Data discovery

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Data discovery is a business intelligence architecture aimed at interactive reports and explorable data from multiple sources. According to the American information technology research and advisory firm Gartner "Data discovery has become a mainstream architecture in 2012".[1]


Jill Dyche calls data discovery 'Knowledge discovery' and defines it as "the detection of patterns in data. [...] These patterns are too specific and seemingly arbitrary to specify, and the analyst would be playing a perpetual guessing-game trying to figure out all the possible patterns in the database. Instead, special knowledge discovery software tools find the patterns and tell the analyst what--and where--they are."[2]

As the current (2013-2014) Vice-President for Best Practices at SAS Institute her definition not surprisingly resembles the definition of Data mining:

"Data mining (...) an interdisciplinary subfield of computer science, is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. Aside from the raw analysis step, it involves database and data management aspects, data preprocessing, model and inference considerations, interestingness metrics, complexity considerations, postprocessing of discovered structures, visualization, and online updating."

It can also be referred to as Business Discovery.

DD vs. BI[edit]

Data discovery and business intelligence are similar in that they provide the end-user with an application that visualizes data or big data. Yet the focus in data discovery lies more on the users of the application, less on the technical aspects. Furthermore data discovery focuses on dynamic, easy-to-use reports, whereas traditional business intelligence reports are static reports.[3][4]


Gartner first recognized the market for Data Discovery in 2011 citing QlikView as the "poster child for a new end-user-driven approach to BI". Today there are a number of vendors who are positioned as 'Data Discovery' vendors in the annual Gartner BI & Analytics Magic Quadrant report.[5] And in 2014, the Gartner Magic Quadrant for Business Intelligence and Analytics Platforms was significantly restructured to highlight the shift in the market toward these new vendors.[6]

According to GigaOm Sector RoadMap 2014,[7] [...] business users want streamlined applications that allow them to query and visualize data[...] They identified six Disruption Vectors to score DD players and based on that, Datameer and Tableau are the best-positioned data-discovery suppliers. Splunk, MicroStrategy, and SiSense are also strong, and Roambi is on the radar.


  1. ^ Kern, J., (2013), Data Discovery, SaaS Lead BI Market Review, Information Management/
  2. ^ Dyche, J., (Nov. 20, 2012), Big Data "Eurekas!" Don't Just Happen, Harvard Business Review.
  3. ^ Haggerty, John; Sallam, Rita; Richardson, James (6 February 2012). "Gartner Magic Quadrant for Business Intelligence platforms 2012". Gartner. 
  4. ^ Schlegel, Kurt; Sallam, Rita; Yuen, Daniel; Tapadinhas, Joao (5 February 2013). "Gartner Magic Quadrant for Business Intelligence and Analytics platforms 2013". Gartner. 
  5. ^ "Gartner 2014 BI Magic Quadrant". 
  6. ^ "Gartner 2014 BI Magic Quadrant". 
  7. ^ Brust, Andrew J (19 February 2014). "Sector RoadMap: data discovery in 2014". 

See also[edit]