Author : admin | Monday, 6 May 2019
From environmental disasters to financial crashes and political shocks, we live in a world that is gradually difficult to predict. It’s a fact that is compounded by the accelerating pace of change of the digital age. Nevertheless, against this backdrop, businesses should be able to make decisions and make them quickly.
Making the right choice requires a company to understand every aspect of its business — over the years, the present, and the future — and to recognize the value of the data available to them and what it tells them about their business. In the long run, the aim of analytics within the enterprise should therefore not simply be to report on what has been, but also to enable everyone at every level of an organization to make decisions with confidence.
It’s a big ask of any company, but since we head deeper into 2019, what are the analytics tools, features, and functionalities we can expect to see to help businesses do exactly that?
While the adoption of predictive analytics methodologies is certainly growing, this change has been mostly 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 are seeking to bridge the gap by introducing self-service capabilities to their employees. It’s a movement that results both sides – 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 focus on more complex and higher value projects.
In this case, what was once known as “advanced analytics” will immediately turned out to be a part of the standard toolset of everyone from marketing professionals to accountants. As indicated by research company Gartner, this shift will mean that “by 2020, even more than 40 percent of data science tasks will probably be automated, leading to increased productivity and much wider use by citizen data scientists.”
Business Intelligence (BI) will evolve to add in advanced analytics capabilities like automatic data discovery. And while such developments of course add to the tools available to users, significantly enhancing their ability to make strategic decisions, they also go one step further: They prevent users from getting into the bias trap, whereby data discovery justifies an effect in place of reveals a new insight.
Left to our own devices, it is an naturally human trait to get only what we are looking for when analyzing data. Knowingly and unconsciously, we guide the process and sort the data to reach the information that confirms what we expected. But by being so centered on what we think should be there, we can also miss out on important trends.
Smart analytics tools bypass this by actively drawing the user’s attention to information that might prove important but that could otherwise go unnoticed. Behind the scenes, a couple of machine learning models provide a summary of significant patterns, outliers, and key influencers of the business that help users really understand what's going on 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 to be like it is being caused by this…”) analytics practices are actively helping users understand what is happening now, why it is happening, and how that will impact future results, all ultimately improving the speed of decision making.
The Consumer-Grade Analytics Experience
In the same way, thanks to advances in areas such as natural language processing, solving business problems should be as simple as “Googling” all other question. Just as no one needs to comprehend 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 seeking in their analytics solution. We can expect to see tools that enable users to do just that — profiting 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 such as 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 influence the tools of a data scientist without the need to actually come to be 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 set up a data- and machine learning-driven culture in an organization.
In 2019, don't be surprised to experience augmented analytics methodologies being used pervasively across companies. From the boardroom to the shop floor, analytics has become a tool that can be gotten to by everyone. As users and as people, we bring our own unique viewpoint to our analysis of the data. It is specifically 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 manufacturing.net