Eight Classic Big Data Mistakes

Eight Classic Big Data Mistakes

Focusing on Technologies Instead of BusinessFocusing on Technologies Instead of Business

CIOs and IT leaders too often lose sight of business requirements because they get caught up in the big data infrastructure planning. Work with business closely to ensure tech aligns with needed outcomes.

Not Knowing What You're Looking ForNot Knowing What You’re Looking For

Always precisely determine what you seek at all stages. Otherwise, your analytics team will get lost amidst the big data.

Disregarding ContextDisregarding Context

All data should be assessed for context and relevancy or it will never translate to usable business value.

Dismissing BiasDismissing Bias

Preconceptions help guide teams toward effective analytics. But they can also skew conclusions.

Shortchanging Data QualityShortchanging Data Quality

Deploy tools such as language correction libraries to process unstructured data in order to ensure its integrity.

Not Securing Data SponsorshipNot Securing Data Sponsorship

To avoid pushback somewhere high along the leadership chain, you need an executive influencer to serve as an advocate for the data.

Not Executing a Cost-Benefit AnalysisNot Executing a Cost-Benefit Analysis

Every project needs its own assessment. If you simply apply an initial use case model to all of those projects which follow, the cost analysis may veer off target.

Dwelling on What Already HappenedDwelling on What Already Happened

While you need to understand why a customer behavior ortrend resulted in a certain metric, the point of analytics is translating that into what’s going to happen next.

Dennis McCafferty
Dennis McCafferty
Dennis McCafferty is a contributor to CIO Insight. He covers topics such as IT leadership, IT strategy, collaboration, and IT for businesses.

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