Open Data Big Data Big Deal - MISA BC 2014
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Big data is driving organizations to a tipping point due to the variety and velocity of petabytes of data. Familiar tools and technologies can provide unique access to both big and small data to help people gain insights and take action. Self-service business intelligence using familiar Office tools combined with cloud computing power allows organizations to leverage the Microsoft data platform.
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Open Data Big Data Big Deal - MISA BC 2014
- 2. Is Big Data a Big Deal?
- 4. Data complexity: variety and velocity Petabytes
- 5. Big data is driving us to a tipping point
- 7. How do you access data, big and small?
- 8. Familiar tools and technology can uniquely speed people to insights and action
- 18. Self-Service BI Self-service BI with the familiarity of Office and the power of the cloud
- 19. 7
Editor's Notes
- Estimated that WalMart Collects 2.5 PetaBytes of data an hour from Customer Transactions - Volume By 2015, organizations that build a modern information management system will outperform their peers financially by 20 percent. – Gartner, Mark Beyer, “Information Management in the 21st Century” The use of big data will become a key basis of competition and growth for individual firms. From the standpoint of competitiveness and the potential capture of value, all companies need to take big data seriously. In most industries, established competitors and new entrants alike will leverage data-driven strategies to innovate, compete, and capture value from deep and up to real time information. Indeed, we found early examples of such use of data in every sector we examined.” – Mckinsey, Big Data report, May 2011 “Information volume is growing worldwide at a minimum rate of 59% annually with 15% of that data as structured data and the rest comprised of new complex data types all helping contribute to Big Data.” - Donald Feinberg, Gartner IT Symposium, October 2010
- 1000 Petabytes = 1 exabytes = 1 Billion GigaBytes 2.5 Exabytes of Data Created Daily Gartner, Mark Beyer says By 2015, organizations that build a modern information management system will outperform their peers financially by 20 percent. 40% Global data growth VS 5% IT spending growth… 4.3 average of connected devices per US adult… Seismic cubes have reached the 100’s of Petabytes… By 2013 15% of BI apps will add collaboration and social capabilities 44% of users (350M people) access Facebook via mobile devices. 33% of BI will be consumed via handheld devices by 2013. 500M Tweets are hosted on Twitter each day. 80% growth of unstructured data is predicted over the next five years.
- Key goal of slide: establish Microsoft’s assertions for this new era when it comes to data. Land the notion of democratization of data – that Microsoft is investing across all stages of the data science process / lifecycle. Take a thought-leader tone. Slide talk track: CLICK 1: For the first step along the data science process – or journey we’ll go on today – what’s key in your success here is getting EASY access to data, big and small, to drive the best business decisions. And we would assert that’s not just about IT’s access to data – it’s also very much about empowering end-users. CLICK 2: Now, the second stop on our journey -- how do you make it easier for the people closest to YOUR business to create a theory, model that theory, refine it and reveal those invaluable business insights? That's all about engaging more people at this second stage, again the people closest to your business, with powerful tools that they're familiar with – so there’s no learning curve. It’s just users and data – and if you can unlock this – you get to that magical point where insights are revealed. CLICK 3: And then finally, at the last stage of the data science process -- you need to be able to create repeatable business process that delivers insights automatically by deploying your “data science process” across a complete data platform. It can't just be about storing relational data, or it can't just be about storing unstructured data in a Hadoop cluster. It really does have to be about thinking about a more complete solution. You want that ROI on your data, and by operationalizing your data science process – you’re going to see those returns.