Digital Enterprise Data Explosion


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A TED-style talk given at Tech Mahindra's i5 event in Dec 2013.

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  • In herein lies the challenge we all are facing. If we are going to harness this new world we will have to become much more data centric in our design of architectures. If you look at this chart showing the progression of technology innovations over the year it tells an interesting story. Wave after wave of new technologies promised more business potential. We saw some big changes, most recently with the introduction of the internet and ecommerce. And now we are yet again at the next wave of the Internet of Things.But, all of this comes at a price for IT. IT just cannot absorb new technologies at the pace of their introduction. There is a complete new learning curve and adoption curve for IT to understand how to program these and harness these. And that complexity drives up costs, increases risk across the board – whether your are building new applications or try to analyze new sets of data.So the first couple of generations of technologies help businesses focus on their processes and optimizing for greater efficiencies. The data integration between apps or data warehouse were done mostly by corporate IT through stand alone projects – separate for different use cases.Then came the social and we started focusing down on people and optimizing the experiences of customers etc, in addition to the processes. In fact, many business processes are enhanced by combining the social type data with our traditional systems. Think of how recruiters now use LinkedIn more and more versus traditional candidate databases. And we are seeing the cracks in our data integration approaches of the past as we now need to think much more in terms of data infrastructure than data integration. We need a platform that can handle all these things. And we also need to collaboratively develop with our lines of business. Its no longer shadow IT but cooperative IT.And then beyond that we are starting to build products that can self-optimize in real-time. Again, it is not replacing the old, but this will even further stress our data infrastrucuture as we need to think of a data ecosystem.A change is needed. IT need to get away from their traditional cycle of new technology adoption through re-skilling and re-programming. Instead, IT need to invest in an information platform that will be able to shield them from the new technology to such an extent that they can harness its power at the speed of business. That is the key to unleashing the true potential of the information in our company.
  • Digital Enterprise Data Explosion

    1. 1. Digital Enterprise Data Explosion Deriving Insights from Convergence of Structured, Unstructured & Semi-structured Data Sanjeev Kumar VP & MD, Informatica India Dec 2013
    2. 2. 2014 The Challenge 2011 Devices & Machines 2007 Communities & Society 1990s Business Ecosystems 1980s BUSINESS 1960s-1970s USERS VALUE TECHNOLOGIES Few Employees Back Office Automation Customers/ Consumers Many Employees Front Office Productivity E-Commerce Line-of-Business Self-Service Real-Time Optimization Social Engagement OS/360 SOURCES TECHNOLOGY MAINFRAME 10 2 CLIENT-SERVER 10 4 WEB 10 6 CLOUD 10 7 SOCIAL 10 9 INTERNET OF THINGS 10 11 2
    3. 3. 2014 The Challenge 2011 Data integration becoming the barrier to business success Devices & Machines 2007 Communities & Society 1990s Business Ecosystems 1980s BUSINESS 1960s-1970s USERS VALUE TECHNOLOGIES SOURCES Few Employees Customers/ Consumers Many Employees Processes Back Office Automation Front Office Productivity E-Commerce + People Line-of-Business Self-Service Real-Time Optimization Social Engagement +Products and things OS/360 Stand alone projects Corporate IT driven Data Data Infrastructure ecosystem LOB driven TECHNOLOGY MAINFRAME 10 2 CLIENT-SERVER 10 4 WEB 10 6 CLOUD 10 7 SOCIAL 10 9 INTERNET OF THINGS 10 11 3
    4. 4. Telecom: Data Stream Flow & Integration Hadoop based “Data Lake” Stream Processing (Real Time) Transactional Application Messaging Collector Agent PowerExchange for MOM Machine Generated Data Streaming Collection Telecom Switches Ultra Messaging Machine Generated Data Messaging Collector Agent PowerCenter + Data Transformation Message Queue Event Feeds DWH / DM Operational Intelligence Event Processing Data Lake Time Sliced Data Telecom Switches (4G in future) Analytic Application Transactional Application Analytics Processing (Batch)
    5. 5. Converged Data Integration (DI) Architecture Real Time + Batch, Core ETL + Big Data Collection Layer EDW Business Intelligence Real-time Streaming Grid NoSQL Social Media Batch Network Logs Content Network Elements BI Layer Staging Layer CEP Transactions External Data Data Integration Layer Hadoop Hadoop Replication MDM Data Quality Management and Operations Archival Data Distribution Data Sources Data Exploration
    6. 6. Lambda Architecture = Batch + RealTime Query = function (all data) New data stream Realtime View All Data Batch Layer Serving Layer Speed Layer Stream Processing Precompute Views Batch View Batch View Query 6