Analytics continue to be one of the most important aspects of enterprise technology, especially as the amount of data we are creating continues to increase exponentially. What major trends will we see in 2017, when it comes to analytics?

Organisations will transition from traditional business intelligence towards data discovery

In the years since big data first appeared on the scene, traditional business intelligence (BI) served a purpose in helping organisations create efficiencies and run more profitably. These days most businesses, including the very smallest, use BI in one form or another. However, the amount of available data in all walks of life and every area of industry has exploded. As an example, the Sloan Digital Sky Survey telescope project in New Mexico collected 140 terabytes [link 1] of information over the course of a decade. The Large Synoptic Survey Telescope, its successor in Chile, is expected to gather that much data every week. 

This upsurge in data means traditional BI systems are no longer able to compile, clean and extract relevant intelligence from the data. What’s more, with these traditional systems, a data scientist would spend most of their time slicing data and generating reports to be tested against narrow hypotheses. But what about the questions businesses don’t know they should be asking? Hypotheses fulfilled by traditional BI reports or standard analytics platforms are not enough. Complicated and time-consuming analytics platforms with limited applications also serve to further muddy the waters. 

In 2017, business intelligence will transition from these traditional, reactive analytics platforms towards data discovery. Data discovery is the process of proactively compiling data from a variety of sources, including from outside the enterprise, and extracting relevant intelligence from the trends found in the data.

Advanced data discovery systems are well equipped to automatically extract and organise metadata categories, dimensions and terms while integrating data, such as market research, customer, social, location and short shelf-life data. This allows multiple analyses of data and project trends, and helps present valuable insight in an easy-to-understand format. 

To wade through the ocean of data that is being created currently, organisations need a platform that can process high quantum of information on a real time basis, and leverage data and analytics for decision making at speed and scale. This will help determine new products and services that can be launched and developed in the market at a rapid pace, and stay ahead of the curve. That platform is data discovery, which more and more organisations will leverage in 2017.

Customer centricity will be key 

Today’s consumers are more informed, discerning and demanding than ever before. Equipped with smartphones and ubiquitous internet connectivity, they are able to quickly and easily research products and services. They are much influenced, and are also instrumental in influencing their peers in the same way. 
For retailers and manufacturers, this means that they have to be more attuned to their customers than ever before, bringing more customised products to market more quickly and efficiently than ever before. Competitive advantage lies in leveraging analytics to better understand these customer behaviour, more efficiently forecast needs and demands, and optimise costs without compromising on quality. The use of analytics to drive customer centricity will be another major trend in 2017. 
For example, a multinational manufacturer might have one client but with tens, even hundreds, of customer touchpoints. This complicates the relationship between manufacturer and supplier, and drives the need for a single view of the customer. However, this can be complicated by issues on the part of the manufacturer, such as siloed operations, a lack of high quality trusted master data and poor data management processes. All of these issues prevent the manufacturer from gaining a single view of the customer, which in turn hinders better planning, mining and forecasting, leading to poor decision making because of redundant data, and an inability of legacy systems to address the dynamic nature of the customer ecosystem. 
The solution to these issues is the implementation of a modern customer data ecosystem that include Customer Master Data structure hosting key attributes such as sales & marketing, finance and customer relationship and the application of advanced analytics. For the manufacturer this means a better ‘single view of customer’, which in turn makes for better insights into the customers, appropriate customer categorisation and more focused offerings. In 2017, applying this kind of advanced analytics to better-extracted and organised data will help businesses across the board drive customer centricity. 

Artificial intelligence and automation will combine with analytics for powerful results

Artificial intelligence (AI) was one of the most talked about technology trends in 2016. While artificial intelligence has been an important area of scientific research for many years and has been a part of the popular imagination since the 1950s, the advancement in computing technology in recent years has strengthened its integration with businesses only now.. In 2017, we’ll start to see it become more firmly embedded into the enterprise with increasing business-use cases. Combined with analytics, it will start to have a powerful effect. 
For example, AI plays a significant role in decision making in the financial services industry. For instance, hedge fund managers are expressing their growing interest in expert systems to reduce errors in investment decisions, while several stock traders are using neural networks to forecast stock performance through the analysis of historical data. AI systems even crawl through and analyse news, press releases and social media content that may impact stock prices to forecast potential changes and help investors bid accordingly.

In parallel, healthcare researchers around the world are showing interest in the use of AI to save lives. The simulation model created by researchers at the Indiana University earlier this year [link2], for instance, analysed patient data to recommend treatment plans that were proven to be 50 per cent more effective at half the cost. 

Clearly, AI has the potential to improve decision-making capabilities, along with cost saving across industries. 

Analytics will go from strength to strength

Analytics has always been a critical part of running a successful enterprise. This has been especially true since the data revolution kicked into gear around 10 years ago, giving businesses the opportunity to analyse mountains of data and extract useful insights. In 2017, we’ll continue to see analytics go from strength to strength within businesses across various industries, with data discovery, customer centricity and artificial intelligence the major trends in this space.   


Pallab Deb, Vice President and Global Head – Analytics, Wipro Limited