Big data belongs to next generation of data warehousing and Business analytics and it is capable to deliver high ROI for organization. Ten years before, we were not using the same amount of data what we use now. The amount of data is doubling every two years. However, organizations have been using the tons of transactional data over the years. Big data didn’t suddenly emerge. There were companies that have dealt with billions of transactions for many years. Then why now, we are talking more about Big Data. Actually, what is Big Data? What makes Big Data is different from regular Data?

Data that becomes large enough and that it cannot be processed using traditional methods. Big Data implies that datasets whose size is beyond the ability of usual database software tools to capture, store manage and analyze. When technology advances over the period of time, the dataset qualify for big data will also move in tandem. Ideally, it depends on the industry as well.  Industries like Manufacture, Automobile, Travel, Healthcare, Banking & Financial services consume more data each and every minute. Some Industry For e.g. Banking/Financial Services industry may consume more data in one day than the other industry. Today, Big data in many sectors will range from a few dozen terabytes to multiple petabytes.

Having said that, one could also ask Whether Big Data is just about quantity. No, but It is the ability to leverage new technology and approaches, which enable us easily to handle more data and take advantage of the variety of data even it is unstructured. Big Data that are based on volume can be troublesome. Some people may define volume by the number of occurrences. The real challenge in the coming days is to identify or develop most cost-effective and reliable methods to extract value from all the terabytes and petabytes of data now available. It’s important to remember that big companies have been collecting and storing large amounts of data for a long time. Accessing ‘New Big Data’ and ‘Old Big Data’ may not be same. Organization forced to retain large amount of information and that too depends on the sector. Organization abides prevailing and new rules and regulation in each sector set in their own countries or different countries. The real challenge for the organization is to access the data and create value out of that.

Technologies like Hadoop, make it practically possible to access a tremendous amount of data and then extract value from it. The availability of low cost hardware makes it easier and more feasible to retrieve and process information, quickly and at lower costs than before. The convergence of several trends is more data for less expensive price and faster hardware. With these available facilities, the ability to do the real-time analysis on very complex sets of data model is possible. Today, real-time analytics started using many market leading companies. Big Data Analytics improves sales revenue; increase profits and provide useful information to make better business decisions.

Big Data is currently defined by three dimensions: Volume, Variety and Velocity. The combination of the three V’s makes it extremely complex and cumbersome with current data management and analytics technology and practices. Data volume can be measured by sheer quantity of transactions. The volume is further exacerbated by the attributes, dimensions or predictive variables. Analytics have used smaller data sets called samples to create predictive models. Most of the time, business predictive insight has been severely damaged when the data volume has purposely been limited to storage or computational processing constraints. By removing the constraint and using larger data sets, organization can discover subtle patterns that can lead to actionable decisions or into predictions that increase the accuracy of the predictive models.

Next is Data variety. It is nothing but assortment of data. If we take operational data, it is ‘Structured’. It is put into database based on the type of data i.e. numeric, character etc. In the recent years, data has increasingly become ‘Unstructured’. Source of data have proliferated beyond operational applications. Unstructured data are nothing but data collected from video, image, Internet data including click streams and log files. Most new data is Unstructured. Specifically, unstructured data represents almost 80 percent of new data, while structured data represents only 20 percent. Unstructured data is basically information that either does not have a predefined data model and/or does not fit well into a relational database. In addition to that, it is typically text heavy but may contain data with facts as well. Of course, not all unstructured data is useful but it has some value. Smart organizations are beginning to capture that value to utilize in their business.

‘Semi-Structured’ data is often a combination of different types of data that has some structure/pattern or it gives minimum information, which is not as strictly defined as structured data. The semi-structured data does not fit into a formal structure of data models. It contains tags that separate semantic elements, which includes the capability to enforce hierarchies within the data. Finally, Data velocity is about the speed at which data is created, accumulated and processed. The business demands to process information in real-time or with near real-time. This means that data is processed on the fly or while streaming by to make quick, real-time decisions. The growing demands for data volume, variety and velocity have placed increasing demands on computing platforms and software technologies to handle the scale, complexity and speed that organization now require remaining competitive in the global marketplace.

Gathering data is easy but all the variables involved in the data are going to be useful or not will be decided by Big Data Analytics. Big Data Analytics uses wide variety of advanced analytics like Descriptive, Data Mining, SQL, Predictive, Simulation and Optimization. It certainly represents enormous opportunity for businesses to exploit their data assets to realize substantial bottom line results for their organization.