We have the keys: the future of the analytics value proposition

Part one of a series of white papers to help analytics executives advance their organizational performance with ‘working’ analytics

Setting the stage

Global business has entered a profound era of change and innovation driven by advanced analytics technologies and the availability of rich data. So much more data about the world is available for computing ….. about people, processes and events that intertwine with products, services and commercial propositions. Examples of the intersection of data and analytics, ‘big’ and small are everywhere. Mobile phones and wearable devices are generating and storing bio-metric data. Internet enabled prescription bottle caps help measure adherence and behaviors of patients on life-saving medications. In some cities, police departments have devices to ‘listen’ for the sound of gun shots and provide the location to the nearest patrol car. ‘Digital exhaust’, hidden data created as a byproduct of customer interactions and business transactions, is on the frontier of usable information that is attracting the interest of data science. (see Katherine Noyes of IDG in “5 Things you need to know about data exhaust”) . Advanced analytics and decision science are the methods of interpreting this data and generating smarter, faster and more consistent decisions. Richer data leads to richer insights, more practical use cases and the advancement of the tools to do the job. This virtuous cycle will remake products, services and strategies for many businesses.

Working Analytics

Analytical insights alone are useful and often the precursor step to making decisions. But ‘working analytics’ means putting algorithms into the flow of 'transactional' decisions, either by improving existing business rules and processes or by informing human decision makers, 'in-line' with their decision-making processes. This ‘run-time’ aspect of analytics makes the development and deployment more challenging. People, process and technology are now intertwined into an analytics application. ‘Working’ analytics is the present challenge for analytic executive in most industries.  

Analytics will take us there

Advanced analytics has matured into the world of business and society in plainly obvious ways. 

It is now common to read articles discussing the algorithms and models of data science in general business articles. Most people in the Millennial age who are power users of social media and the worldwide web are savvy to being directed and informed by search optimization, word pre-fill, recommender algorithms and more. Political scientists and quant journalists are routinely cited in political news for using sophisticated data to predict the outcome of elections. Defense and law enforcement agencies have their algorithms and data spying methods routinely dramatized in television series.  While it is somewhat early days in the integration of complex algorithms into our lives and workplaces, business leaders, workers and consumers are slowly adapting to a world where complex math and data analysis is integrated into their daily lives as they interact with customers and make decisions. Worries about privacy, social effects and job protection are real, but we seem to be mostly enjoying the analytics lifestyles and workplaces that this technology serves up.

In pace, analytics technology itself is at full speed. The leading methods, techniques, algorithms and development tools are now available for free in open software supported by communities of contributing programmers. Computing hardware and storage continue to get cheaper and more flexible. Fantastic advances in ‘in-memory’ and graph databases are enabling so much more, and more useful, analytics to be performed in less time. ‘Deep Learning’ Neural Networks are opening up new approaches and new interest in more complex source data. The technology supporting analytics is accessible and no longer a barrier for general business uses. In fact, new analytics technology emerges regularly and the horizon of feasible data related analytics technologies seem to grow every year.

Slower to advance but also maturing is the ‘artistry’ of analytics: the hard-won wisdom in the minds of data science practitioners.  The real risk of bad mistakes made by inexperience practitioners must be addressed by all businesses that rely on quantitative science. Fortunately, in analytics circles, there are recognized wise-men and women sitting within boutique firms, consulting groups and academic institutions who speak and publish widely for the benefit of junior quant’s. John Elder, of Elder Research, is one such wise-man example. After many years on the speaking circuit, John Elder’s “The Ten Most Common Data Mining Business Mistakes” is still one of his most popular talks he presents and is now published as a folio for all to learn from.  The operations research and analytics technical society, INFORMS (www.Informs.org), has created a certification standard for quants that helps the profession and world to see data science as a more formal discipline. Quant experience is on the move, as data scientists are rewarded with higher salaries and are poached from company to company. This movement inevitably spreads the experience within and across industries. As the intense interest and investments in data science continue, ‘good mistakes’ will continue to enrich companies that foster the right environment, and bad mistakes from inexperience will diminish or be minimized.   

Analytics is ‘here’, the business is not

Analytics is a transformational ‘key’ for improving decision-making and making complex processes faster and more efficient. 

Data Science and Decision Science present an opportunity for discontinuous leaps in the strategy of business. But a key is merely interesting without knowing the lock it opens. Just as in prior periods of hyper-innovation in technology, data science has outstripped the ability of business to apply it. We have lots of ‘keys’ in analytics solutions, data and technologies to which we have not yet found matching locks in business applications of analytics. As a result, today’s analytics footprint has generated only a fraction of the value to be created in the coming years. We have the keys. The current challenge is to find the locks.

Locks and Keys

This series of articles will discuss where and how working analytics has yet to be significantly monetized. This is a moving target, of course, because new approaches and newly available data present a moving horizon.  “Locks and Keys” provides a perspective to help executives manage through the complexity, uncertainty, and the organizational blocks and to achieve analytical transformations faster than their peers in the race to fully monetize this technical innovation.