Last month, the International Monetary Fund (IMF) pledged renewed efforts to bolster data integrity following a data-manipulation scandal involving its managing director, Kristalina Georgieva.
In a nutshell, the World Bank canceled a prominent report rating the business environment of the world’s countries after an investigation concluded that senior bank management pressured staff to alter data affecting the ranking of China and other nations (Georgieva was an executive at the World Bank at the time).
Many organizations don’t fully understand what data integrity is or what’s needed to achieve it.
Even the Treasury Department said that the United States, the largest shareholder at the IMF, saw a need to take proactive steps to “reinforce data integrity and credibility at the IMF.”
However, this whole incident unveiled something further: Many organizations don’t fully understand what data integrity is or what’s needed to achieve it, and yet it’s critically important to their success. In fact, the scandal surrounding the World Bank is one based on a lack of data governance and quality — which is only one of the four pillars of data integrity.
What Is Data Integrity?
Data integrity empowers businesses to make fast, confident decisions based on trusted data that has maximum accuracy, consistency and context. The integrity of data is built on four key pillars: enterprise-wide integration, data governance and quality, location intelligence, and data enrichment.
Here’s a bit more on each pillar.
Enterprise-Wide Data Integration
True data integrity relies on having data that is consistently integrated according to a defined standard that is applied regularly over disparate systems — providing a clear, unified view of the data.
In the case of the World Bank, this pillar was side-stepped. By not enforcing data standards used in the proven model, last-minute adjustments were made to engineer the desired results.
Data Governance and Quality
A sound data integrity strategy also depends on having a robust framework for data governance — which allows businesses to proactively find, understand, and manage their data, and access to that data.
This works in tandem with data quality processes that must be capable of managing and validating data across multiple systems, identifying gaps or discrepancies, as well as triggering workflows and processes to correct those errors. Ultimately, data governance and data quality work together to ensure trustworthy data is available throughout the business in a permission-based, secure, environment.
Data governance and data quality work together to ensure trustworthy data is available throughout the business.
It’s this point that’s most related to the World Bank controversy, as the data was not appropriately governed or validated, and discrepancies were not identified. Having strong data governance and quality processes in place would help to ensure the data being reported on is accurate, consistent and complete.
When fully implemented, a strong integrity program protects data-driven analysis from subjective or desired outcomes, such as those the published by the World Bank.
Virtually every data point in the world can be associated with location in one way or another, and organizations are increasingly using insights from location data for even smarter decision-making.
The third pillar of data integrity is location intelligence — which adds context to an organization’s data by providing additional insights based on the boundaries, movement, and the environment surrounding data related to customers, vendors, store locations, and other entities.
Location data could have been used by the World Bank to monitor and visualize the changes to input data through the process, showing which data was altered and the impact of those changes by country.
To fully build competitive advantage, organizations must also look to data enrichment, the fourth pillar of data integrity. When accurate third-party datasets are used to build further context, deeper and more thorough analysis is performed.
When accurate third-party datasets are used to build further context, deeper and more thorough analysis is performed.
Data related to location, business, climate, demographics, or other important factors is combined with existing data, making those assets more complete — and even more valuable.
The World Bank’s processes relied heavily on third-party data to build the “Doing Business” rankings. Had they better implemented the other aspects of integrity, they would have likely accurately reported the content of the gathered data.
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Everyone Is Solving for Data Integrity
Data integrity provides a firm foundation for data analytics and confident business decisions. But the IMF isn’t the only organization struggling to achieve data integrity.
In fact, according to recent research by Precisely and Corinium Global Intelligence, 82% of C-level data executives say data quality concerns represent a barrier to data integration projects. Further, 80% find it challenging to ensure data is consistently enriched with proper context at scale.
However, achieving integrity in data is critical, especially in today’s rapidly changing world as more companies undergo digital transformation and the rate at which data is created increases.
Data integrity is needed to provide a firm foundation for confident decision-making. It’s imperative that businesses begin to look beyond a focus on data governance and quality processes to ensure a solid data integrity framework that is also built upon data integration, data enrichment, and location intelligence.
It’s not just a matter of ensuring accurate data, but trusting that it has high integrity for confident decision-making.
If a company is not approaching data integrity holistically, they could be leaving money on the table. For example, a retailer looking to open a new store may choose a store location based on data that shows the area’s demographic meets its shoppers’ profile. But without location data, they may select a location that has limited parking options and not see the number of shoppers they hope for.
Data integrity is bigger and broader than many businesses realize. It’s not just a matter of ensuring accurate data, but trusting that it has high integrity for confident decision-making. By building a meaningful strategy around data integration, data governance and quality, location intelligence, and data enrichment, organizations can be confident that they are making smarter business decisions, and providing reports based on data they can trust.
The World Bank is a recent example of mismanaging data integrity in a way that impacts reputation, brand, and trust in a business or institution. The integrity of data is increasingly an extension of business integrity, and is a topic that businesses cannot afford to get wrong.
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