Big Data 3.0: Delivering on the Promise

There is unusual consensus among economists that 2016 will be a more-of-the-same year of moderate growth, low inflation and emerging market risks. Against this unspectacular backdrop, how do corporations achieve spectacular outperformance?

One clear weapon of choice is big data. Last year, the world received further affirmation of big data’s real world applicability. The largest ever deal in IT industry history – courtesy of Michael Dell and data storage provider, EMC – was just icing on the cake.

Here is a recap of big data’s big achievements in 2015, and what lies in store for 2016:

Managed Healthcare

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Most heartening for big data proponents were the strides made in healthcare in 2015. In June for example, Forbes reported on the way data is now being collected from every US cancer patient in order to detect patterns, not just of the emergence of the disease, but also of patients’ responses to treatment.

In 2016, the focus looks set to shift from battling illnesses to maintaining good health. Biometric data from fitness apps and IoT-based wearable healthcare devices is likely to come to the fore. The challenge to date has been the technical and intellectual capabilities required to make sense of this overwhelming amount of data. As the Internet-of-Things comes of age, the opportunities for innovation will fall to those able to master AoT – the Analytics of Things.

Machine Learning

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Machine learning was hard to ignore in 2015. Algorithms moved from a term familiar only to maths geeks to one used in the trendiest of places. Last month, fashion retailer, The North Face, launched its virtual personal shopper service, providing style trends, fashion recommendations and practical travel advice, all powered by a big data machine learning platform.

However, machine learning in 2016 may take on a different flavour. Rather than replacing humans, machine learning is moving to the incorporation of human intervention. “Human-in-the-loop” computing is based on a continual cycle of 1. ascertaining an algorithm’s ability to solve a problem, 2. seeking human input where necessary, and 3. feeding this back into the model. A range of automated services from self driving cars to natural language processing (NLP) will likely continue to rely on humans teaching machines a thing or two.

Dynamic Pricing

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Talk advanced retail pricing and you are likely to think of price-matching. Some of the biggest names in retail have relied on price-matching strategies to build customer loyalty. However, Walmart took the concept to a new level in 2015 with its Savings Catcher mobile app, which allows shoppers to scan their receipts and claim for items more expensive than at competitor stores. This feature alone is credited with taking Walmart’s app usage from 5 million to 22 million in just one year.

That said, the evolution of big data use is paving the way for retailers to improve their margins. In 2016, rather than the intense focus on price, big data will offer retailers the ability to price based on a variety of highly dynamic and complex factors, including pop-up trends, time of day, weather conditions, underperforming products, etc. Amazon, for example, is known to change prices up to 2.5 million times a day during the busy holiday season. According to IDC, by the end of 2016, product intelligence will inform 90% of pricing decisions made by the top 10 US e-Commerce retailers.

Chief Data Officers

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A survey conducted in 2015 found that 61% of CIOs wanted to hire chief data officer (CDOs) within the next 12 months. This suggests that in 2016, CDOs will be a significantly more common sight on company payrolls than at present.

Although the CDO designation has been in place in some US banks since the 2008 financial crisis to oversee data quality, the role in its current form has only surfaced in the last couple of years. In 2014, about 25% of Fortune 500 companies claimed to have one appointed to optimise their use of data.

Despite this, a study by IBM suggests that many CDO employers are not clear what this role is all about. Furthermore, while the growing importance of big data will see CDOs gaining more power in 2016, research firm Forrester note in an intriguing report that this power may be eroded by an increased trend to outsource data solutioning, and to succumb to data security concerns.

Big Data 3.0

 Forrester’s report also notes that 2016 may see on the end of big data’s honeymoon. The report warns that big data tools alone will not be enough, if not accompanied by enterprise-wide commitment and visionary management.

If Big Data 1.0 was the era of discovery, and Big Data 2.0 was marked by a period of experimentation and application, then what we see unfolding in 2016 must surely be Big Data 3.0, when tangible results will be key. All I can say is, bring it on!

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