TIBBS PEREIRA, a recognised expert data analyst, shared his perspective on the role of data analytics with a group of third level lecturers in Microsoft-Ireland this morning. Some of the coolest jobs in the fourth industrial revolution are earmarked for data specialists and data scientists today.
Data Science is not a tech bubble. Everyone is in the data business--some may not recognise this fact but they are probably sharing little data points that mean a lot when aggregated. And knowing how to mine that aggregated data is why I'm interested in today's professional development session.
The win for Microsoft involves leveraging the cloud. If you know what you need to mine from your aggregate, Microsoft's Azure can easily and quickly set up an array of supercomputers that few businesses can afford. This is a quickly expanding field. Gartner showed $35 billion of IT spending in 2013 for big data. That number has more than doubled in three years through increases in cloud services and acquisition of specialized services. For example, McLaren's F1 predictive analytics were snapped up by KPMG to analyse supply chain optimisation. The entire chocolate industry has survived a downturn in cocoa production by leveraging data analytics.
Traditional business intelligence based on simple information, has evolved into advanced analytics with predictive analytics and prescriptive analytics. All around the world, people (often customers using free apps) are happy to share their data into massive open data sets. Because of this tendency for sharing, global sleeping patterns are revealed by apps. I can see highest cinema ticket demands through simple Google venue searches.
To leverage this fourth industrial revolution, third level institutions should be training data wranglers, data stewards, data scientists, business analysts and application developers. All these people need to know how to build Big Data Sets, how to leverage cloud computing services and how to produce meaningful business intelligence as an output.
Microsoft-Ireland evangelizes a handful of proper data analytics tools, including R, a language platform. This data visualization framework is Open Source. The enterprise use of Open Source R has some limitations. R needs data in memory to start a computation. R is single threaded. R requires a skilled resource to scale out computations across a cluster.
So Microsoft built Cortana Intelligence to pull data sources, apps and sensors into the storage and computational power of cloud services and then pushing the business intelligence out to people, apps and business systems.
And today, I get some hands-on usage of advanced intelligence technologies in the form of pre-configured solutions, machine learning and a big data store. I expect to start the autumn semester with an iteration of today's managed services to improve my learning analytics in three different third level degree programmes.