The Rise of Data Science and Big Data
Big Data is one of the latest buzz words heard wandering the halls of large enterprises and now small businesses alike. A broad term for large data sets that can bring anxiety to even senior IT staff due to the complexities of capture, storage, analysis, processing, correlation and searching. Big data has become so complex that it has given rise to the new role Data Scientist. Scientist is the perfect term due to the systematic study and research that these individuals perform.
According to the McKinsey Global Institute, by 2018 the United States will face a shortage of “1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions”. More recently, IDC has predicted that the need for deep analytics roles will see a shortage of 181,000 staff in the U.S. To meet the demand, universities like NYU and Washington University have even created a Master of Science program for Data Science to teach new researchers and professionals. It’s undeniable that organizations will start to rely on big data to make faster and smarter decisions now and in the coming years. The question is “What is Big Data to your company and how will you harness its power?”
Big Data As A Service (BDaaS)
Predicting the requirements for a big data initiative can be daunting. The mere thought of what data will be stored, how much data storage will be needed, and how much power it will take to analyze can cause paralysis by analysis. Like yin and yang, what makes designing a big data system so expensive and complex marries near perfectly with the benefits to cloud computing. BDaaS is offering enterprises environments with the agility, elasticity and the near limitless processing capabilities of the cloud.
Even with the benefits to cloud computing, a single centralized BDaaS instance may not be right for everyone. In some cases, as DATAVERSITY has outlined, clients are taking a hybrid approach to BDaaS with a central BDaaS instance for unified data for mission-critical decisions while business units, divisions or separate operating companies have smaller decentralized data warehouses for micro level decisions. This hybrid approach along with data abstraction enables enterprise grade and commodity grade environments to co-exist but content ownership to remain the purview of the enterprise. A landscape consisting of 'many clouds' is the best way an organization can hope to scale from terabytes to exabytes in this decentralized hybrid model.
Regardless of the Big Data as a Service model used, big data analysis is soon to become a key differentiator that results in your company either thriving or withering. NTT DATA has a strong focus on the analytics and science of data in your enterprise. Contact NTT DATA to find out how we can help define your approach to big data so that you can think smarter, act faster, and flex your business.
- Nathan Aeder, Associate Director, Cloud Advisory Services – Senior Cloud Strategist
Post Date: 2015-04-08