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The next big revolution in the information industry: Your 2017 guide to big data

(Image credit: Image source: Shutterstock/ESB Professional)

The big challenges for the big data industry in 2017?

Without a doubt, productionising analytics - bringing analytics and model management into production in a repeatable way, is going be a huge challenge next year. The companies that are doing this successfully are beginning to realise value from their big data investment and applying that knowledge in new ways. Achieving the right levels of governance will also be a challenge that big data-driven organisations will aim to conquer. I spoke to Prof Daniel Ray, Director of Data Science at one of our key customers NHS Digital, and here’s what he has to say about governance:   

“Governance will become a bigger focus point than big data technology solutions. It’s essential that all organisations using public records follow and comply with correct governance. We can’t lock up everyone’s data, so no one has access to anything. But on the other end of the spectrum, at the moment you’ve got everyone able to see everyone’s data in order to use analysis for the common good. It’s a balance in ensuring data is safe and secure so that we’ve got the public’s trust, and in being able to use that data to add value and understanding to planning, variability and the inequalities that may exist today. If we can follow and comply with correct governance and maintain public trust, we can use big data in the right way to improve care.” 

And the next big revolution in the information is….

Predictive analytics will be huge. The first element you unlock when you can do predictive analytics is new insight and predictions, but after that, if you continue, you can automate manual processes, and you can replace them with predictive analytics, which is a huge cost saver.  Banking is a good example. Anti-money laundering means a lot of processing and inspection to prevent fraud, but if you remove the tedious, repetitive manual decision making, you can replace it with predictive analytics.   

Retail is another area where predictive analytics will be big. I was recently speaking to customer Jon Everitt, Group Data Architect, Camelot Global, who said:   

“Predictive analytics is a clear target use case and will be very big in 2017. Modern data architecture is able to stream information in real-time to bring in greater and more disparate data sets. The classic example being consumer behaviour analysis for retail - what adverts people consume, website dwell times, click through rates - the list is long. The next step is not just about seeing what people have done, but what they didn’t do, and why. Predictive analysis means understanding how, when and where consumers transact, although this brings with it the added security challenge of understanding anonymous customer movements.” 

The biggest use cases for big data and data analytics in 2017

Deep learning driven initiatives are probably one of the most exciting areas, mainly because there are so many possibilities, from fraud detection to self-driving cars. Banks have been talking about applying deep learning for money laundering and other types of fraud detection for a while, but they aren’t doing it properly.  This is mainly because most are still trying to build effective data lakes, and failing to efficiently ingest data, and to extract and use it in an efficient and useful way that will actually help business as the process takes too long to be useful. 

Which sectors will be the big movers?

One of the biggest movers will be agriculture. We see huge use cases around the industry. When you look into the data, it’s becoming a very detailed industry. Other sectors likely to see greater uptake with big data are manufacturing, which has only just scratched the surface in terms of what big data can deliver and healthcare. It has huge potential, but it hasn’t yet taken off because of the risks associated with the industry and time required for testing. 

The big data skills shortage

Big data innovation will simply not move forward without new and experienced talent driving it. Companies need to look into innovative ways to help to address the skills gap, such as working with universities to try and make degree courses more closely aligned with the skills needed.

2017 is certainly set to be an interesting year, one where we can looking forward to not only seeing great new technologies and use cases emerge but also one where we hope to see enthusiastic new talent enter the big data workforce to help accelerate big data innovation in both enterprises and across agile SMEs.   

Image Credit: ESB Professional / Shutterstock

Tim Seears
Tim Seears, VP Emerging Technology at Think Big, A Teradata Company