The June, 2011 Gartner report, “Emerging Technology Analysis: Visualization-Based Data Discovery Tools” (available only to Gartner subscribers) is chock full of insights. Here are a few of my favorites:
- Business users are driving adoption of data discovery platforms. Because data discovery tools have light infrastructure requirements, are fast to deploy, and have relatively short sales cycles, they are spreading to places traditional BI tools haven't been able to touch. The gap between traditional BI platforms and data discovery platforms is widening because business users find the benefits of data discovery tools so compelling that they make this choice despite the risk of creating fragmented silos of data, definitions, and tools.
- The data discovery platform will surpass $1 billion USD in sales as early as 2013. Data discovery tools comprise one of the fastest-growing segments of the BI platforms market, mainly because they enable self-service BI. Gartner expects that by the end of 2013, data discovery tools will surpass $1 billion in annual software sales, and that until 2015 data discovery tools will outgrow the overall BI platforms market by a factor of three.
- IT organizations should consider deploying data discovery platforms as analysis sandboxes. Gartner recommends that instead of trying to prevent the spread of data discovery tools, IT organizations should position these tools as analysis sandboxes. (See related QlikView blog posts, “Analysis Sandboxes: Indispensable Tools for Insight Discovery” and “Traditional Analysis Sandbox Approaches Fall Short” and “Analysis Sandboxes the QlikView Way.”) Gartner points out that business users who "play with" these sandboxes will make discoveries that can then be shared with the broader user community, and that new data models can serve as rapid prototypes for sanctioned, corporate data models.
What Is a "Visualization-Based Data Discovery Platform," Anyway?
Below are Gartner's descriptions of the characteristics of visualization-based data discovery platforms, as well as some specifics about QlikView's capabilities.
|Characteristics of visualization-based data discovery platforms||QlikView's capabilities|
A proprietary data structure to store and model data gathered from disparate sources, which minimizes reliance on predefined drill paths and dimensional hierarchies
With QlikView, developers or business users extract the data needed for analysis from multiple sources (e.g., spreadsheets, web services, databases, and enterprise applications). This extracted data is stored in a single QlikView file (.QVW). The QVW also contains the user interface and scripts—everything the user needs to perform analysis. QlikView holds all data needed for analysis in memory, where it is available for immediate exploration by users. Users can quickly and easily see relationships and find meaning in the data, for a quick path to insight. A user can continue to click on field values in the application, further filtering the data based on additional questions that come to mind.
A built-in performance layer that obviates the need for aggregates, summaries, and precalculations
With QlikView, users experience zero wait time as the QlikView engine performs the calculations needed to deliver the aggregations users request. QlikView stores common calculations and shares them among users, so they don’t have to be recalculated every time someone needs them. QlikView also compresses data down to 10% of its original size. As a result, QlikView can scale to handle very large data sets rather than duplicating hardware investment costs to simply move the entire data set into memory. And unlike technologies that simply “support” multi-processor hardware, QlikView is optimized to take full advantage of all the power of multi-processor hardware, thereby maximizing performance and the hardware investment.
An intuitive interface that enables users to explore data without much training
Business users can get up and running with QlikView in no time at all. Once they learn the core concepts of QlikView's associative experience, they can immediately begin exploring data. Users can ask a question in QlikView in many ways, such as lassoing data in charts and graphs and maps, clicking on items in list boxes, manipulating sliders, and selecting dates in calendars. Instantly, all the data in the entire application filters itself instantly around these selections.
With QlikView, users can literally see relationships in the data. They can see not just which data is associated with the user’s selections—they can just as easily see which data is not associated. The user’s selections are highlighted in green. Field values related to the user’s selection are highlighted in white. Unrelated data is highlighted in gray.
Gartner summed up its take on data discovery tools with: “Although still a small part of the market, visualization-based data discovery tools have far-reaching implications for how business information is consumed, and will take an increasingly large stake as the front end for analysis, querying, exploration and ‘dashboarding.’”
At QlikTech we are seeing that business users (not “end users”) are buying QlikView to explore and solve business problems (not data problems). Thus the emergence of the term Business Discovery. For more info, please see the QlikView White Paper, “Business Discovery: Powerful, User-Driven BI.”