Everyone’s talking about big data and data quality services. The hubbub isn’t likely to shrink any time soon because big data is only getting bigger and becoming increasingly important to rein in and manage. As with any topic receiving a great deal of attention, several myths have emerged. Ready for some myth-busting?
Ted Friedman, Vice President and analyst from Gartner, debunked this myth at Gartner’s 2014 Business Intelligence & Analytics Summit in Munich, Germany. IT leaders believe that the huge volume of data that organizations now manage makes individual data quality flaws insignificant due to the “law of large numbers.” Their view is that individual data quality flaws don’t influence the overall outcome when the data is analyzed because each flaw is only a tiny part of the mass of data in their organization. “In reality, although each individual flaw has a much smaller impact on the whole dataset than it did when there was less data, there are more flaws than before because there is more data,”1 said Ted Friedman, vice president and distinguished analyst at Gartner. Instead of ignoring minor flaws, easy-to-use data quality tools can quickly correct or remove them.
This myth comes courtesy of Michel Guillet of Juice Analytics who shared some myths in an INC article, 3 Big Myths about Big Data. Comparing big data choices to grocery store shelves, Guillet illustrated how too many big data choices (i.e., metrics, chart choices, and so on) can quickly become overwhelming. Guillet suggests that when faced with uncertainty about data options, users may simply ask for all of it.
What he doesn’t say, however, is that too many choices often lead to indecision. For example, if you’re faced with, and overwhelmed by, too many different potato chip flavors plus traditional, low-fat, gluten-free, and baked options, name brands and store brands, you may grab one just to be done with it. Or, you might not choose one at all.
Guillet says that users want guidance, not more uncertainty and that expressing an interest in more data is an indicator of uncertainty. What they really want? According to Guillet, “…they want the data presented so as to remove uncertainty, not just raise more questions. They won’t invest more than a few minutes using data to try to answer their questions.”
Sure, customers may own their own data, but they don’t necessarily know how to extract meaningful information from that raw data. Guillet explains that you’re not selling them access to their own data. Rather, you’re selling algorithms, metrics, insights, benchmarks, visualizations, and other data quality services that increase the data’s value.
Not necessarily. While it seems like everyone else has already adopted a big data solution, or is in the process of doing so, according to Gartner, interest in big data technologies and services is at a record high, with 73 percent of the organizations Gartner surveyed in 2014 investing or planning to invest in them. But most organizations are still in the very early stages of adoption — only 13 percent of those we surveyed had actually deployed these solutions.1 The majority remain in the early stages of adoption.
While you may feel like you’re late to the big data and data quality services party, the party is just getting started! In fact, this is the perfect time to investigate your big data options. Data quality tools have been around long enough to be both innovative and yet mature enough to have had the bugs worked out of them.
These myths continue to make their rounds, but rest assured: our data quality services take care of data quality so even the tiniest of flaws are removed or corrected. We provide expert guidance, helping you to get the most out of the data quality tools available to you. Moreover, it’s not too late to get started. Simply sign up for a free trial key and try our data quality services in a matter of minutes.
Sources:
1 Gartner Newsroom, Gartner Debunks Five of the Biggest Data Myths,
https://www.gartner.com/en/newsroom/press-releases/2014-09-25-gartner-debunks-five-of-the-biggest-data-myths
http://www.inc.com/eric-holtzclaw/3-big-myths-about-big-data-.html
