With a flexible approach to legacy applications, businesses have some leeway in how they introduce major infrastructural changes. Before committing to a new means of accessing data, though, organizations can try to predict the amount of value they will gain from faster, more efficient processes. Analytics and data sharing will grant enterprises more insight to work from.
Forbes recently referenced information from a CrowdFlower survey on the most common Big Data tasks. According to nearly 80 percent of respondents, data scientists spend the majority of their time "cleaning and organizing data," rather than collecting data sets, mining data for patterns or refining algorithms. At the same time, 57 percent of those surveyed found this to be the "least enjoyable part of data science."
Working with more data shouldn't mean more unnecessary complexity.In a recent ITProPortal piece, Mike Merritt-Holmes described the importance of gaining real positive impact from data options. He adds value to the traditional traits associated with Big data and calls it "key."
"Without extracting tangible business value from your Big Data solution, it is irrelevant how well the other 3 Vs are managed," Merritt-Holmes wrote. "To get maximum value from a Big Data strategy, you need to be organised internally, and to discover the full potential of your Big Data strategy, your entire organisation needs to have at least a baseline understanding of its potential."
Keeping value as a prominent goal may be increasingly important with the growth of more connected technologies. Iron Paper recently cited BI Intelligence Estimates data that shows nearly 35 billion devices will become part of the "Internet of Everything" by 2019. Setting up a mainframe emulator that anticipates this could continue to keep data "clean" and avoid unnecessary complication.
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