WHERE IS THE VALUE of Big Data

Extracting value is much more easily said than done. Big Data is full of challenges, ranging from the technical to the conceptual to the operational, any of which can derail the ability to discover value and leverage what Big Data is all about.

Perhaps it is best to think of Big Data in multidimensional terms, in which four dimensions relate to the primary aspects of Big Data. These dimensions can be defined as follows:

1. Volume. Big Data comes in one size: large. Enterprises are awash with data, easily amassing terabytes and even petabytes of information.

2. Variety. Big Data extends beyond structured data to include unstructured data of all varieties: text, audio, video, click streams, log files, and more.

3. Veracity. The massive amounts of data collected for Big Data purposes can lead to statistical errors and misinterpretation of the collected information. Purity of the information is critical for value.

4. Velocity. Often time sensitive, Big Data must be used as it is streaming into the enterprise in order to maximize its value to the business, but it must also still be available from the archival sources as well.

These 4Vs of Big Data lay out the path to analytics, with each having intrinsic value in the process of discovering value. Nevertheless, the complexity of Big Data does not end with just four dimensions. There are other factors at work as well: the processes that Big Data drives. These processes are a conglomeration of technologies and analytics that are used to define the value of data sources, which translates to actionable elements that move businesses forward.

Many of those technologies or concepts are not new but have come to fall under the umbrella of Big Data. Best defined as analysis categories, these technologies and concepts include the following:

- Traditional business intelligence (BI). This consists of a broad category of applications and technologies for gathering, storing, analyzing, and providing access to data. BI delivers actionable information, which helps enterprise users make better business decisions using fact-based support systems. BI works by using an in-depth analysis of detailed business data, provided by databases, application data, and other tangible data sources. In some circles, BI can provide historical, current, and predictive views of business operations.

- Data mining. This is a process in which data are analyzed from different perspectives and then turned into summary data that are deemed useful. Data mining is normally used with data at rest or with archival data. Data mining techniques focus on modeling and knowledge discovery for predictive, rather than purely descriptive, purposes—an ideal process for uncovering new patterns from large data sets.

- Statistical applications. These look at data using algorithms based on statistical principles and normally concentrate on data sets related to polls, census, and other static data sets. Statistical applications ideally deliver sample observations that can be used to study populated data sets for the purpose of estimating, testing, and predictive analysis. Empirical data, such as surveys and experimental reporting, are the primary sources for analyzable information.

- Predictive analysis. This is a subset of statistical applications in which data sets are examined to come up with predictions, based on trends and information gleaned from databases. Predictive analysis tends to be big in the financial and scientific worlds, where trending tends to drive predictions, once external elements are added to the data set. One of the main goals of predictive analysis is to identify the risks and opportunities for business process, markets, and manufacturing.

- Data modeling. This is a conceptual application of analytics in which multiple “what-if” scenarios can be applied via algorithms to multiple data sets. Ideally, the modeled information changes based on the information made available to the algorithms, which then provide insight to the effects of the change on the data sets. Data modeling works hand in hand with data visualization, in which uncovering information can help with a particular business endeavor.

The preceding analysis categories constitute only a portion of where Big Data is headed and why it has intrinsic value to business. That value is driven by the never-ending quest for a competitive advantage, encouraging organizations to turn to large repositories of corporate and external data to uncover trends, statistics, and other actionable information to help them decide on their next move. This has helped the concept of Big Data to gain popularity with technologists and executives alike, along with its associated tools, platforms, and analytics.

Taken from : Big Data Analytics: Turning Big Data into Big Money