1. My prediction is a year of predictions Data analysis focus will change from analysing historic information, to predicting what will happen in the future on a continual on-going basis. Business can learn a few lessons from the scientific community here, and they will learn that a fast database cannot deliver a predictive solution. It is a means to hold huge amounts of data, but to analyse huge amounts of data, learn new relationships, train predictive applications and to construct algorithmic automated decision-making machines running in real-time, it needs scientific, business algorithms. It is only recently that commercial applications are catching up with science. 2016 will be the year that more and more decision-makers will understand the difference between a database and a predictive application, and market research analyst Gartner shares this same opinion as well.
2. Business algorithms and the Industry of Things (IoT)
Businesses which have not yet started looking at predictive and algorithmic strategies face a bleak future. As well as the rise in algorithm application, we will see a better understanding of big data analytics. To date, much of the conversation has not differentiated between the different type of statistics drawn from data, and still seems to have focussed on descriptive analytics, ie analysing data and understanding the past. As mentioned above this will now move onto predicting the future.
The IoT is taking off. Products such as Google’s driverless car, smart phones and connected household appliances all produce more and more data. This data needs algorithms to make sense of it and deliver on the promise big data has tantalised us with. These would allow the prediction of outcomes and would automatically make decisions based on those predictions. Artificial Intelligence (AI) moves forward.
Humans will always have a vital role in decision-making, but AI has the power to enhance our lives today. Humans alone decide what the strategy is and what goals should be reached, but algorithms can break down this strategic goal to an optimisation of the often very large number of (repeating) operative decisions. And this makes a lot of a difference — not just in the far future, but today.
3. Data scientists will be in high demand by human resources and Data-as-a-Service (DaaS) will be popular
The skills of a proper data scientist have long been recognised in the banking and finance sector and 2016 will see this recognition and requirement spread to other sectors. Some companies will undoubtedly create their own departments for it but for others it will be more logical to buy in the services of data scientists with creditable business knowledge from supplier organisations. This is what DaaS will be about.
