Dan Darnell - The Future of Analytics

by Dan Darnell , Rafael Coss , and Patrick Hall

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[LSI]

In 2015, when I started writing The Evolution of Analytics with my colleagues Wen Phan and Katie Whitson, we made the case for machine learning in business. Five years later, its time to make the case to use machine learning the right way in business. While I certainly dont know all the answers, a few themes stand out to me, looking back over the past five years. On the negative side, theres the endless hype about artificial intelligence (AI) and the tendency to deploy it in creepy and discriminatory ways. On the positive side, I see the growing government and public awareness of AI. I hope this awareness translates into the regulation of AI, improved interaction design in AI apps, and more corporate responsibility and governance for AI.

In this report, well introduce AI-driven applications that boost traditional data analytics with machine learning. While these apps may beat the odds, provide useful insights, and drive organizational value, such success stories dont serve as guarantees. In fact, everyone involved in organizational AI projects would be wise to take an inventory of AIs impacts on businesses, consumers, and the general public.

Another bright spot in AI over the last five years has been the development of technologies that increase human trust and understanding in machine learning. These inventions have transformed machine learning from a field of black-box algorithms to a field that is now capable of fierce debate around the concepts of algorithmic transparency, accountability, and fairness. This technological progress not only enables the nuts and bolts of regulatory oversight, but it also gives companies the power to govern their AI systems like the enterprise software assets they are. If you can block out the hype, youll see that AI is really just software. And like all other enterprise IT resources, AI systems should be documented, managed, monitored, and governed.

Looking forward, I see successful AI deployments being aware of the risks of AI, taking on the associated governance burdens, and enabling humans to work together with computers to solve big problems. For businesses climbing to the next plateau in digital transformation, dont settle for any AI system. Youll need AI systems that are documented, transparent, managed, monitored, and minimally discriminatory. Moreover, these AI systems must support AI apps that are flexible, explainable, and, when appropriate, automatic. Thats why Dan, Rafael, and I have written this new report, The Future of Analytics. Its a necessary update to the original Evolution of Analytics report, and we hope you find it to be a timely and useful guide through the new world of AI-powered analytics apps.

Patrick Hall

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In the broadest sense, analytics is the systematic analysis of data. This analysis makes the data consumable by people and systems, with the goal of understanding past outcomes and helping to predict future events. The adoption of analytics has driven a wave of digital transformation across industries where companies use data to power decision-making processes. Analytics projects, however, have not been without their drawbacks.

Challenges in Current Analytics Projects

Like many changes in business thinking, the first forays into data-driven decisions led down accessible but less useful paths. One such path was using dashboards to view historical trends to drive human insights from data, as shown in . We now know that these traditional analytics dashboards alone can be insufficient to make better decisions as they provide only a historical summary.

Figure 1-1. Typical dashboard with historical information

While historical trends are useful and can be predictive, they can also provide a false sense of confidence, when the future does not mirror the past. In the end, historical information and descriptive analytics alone leave business leaders to use their best judgment about trends in order to make decisions based on their own experience and limited view of the data. The result of this process is then highly dependent on the individual decision-makers expertise, which yields highly variable outputs.

Using machines to find patterns in data and make predictions is another area of great promise for decision support. Machine learning models, trained on historical data, can look at new data and predict what is likely to happen. For example, credit card companies use machine learning models to determine who has access to credit and is likely to carry a balance on their credit card bill each month. Such models can even prescribe actions for users to take and recommend products or content of interest. This ability to predict future outcomes and prescribe actions has made machine learning a hot technologyand data science a hot profession.

For all its promise, machine learning has not reached widespread usage in production or within business applications where it can provide value and support business decisions. The challenges vary by organization and use case. However, the common themes in AI and machine learning adoption revolve around a few key areas, including a lack of resources, lack of business trust in models and their outputs, difficulty putting models into production and keeping them running, lack of consistent business involvement, and bottlenecks in putting predictive results into business applications. Lets discuss some of these dilemmas below: