Eight Classic Big Data Mistakes

Eight Classic Big Data Mistakes

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.

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

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

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

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

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

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.

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.