Big Data Challenges | Big Business Successes

Big Data's 3 Great Challenges | Volume, Velocity and Variety

Business analysts and data scientists call the three main Big Data challenges the "3 V's," as were first detailed in a fundamental work written by Gartner Research VP and data analysis professional Doug Laney, back in 2001. The 3 V's that Laney articulated are known as Volume, Velocity and Variety. While more V’s seem to be talked about today, Laney’s foundation is still the underpinning for most thought and philosophy on Big Data strategy.

Volume

Volume is at once a two prong challenge. One problem has been easily solved with digital technology: storage space. With clouds and the ever increasing disk space, it is simple to store data, but because of this we tend to store everything.

The other problem is sorting through bad, dead, irrelevant or old data. This hassle becomes the new norm ultimately slowing down analysis by decreasing sight of good data, and creating new varieties that are being housed without an ability to read or relate that data yet.

Style Intelligence can attach to a wide variety of data sources, from cloud-based sources like Salesforce to Big Data sources such as Hadoop. It can also be programmed to preprocess and filter incoming irrelevant data, eliminating old and dead data without needing to delete it or modify the data source itself. InetSoft even offers its own remote cloud hosting which amplifies the Style Intelligence convenience. Compatibility with Big Data sources as well as a proactive approach to relevancy helps the user cope with massive amounts of data without worry of corrupting or losing information stored in a data source.

Velocity

Velocity is a challenge that arises from having such high volumes and huge varieties of data. Velocity is only partially addressed with internet and computer speed technology. Methods need to be developed to break up data sets and pull out data with minimal wait times. Managing velocity in today’s Big Data atmosphere means keeping as much data as up to date as possible, and even making data display live. This is especially difficult with giant data sets.

Plus, when executives are reconciling data that is pulled out of a huge source, huge volume and wide variety are going to create a new velocity challenge: the slow down on the path to actionable information.

To capture and report specific data nestled in a super large data set quickly, Style Intelligence employs technologies such as data caching and the InetSoft patented Data Block technology. Data heavy report production is accelerated through bursting, in essence, combining many reports into one larger report and automatically delivering relevant information to whoever needs it. InetSoft also offers data grid caching, scheduled retrieval and storage of a dashboard's required data, so the massive amount of time it takes the program to pull data out of a Big Data source can be metered and managed without suffering through request wait times. Combining a wide variety of approaches to technological agility speaks to the big business need for speed and results in avoiding the organizational clutter that slows down the path to decisions.

Variety

Variety is Big Data's most dynamic challenge because new data types are being produced all the time. Big challenges like nuanced or incompatible formats, incongruent data structures and relational problems, and inconsistent methods that applications use to relate their data to their businesses coalesce into an explosion of new storage and organizational needs as well as a more arduous path toward accurate reproducible analysis.

Variety comes with demand for better communication between data sources and data analyzers and visualizers, more storage space, and ever more refined query methods.

Compatibility is the key to solving Variety challenges. Style Intelligence was developed to be a conduit through which multiple data stores, sources, and warehouses are related by the program itself. This is called data mashup and it approaches the variety challenge through several avenues. The InetSoft mashup strategy includes connectivity to relational and multidimensional databases which translates to faster access to internal and external data sources, regardless of formatting. Further, Style Intelligence is capable of pre-processing data and revealing relationships simply without editing the data or data source itself.

It can take advantage of OLAP, XML, SOAP, Java beans, Google Adwords, even connecting directly to spreadsheets themselves and even more compatibilities. If the solution to the variety challenge can be looked at as uniformity across platforms, Style Intelligence represents a truly homogenized environment through which all data can interact.