From the environmental disasters to financial crashes and political shocks, we live in a world that is gradually hard to predict. It's a fact that is compounded by the accelerating pace of change of the digital age. But still, against this backdrop, businesses should be able to make decisions and make them quickly.
 
Making the right choice requires a company to understand all facets of its business — in the past, the present, and the future — and to identify the value of the data available to them and what it tells them about their business. In the long run, the purpose of analytics within the enterprise should therefore not simply be to report on what has been, but to enable everyone at every level of an organization to make decisions with confidence.
 
It’s a big ask of any company, but even as we head deeper into 2019, what are the analytics tools, features, and functionalities we can expect to see to help businesses do exactly that?
 
Next-Generation BI
 
While the adoption of predictive analytics methodologies is unquestionably enhancing, this change has been principally driven by their IT specialists, with business users having to make requests for (and wait on) their reports. With demand for data scientists outstripping supply, however, many companies would like to bridge the gap by introducing self-service capabilities to their employees. It’s a trend that benefits all parties – while the business users can access capabilities previously out of bounds, by not having to spend their time on such tasks, the data scientists can pay attention on more complicated and higher value projects.
 
Therefore, what was once known as “advanced analytics” will quickly come to be part of the standard toolset of everyone from marketing professionals to accountants. As stated by research company Gartner, this shift will mean that “by 2020, more than 40 percent of data science tasks will be automated, bringing about increased productivity and larger use by citizen data scientists.”
 
Business Intelligence (BI) will evolve to add in advanced analytics capabilities such as for example automatic data discovery. And while such developments for certain add to the tools available to users, tremendously enhancing their ability to make strategic decisions, they also go one step further: They prevent users from coming under the bias trap, whereby data discovery justifies an outcome instead of reveals a new insight.
 
Left to our own devices, it is an inherently human trait to search for only what we are looking for when analyzing data. Knowingly and instinctively, we guide the process and sort the data to obtain the information that confirms what we expected. But by being so centered on what we think should be there, we can also miss important trends.
 
Smart analytics tools prevent this by actively drawing the user’s attention to information that might prove important but that could otherwise go unnoticed. Behind-the-scenes, a set of machine learning models provide a review of significant patterns, outliers, and key influencers of the business that help users really understand what is happening in their business. By changing from a passive system (“here’s some data, interpret it how you will”) to an active system (“have you seen this unusual development over here? It seems like it is being caused by this…”) analytics practices are continually helping users understand what is happening now, why it is happening, and how that will affect future results, all ultimately improving the speed of decision making.
 
The Consumer-Grade Analytics Experience
 
In the same manner, thanks to advances in areas such as natural language processing, solving business problems should be as simple as “Googling” all other question. Nearly as no one needs to realize the programming behind a search engine to be able to use it, no one should have to first learn coding to get the answer to the question they are looking for in their analytics solution. We can expect to see tools that enable users to do just that — benefiting from conversational technologies to get the answers to questions such as “what are the top ten stores by sales revenue in Germany?” by simply typing the question.
 
Finally, tools for example automated model builders represent another essential development. By giving business users access to capabilities that allow them to solve standard predictive modeling tasks, they can leverage the tools of a data scientist without having to actually become an expert themselves. Gradually introducing and exposing users to such concepts — and all without any significant upfront training requirements — also plays a key role in helping to establish a data- and machine learning-driven culture in an organization.
 
In 2019, we can expect to see augmented analytics methodologies being used pervasively across companies. From the boardroom to the shop floor, analytics has become a tool that can be viewed by everyone. As users and as people, we bring our own unique point of view to our analysis of the data. It is completely this combination of such powerful artificial intelligence and the inherent creativity of the people who use it that ultimately enables us to make resolutions a lot faster and with greater confidence than ever before.
 
This article is originally posted on TRONSERVE.COM