How Data Discovery Tools Influence Business Users' BI Product Buying Process

By CIOReview | Friday, August 26, 2016
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Until recently enterprises were often frustrated with the lack of a significant tool for gaining relevant insights from the large datasets at their disposition. The existing Business Intelligence (BI) solutions were squandering the company’s resources, both money as well as time, though they had been packed with so many capabilities. Then came a new set of tools for data discovery which brought a new wave in the field of data analytics for enterprises. Data discovery tools soon became enterprise favorites as it had its own intuitive features as well as new ones borrowed from the legacy BI solutions. With data discovery tools, enterprises gained insights, much faster and more relevant, in addition to attaining better decision making abilities through predictive analytics. Following are the most essential elements of data discovery that influences the current BI buying process for business users.

Data Discovery Tools: Search Based

Data discovery tools allow users to analyze structured and unstructured data via queries through text. Data discovery, more often than not, is designed for displaying quantitative data, but with the advent of search tools, their application now extends to qualitative data as well. In addition, search tools employ modeling and storing data which reduces enterprises’ dependence on traditional BI. Moreover, with the entry of data visualization tools users can now search both quantitative data as well as qualitative data via text or through visualizations.

Data Visualization Tools

As mentioned earlier, data visualization has given a new paradigm shift for data discovery applications. Data visualization tools enable users to “visualize” data in different forms other than the usual spreadsheets, reports, or tables. Also, it allows users to alter images, colors, and brightness; thereby enabling them to interpret patterns hidden within datasets, faster and better.

Dashboards are a type of visualization tool, but are usually considered distinct from regular data visualization as they perform diverse functionalities. Dashboards display different sources and forms of data, while data visualization tools are used for exploration and in detecting latest trends. Moreover, dashboards are much more suited for real time data monitoring as it shows data in easy ways than visualization tools.

 In addition, the arrival of GPS technologies along with the mobile wave has led to the introduction of “geospatial technologies” in visualization. Businesses can leverage this technology for improving their field sales through mobile employees.

Data Mashup Capabilities

Data discovery vendors are now integrating data mashup capabilities into their products which allow users to augment their analytics as well as business intelligence. Data mashup capabilities also help businesses in pin-pointing the required bit of data rather than chopping down the entire forest for it. Furthermore, data mashup refines business intelligence through predictive analytics by finding and combining data from different or disparate sources. This feature enables enterprises to generate value from unlikely and unexpected venues.

In-Memory Analytics

Like both data visualization and data mashup, in-memory analytics has now become a core feature in data discovery tools. Typically, information in business intelligence in stored upon physical disks while in-memory analytics stores information, datasets or queries, in the RAM.

According to Gartner, “In-memory computing (IMC) is an emerging paradigm that enables user organizations to develop applications that run queries on very large datasets or perform complex transactions at least one order of magnitude faster – and in a more scalable way – than when using conventional architectures.”

In-memory analytics is now possible because of the emergence of 64-bit architectures. Compared with 32-bit architectures, 64-bit architectures can deal with more memory resources as well as bulkier files. With this feature enterprises can now process large datasets in much lesser time than it took in traditional physical disk-based analytics.

Conclusion

The line between data discovery applications and BI is getting more blurred, with every passing day. Moreover, with the arrival of new features like data mashup and in-memory analytics, enterprises are entering a new of dawn of predictive analytics, leaving behind their traditional analytical techniques. Enterprises have always faced problems on gaining relevant insights, though there were a number of analytical tools available. These tools used complex models which in turn resulted in IT backlogs and more obstacles than insights. However, with the latest data discovery tools in hand, securing insights doesn’t seem much of a problem.