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Data Integration: The Evolution of Analytics

The massive growth of technology has introduced new and improved ways to collect and analyze data. Further, the capabilities of these new technologies are leading to data integration never before seen or used – and they’re showing no signs of slowing down.

A Brief History of Data

Without scrolling as far back as ancient Egypt to the first hypothesized statistical data gathering, data analysis has been used historically in the corporate world as a way to evaluate both company performance and identify growth or limitations within any environment (expansion opportunities, expectations in employment growth, etc.).  However, over time, the value of data has morphed into something so much greater. It’s not just important, it’s vital, and it’s no longer limited to traditional business intelligence.

Advanced Analytics Becoming More Accessible

The days of statisticians being absolutely necessary are long gone given the list of alternatives out there. While still incredibly valuable, their responsibilities have expanded from data gathering and analyzing to interpreting. Statistical software programs such as Alteryx, SPSS, and SAS are now providing advanced analytics capabilities to novices and masters alike. Even with all the software programs available, the need for integration has become increasingly necessary. Data streams require constant evaluation and interpretation and.

Data Integration or Bust

Proprietary data, while still vital, has been inundated with additional data streams thus preventing it from being completely sufficient on its own when digging deep for the "big picture.” But more data means more information to sift through. “The majority of raw data, particularly big data, doesn't offer a lot of value in its unprocessed state. Of course, by applying the right set of tools, we can pull powerful insights from this stockpile of bits,” says Dr. Michael Wu, chief scientist of San Francisco-based Lithium Technologies, which develops social customer experience management software for businesses. Market research companies are now integrating multiple data streams in order to produce more robust research studies to provide a more in-depth picture to their clients. These data streams can come from multiple research studies and other various types of data such as:

  • Geospatial data (location)
  • Qualitative data gathering, scraping, and analysis
  • Large quantitative data sets that use an increased sample size to allow for customer confidence

The key is to integrate all data sets to establish patterns, then use those patterns to identify value. So what’s next?

The Future Data Analytics

The growth of qualitative data has resulted in the creative integration of data sets like social media, standard survey responses, video and sound analysis. The future of data analytics is continuously taking shape and developing into more experimental tools, such as geospatial and real-time mobile tracking technologies. The changes in technology and the steadily-increasing access to information have allowed us to gather insights from virtually every data point. But, no one can truly anticipate what the future holds…or can they?

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