Evolution of Data Science Architecture

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Times have changed. We have entered in era of IoT, big data, small data and social media analytics; however, some key questions remain: Why do we need Data Architecture? How is it relevant for an intelligent enterprise? Why is it critical for any software application? We have entered in era of a digital economy where data is more precious than ever. This first series of my posts on Data Architecture is an attempt to introduce Data Science Architecture and its relevance.

For decades, Data Architecture has been one of the most important and critical domains for any business application and intelligent enterprise. It’s always hard to find a universally accepted definition of Data Architecture. Let’s go back to the fundamental and traditional way of Enterprise Data Architecture, where it lives outside the IT and Business Entities but it represents the complete Enterprise Architecture view of Data, Application, and Information. This complete view of Data Architecture enables integrated processes, utilizes industry best practices, standards and policies, and finally establishes a "foundation architecture" as the “single version of the truth” for enterprise data and information activities to be shared.

Data Science Architecture 

Data has always been one of the most important and valuable assets for any corporation. It was not the first time in early 2012 when a prominent investor and strategist said that Data is the new oil and that companies must begin treating data as an enterprise-wide corporate asset while also managing the data locally within business units. Some data evangelists even say that data is a commodity.

In today’s fast-moving business world, we need to constantly augment data to keep pace with the market. Enterprise data (structured, unstructured, digital) is usually spread across many different systems, all using different technologies. To bring this dispersed data together is always a major technical challenge.

However, Enterprise Data Architecture for an intelligent enterprise has taken a quantum leap with the advent of social media, social network analysis, increased mobile subscriptions (for telecom companies) and the market research done by Gartner and Forrester in Big Data and IoT.

Data is ubiquitous. The volume of data is growing at alarming speed.. and the pace is accelerating every day. In the next few years, digital capabilities and data volume will grow profoundly. Now you wonder what Data Architecture has to do with this? Well the answer lies in the question: How do we harness this digital and unstructured data? This is the beginning of Data Science Architecture in digital economy world. Enterprises have to become more intelligent and find innovative ways to drive smarter decisions to enable new services and business models to help reduce the costs.

We all know terms like machine learning algorithms, modeling, analytics, mathematics & statistics. Let’s put it all together and think about Data Science.

Data Science is evolving because the world is changing and so is the business. Professionals in the data world started from traditional Data Strategy & Architecture, and then next evolution came with Business Intelligence Strategy & Architecture. Today data-savvy professionals are in high demand, thanks to the emerging world of Data Science.

More on transition from Traditional Data Architecture to Data Science Architecture will be covered in my next post.

Post Date: 7/14/2015

Prakash Mishra - NTT DATA Prakash Mishra

About the author

Prakash Mishra leads NTT Data’s Data Architecture and Management Practice. A solutions-driven, results-oriented, self-motivated leader, Prakash has a proven record of extensive data architecture leadership in a complex environment. Prakash has been involved in developing and leading the implementation of traditional and innovative big data strategies and solutions, data modernization and master data management solutions for small to large organizations. Prakash is a master in building and motivating high-performance teams, cultivating a positive work environment and promoting a spirit of teamwork and idea-sharing to maximize individual contributions. Prakash holds a master’s degree in computer science , with two decades of experience specialized in enterprise data architecture and management.

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