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Analytics vs. Data Discovery

By   /  October 17, 2018  /  No Comments

Analytics vs. Data DiscoveryNewer and faster data channels are enveloping the digital business landscape, so business operators and users are becoming more demanding about what they want from their data. Now, the traditional Business Intelligence (BI) platforms are no longer sufficient to address all types of business queries; users are increasingly looking toward the Data Discovery capabilities of BI solutions to meet their daily needs. Today, every business decision is guided by Analytics, and Data Discovery tools provide custom solutions to situation-specific information needs.

As The Battle of Business Intelligence: Data Discovery vs. Traditional BI suggests, the visual features of standard Data Discovery platforms enable the users to pull data from different types of sources, and avoid the complexities of Excel to get answers to ad hoc queries. In a nutshell, Data Discovery offers Advanced Analytics without sacrificing speed of execution.


Traditional BI: The Emergence of Analytics through Exploration

For many years, all the major BI platforms offered a handful of tools to analyze raw data and report results. The visual representation of results was limited to pie charts, graphs, and pivot tables. The primary drawback was that business analysts had to be tools experts, which significantly limited the use of these tools within the IT corridors.

Then came intelligent dashboards, which beckoned the era of Data Exploration and Data Discovery. The dashboards managed to impress a wide range of users, from the C-suite executives to business staff, but still required the technical know-how of IT staff for comprehending the contents of the dashboard.

Data Discovery: Is it Just Exploration or More?

So What is Data Discovery Anyway? explains what separates Data Discovery from traditional BI: it offers speed, accuracy, usability, flexibility, and collaboration.

  • Data Discovery allows users to get quick answers to ad hoc queries, which may be mined from different sources.
  • The “visual” aspect of reporting makes comprehension easy.
  • Data Discovery is designed for custom solutions for individual problems rather than the standard solutions to standard problems of traditional BI.
  • The ease of data access enables enterprise-wide users to find and reuse visual “snapshots” for specific business situations.
  • Data Discovery adds some sort of “governance” to BI environments while working seamlessly as a core component of a traditional BI system.

Take the case of Big Data Analytics. The acquired data has to be explored and tuned before it can be fed to a Data Discovery tool. What does that mean? Typically, during the data exploration phase, both data and business experts are involved to refine the raw data to make it ready for further discovery. Later, during the Data Discovery phase, business users can retrieve answers from the visual representation of data, as explained in Data Exploration vs. Data Discovery.

This data exploration phase is critical to Business Analytics, because without this step, the users can easily end up using the “wrong” type of data, resulting in erroneous results. The basic shift from reporting results in traditional BI to visually uncovering insights in Data Discovery has prompted Citizen Data Scientists to readily adopt the platform. The dual assurance of data exploration, followed by data discovery, ensures that ordinary business users without deep tools skills can find solutions to their daily problems through “show and tell” methods.

Vendors like Qlik and Tableau have transformed the lives of analysts by adding data Discovery features in their traditional BI systems. Characteristics of Good Visual Analytics and Data Discovery Tools claims that “visual Analytics” makes it easy to convert insights into actionable decisions because the user has the privilege of viewing the results of an action.

A Notch Higher: Smart Data Discovery

Business Analytics has evolved to such a degree in the last decade or more, that along with modern technology and tools, the users have also gone through a radical mind shift from data-crunching to data exploration. Smart Data Discovery Will Radically Transform Analytics suggests that the days of complex data modeling are gone. Now, billions of data piles from different sources can be compiled and analyzed in seconds with advanced Machine Learning (ML) algorithms.

Gartner reports that:

“By 2021, the number of users of modern BI and Analytics platforms that are differentiated by smart data discovery capabilities will grow at twice the rate of those that are not and will deliver twice the business value.”

According to reputed research firms, Smart Data Discovery will be the next big thing in Analytics.

Both Data Scientists and BI experts act as facilitators of technology in business in the sense that they each look for hidden trends in data. The Data Discovery platform, in a way, brings Data Science closer to traditional BI. With the rising importance of data-driven decision-making in businesses, both BI and Data Discovery will play critical roles in ensuring business success.

The Battle for Supremacy: Traditional BI vs. Data Discovery

As businesses perceive “Analytics” as a game-changer in modern businesses, the war between data platforms will continue. Most traditional BI systems like Tibco Spotfire, Tableau, or Qlik have integrated Data Discovery features, but currently the all-encompassing preoccupation with AI has overshadowed all other technologies.

Very soon, we may see all major BI vendors offering bundled ML algorithms in their Analytics solutions; thus, the war may shift from Data Discovery vs. Traditional BI to Data Discovery vs. ML-enabled BI (Smart Analytics) systems.

The popularity of Self-service BI indicates that prepackaged, visual platforms may become the preferred BI platform for average businesses in future. An SAS Webinar discusses Advanced Analytics technologies like Self-service Data Mining tools for business analysts, Entity Analytics, and recommendation engines. As the AI/ML adoption grows for BI solutions, the strong indication is that business analysts and Data Scientists work together to deliver solutions to business problems.

McKinsey claims that for future businesses to take full advantage of data technologies, the “data” and “Analytics” must be integrated with the core business vision. The effects of data technologies will only become apparent when the internal processes, manpower, and core visions align to make technology adoption and execution a top priority.

Advanced Analytics: Nine Insights from the C-suite seems to indicate the Analytics Revolution has begun, and at least 50 percent of CEOs consider themselves active leaders of their enterprise Analytics agenda. However, the top management has to work with the CIOs to transform advanced data technologies into a goldmine of business opportunities.

Periodic Forrester reshuffles confirm that while the aggressive upstarts in the BI vendor market replaced the old guards, these upstarts themselves were replaced by some runaway winners. Thus the constant musical chairs continues in the volatile BI vendor market. According to Forrester, what will matter most for future BI systems is a healthy balance between self-service, enterprise, and Data Discovery features.

Augmented Analytics is the future of data and Analytics as ML-driven Analytics platforms continue to grow in popularity. With future Analytics powered by ML and NLP, who will need to worry about data centers and expert Data Scientists? Global businesses are probably heading toward an era of Business Analytics by functional heads.

Image Credit: kentoh/Shutterstock.com

About the author

Paramita Ghosh has over two and a half decades of business writing experience, much of which has been writing for technology and business domains. She has written extensively for a broad range of industries, including but not limited to data management and data technologies. Paramita has also contributed to blended learning projects. She received her M.A. degree in English Literature in 1984 from Jadavpur University in India, and embarked on her career in the United States in 1989 after completing professional coursework. Having ghostwritten and authored hundreds of articles, blog posts, white papers, case studies, marketing content, and learning modules, Paramita has included authorship of one or two books on the business of business writing as part of her post-retirement projects. She thinks her professional strength is “lifelong learning.”

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