2Myth 1: Having More Rapidly Accessible Data Is Everything
3Myth 2: Siloed Data Can Easily Be Connected Across Business Units
When architecting big data initiatives, it’s easy to assume that connecting the data will be a straightforward, simple process. However, there are organizational and technical challenges. Data managers from different business units often disagree on the best methods for sharing data and what to standardize on (e.g., unique identifiers or primary keys). Typically, each business unit focuses on summary data to answer specific questions—excluding data important to other units—and frequently lose sight of the entire picture.
4Myth 3: Insights Remain Relevant Indefinitely
5Myth 4: Tracking, Measuring All Enterprise Data Sets Is Key to Success
Simply tracking and measuring everything without tools to filter the noise results in unnecessary technical overhead, processing and access control issues, or worse—misleading conclusions. With the right tools in place, the least-suspected data element can be the one that provides the “Ah-ha!” moment.
6Myth 5: One IT Solution Is the Answer to Big Data
7Myth 6: Big Data Transforms Everything and Everyone
The impact of big data is likely to be incremental and subtle. We don’t need volumes of data to identify the biggest trends—they’re obvious. It’s getting to the nuggets of critical information hidden in the exabytes of stored data that will help decision-makers make the best choices at the best time in context.
8Myth 7: Big Data Requires Data Scientists for Any Successful Project
9Myth 8: Big Data Is Brand New
10Myth 9: Big Data Requires a Big Budget
11Myth 10: Big Data Is Clean, Structured Data
Big data is more likely to be messy, unstructured, “dirty” data that requires a lot of scrubbing—sometimes manual, but more often automated—to perform meaningful analytics.