Although visual data discovery tools give a faster insight and better exploration than traditional BI tools, these have two things in common i.e. data validation and user training. With the help of both visualization of data discovery tools and BI tools users can dig into the data with simple queries, find out patterns and trends by looking at the data graphically. Previously, there was involvement of IT in data analysis in the form of building a metadata layer of abstraction from a physical database scheme with many tables, but now the visual data discovery tools automatically models a metadata layer. It is of utmost importance to know how to use visualization effectively for data analysis. Thus, user training is still important for gaining success. Also data validation cannot be ignored because data validation policies and procedures must be applied before the data is entered into a database.To read more follow:- http://it.toolbox.com/blogs/it-solutions/two-ways-visual-data-discovery-tools-differ-from-traditional-biand-two-ways-they-are-the-same-74024

A metadata fabric that provides efficient data analysis and data driven decisions, is of great value to an enterprise which utilises IOT generated data. The metadata fabric presents data and analytics, together in a business consumable format and interface. The major types of metadata are maps, derivations and complex events. An enterprise’s first concern is the ability to create and store the metadata in a business friendly interface, which will also enable its exploration, its usage in data analysis and will accept updates with changing business trends. The metadata fabric needs to adapt itself to situations, where data values can change over time or appear in a fragmented manner or even encrypted, at times. The information, regarding the analyses performed by previous personnel needs to be a part of the metadata layer, available for successive employees. When users and systems are not able to search through, or update metadata, the metadata fabric is probably broken, and so it is, when data exists in disconnected islands. Read more at:

http://www.cio.com/article/2939114/data-analytics/the-grand-unified-theory-of-metadata-governance.html

Like everything else, businesses must also do yearly cleaning of their contents to remove redundant, obsolete and trivial data from their systems commonly known as ROT. This ROT makes it difficult to find data which are important and useful. A few tips to do this daunting tasks of data cleaning given by experts are: • Identification of relevant and critical data for the business from the identified repositories with the help of Subject Matter Experts (SME)

• Analyzing data in these repositories which are suspected to be important with the help of SMEs, automated processes and leveraging software.

• Establishing metadata for finding and retrieving documents, access control, privacy policies and potential business value

• Metadata needs to be classified for all documents in the repository. This classification process reveals the ROT.

These essential chores can lead to cost-effective information governance by eliminating ROT.

Read more at: http://www.cmswire.com/cms/information-management/do-your-chores-clean-out-your-data-029232.php