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How Do Data Discovery Tools Differ From Traditional BI Systems An easy way to understand this difference is to look at the history of BI solutions.Traditional BI systems were an attempt to solve the difficulty of writing SQL queries in order to retrieve data such as sales information, customer information, shipping records etc.Before BI, users had to be highly familiar with SQL to get the data they needed out of such databases.Thus, traditional BI systems mapped a layer of familiar business terms known as a semantic layer onto the relational databases storage schemas, thereby allowing users to retrieve and combine data without knowing SQL at all.Traditional BI Semantic Layer.The semantic layer is a way of expressing a data model, or a schematic representation of the relationships between data in one or multiple datasets.In particular, the semantic layer schematizes the relationships between data residing in different data sourcesdatabases.For instance, the dimension customer in the semantic layer may be defined as grouping together information from both the sales orders database as well as the customer records database.Business. Objectslater acquired by SAPwas the first BI vendor to use the semantic layer model, and remains one of the most popular semantic layer based solutions.The semantic layer model is still suitable for large enterprises that need unified access to data stored in numerous operational databases.The problem with this model is that the semantic layer needs to be standardized across the organization.In other words, various business units must agree on which databases and tables in these databases the dimension customer will pull from.Moreover, once the semantic layer has been standardized, it remains under IT control.As you can see in the above diagram, traditional tools for ad hoc queries pass analysts queries through the semantic layer, which automatically translates them into SQL queries to retrieve data from SQL databases and other data sources that support SQL querying.Thus, traditional querying tools can only work with data sources that have already been integrated into the semantic layer.Data sources outside the semantic layer a spreadsheet sent in an email, a public data source on the web, 5.Tweets about a product recall etc.IT develops new processes.And, of course, IT cant develop a process for every new data source.When the semantic layer is standardized across the organization, the paths that analysts follow to retrieve and combine data get frozen into place.For instance, if the organization defines store as a subcategory of branch, and branch as a subcategory of sales region, while neglecting to slot customer somewhere into this hierarchy, blended analysis of sales and customer data can become overly complex.Business terms mapped to operational data in SAP Business.Objects. Data discovery tools remedy this situation by providing direct access to the operational databases shown in our chart, instead of going through a semantic layer.This allows users to combine spreadsheets and other data sources outside the semantic layer with operational data.Any data preparation work that needs to be done to combine data sources e.ID to customer is done on the fly, instead of forcing IT to standardize terminology across the organization.Additionally, users can develop their own data models during analysis, instead of being bound to the data model encoded in the semantic layer.This allows greater flexibility for sophisticated queries that depend on blending data from multiple sources.Capabilities of Data Discovery Software.Theres a wide range of data discovery platforms, meaning that listing specific features is pointless.Instead, lets take a quick look at the broad capabilities that define these solutions Graphical front end for data manipulation.Allows for data access and manipulation via visualizations of data sources and patterns in data.Instead of writing a query, you can simply click on a wedge of a pie chart to drill down, or choose a heat map visualization for your data.In memory processing.Processes data by storing it in RAM random access memory instead of writing it to disk.This gives them the processing power to blend massive data sets on a users laptop, instead of doing the blends in the database as traditional BI tools do.See our data blending report for more details.Big data connections.Supports direct connections to data sources, instead of confining access to sources within the semantic layer.Support for flat files.SQL databases. Beyond that, the range of data sources a tool can connect to is generally a point of competitive differentiation.Data cleaningpreparation.Offers features for cleaning and preparing data, since analysts cant rely on pre integration of data sources via a semantic layer.These features are for normalizing dimensions, removing trailing spaces, testing the accuracy of joins etc.Note Several of these definitions of data discovery capabilities were adapted from Gartner research reports, specifically What Data Discovery Means for You by Joao Tapadinhas and Dan Sommer available to Gartner clients.Types of Data Discovery Tools.Data discovery has been an emerging market for at least a decade, but instead of solidifying around a core set of concepts and features, the market has continued to evolve.Data discovery functionality has also been added to traditional systems that use semantic layers, though such systems will still be overkill for many small businesses.There are essentially three categories of data discovery solutions currently on the market Search engine like tools for textual searches of data.Visual interaction tools that provide a graphical front end for data manipulationAI based tools that do the bulk of the pattern recognition for you.Visual data interaction tools are analytics tools that directly access data sources instead of going through a semantic layer.They allow users to process massive datasets on their laptops via in memory caching engines and spot patterns using a visual interface.Data visualizations in Tableau.The point of a visual data discovery tool isnt simply to crunch numbers and then output pretty charts and graphs, which can easily be done with Excel and Powerpoint.Instead, these tools are for interactive manipulation of data via visualizations.For example, you can click on a particular city in a heat map to begin analyzing sales just within that citys stores.You can then add another dimension to your mapsay, aggregate payroll expenses per storeto blend sales and payroll data and spot new patterns.As you click on visualization elements and drag and drop dimensions and measures into your visualizations, an engine within the data discovery tool translates your gestures into SQL queries.Changing the visualization automatically refreshes it with newly processed data from your databases.These tools thus allow for highly interactive and sophisticated database querying without forcing users to learn SQL.Moreover, they allow users to access and blend data from multiple data sources that havent been integrated via a semantic layer. 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