Just like Cloud Computing, Social Media and BYOD. Big Data has fast emerged as one of the most popular IT terms of today. But does the expression (used to describe the explosion in the growth of data and its availability and usage) have any significance or is it just big hype? While critical data and information are getting generated at a mind-boggling pace in enterprises, it still cannot be dubbed as Big Data – massive volumes that are growing beyond the performance capacity of traditional database management systems and data warehouse. Some sectors not only generate humungous amounts of data but also need to run this data through analytics for continuous growth and performance. It is no longer a subject of debate that Big Data enables enterprises to become more productive. It helps corporate become smarter by exploiting data in a hitherto unavailable manner thereby presenting newer growth opportunities. At the same time, however, technology leaders in most sectors need to carefully evaluate whether their businesses actually demand Big Data solutions or not. They should cautiously assess vendors pushing Big Data solutions.
First Big Data solutions are expensive. Secondly, it impacts the traditional approaches to Enterprise Architecture (EA).While Big Data (both from a management and implementation perspective) could be a challenge. It is also an opportunity for technology leaders. Big Data demands new business models. Some define Big Data in terms of being larger than a certain number of terabytes. As technology advances overtime, the size of the datasets that qualify as Big Data will also increase. Also, the definition can vary by sector, depending on what kinds of software tools are commonly available and what size of datasets is common to particular industry.
Staffing could be one of the biggest challenges for big data deployments. For a large scale deployment, enterprises would need to invest into training the staff on Big Data technologies. Moreover, cultural mind mindsets need to change to allow use of open source technologies as many Big Data tools are open source.Big Data can turn into opportunity if handled well. The data can be segregated under three buckets:
Customer Centric – Required for customer services.
Business Data – Required for analytics, trend analysis and business forecast etc.
Legal Data – Managed for regulatory requirements.
Adoption of Big Data analytics will lead to faster rollout of many customer-facing services and by applying analytics one can really change the game in the market. Analytics plays a major role in making the business enlightened on the power of information that can be carved out of the Big Data mart.In the last 6-7 years, advancement in Big Data technologies has considerably improved analytics on extremely large datasets. Enterprises need to think how data in their company is getting created and how it is being stored. Storage tiering is required to get optimal level of performance before adopting Big Data analytics. The success rate of a Big Data deployment does not depend on the scale of deployment rather it is more to do with the alignment of IT and business. Value of Big Data deployment can be measured in terms of accuracy of analysis of data. IT can also be measured in terms of business efficiency improvement and insights that it offers.