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 Gartner "Data discovery has become a mainstream architecture in 2012".[1]
Definition
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) SAS Vice-President for Best Practices 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 pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating."
DD vs. BI
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]
However, according to Wikipidia, Business intelligence is:
"Business intelligence (BI) is a set of theories, methodologies, architectures, and technologies that transform raw data into meaningful and useful information for business purposes. BI can handle large amounts of unstructured data to help identify and develop new opportunities. Making use of new opportunities and implementing an effective strategy can provide a competitive market advantage and long-term stability."
Which is far from a mere static report. So Data Discovery still waits for a more clear definition to set it in its own niche inside the BI space of tools, techniques and theories.
Players
According to GigaOm Sector RoadMap 2014, [5] [...] 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.
References
- ^ Kern, J., (2013), Data Discovery, SaaS Lead BI Market Review, Information Management
- ^ Dyche, J., (Nov. 20, 2012), Big Data "Eurekas!" Don't Just Happen, Harvard Business Review
- ^ Haggerty, John; Sallam, Rita; Richardson, James (6 February 2012). "Gartner Magic Quadrant for Business Intelligence platforms 2012". Gartner.
- ^ Schlegel, Kurt; Sallam, Rita; Yuen, Daniel; Tapadinhas, Joao (5 February 2013). "Gartner Magic Quadrant for Business Intelligence and Analytics platforms 2013". Gartner.
- ^ Brust, Andrew J (19 February 2014). "Sector RoadMap: data discovery in 2014".