As organizations continue to wrestle with big data, they are quickly realizing that the real challenge lies in the volume. Today, companies of all sizes and budgets can throw their hats into the big data arena. Studies show that most businesses are clearly invested in capturing and storing data. But, just having data in your reservoirs doesn’t quite cut the mustard. Raw data alone won’t reveal any secrets. You need valuable data. For data to be valuable, it should be able to predict business outcomes. Failing to process data effectively could lead to “data obesity” – abundance of non-actionable data. How can you overcome this problem? I’ll discuss three ways to maximize your profits when working with big data.
Contextualize data
In business intelligence, it is extremely important to break down big data sets into smaller subsets. This is based on their context of application. Most businesses can build contextualization around three basic data types: Demographic data, historical data, and situational data.
Demographic data helps to shape the buyers’ personas by analyzing purchase preferences, patterns, and/or digital channel interactions. Historical data tracks the past records of customer interactions and predicts future behavior based on those insights. Situational data reveals the changing patterns of customer behavior across various points in time.
In order to gain the correct insights, organizations need to narrow down the data they are analyzing, eliminating unimportant data sets and focusing instead on the most useful ones for their business.
Capitalize on both structured and unstructured data
When it comes to analytics, the four Vs of data – volume, variety, velocity, and veracity – beat out algorithms currently in place. Customer calls, emails, text messages, and social media feeds generate unstructured data. This contributes to 80 percent of the data organizations store. However, unstructured data is the most difficult to understand. As a result, it remains largely unused by many organizations. While the maximum effort is put towards processing structured data, organizations should pay attention to unstructured data sitting idle in their data lakes.
Tie information together to create business insights
The final step is assimilating all the information extracted from the data — structured and unstructured — to create a complete picture. This should clearly provide answers to the business questions they are expected to solve. The right visualization tools are extremely helpful in making data results comprehensible and tangible.
The future of big data isn’t in the “amount” of data we are gathering today – which can actually be crippling. The future of big data lies in corporations filtering out the most accurate business insights. This will also lead to potential growth area for MSPs and VARs. They could tackle data requirements of many unequipped companies by providing an “As-A-Service” model for delivering big data insights. As companies use big data to outmaneuver their competitors, leveraging data insights will be a major determining factor in their success.
This post was brought to you by IBM for Midsize Business (Now PivotPoint) and opinions are my own. To read more on this topic, visit IBM’s Midsize Insider. Dedicated to providing businesses with expertise, solutions and tools that are specific to small and midsized companies, the Midsize Business program provides businesses with the materials and knowledge they need to become engines of a smarter planet.
Image: Creative Commons
