Problem: Big Data greatly increases the volume, velocity, and complexity of data streaming into the organization. Data comes from numerous disparate sources, which makes it harder to find correlations between seemingly unrelated phenomena. This is rather problematic when attempting to analyze this data.
Solution: By the nature of Big Data, collecting input from social networks, blogs, and public sources, along with your traditional customer, market, and operational data, results in significantly greater amounts of noise. Whatever is irrelevant to your goal analyses should be filtered out early on.
Heuristics based on domain expertise, along with time-series analysis, will throw out the noise yet leave in exceptions that might be important to detect trend breaks and quickly identify market changes and performance issues or opportunities.
The challenge of Big Data and manual analytical methodologies grows. With Big Data enterprise adoption rates growing year over year, companies face the daunting task of integrating and making sense of all this new information. Millions of tracked statistics, figures, and reports are coming in every week. These are a few of the many obstacles that stand in the way of harnessing the value of enterprise data.
For companies that look for real business value by analyzing this data, Verixhas identified five tips to help you through this process.