CIO Insight https://www.cioinsight.com/ The Voice of the CIO Community Wed, 15 Feb 2023 22:02:32 +0000 en-US hourly 1 https://wordpress.org/?v=6.4.3 Storage Vulnerabilities: The Neglected Cybersecurity Frontier https://www.cioinsight.com/security/storage-vulnerabilities-the-neglected-cybersecurity-frontier/ Wed, 07 Sep 2022 15:45:07 +0000 https://www.cioinsight.com/?p=28142 A 2021 study by Continuity details the fact that lack of storage security is putting a great many organizations at risk. Based on an analysis of more than 400 high-end storage devices, over 6,000 discrete vulnerabilities, misconfigurations, and other security issues were detected.  “On average, an enterprise storage device has around 15 security vulnerabilities, out […]

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A 2021 study by Continuity details the fact that lack of storage security is putting a great many organizations at risk. Based on an analysis of more than 400 high-end storage devices, over 6,000 discrete vulnerabilities, misconfigurations, and other security issues were detected. 

“On average, an enterprise storage device has around 15 security vulnerabilities, out of which three can be regarded as being at a high or critical risk rating that could present significant compromise if exploited,” said Doron Pinhas, CTO at Continuity. “This is evidence of the fact that storage and backup systems have a significantly weaker security posture than the compute and network infrastructure layers.” 

He laid out some of the common vulnerabilities in storage and backup systems such as: 

  • Not disabling legacy versions of storage protocols, or defaulting to their use (e.g., SMBv1, NFSv3).  
  • Using cypher suites that are no longer recommended, such as allowing TLS 1.0 and 1.1 and not disabling SSL 2.0 and 3.0. 
  • Not enforcing data encryption for critical data feeds such as management transport, replication transport, and backup transport). 
  • Allowing cleartext HTTP sessions.
  • Lack of central user management. 

CVEs Impacting Storage 

Common Vulnerability and Exposure (CVE) records are published regularly to alert the world of IT about the latest threats as well as well-known threats the bad guys keep exploiting. These records typically offer a solution in the form of a patch, a recommended upgrade, or a suggested configuration change. 

Unfortunately, a surprising number of organizations fall behind on patching. Cybercriminals are still finding, for example, Internet Explorer and Windows XP systems lurking in organizations that are riddled with vulnerabilities as they have been unsupported for years. 

Further, patches that have been issued to fix major bugs on critical systems can remain unpatched for months due to neglect, slow testing of patches, or lack of personnel resources. To make matters worse, vulnerability scanning tools often miss the CVEs that apply to storage and backup systems.

“Common vulnerability management tools used by enterprises do not detect many storage CVEs as they tend to focus on server OSes, traditional network gear, and software products,” said Pinhas. 

His research found that close to 20 percent of storage devices are badly exposed. Around 70 different CVEs were detected in the sampled storage environments that could be used to exfiltrate files, initiate denial-of-service attacks, take ownership of files, and block devices. 

Ransomware Weaknesses 

Perhaps the most shocking finding is the susceptibility of storage and backup to ransomware. In the fight against ransomware, IT is focused on the front door. They set up firewalls, website defenses, endpoint detection and response, and other tools to prevent direct incursion. Yet the back door may be wide open via storage or backup systems. 

“Although modern storage devices offer ransomware detection and prevention capabilities, as well as advanced capabilities for locking retained copies, protecting critical data from tampering and deletion, and certain forms of air-gapping, these features are often overlooked,” said Pinhas. “Even when used, many configurations did not meet vendor best practices and left the organization exposed to the threat of ransomware. 

Due to these threats, Continuity has pivoted from its traditional market of maintaining availability onto the scanning of data storage, storage management, and backup systems to look for vulnerabilities and security misconfigurations.

“Our StorageGuard product provides continuous scanning and analysis of data storage and backups, as well as automatic detection of security risks,” said Pinhas. 

The goal is to give enterprises complete visibility into storage and backup blind spots and to automatically prioritize the most urgent risks.

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7 Principles of Quality Management https://www.cioinsight.com/enterprise-apps/quality-management-principles/ Fri, 02 Sep 2022 20:16:08 +0000 https://www.cioinsight.com/?p=28138 Quality management can be broken down into seven essential principles that are necessary for success. In fact, the seven principles of quality management are all clearly laid out in a set of codes called ISO 9001. Read on to discover exactly what ISO 9001 is, what the seven principles of quality management are, and why […]

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Quality management can be broken down into seven essential principles that are necessary for success. In fact, the seven principles of quality management are all clearly laid out in a set of codes called ISO 9001. Read on to discover exactly what ISO 9001 is, what the seven principles of quality management are, and why quality management matters to all businesses.

What is ISO 9001?

ISO 9001 is an overarching set of principles that help businesses meet quality management standards to ensure that customers are receiving satisfactory and consistent goods and services. 

This set of quality management standards applies to businesses in all industries. There are also subsets of ISO codes that apply to special industries such as medical devices and railways.

The Seven Principles of Quality Management

As laid out in ISO 9001, the seven principles of quality management are:

  1. Customer focus
  2. Leadership
  3. Engagement of people
  4. Process approach
  5. Improvement
  6. Evidence-based decision-making
  7. Relationship management

Instead of focusing on the nitty-gritty of creating and upholding quality standards, These principles focus on the building blocks that it takes to create a culture of quality management at every level of the organization. 

Customer focus

Customer focus is key to the mission of quality management. Without loyal customers, businesses can’t stay afloat, which is why customer satisfaction should be the ultimate goal of every business’s quality management system.

Gathering customer feedback can help a company identify areas where your business can improve, creating a positive cycle of feedback and improvement. Higher customer satisfaction also means more sales and greater profits, improving the business’s bottom line.

Leadership

If the leaders of a business don’t believe in quality management or don’t make it a strategic priority, then the quality management initiatives will fail. That’s why leadership is the second principle of quality management laid out in ISO 9001. Dedication to quality management must come from the top of the organization.

Strong leadership means that the C-suite and other supervisors are setting the organization’s goals for quality management and creating a culture of quality from the top-down. Without a strong vision for quality management from leaders (not to mention a budgetary commitment), businesses will fall short of their quality management goals.

Engagement of people

Engagement of people refers to having employees at every level of the organization who are committed to meeting the company’s quality management standards and delivering goods or services that result in a high level of customer satisfaction.

Because entry-level and mid-level employees are the ones actually responsible for executing the quality management directives, getting them on board is extremely important for carrying out quality management strategies and implementing quality management systems. If leadership covers the “top-down” portion of quality management, then engagement of people is the “bottom-up” portion of creating a culture of quality.

Process approach

Process approach refers to seeing a business as a series of interconnected processes rather than separate departments or silos. It means focusing on the dynamic business processes rather than individual positions, team divisions, and other static elements.

This process approach encourages efficiency, contributes to better resource management, and helps support continuity when team members move on and have to be replaced. When combined, all of these factors contribute to better quality management and also a more flexible business that can respond quickly to market changes.

Improvement

Companies never “arrive” or fully achieve their goals when it comes to quality management. Instead, they must reject complacency and be engaged in an ongoing focus on improvement, which is what this principle refers to. When they achieve one quality management goal, companies who are serious about quality management will simply create another, more far-reaching target, so the metaphorical goalposts are always moving.

This focus on continual improvement also helps companies to stay competitive and continue delivering goods and services that satisfy customers. Customers will take their business to the companies that are continuing to improve, so companies must focus on pushing their standards for customer success.

Evidence-based decision-making

Evidence-based decision-making means making decisions based on facts, data, and observation rather than unsupported assumptions. Fortunately, quality management software allows companies to make evidence-based decisions quickly.

While there will always be some level of uncertainty when it comes to setting business strategies, following an evidence-based approach will help reduce the levels of uncertainty and give business leaders more confidence in the accuracy of their decisions. This is related to quality management because more accuracy in business decisions translates to higher quality goods and services and in turn, better customer satisfaction.

Relationship management

If relationship management sounds more relevant to sales than to quality management, think again. Quality management requires maintaining healthy relationships with all stakeholders—not only customers, but also suppliers, vendors, and distributors as well.

Just because these third-party partners are not technically part of a company does not mean that their actions aren’t going to impact that company’s ability to meet its quality goals and satisfy its customers. Cultivating strong relationships with these outside partners and getting them to understand the vision for quality management is key to achieving quality goals.

Ready to get serious about quality management at your company? Explore top solutions on our list of the best quality management software on the market today.

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Domo vs Tableau: Which is the better BI solution? https://www.cioinsight.com/big-data/domo-vs-tableau/ Sat, 27 Aug 2022 02:11:31 +0000 https://www.cioinsight.com/?p=28127 Choosing the right business intelligence (BI) software can make the difference between a company meeting business objectives or falling by the wayside. These solutions don’t just help businesses manage their data—they can take business data from multiple sources to generate valuable insights for a company that can drive their business decisions and fuels them for […]

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Choosing the right business intelligence (BI) software can make the difference between a company meeting business objectives or falling by the wayside. These solutions don’t just help businesses manage their data—they can take business data from multiple sources to generate valuable insights for a company that can drive their business decisions and fuels them for success.

With so many business intelligence products on the market today that offer unique features and capabilities, selecting the right one to suit your company’s needs is vital. Domo and Tableau are two of the most popular BI tools on the market, but they each have different approaches to their key features. Keep your business’s unique needs in mind as you consider Domo and Tableau’s similarities and differences.

What is Domo?

Domo is an end-to-end business intelligence solution that provides users with data analysis and visualization features. This product can utilize a business’s information from multiple data sources and transforms its data to leverage it in new ways. 

With its broad integration capabilities, this solution enables users to get the most from their data and generate app-led workflows that can help them take on an intelligent approach to business challenges.

What are the key features of Domo?

Domo offers more than 1,000 pre-built connectors and other data connection methods so users can leverage data from each area of their business processes. Data is then processed within the system, where its ETL, SQL dataflow, data science, and data preparation features can transform the data to support business activities. 

The software solution also provides machine learning (ML) capabilities, enabling users to train their ML models and develop new ways to generate valuable insights from their datasets. The product also comes with features for data querying, and users can even query and visualize the data within data warehouses without needing to duplicate it.

Domo users can generate over 150 chart types and thousands of custom maps to visually represent user data. The reporting and dashboard features of the product make it easy to understand and represent data through powerful visualizations. These can even be shared with others using the platform’s collaboration features.

Finally, the tool’s self-service and embedded data analytics features can support users in their business processes, allowing them to use their data to answer questions and develop plans based on their data insights. 

What is Tableau?

Tableau is a data visualization solution that helps users better understand and analyze their business data. The integrated platform can connect with many data sources to access business data for analysis and valuable insights. 

With AI/ML capabilities, data management, and visual storytelling features, the intuitive analytics solution provides plenty of ways for users to make sense of and leverage their data. The system also provides dashboards, advanced deployment monitoring, content management, and various options for sharing custom views. 

Therefore, through this solution, everyone can access the right data visualizations at the right time to support their business processes.

What are the key features of Tableau?

Tableau’s features enable users to take their data to the next level. Its visualization options can help users make sense of their data and manage their data activities through their dashboards. In addition, the solution lets users interact with their data to identify key insights and create and share custom views. 

When it comes to sharing, Tableau has several features to assist users with their collaboration efforts. For example, users can create subscriptions for other people and themselves, and authorized users can comment on dashboards and visualizations within the platform. Users can even set up data-driven alerts and create/publish workbooks and data sources.

Tableau also has data preparation capabilities, as users can create, edit, and modify their data flows to meet their needs. They can also publish and run data flows, schedule their data flows, and monitor the health and performance of their flows to ensure that everything is running smoothly.

Another beneficial aspect of Tableau would be the governance features. The system provides several functional capabilities for managing content and certifying data sources. In addition, the governance tools are excellent for ensuring the quality of users’ data.

Domo vs. Tableau: Deployment methods

Domo

Domo enables users to build their own apps and solutions through its infrastructure and development tools. The Domo App Framework provides features for developers in a familiar development environment so that they can build scalable data apps that are customized to their needs. 

Source: Domo

Domo provides an entire integration studio and over 1000 connectors, so users can easily access their data and deploy their data products faster. The Domo interface is also mobile-optimized, so users can access their data remotely. 

As an open-cloud platform, the Domo system supports hybrid deployment options so users can develop data apps with data sourced from any cloud or across multiple clouds. Additionally, with Domo Workbench, users can simply and safely move their data into Domo from their on-premises systems. 

Tableau

Tableau supports on-premises, cloud, and hosted deployment options so that users can take advantage of its self-service tools at scale with unified data from various sources. Buyers can choose to utilize the Tableau Server or Tableau Cloud products depending on their technology requirements. 

Tableau Cloud scales and serves thousands of sites within a multi-tenant environment. The software system provides users with the freedom to utilize its features effectively within their IT infrastructure, as it supports a hybrid data architecture that includes on-premises, private cloud, and public cloud networks. 

Tableau also provides mobile app deployment, so any workbooks created are accessible from mobile devices and are automatically mobile-enabled. Users can also edit workbooks and create new views in their workbooks and can access these capabilities with embedded visualizations in custom mobile apps, through a mobile browser, and within the Tableau Mobile app.

Source: Tableau

Domo vs. Tableau: Data analysis and visualization capabilities

Domo

Domo’s data analysis features help users visualize and interpret their complex data in a variety of ways. Users can choose from the platform’s collection of visualization options to transform their data into maps, data science charts, tables, Trellis charts, and more.

Domo Analyzer is a tool that helps users analyze their data with predefined filters, allowing them to reach conclusions and answer questions quickly. Through the drag-and-drop interface, any user can easily sort and filter data without impacting other users’ views.

Source: Domo

The solution’s integrated data science features can provide even more insight into users’ data with natural language queries, automated data discovery, and automated machine learning capabilities. Users can even adjust the appearance of their analytics through the branding toolkit to fit their business’s brand identity.

Tableau

Tableau analyzes users’ datasets to uncover patterns and trends and identify key insights. The system uses data science and analytics to run and scale users’ ML models and transform their data into useful visualizations. Some of its other helpful features include R and Python integration, rapid cohort analysis, sophisticated calculations, and predictive capabilities.

Source: Tableau

As a solution that focuses on enabling users to understand their data through visualizations, it provides many options for transforming data through its analytics platform. Tableau’s technology can translate drag-and-drop actions into data queries, so it’s easy to learn from data and uncover actionable insights.

Tableau’s augmented analytics features can allow users to reach insights and find the answers to their important questions. Users can ask questions regarding their data in natural language, or they can use no-code AI to make smarter business decisions by leveraging the business science technologies that Tableau has to offer.

Domo vs. Tableau: Integrations

Domo

Domo lets users connect data from the cloud, on-premises devices, and more. The system supports bi-directional connections between all systems and ways for securely connecting to on-prem data. Users can pull data from their files and move them into a single platform, so it is easy to see all of their important data in one place. 

Domo even has proprietary systems connectors. Users can build their own data connections using secure SDKs, HTTP server-to-server callbacks, or by generating custom connectors through the Connector Dev Studio.

Domo has robust capabilities for integrating data from different sources. However, as a solution that works to connect data of large volumes and from various sources, this can cause challenges with the speed at which data is extracted and analyzed in some scenarios.

Source: Domo

Tableau

Tableau has a wide variety of native data connectors for accessing data from various files and databases. The Tableau partner ecosystem allows users to get more from their data pipelines and IT infrastructures. 

Users can even extend their Tableau systems with the Extensions API, letting users interact with their data from other sources directly in the system. Additionally, they can integrate embedded, interactive analytics from other applications into their Tableau dashboards for easier access and use.

While users can access data through the system’s many different integration options, Tableau cannot work with uncleaned data. Therefore, users would need to clean their data before connecting it to their Tableau systems.

Source: Tableau

Domo vs. Tableau: Ease of use

Domo

Domo is a service that provides solutions enabling developers and non-technical users to leverage their data. By not requiring coding knowledge, more users are able to analyze their data to reach actionable insights. 

Domo provides users with access to Domo University, which contains learning content to help them use and optimize the Domo software. Still, many online reviewers have found the solution to be challenging to learn. 

Tableau

While users do not need to understand coding to benefit from Tableau, incorporating other scripting languages can be helpful in avoiding performance problems when analyzing complex data tables. 

Also, the system features single-value parameters that must be updated manually each time they are changed. Finally, its data preprocessing capabilities are limited, so analysts will likely require a separate product like Tableau Prep to perform data cleansing.

Domo vs. Tableau: Which is the better data analytics solution?

Domo and Tableau each provide excellent features for business intelligence processes, but which solution is the better data analytics tool? The answer depends on each organization’s unique needs.

Domo is an ideal option for any organization looking to use a hybrid solution that can help them transform and leverage their data. Additionally, professionals who require tools with data governance features will appreciate Domo’s intelligent tools and certification processes. 

Of course, analysts who handle numerous data sources are sure to enjoy Domo as well, as the solution’s many connectors make it easy to access, clean, and utilize important data, saving users time and effort.

Alternatively, Tableau can be a fantastic resource for organizations that want to take advantage of modern business intelligence through visual representations. 

The product can also be extremely helpful for anyone needing to save time generating useful reports and visualizations that will allow them to better comprehend their data. Additionally, with their offline-analysis capabilities and collaboration features, it’s great for users that need to easily access and share their data findings on the go.

Still not sure which solution is right for you? Compare Domo and Tableau with other top solutions on our list of Best Business Intelligence Software.

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Taking the Complexity Out of the Cloud Journey https://www.cioinsight.com/cloud-virtualization/complexity-cloud-journey/ Thu, 25 Aug 2022 23:54:02 +0000 https://www.cioinsight.com/?p=28125 According to a study by Wakefield Research, 92 percent of organizations are either in the midst of app modernization or are planning to. Unfortunately, many of these modernization projects run into trouble. As they progress, the projects grow more complex, more expensive, and riskier.  Around 80 percent of software developers and architecture engineers admit to […]

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According to a study by Wakefield Research, 92 percent of organizations are either in the midst of app modernization or are planning to. Unfortunately, many of these modernization projects run into trouble. As they progress, the projects grow more complex, more expensive, and riskier. 

Around 80 percent of software developers and architecture engineers admit to a failure in an app modernization effort. Three out of four of survey respondents complained about cost—many reported that typical application modernization project costs nearly $1.5 million. Another 58 percent said such projects usually take around 16 months, with 27 percent saying they can run for two or more years. Developers, too, felt that a lack of integration tools was holding them back. 

These reports aren’t discouraging organizations from migrating more applications to the cloud. Instead, app modernization teams are seeking tools that automate and simplify the cloud journey. Such tools must be able to connect and integrate applications between clouds, as well as between the cloud and those applications that must remain on-premises. This is a lot harder than it sounds, as many in IT have discovered. 

Hyperscaler envy

Many organizations hear about how Google Cloud, Amazon, Facebook, and other hyperscalers have a cloud-only architecture. They admire the flexibility this offers and become envious of the capabilities, performance, and cost-efficiency these providers build into their data centers and application portfolios. Businesses want that for themselves, and they often rush headlong into the cloud in their digital transformation efforts.

But they are forgetting one major point that explains why the hyperscalers make it look easy: starting with cloud-native applications. Hyperscalers had the luxury of being able to architect everything for the cloud, so they weren’t bogged down in decades-old legacy applications. 

Most established organizations are coming from a very different position. Many of their apps were designed for on-premises deployment. Even when they develop newer apps for the cloud, they often discover that these apps still need to interact with legacy on-premises systems. 

For example, banking systems that are cloud-based and customer-facing typically suffer from dependencies that require data to be passed through a legacy system or at least interact with that system for verification purposes. There are also a great many regulations that require data to remain on-premises, not leave the country, guarantee privacy, and meet other standards. 

Financial services requirements can get particularly complex: regulations about how and where money can be moved, the many taxation jurisdictions and responsibilities, data sovereignty rules, etc. These factors tend to mire down application modernization efforts. 

All of this makes the move to the cloud far more challenging. When you then factor in the complexity within modern IT itself—Kubernetes clusters, virtualization, software-defined computing, and other factors—it’s no wonder so many digital transformation projects and cloud enablement projects are stalling. 

Also read: Top Cloud Computing Companies

Automated integration 

Fortunately, several providers are coming to bat for those wanting to move to the cloud. Companies like vFunction and Ori are developing tools to eliminate this complexity and automate the cloud journey. Ori, for example, offers the Ori Global Cloud as a service. It functions as a kind of middleware or automated orchestration/integration platform that promises to take apps to the cloud at Internet speed. 

“It is now possible to deploy a single app to the cloud in minutes and as many as 30 common apps to the cloud within a day,” said Rick Taylor, CTO at Ori. “The Ori Global Cloud reduces the need to develop in-house technology expertise by abstracting away any complexity and eliminating time-consuming manual plumbing. This allows staff to focus on higher-level IT functions.” 

The software achieves this efficiency via a combination of automation and intelligent orchestration. AI built into Ori Global Cloud administers application compute destinations based on criteria such as availability, operational cost, location, and performance requirements. 

Plus, the supporting automation features take the manual labor out of the move to the cloud. Ori automatically manages the underlying networking, security, and installation processes for all deployments. IT only has lay out a few application requirements and the platform takes care of the rest. 

If such platforms deliver what they promise, this could bring a new lease of life to the world of digital transformation and cloud migration.

Read next: Creating a Cloud Strategy: Tips for Success

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Top Container Software Available in 2022 https://www.cioinsight.com/application-development/top-container-software/ Thu, 25 Aug 2022 17:15:44 +0000 https://www.cioinsight.com/?p=28122 Container software is a key tool in the software developer’s toolkit. Containerized applications can run independently across multiple computing environments, making it easier for DevOps teams to deploy software updates and shift resources without fear of major disruptions. However, it’s important that organizations choose the right container software for their unique needs and challenges. Though […]

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Container software is a key tool in the software developer’s toolkit. Containerized applications can run independently across multiple computing environments, making it easier for DevOps teams to deploy software updates and shift resources without fear of major disruptions.

However, it’s important that organizations choose the right container software for their unique needs and challenges. Though the top container software solutions are versatile, they each have advantages and drawbacks that make them better suited to some software buyers’ needs than others.

Top container software comparison

Container Auto-recoveryAuto-scalingLoad-balancingIntegration with CI/CDHIPAA or PCI-DSS compliant
Amazon ECS✅✅✅✅❌
Docker❌❌✅✅❌
Google Kubernetes Engine❌✅✅✅✅
Microsoft Azure❌✅✅✅❌
OpenVZ❌❌❌✅❌

Amazon ECS

Elastic Container Service (ECS) is a managed container orchestration service from Amazon Web Services (AWS).

This solution is best for customers who are already using other AWS products, 

Amazon ECS is most often used by large computer software companies with thousands of employees, but it can work well for smaller companies as well.

Screenshot of Amazon ECS.
Source: AWS

Customer Reviews

AWS’s extensive documentation and support center received favorable reviews from multiple customers. ECS integrates easily with the CI/CD pipeline and other Amazon cloud services. Additionally, the scalability of ECS is rated highly by customers, and the interface is user-friendly. 

However, some users said the AWS cloud formation designer template could be improved, and the application can be challenging to use for beginners. In addition, the load balancing service can be difficult to use. One customer also expressed frustration with the lack of integrations between ECS and third-party applications.

Docker

Docker is one of the best-known containerization solutions on the market. It is an open-source platform, allowing developers to use Windows, Mac, or Linux computers. This kind of platform agnosticism is ideal for organizations that need to run applications across multiple operating systems and environments.

Screenshot of Docker.
Source: Docker

Customer Reviews

The Docker platform helps ensure the software is easily deployed without having any system dependencies. Another positive comment referred to Docker as one of the best container runtime products on the market. The product is easily installed and integrates well with most cloud-based servers running various operating systems. Docker also can accept a variety of plugins to enhance its features.

On the downside, Docker does not have a GUI nor built-in support for Macs. The concept of containers can be challenging to understand, and making a lightweight container requires more than a basic understanding of container concepts. 

Furthermore, when a container becomes inoperable, there is no available data recovery solution. Docker’s online documentation is not up to par, and another drawback is that there is no monitoring feature available.

Google Kubernetes Engine (GKE)

Google Kubernetes Engine (GKE) is a container orchestration tool that helps organizations migrate, deploy, manage, and containerized scale applications via Kubernetes.

GKE is PCI-DSS compliant, which makes it a suitable solution for credit card payment processes, including storing, processing, and transmitting cardholder data (CHD). GKE is HIPAA compliant too, making it a favorable platform for healthcare organizations.

In addition, GKE has a 99.5 percent service level agreement (SLA) standard that incentivizes GKE to meet its service level objective (SLO). If GKE does not meet its SLO and the customer meets its SLA, the customer will receive financial credit. This dedication to customer satisfaction may be a compelling advantage for customers who prioritize reliability from their container software provider.

Screenshot of Google Kubernetes Engine.
Source: Google

Customer Reviews

Google Kubernetes Engine integrates well with several Google Cloud components. Setting up Google Cloud is simple, and the user interface is easy to use. Other complimented features are the native monitoring tool and the cluster auto-scaler for managing containers in a cluster.

Some pain points mentioned with GKE are inconsistency and lack of expected functionality with the console and shell. Specific help documentation can be challenging to find, and this documentation may be insufficient for new users. Customer support is mentioned as needing improvement. 

Microsoft Azure

Microsoft Azure offers multiple solutions for various container needs. It supports multiple programming languages, including deployment flexibility within the CI/CD process. In addition, this product is compatible with a range of open-source, client-side tools. 

Screenshot of Azure platform.
Source: Microsoft

Customer Reviews

Azure’s documentation site is rated as excellent and is noted for providing great tutorials and examples. Azure is a fully managed, user-friendly container platform with point-and-click publishing. In addition, Azure DevOps features like automation deployment and managing scrums help guide development teams in the iterative and incremental delivery process. 

One drawback customers noted was Azure’s customer support. Some users commented that the learning curve may be very steep for beginners, so it may not be a suitable solution for teams that don’t have a lot of technical expertise. Some customers also noted frustration with Azure’s pricing structure and hidden fees they hadn’t anticipated.

OpenVZ

OpenVZ uses a Linux OS with container-based virtualization that can create multiple isolated containers. This product supports Docker containers and promotes DevOps automation with Kubernetes hosting to enhance containers’ development and deployment processes. 

Virtuzzo uses a pay-per-use price structure. This flexibility may be especially beneficial for small and mid-sized companies that have lean budgets but need scalability.

Screenshot of OpenVZ.
Source: OpenVZ

Customer Reviews

With OpenVZ, provisioning containers is fast and easy, and some customers praised the platform’s unique features. Container virtualization is highly rated since it uses a shared kernel and therefore fewer computer resources.

One pain point customers mentioned is OpenVZ’s lack of image templates. Another drawback is that some containers are not as lightweight as Docker containers. The platform is not easy to configure relative to competitors—users must manually configure OpenVZ from scratch, whereas Docker is ready to go out of the box. 

What are the benefits of using software containers?

Containers promote efficiency when operating in a computing environment because they require fewer computer resources than monolithic applications or virtual machines. The container uses the host operating system (OS) without the OS being a part of the container. This lessens the overhead for containers and makes them highly portable to other computing environments and smaller in file size. 

Other benefits of using software containers include:

  • More consistent operation: DevOps teams can rely on consistent container performance regardless of the host OS in use.
  • Better application development: Containerization facilitates quicker development, test, and production cycles.
  • Greater efficiency: Containers enhance the ability of applications to be quickly deployed, patched, or scaled as necessary. 

How to choose the best container software

Container software must meet the unique needs and challenges a business faces in its software environment. It must be compatible with the right systems, programming languages, and operating systems the organization uses to operate. It must also fit the technical expertise of the development team and provide adequate technical support in the event something doesn’t work as expected.

Cost is another factor to consider. Some organizations might find more success with a per-user pricing structure, whereas others may prefer to pay based on the resources they use. If flexibility is the primary concern, customers should look for solutions with auto-scaling capabilities that can scale up or down as demand fluctuates.

Read next: Are Your Containers Secure?

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What is a Quality Management System (QMS)? https://www.cioinsight.com/enterprise-apps/what-is-a-qms/ Thu, 25 Aug 2022 17:00:52 +0000 https://www.cioinsight.com/?p=28120 Quality management systems (QMS) are a critical tool in an enterprise company’s toolkit. Even minor instances of inconsistent quality can have major revenue implications, so it’s essential to have a system in place that can quickly identify quality issues and take steps to remediate them.  However, before you can choose the right QMS for your […]

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Quality management systems (QMS) are a critical tool in an enterprise company’s toolkit. Even minor instances of inconsistent quality can have major revenue implications, so it’s essential to have a system in place that can quickly identify quality issues and take steps to remediate them. 

However, before you can choose the right QMS for your organization, it’s important to understand the basics of quality management and the benefits this kind of software can offer.

What is a quality management system?

Quality management systems are designed to help businesses ensure their goods are meeting industry standards. This software helps manufacturers maintain product consistency, so customers will receive the same standard of quality with each purchase.

QMS platforms are common in heavily regulated manufacturing industries such as medical equipment, automotive, and food and beverage, but they have applications for many other areas as well. Most businesses in any industry and of any size—whether that’s a small family business or a large, multinational corporation—can benefit from using the principles and tools of quality management.

To be effective and successful, quality management initiatives must incorporate two main processes: quality assurance and quality control. 

Quality assurance

Quality assurance (QA) refers to the systems and plans in place to create satisfactory goods. QA is about proactively ensuring quality during the manufacturing or development process—the goal is to prevent defects from the beginning. Because of this, QA is very broad in its nature.

Quality control

Quality control (QC) refers to the actions that confirm a product meets or exceeds its quality objectives. It’s entirely focused on the goods that have already been created, and it involves rigorous testing, inspections, and sampling to ensure consistent product quality. Issues that arise in QC checks may warrant further QA scrutiny. Because of this, QC is more narrow than QA.

What are the benefits of using a QMS? 

There are many benefits of adopting a quality management system, including stronger compliance, streamlined management, improved efficiency, and better customer satisfaction.

Stronger compliance

The main goal of a quality management system is to help businesses maintain compliance by recording quality metrics at each step of the production process. Not only does this track and confirm that companies are meeting their quality standards, but it also means that they will be prepared with sufficient documentation in the event of an audit.

Streamlined management

Similarly, QMS platforms consolidate all compliance documentation into a single platform, which is extremely helpful for national or multinational corporations with numerous vendors in the supply chain. A streamlined management system means that businesses don’t have to maintain multiple sources of documentation to confirm that third-party partners are meeting acceptable standards.

Improved efficiency

Implementing a quality management system helps reduce rote or redundant work that results from manually tracking quality data. This frees up workers to spend their time and effort on more important tasks that make a more significant impact. 

QMS platforms can also help businesses more quickly identify systemic causes of repeat errors that impact profitability and efficiency, such as poor product designs or equipment failures. It’s then easier for teams to identify solutions to these challenges and avoid any bottlenecks or delays.

Better customer satisfaction

QMS solutions increase product consistency, which in turn improves customer satisfaction. When quality varies widely from one purchase to the next, consumers are likely to look for more reliable alternatives. An effectively implemented quality management system helps avoid inconsistencies and drive customer loyalty, thereby maintaining a competitive advantage.

How to choose a QMS

Most quality management systems are tailored to particular industries and based on at least one set of ISO standards. Some QMS platforms may integrate other management approaches, such as Six Sigma and Capability Maturity Model Integration (CMMI). 

General features to look for in a QMS platform include document management, audit management, risk management, supplier or vendor management, and change control.

However, there is no one-size-fits-all approach when it comes to navigating the types of quality management systems that are available today. Each company has specific needs for a QMS system based on its size, industry, and other needs. 

If you’re ready to choose a QMS platform, start by reviewing our list of the Best Quality Management System Software to learn more about the different kinds of QMS solutions that are available across various industries.

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Sisense vs Power BI: Battle of Top BI Software https://www.cioinsight.com/big-data/sisense-vs-power-bi/ Thu, 25 Aug 2022 16:48:05 +0000 https://www.cioinsight.com/?p=28119 Sisense and Power BI are two of the most popular business intelligence tools. Both platforms offer solutions for different use cases, such as querying raw data or generating visualizations to provide executives with at-a-glance views of their most important metrics.  But which one is right for your organization? Keep your business’s unique needs in mind […]

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Sisense and Power BI are two of the most popular business intelligence tools. Both platforms offer solutions for different use cases, such as querying raw data or generating visualizations to provide executives with at-a-glance views of their most important metrics. 

But which one is right for your organization? Keep your business’s unique needs in mind as you compare both platforms’ user experience, features, and pricing to determine which one is the right fit.

What is Sisense?

Sisense offers an end-to-end solution for data preparation, analysis, and visualization. With Sisense, users can easily connect to various data sources, clean and prepare data, build dashboards and reports, and share insights with relevant parties. 

What sets Sisense apart from other business intelligence software is its ease of use. With its drag-and-drop interface, users can quickly build complex dashboards without any coding required. In addition, the platform is accessible on desktop and mobile devices, giving users access to their data anytime, anywhere. Sisense also provides integrations with many popular apps, including Salesforce, Zapier, and Slack. 

What is Power BI?

Microsoft’s ​​Power BI tool allows users to visualize data, share insights, and collaborate with others in real-time. Power BI is a part of the Microsoft Office 365 suite of applications and services. 

Using Power BI’s desktop application, users can upload their data from on-premises sources such as SQL Server or other relational databases. Once imported into the dashboard, they can create reports using visualizations like graphs, charts, maps, dashboards, and scorecards. 

Users can customize these visuals using different formatting options for their metrics, such as color coding or shading within the visualization itself. These features allow for more detailed analysis at a glance and provide valuable insight into the company’s activities. 

Sisense vs Power BI: Ease of use

If you’re looking for business intelligence software that is easy to use, Sisense is an excellent option. With its straightforward interface, users can quickly start building dashboards and reports. Sisense is very intuitive and doesn’t require users to be familiar with complex formulas. 

On the other hand, Power BI might be difficult for some users because it requires a certain level of Excel knowledge. Although Power BI does not require coding knowledge, if you want to operate outside of the Power BI GUI, you must understand Data Analysis Expressions (DAX) – a formula expression used in analysis services, Power BI, and Power Pivot in Excel.

If ease of use is your priority, Sisense might be the best choice since Power BI requires more time investment to learn.

Sisense vs Power BI: Data visualization

Data visualization is a key part of business intelligence software. Both BI tools offer various visualization options, including charts and graphs. 

Sisense

Sisense allows users to track metrics and embed dashboards, widgets, and filters into applications for a fully customizable and interactive experience. Users can save their visualizations as web-based dashboards that are interactive and can be embedded in non-Sisense environments, such as your websites or applications.

Sisense’s drag-and-drop interface for dashboard and visualization building makes it robust for business users. With a drag-and-drop interface that makes it easy to visualize data without writing any code, Sisense eliminates barriers to entry in BI software. Even those with no technical background will find this intuitive interface appealing, enabling them to build powerful visualizations and dashboards quickly.

Screenshot of Sisense data visualization.
Source: Sisense

Power BI

Power BI allows users to create interactive, shareable dashboards. It offers a wide range of features, including built-in templates, drag-and-drop canvas, and real-time data analysis. Plus, it integrates with other Microsoft products like Excel and SharePoint. 

Users can leverage a variety of Power BI’s library of pre-configured data visualizations including charts, graphs, timelines, and calendars. They can also embed interactive Power BI content in blog posts, websites, emails, or social media using the publish-to-web option.

Screenshot of Power BI data visualization.
Source: Power BI

Sisense vs Power BI: Business analytics 

Business analytics features give organizations the ability to collect, organize, and analyze data so they can maintain a competitive advantage. Both Sisense and Power BI offer these features on their respective platforms.

Sisense

Sisense helps businesses analyze and make better decisions with data. Sisense allows you to “infuse insights everywhere you or your customers work” by enabling you to connect with, explore, and visualize any type of data in the cloud. 

Its business analytics capabilities allow users to process raw data from multiple sources into a single location for querying and visualization. Sisense can simplify and analyze complex data, create visualizations, and discover and share insights with key decision-makers in an organization.

Screenshot of Sisense business analytics.
Source: Sisense

Power BI

Power BI is an enterprise-class analytical tool that delivers compelling visualization and analysis experience. Its business analytics feature provides real-time data insights, allowing users to analyze trends and patterns from both historical and current data.

It offers dashboards with prebuilt content, interactive reports with KPIs that are easy to configure, and intuitively connected mobile experiences across various platforms. 

Power BI provides intelligent answers for a modern workplace through instant access to insights about the key performance indicators of your business anytime you need them.

Screenshot of Power BI business analytics.
Source: Microsoft

Sisense vs Power BI: Ad hoc analysis

Both Sisense and Power BI support ad hoc analysis—a business intelligence process where analysts create reports and perform analysis on data without pre-determined templates or question types.

With Sisense, businesses can use embedded analytics to help their teams easily view and digest big data from a variety of sources on the fly. 

Furthermore, with Sisense’s self-service reporting, business users can make queries, create reports and visualizations, and share them with other team members, who do not need any technical knowledge of SQL or complex query languages. This reduces business teams’ dependency on data teams for their analytical needs.

Power BI also supports ad hoc analysis and reporting. Users can get additional insights via the ad hoc analysis of a Power BI report’s visuals. Power BI provides on-the-fly access to reports for data analysis and alerts users if there is any update or change in the information, allowing them to rapidly assess the most important KPIs and take deliberate actions. 

Sisense vs Power BI: Data connections

Data connections are important in any BI software because they provide the foundation for creating reports and visualizations. Both tools offer the ability to connect to multiple data sources simultaneously.

Sisense

​​Sisense provides an easy way to connect to your data, regardless of where it’s stored. With support for over 80 data sources, including relational databases, NoSQL databases, cloud-based data warehouses, and flat files, you can be sure that Sisense will be able to connect to your data. 

You can also build relationships between multiple tables using a graphical interface. Custom ODBC and JDBC drivers are also available if this doesn’t cover your needs. Sisense uses ElastiCube, a proprietary high-performance analytics database that allows users to import data from multiple sources and store that data.

Screenshot of Sisense data connections.
Source: Sisense

Power BI

Power BI has native connectivity for almost any source imaginable. Microsoft has worked hard to maintain a robust list of connectivity options for many use cases with over 100 data connectors, including SQL Server, Oracle, MySQL, Teradata, Netezza, and others. Power BI also has features that simplify the process of connecting datasets, such as importing tables into spreadsheets and generating API scripts for live connections with RESTful services.

Microsoft has prioritized business intelligence capabilities across platforms. They have options for those looking to work with data inside and outside of Power BI. For example, Microsoft Office 365 Enterprise includes business intelligence capabilities in Excel and SharePoint Online. These platforms allow users to collect data, visualize data, and share information with colleagues across multiple devices.

In addition, they’ve invested heavily in integrating Power BI with Azure technologies such as Machine Learning (ML) services and Cortana Intelligence Suite AI Services, which opens up more avenues for advanced analytics. 

Screenshot of Power BI data connections.
Source: Microsoft

Sisense vs Power BI: AI analytics

Artificial intelligence (AI) helps businesses derive next-level insights from large volumes of data that wouldn’t be possible with traditional data analytics approaches alone. Both solutions offer AI-powered analytics that can help you make sense of large data sets.

Sisense

Sisense offers artificial intelligence (AI) capabilities within its BI software. This enables users to get predictive insights from their data and recommendations on how to improve their business. Sisense also has a natural language processing (NLP) engine, which allows users to ask questions in plain English and get answers in the form of data visualizations. 

The UI is customizable so that you can create dashboards for different types of employees with diverse needs. For example, salespeople may need a dashboard focused on leads, whereas marketing managers may need information about campaign performance.

Power BI

With Power BI’s AI Insights, your data analyst can access a collection of pre-trained machine learning models that enhance data preparation. AI Insight is accessible via the power query editor and provides insights on trends and patterns depending on the data set and business goal. 

Power BI also supports cognitive services such as text analytics and vision. Users can leverage multiple algorithms from Azure Cognitive Services to enrich their data in Power Query. Sentiment analysis, key phrase extraction, language detection, and image tagging capabilities are provided services for visualizing various data points. 

Sisense vs Power BI: Pricing

Regarding pricing, Sisense doesn’t provide any details on their website. They want you to contact them for a custom quote based on your specific needs. 

Power BI, on the other hand, has a detailed pricing page. They have three plans: 

  • Pro plan: Starts at US$9.99 per user per month
  • Premium plan: Starts at US$20 per user per month
  • Premium per capacity plan: Starts at US$4,995 per capacity per month

The decision of which software to use largely depends on what your needs are, but in terms of pricing, Sisense is more expensive than Power BI if you’re looking for a one-time purchase with annual billing or an all-inclusive price. 

However, suppose you need customization and scalability features. In that case, Sisense might be worth the cost depending on how much they charge per project and the number of concurrent users needed by that project.

Both providers offer free trials, so you can try out each product before deciding which one to buy. 

Sisense vs Power BI: Which one to choose?

Sisense and Power BI offer a great set of features for business intelligence for enterprises of all sizes. For example, both BI solutions provide dashboards, reporting, and data visualization capabilities. Both tools allow you to easily track, analyze, visualize, explore, and understand your data process.

However, if you develop products that require near real-time updates, need embedded analytics, or work with non-technical users, then it might be better to choose Sisense. Alternatively, if you are looking for the best performance across multiple servers, platforms, and databases, then Power BI would be a good choice. 

Ultimately, choosing between Sisense and Power BI comes down to understanding your company’s needs and knowing which tool will fulfill them.

Read next: Best Business Intelligence Software: BI Tools Comparison

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Looker vs. Tableau: Top BI Software Comparison https://www.cioinsight.com/big-data/looker-vs-tableau/ Fri, 19 Aug 2022 21:44:44 +0000 https://www.cioinsight.com/?p=28109 Business intelligence and data analytics tools like Looker and Tableau give enterprise data and BI teams tools to visualize their company data and connect disparate data sources. These tools  offer businesses analytics, visualization, and software integrations to better study their data and apply it to their operations. To decide which BI solution is better suited […]

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Business intelligence and data analytics tools like Looker and Tableau give enterprise data and BI teams tools to visualize their company data and connect disparate data sources. These tools  offer businesses analytics, visualization, and software integrations to better study their data and apply it to their operations. To decide which BI solution is better suited to your enterprise, consider the main tools your business needs, your analysts’ BI experience, and your most prioritized software integrations.

What is Looker?

Looker logo.Looker is a cloud-based business intelligence and embedded analytics platform that belongs to the Google Cloud portfolio. It offers data visualization and modeling capabilities for enterprises. 

Looker’s data modeling tools allow teams to link Looker to a database and use Structured Query Language (SQL) to define a data model. Looker provides pre-built modeling patterns so teams can immediately begin modeling without needing to write all of their own models. Looker also provides pre-built templates for quickly building dashboards from commonly accessed data sources like Google. 

For developers, Looker offers an API for customizing workflows or developing enterprise-specific applications. Dev teams also have the option to build Looker extensions to share with Looker users outside their organization. Businesses that want to solve BI questions with other organizations will benefit from Looker’s collaborative development features. 

What is Tableau?

Tableau logo.Tableau is a data visualization and analytics solution owned by CRM provider Salesforce. Tableau connects to a wide variety of data sources, from databases to Google Workspace applications. It also offers analytics for Salesforce customers, analyzing data from the CRM alongside data from other business applications. 

Tableau offers drag-and-drop functionality for creating data visualizations and allows inexperienced users to immediately begin designing dashboards. Tableau supports enormous warehouse workloads and large unstructured datasets for enterprises that need to analyze large stored volumes or data from high-performance applications. 

What are the differences between Looker and Tableau?

Looker and Tableau differ in data modeling technology, integrations, and ease of use. Consider your business’s specific needs when evaluating each of these BI tools. 

Looker vs. Tableau: Data visualization and modeling

For data modeling, Looker uses LookML, a coding system similar to SQL. When Looker is connected to an SQL database, users are able to automatically generate a LookML data model. 

Looker offers over 100 prebuilt modeling patterns to help teams build their models. These patterns allow them to customize models based on enterprise-specific data measurement needs. Looker also develops a basic model for businesses by observing relationships between the tables in the organization’s data schema. 

Looker offers visualization tools like dynamic dashboard filtering, which allows users to filter for individuals and groups, and an entire marketplace of visualization types, including maps, funnels, and timelines. Some customers felt that Looker’s visualization tools were weaker, though, and would have preferred the ability to customize them further.  

Tableau offers visualization features like drag-and-drop, which allows data analysts to easily select dimensions like customer ID or postal code to view sales data. Analysts can also use natural language queries to view a graph of answers. Note that Tableau has no built-in template for a data dictionary, so creating visualizations from many different data sources may require manual work. 

Tableau doesn’t provide strong data modeling features. If your data team needs to create models as well as visualizations, Tableau won’t provide sufficient tools to do so. 

Bottom line: Consider Looker if your data teams need to easily build data modeling. If you need easy-to-learn visualization capabilities, especially for newer teams, consider Tableau. 

Looker vs. Tableau: Integrations

Important Looker integrations include Salesforce and Slack. Looker allows teams to send data directly to Slack channels and use slash commands to search data within Slack. Looker is a useful solution for teams that heavily communicate via Slack and want to share LookML dashboards in those conversations. 

Looker also integrates with Snowflake, among other SQL databases and data sources. For data teams experienced in SQL, Looker provides the tools to create an infrastructure of databases with a central source of data insights. Consider Looker if your BI professionals need database-intensive data analytics and integrations or want to use Snowflake alongside Looker. 

Tableau offers API connections and native integrations with many other tools, including Splunk, Amazon Athena, and SAP HANA. It works with SQL databases and can retrieve data from tools like Shopify, Zendesk, and Snowflake. Tableau also has extensions for languages like Python and programs like MATLAB. If your team has data scientists or engineers that want to link their data models with their most commonly used programs or languages, consider Tableau. 

Salesforce acquired Tableau in 2019, and customers that use both benefit from being able to analyze Salesforce data in Tableau. If your enterprise has an established Salesforce platform, your sales, marketing, and operations teams may also benefit from a partnership with the business intelligence team to better understand CRM data and customer behavior. 

Bottom line: Tableau is beneficial for businesses that use Salesforce or store data in many applications. Looker is useful for businesses that want to compile data from many databases and is especially helpful for SQL-experienced teams. 

Learn more about APIs and connecting applications: What is API Integration?  

Looker vs. Tableau: Ease of use 

Looker works on Mac, Windows, and Linux systems; it’s a good choice for data teams that use a variety of devices and operating systems. It’s highly customizable, which gives BI professionals flexibility in designing models. However, its customizability may mean a learning curve, especially for inexperienced team members. 

Looker also receives highly complimentary user reviews for its customer support team. Consider Looker if your data team needs additional support from a BI vendor when implementing or using the solution. 

Tableau is a tool for new or small data teams that need to start creating visualizations and dashboards immediately. It’s relatively easy to learn. Aside from desktop and laptop computers, Tableau works on both iOS and Android devices. 

It has its own query language, VizQL, which allows teams to create visualizations based on the results from a query. VizQL supports SQL analytics capabilities while also letting data teams create temporary variables that don’t actually change the source data.  

Keep in mind that Tableau’s many features can be overwhelming for inexperienced BI professionals. For advanced data operations like modeling, Tableau doesn’t provide native features or templates. 

Bottom line: Tableau makes it easy to start creating visualizations, but exploring its other features takes time, as does the manual work required to compensate for its lack of modeling functionality. Although Looker’s high customizability might require heavy learning in the first few weeks or months, its customer support is a plus for teams that need extra help. 

Looker vs. Tableau: Pricing 

Looker pricing is available by requesting a quote from Google Cloud, its parent platform. Looker has multiple business plans, and generally speaking, its pricing is more affordable for larger data teams that have multiple users.   

A single Tableau creator license costs $70/month, and the price is multiplied for each additional analyst on the plan. Viewer licenses, designed for executives, cost $15/user/month and allow leaders to view dashboards and visualizations created by the analytics team. Tableau’s pricing is suitable for both SMBs and larger companies.

Bottom line: Tableau has the edge on pricing for small businesses. Looker’s customizable pricing plans allow enterprises to design a solution with the features and capacity they need. 

Is Looker or Tableau better for your business? 

Looker is a good choice for teams experienced in SQL and organizations with multiple SQL databases. It can be used alongside other data analytics tools like Snowflake. 

Large data teams that need the ability to develop models will benefit from Looker’s modeling tools. Looker is also a good solution for BI teams that have development experience and want to create their own apps or embed Looker analytics within existing company software. 

Small and inexperienced teams will benefit from the ability to immediately create visualizations in Tableau. Enterprises with many data sources, including multiple databases, also benefit from Tableau’s integration options. 

Consider Tableau if your enterprise or small business uses a variety of data sources or multiple non-Microsoft applications. Because Tableau supports so many data connectors, it’s a good choice for teams with scattered applications that need to pull all their information into one location for analytics. 

Considering other BI solutions? Read Top Business Intelligence Software next. 

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Best Business Intelligence Software: BI Tools Comparison https://www.cioinsight.com/big-data/business-intelligence-software/ Fri, 19 Aug 2022 21:32:22 +0000 https://www.cioinsight.com/?p=28101 Business intelligence (BI) and data analytics tools pull disparate data sources into a single platform for enterprise data and BI teams to glean comprehensive insights from their organization’s information. These software solutions make it easier to visualize trends in customer purchases, failed business operations, or company cybersecurity. Enterprises should consider which data analytics features are […]

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Business intelligence (BI) and data analytics tools pull disparate data sources into a single platform for enterprise data and BI teams to glean comprehensive insights from their organization’s information. These software solutions make it easier to visualize trends in customer purchases, failed business operations, or company cybersecurity. Enterprises should consider which data analytics features are the highest priorities for their team before deciding to implement a BI solution. 

Best business intelligence software

Built-in ETL capabilitiesPredictive analyticsFree version
Tableau❌✅❌
Sisense✅✅❌
Power BI✅❌✅
Domo✅✅❌
Dundas✅✅❌

Tableau

Tableau logo.Tableau is a data visualization and business intelligence tool for businesses of all sizes. It offers connections to many business data sources, which are applications or systems that store enterprise information. Users can also import data sets into Tableau from CSVs and Excel sheets. Consider Tableau if your business stores data in many different applications like SAP HANA, Salesforce, or Teradata. Tableau also integrates with multiple databases, including MySQL and MariaDB. 

Tableau offers drag and drop visualization features and provides relatively inexperienced BI software users with an easy starting point; it’s simple to jump into Tableau even without extensive prior experience. However, Tableau does have extensive features, so learning to use it in depth might require more time. Tableau is also a good choice for data teams that have limited or no experience with programming or databases because many of its features don’t require coding.  

Tableau doesn’t have native extract, transform, and load (ETL) operations, so data will need to be cleaned and pre-processed before analysis within Tableau. It also doesn’t work well with unstructured data, requiring a query language to parse. 

Additionally, although Tableau is able to analyze large data sets, it can run slowly while doing so. Consider your business’s data processing needs before purchasing Tableau. 

Tableau has no native data modeling capabilities. It’s one of the most popular visualization tools, but consider another data solution if your BI team needs extensive modeling features. 

Considering a data modeling solution? Read Best Data Modeling Tools.

Sisense

Sisense logo.Sisense is an embedded analytics platform for businesses that need BI installed within their most frequently used applications. Sisense can be deployed in the cloud, on-premises, or in a hybrid environment. 

Sisense has a containerized microservices architecture, which allows it to scale when enterprise data requirements increase. For example, when a high-performing application needs heavier analytics to parse large volumes of data, Sisense automatically uses more processing resources to analyze that data.

Through the containerized architecture, Sisense is also able to support auto-scaling environments like Amazon Elastic Kubernetes Service. Consider Sisense if your enterprise runs container services and needs to scale analytics for those platforms on demand. 

Sisense has APIs for each endpoint, which allow users to customize both the front-end visuals and back-end security and monitoring of their applications. Enterprise data teams that want the opportunity to heavily change and develop their BI solution will benefit from Sisense’s customizability.

Note that some operations in Sisense require knowledge of languages like SQL or JavaScript. BI teams with no development experience may struggle with editing elements in Sisense. However, if your business’s data team members have a background in development, they may enjoy the opportunity to use the multiple programming languages supported by Sisense. The coding features are useful for teams that want to specifically customize analytics for their business applications and have the expertise to do so.  

Power BI

Power BI logo.Microsoft Power BI integrates with many solutions in Microsoft’s extensive enterprise portfolio, including Excel, SQL Server, and Azure Cloud storage. Consider Power BI if your organization uses Azure Cloud, Azure Database or SQL Server, or Excel for the majority of its data storage. However, if your business and data analytics teams frequently use tools and data sources that aren’t Microsoft Office or Azure, Power BI may be difficult to implement. Though it works well with Microsoft products, Power BI integrations might be inconsistent or entirely unavailable for data sources outside the Microsoft umbrella.

Power BI offers data visualization features that teams can use to create detailed reports. Power BI Desktop also has a free version—smaller businesses with limited budgets will benefit from its free visualization and reporting capabilities.  

Users can configure row-level security for data models imported into Power BI, which restricts other users’ access to certain rows of data. Note that row-level security doesn’t restrict data access for any users within a workspace, but for individual rows in models or datasets. 

Power BI also offers data access monitoring, which helps businesses comply with the access requirements of regulatory standards.

Power BI  consumes heavy amounts of RAM; ensure that your machines can handle processing large volumes of data before purchasing the solution. 

Domo

Domo logo.Domo is a solution for larger organizations and teams focused on data reporting. Domo offers ETL features that require no knowledge of SQL—users can drag and drop datasets to have them cleaned and transformed. 

Domo offers a feature called Stories for customizing dashboards; users can design the layout of their dashboard and add filters for viewers to apply. Users are also able to customize the layout with templates, drag and drop cards, and headers. 

Stories are available on computer, tablet, and mobile interfaces. If your enterprise’s data team plans to make regular data presentations to executives or other business leaders, Domo may be a good choice.  

Domo’s embedded analytics solution allows data teams to schedule automated reports and share dashboards and visualizations with the organization’s customers and partners. Partners are able to create their own visualizations using the available data. Domo is a good choice for businesses that regularly communicate with third parties and want to give them better access to analytics. 

Some users experienced a learning curve when they first began using Domo; it might take time for data teams, especially inexperienced members, to learn. Additionally, Domo can be expensive for small businesses or companies on a budget. 

Dundas

Dundas logo.Dundas is a data analytics platform for enterprise-sized businesses and software providers. It offers a wide range of business intelligence and analytics features to customers, including predictive analytics and performance metrics. Dundas is known for its many features, and large or experienced data teams will particularly benefit from its advanced BI tools. 

Dundas has open APIs and also connects to multiple data sources, including OLAP, relational databases, and text files. Consider Dundas if your enterprise needs to analyze data from many disparate sources or applications, particularly data living in text files and databases.  

Dundas provides white-label analytics services, which allow businesses—specifically software providers—to customize their analytics to match their own company’s branding, giving their customers a unified brand experience. Dundas’s solutions for software vendors provide interactive dashboards and data preparation. Dundas is a programmable platform designed with developers and software providers in mind. 

Dundas may be expensive for startups or very small businesses. Keep in mind that although its many features are beneficial for larger enterprises, they might be overwhelming for small companies or inexperienced BI teams. 

What does BI software do?

BI tools use aggregated data from storage like databases, data warehouses, data lakes, and cloud storage solutions. They can also use APIs to draw data from business applications, like CRM, ERP, and collaboration tools. They provide insights about data that otherwise would be sitting in large quantities, siloed from other enterprise information. BI tools make data palatable for enterprises. 

BI tools allow teams to create dashboards of distilled information. Dashboards include reports, charts, and graphs that help businesses visualize their data. They’re able to more clearly see when sales or production is improving or declining. Dashboards also give teams a reporting tool for presenting data to executives or other business leaders. 

BI software alerts data teams when data significantly changes, in case something needs to be addressed immediately. Examples include a sudden drop in sales, strange website traffic numbers, or supply chain/shipping delays. 

Some BI solutions also include predictive business analytics, which takes data from events that have already happened and uses that information to predict or prescribe what may happen later. Predictive analytics help businesses make preemptive decisions and avoid future problems. 

Why use BI software?

Business intelligence software and analytics tools observe trends within data that would take a much longer time for human analysts to track. Although data teams are necessary to understand the full context of a potential problem or opportunity, they save significant manual work by letting a BI solution gather initial information, identify trends or anomalies, and present the data visually. BI tools speed and simplify the analytics process.

BI tools provide real-time reporting, so businesses can plan for rapid changes as well as long-term ones. Organizations will be less likely to experience disruption to an unexpected problem if they can identify and mitigate it earlier. 

Overall, business intelligence is a resource for more reliable, data-based decisions. It serves BI and data teams by giving them a single location with data insights through charts, reports, and dashboards. 

Choosing the right BI tool for your business

To select a business intelligence solution for your organization, consider the tools your company needs, the ideal deployment, compatibility with data sources, and how easy the BI software is to learn and use.  

Critical features 

Select a tool that offers the features your business and data team specifically need. You’ll likely need to look to the future and your data strategy in the next 5-10 years to find a tool that will fit the company’s growth, too. 

Deployment options 

Do you need a cloud deployment, an on-premises application, or a hybrid solution? Choose BI software with the deployment model your business needs. 

Integrations

Choose a solution that integrates with all your current data sources or offers a way to do this manually (through an API or data transfer, for example). Your teams will need to pull all relevant data into the software for it to be accurate and effective.  

Ease of use

Choose a tool with a learning curve that’s relative to the ability or experience of your team. Data teams with more experience will be better prepared to learn a more complicated, advanced solution. Consider what your organization plans to invest in the data team—for instance, giving employees time to get a Tableau certification. 

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Best Machine Learning Books to Read in 2022 https://www.cioinsight.com/books-for-cios/machine-learning-books/ Fri, 19 Aug 2022 18:06:39 +0000 https://www.cioinsight.com/?p=28088 Advertiser disclosure: We may be compensated by vendors who appear on this page through methods such as affiliate links or sponsored partnerships. This may influence how and where their products appear on our site, but vendors cannot pay to influence the content of our reviews. For more info, visit our Terms of Use page. Machine learning […]

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Advertiser disclosure: We may be compensated by vendors who appear on this page through methods such as affiliate links or sponsored partnerships. This may influence how and where their products appear on our site, but vendors cannot pay to influence the content of our reviews. For more info, visit our Terms of Use page.

Machine learning (ML) books are a valuable resource for IT professionals looking to expand their ML skills or pursue a career in machine learning. In turn, this expertise helps organizations automate and optimize their processes and make data-driven decisions. Machine learning books can help ML engineers learn a new skill or brush up on old ones.

The most popular machine learning books

Beginners and seasoned experts alike can benefit from adding machine learning books to their reading lists, though the right book depends on the learner’s goals. Some books serve as an entry point to the world of machine learning, while others build on existing knowledge. 

The books in this list are roughly ranked in order of difficulty—beginners should avoid pursuing the books toward the end until they’ve mastered the concepts introduced in the books at the top of the list.

Machine Learning for Absolute Beginners: A Plain Introduction

Cover of Machine Learning for Absolute Beginners book.

Machine Learning for Absolute Beginners is an excellent introduction to the machine learning field of study. It’s a clear and concise overview of the high-level concepts that drive machine learning, so it’s ideal for beginners. The e-book format has free downloadable resources, code exercises, and video tutorials to satisfy a variety of learning styles.

Readers will learn the basic ML libraries and other tools needed to build their first model. In addition, this book covers data scrubbing techniques, data preparation, regression analysis, clustering, and bias/variance. This book may be a bit too basic for readers who are interested in learning more about coding, deep learning, or other advanced skills.

The Hundred-Page Machine Learning Book

Cover of The Hundred-Page Machine Learning Book.

As the name implies, The Hundred-Page Machine Learning Book provides a brief overview of machine learning and the mathematics involved. It’s suitable for beginners, but some knowledge of probability, statistics, and applied mathematics will help readers get through the material faster.

The book covers a broad range of ML topics at a high level and focuses on the aspects of ML that are of significant practical value. These include:

  • Types of machine learning
  • Notation and common definitions
  • Fundamental algorithms
  • Anatomy of a learning algorithm
  • Neural networks and deep learning

Several reviewers said that the text explains complicated topics in a way that is easy for most readers to understand. It doesn’t dive into any one topic too deeply, but it provides several practice exercises and links to other resources for further reading.

Introduction to Machine Learning with Python

Cover of Introduction to Machine Learning with Python book.

Introduction to Machine Learning with Python is a starting point for aspiring data scientists who want to learn about machine learning through Python frameworks. It doesn’t require any prior knowledge of machine learning or Python, though familiarity with NumPy and matplotlib libraries will enhance the learning experience.

In this book, readers will gain a foundational understanding of machine learning concepts and the benefits and drawbacks of using standard ML algorithms. It also explains how all of the algorithms behind various Python libraries fit together in a way that’s easy to understand for even the most novice learners.

Python Machine Learning by Example

Cover of Python Machine Learning By Example book.

Python Machine Learning by Example builds on existing machine learning knowledge for engineers who want to dive deeper into Python programming. Each chapter demonstrates the practical application of common Python ML skills through concrete examples. These skills include:

  • Exploratory data analysis
  • Feature engineering
  • Classification
  • Regression
  • Clustering
  • Natural language processing

This book walks through each problem with a step-by-step guide for implementing the right Python technique. Readers should have prior knowledge of both machine learning and Python, and some reviewers recommended supplementing this guide with more theoretical reference materials for advanced comprehension.

Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow

Cover of Hands on Machine Learning with Scikit_Learn book.

Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow provides a practical introduction to machine learning with a focus on three Python frameworks. Readers will gain an understanding of numerous machine learning concepts and techniques, including linear regression, neural networks, and deep learning. Then, readers can apply what they learn to practical exercises throughout the book.

Though this book is marketed toward beginners, some reviewers said it requires a basic understanding of machine learning principles. With this in mind, it may be better suited for readers who want to refresh their existing knowledge through concrete examples. 

Machine Learning for Hackers: Case Studies and Algorithms to Get You Started

Cover of Machine Learning for Hackers book.

Machine learning for Hackers is written for experienced programmers who want to maximize the impact of their data. The text builds on existing knowledge of the R programming language to create basic machine learning algorithms and analyze datasets.

Each chapter walks through a different machine learning challenge to illustrate various concepts. These include:

  • Data exploration
  • Classification
  • Ranking
  • Regression
  • Regularization
  • Optimization

This book is best suited for intermediate learners who are fluent in R and want to learn more about the practical applications of machine learning code. Students looking to delve into machine learning theory should opt for a more advanced book like Deep Learning, Hands-on Machine Learning, or Mathematics for Machine Learning.

Pattern Recognition and Machine Learning

Cover of Pattern Recognition and Machine Learning book.

Pattern Recognition and Machine Learning is an excellent reference for understanding statistical methods in machine learning. It provides practical exercises to introduce the reader to comprehensive pattern recognition techniques.

The text is broken into chapters that cover the following concepts:

  • Probability distributions
  • Linear regression and classification models
  • Neural networks
  • Kernel methods and sparse kernel machines
  • Graphical models
  • Mixture models and expectation-maximization
  • Approximate inference
  • Sampling methods
  • Continuous latent variables
  • Sequential data

Readers should have a thorough understanding of linear algebra and multivariable calculus, so it may be too advanced for beginners. Familiarity with basic probability theory, decision theory, and information theory will make the material easier to understand as well.

Mathematics for Machine Learning

Cover of Mathematics for Machine Learning book.

Mathematics for Machine Learning teaches the fundamental mathematical concepts necessary for machine learning. These topics include:

  • Linear algebra
  • Analytic geometry
  • Matrix decompositions
  • Vector calculus
  • Probability and statistics

Some reviewers said this book leans more into mathematical theorems than practical application, so it’s not recommended for those without prior experience in applied mathematics. However, it’s one of the few resources that bridge the gap between mathematics and machine learning, so it’s a worthwhile investment for intermediate learners.

Deep Learning (Adaptive Computation and Machine Learning Series)

Cover of Deep Learning- Adaptive Computation and Machine Learning Series book.

For advanced learners, Deep Learning covers the mathematics and concepts that power deep learning, a subset of machine learning that makes human-like decisions. This book walks through deep learning computations, techniques, and research including:

  • Deep feedforward networks
  • Regularization
  • Convolutional networks
  • Sequence modeling
  • Linear factor models
  • Autoencoders
  • Representation learning

There are about 30 pages that cover practical applications of deep learning like computer vision and natural language processing, but the majority of the book deals with the theory behind deep learning. With this in mind, readers should have a working knowledge of machine learning concepts before delving into this text.

Read next: Ultimate Machine Learning Certification Guide

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