Happy Friday. 5 Big Data Best Practices.
Big data is a big buzzword. It also fuels a big, powerful tool: Business Intelligence (BI). But how do you know if your company is using big data and BI for the best results?
In this week’s Happy Friday post, we share resources to help you ensure you’re doing it right!
In her 2014 TED Talk, Susan Etlinger, an industry analyst with Altimeter Group shares a very personal story where she focuses on data intelligence, analytics and strategy. She also challenges her audience to consider how we create meaning from data (rather than how data creates meaning).
Christopher Surdak, author of Data Crush: How the Information Tidal Wave Is Driving New Business Opportunities, writes about the six signs that you’re going to fail at big data, and The Boston Consulting Group offers advice on how to avoid the big data trap.
Also, Seth Demsey, Chief Technology Officer for AOL Platforms, points out that BI isn’t the same thing as data science.
So, where do you begin with big data and BI?
1. Think about your questions. No matter how much data you collect, if it’s not the data you need to answer your primary business questions, almost all of your big data will be (virtually) useless.
2. Consider your storage and access. Ensure the right people have access to the right data—and that your data is safe and secure. Today, quality cloud services can resolve many of these headaches.
3. Examine your data. Making assumptions about the data and then analyzing “bad” (e.g., inaccurate, incomplete, or untimely) data costs you time and money. Making decisions based on bad data costs you even more.
4. Assign an owner. If no one is responsible for the data you collect, store, analyze, and base your business decisions on, you risk bad big data. You may consider multiple owners for different data sets, or a coalition across business teams, but don’t make the mistake of assuming or failing to assign ownership.
5. Pay attention to the gaps. Although you will collect data to answer your questions, you may notice gaps that lead to more questions. Pay attention to these gaps, especially as you revisit and revise your (big) data collection and BI approach.
Have a Big Data horror story? Tell us in the comments!
P.S. Check out this playlist of 13 TED Talks: Making sense of too much data.