Introduction
If you search for “big data,” you will find many results. They number almost a billion. A third of those results will be startups that claim to be using big data to change the world.
A further third are VCs, including OpenView, which was mentioned in TechCrunch in their most recent Big data investment.
The remaining sections include Fortune 500 business leaders discussing why Big data development company is their top priority for 2013.
Everyone needs data, and the more remarkable, the better, the better.
Consider the following seven uncomfortable realities before you fire all of your mid-level executives:
1) Data ≠Knowledge. Data x Analysis = Knowledge
A massive data set may be a gold mine of insights in the hands of a skilled analyst with the appropriate tools and enough time. However, data is merely a set of numbers, and numbers cannot manage a business.
I previously touched on this subject in a blog post about big analytics. Still, I should have mentioned the inverse, which is also true: no matter how many advanced degrees and expensive tools your data scientist possesses, they will be able to provide you with any helpful insight with the appropriate data. A project needs both of these inputs to be successful.
2) Analysis and Data Compete for Important Resources
Large data sets are costly and time-consuming, regardless of how you decide to compile, store, and clean your data.
Building massive data will divert resources from studying the same dataset if there is a restricted budget (since what budget isn’t constrained?). A lack of resources for one input will impair your capacity to create data-driven judgments since Data x Analysis = Knowledge.
3) Data Always Appears to Be Vital. Sometimes it’s not.
Suppose you want to gather information about potential clients so your salespeople can make more individualized pitches. More info can’t hurt.
A remarkable study from Princeton and Stanford found that gathering relatively insignificant information might cause us to become distracted from the few crucial data.
4) Small Data Can Make a Big Difference
Another common misunderstanding among executives is that vast amounts of data are necessary to make data-driven choices. Frequently, only a few facts are sufficient.
Small data sets will only sometimes tell you the whole story, but the most significant findings frequently require the least amount of information. Statistical significance must be reached using a larger sample size for less obvious disparities in a data set.
5) The Complete Data Set Does Not Exist
The phrase “full data set” in the previous sentence is in quotes since there isn’t one. There is an endless quantity of data in the world, and practically all of it has no bearing on the thing you’re attempting to measure.
In addition to being a waste of time and effort, trying to quantify everything’s impact on everything else will probably result in type I errors like the Superbowl Indicator. If there isn’t a good chance that the data you are gathering will be helpful, don’t bother collecting it.
4) Decision-making is frequently made harder, not easier, by granularity.
Imagine you’re a B2B firm attempting to determine which sector attracts the most significant clients. Many executives will select the most granular option available when given a choice between several SIC code levels of granularity.
5) If you can communicate big data to the end user, it’s useful.
If my statistics or econometrics instructors do see this, they could retroactively fail me. But how you present your research is frequently much more crucial than the accuracy of your model or the comprehensiveness of your data collection.
Conclusion
It is advised to CEOs trying to use big data within their organizations, and it would be to concentrate on the insights rather than the scale of the data collection.
Frequently, a little, focused sample that has been adequately cleaned, gathered, and examined may teach us far more than a large, clumpy sample that has been contaminated, aged, and grainy.
Big data consulting company has the potential to shine for your company, but it needs the appropriate technology and personnel to back it up.
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