What does big data mean for you?

Big data has become a management fad like TQM, Six Sigma and other concepts that propagate like wildfire.

Wikipedia defines big data as “a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications. The challenges include capture, curation, storage, search, sharing, transfer, analysis and visualization.” The key word here is big, I suppose. But everything is relative.

 

Using data to create value

Lately almost everything in data analytics has been linked to big data, whether relevant or not. For small and midsized businesses, it doesn’t matter whether your data is small, medium or extra large. What matters is that you find the data, connect and integrate it, analyze it and use it to create value for your business.

The latter part of the equation is the most important — proving the positive impact of big data on the profit performance of firms. Although the concept has taken analytics by storm, more evidence-based research needs to demonstrate its profit power.

 

Getting started with data analytics

The power of data analytics is very real. Whether your data sets are big or small, here are some recommendations to get you started:

  • Don’t worry about size; focus on the data. Start by finding all sources of data in your organization. Try to create a map of where data is produced, stored and what it is used for. You will be surprised by how much data your business generates. From invoice transaction to product data, you probably store billions of records already.
  • Leverage the integrative power of all data. Once you identify and extract all your data, you might be ready to run some analytics. The key is to analyze each data set. The power of big data is to connect the data sets — whether marketing, sales transaction or supply chain data — and create predictive and explanatory models that help you make better decisions.
  • Blend intuition and science in your decision-making. All analytical models are flawed; they are incomplete and built on specific assumptions and hypotheses. The goal is to narrow down the level of uncertainty in your decision-making process. Intuition and experience still play a role. Try to reverse the balance of 80 percent intuition/20 percent science to 80 percent science/20 percent intuition.
  • Walk before you run. Depending on where you are in the analytics spectrum (from nowhere to embedded analytics), start slow. Before calling the big gun consulting companies, establish basic internal capabilities and run an audit of your infrastructure. IT systems from the ’80s and ’90s might have issues extracting the data and linking it.

Did I lose you? Don’t worry about it. “Big data sizes are a constantly moving target … ranging from a few dozen terabytes to many petabytes of data in a single data set,” according to Wikipedia. Did that say petabyte?

The point is to take your “big” data and pay more attention to the power of data analytics to support your decision-making process.