How Big Data Can Be A Big Problem

Big Data is considered a business’s most valuable asset, so it’s understandable that organizations often strive to collect as much of it as they can. The more data you have, the more opportunity for insight, right? That mentality has spawned the era of big data — which is the attempt to analyze and extract patterns, trends or associations from data sets that are too large to handle through traditional computation. Yet in my experience working for a company specializing in driving data in real time, I’ve found that for many organizations, fixation on endlessly collecting more and more data in the belief that it will magically generate value can be a fool’s errand. Simply accumulating every bit of data available to the business isn’t always the key to better results. It can actually lead to serious obstacles when trying to derive value from what has been collected. 

The more data businesses have, the more difficult and time-intensive it is to process and analyze it. This added complexity can delay crucial decisions and actions, delays that will ultimately hurt the business. It also puts the most predictive and important data — recent data — further out of reach as it’s stored for review at a later date. 

Here’s a simple thought experiment: Am I better able to make a decision or take advantage of an opportunity if I know what’s happening right now in the present moment, or what was happening at some arbitrary point weeks, months or years ago? In most instances, it’s that immediacy that offers the most valuable insight. And anything that hinders fast decisions based on “right now” data hampers success. For many organizations, this requires a complete 180-degree turn in thinking. Instead of focusing on accumulating more data and hoping to derive value later, businesses should focus on getting immediate value out of key information as it streams into the organization. Acting and reacting to this fast data can lead to real progress toward specific business objectives but requires teams to approach their data in new ways. 

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Nastel Technologies uses machine learning to detect anomalies, behavior and sentiment, accelerate decisions, satisfy customers, innovate continuously.  To answer business-centric questions and provide actionable guidance for decision-makers, Nastel’s AutoPilot® for Analytics fuses:

  • Advanced predictive anomaly detection, Bayesian Classification and other machine learning algorithms
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