I was fortunate enough to have recently presented at the Data on Purpose conference at Stanford University, where some of the best minds discussed the virtues of using data to further the causes of corporate social responsibility, targeted allocation of resources for the common good, alleviating poverty and more. It got me thinking how data and analytics are more than just drivers of successful outcomes in a business setting, but are also methods we can leverage to make a difference.
The era of algorithms
The use of a data-driven policy evaluation and implementation in the not-for-profit world is rooted in prior work done in the private sector.
Think of credit scoring algorithms or predictive models that provide consumer purchase likelihoods, or customer churn models that determine an individual’s propensity to take her business elsewhere. The same principles have been effectively repurposed toward addressing some vexing questions that plague us as a society.
Projects like the Impact Genome Project have been created with the sole purpose of using program evaluations data to deliver a set of solid criteria to determine the effectiveness and the efficacy of social programs. The Impact Genome Project has collected more than 75,000 data points and has identified at least 125 types of social outcomes. The added impact to all of this is greater efficiency in administering a social program and maximally impacting a given population.
Here are a few areas where advanced analytics is poised to be a formidable ally in delivering an outcome-based evaluation of social programs for the greater good of humanity.
Disease control and prevention
The default method of disease prevention is to monitor patient progress after a set of symptoms manifest or upon the full onset of an affliction. Then caregivers devise a therapeutic course of action to prevent its spread. While this often results in complete cure, the physical, psychological and economic damage may already be significant. In many hapless cases the cure is well beyond the reach of the treatment, and sometimes the damages are long lasting.
Enter the world of precision medicine. In this new era, a lot of data is collected on widely diverse variables like genes, localized environment, individual lifestyle, pre-existing conditions, socioeconomic factors and demographics to develop predictive models that pinpoint individual susceptibility to diseases.
Imagine knowing far in advance of the onset of a debilitating condition that you are likely to be struck by a certain illness. Such predictive models may give us enough information to take corrective actions in our lifestyles to sufficiently disrupt or reverse the progress of a disease, or perhaps prepare for its arrival by taking the necessary precautions. Further, imagine this being applied to an entire population that is particularly vulnerable to certain kinds of afflictions. The more we are delivering powerful predictive models that foreshadow the incidence of these ailments, the better off we will be in preparing ourselves to successfully combat endemic diseases when they actually arrive.
In some cases, we could even prevent them from making their arrival. Stanford University’s pioneering MyHeart Counts app is one example of the enormous power of crowdsourcing health data to develop models in a bid to prevent heart disease.
Public safety and national security
It takes no special intellect to recognize that global perils such as terrorism, cyberattacks and personal security dangers have increased in the last 20 years. Cyberattacks are now possible from remote corners of the globe and can affect sensitive infrastructure anywhere at will. This results in incalculable damage to national security in the form of sensitive information leaks to individuals with nefarious intent.
There is currently a need to systematically sift through millions or even billions of pieces of data — numbers, text, graphics, videos and sensor information — to glean insights that can’t be detected through standard methods.
This is leading to a more coordinated view into an asymmetric set of activities that have no apparent connection to each other but, when looked at as an ensemble of behaviors, provide a clear indication of the nature and intensity of the threats posed to a nation and its people. To review these behaviors as an ensemble requires the intelligent application of analytics techniques — a single approach won’t work given the various data sources — and innovative ways of visualizing the insights so policy imperatives become clearer.
The era of big data has been upon us for a while. Many for-profit organizations created solutions, products and platforms that are hyper-versatile in terms of ingesting, curating, processing, transforming and analyzing data while also providing intuitive ways of visualizing and operationalizing insights, often in near-real time. There are many examples in the commercial world where data is not only an asset but also a positive force and makes companies successful, consumers happier, processes more efficient, patients healthier and products better.
In the nonprofit world, the magnitude of challenges is no less serious and may be more critical for human progress. It is a cause for warm comfort that technology companies have contributed handsomely towards delivering the expertise and solutions that make all our lives better. More needs to be done, but the culture of making decisions based on solid data analysis is here to stay and is getting more embedded in the day-to-day operational and longer term strategic ethos across all organizations.
If we believe in the value of data to help solve problems and improve the quality of life, we must begin new conversations that spark progress — and call attention to the great individuals who are already beginning to make good things happen with analytics.
Sri Raghavan is a Senior Global Product Marketing Manager at Teradata and is in the big data area with responsibility for the AsterAnalytics solution and all ecosystem partner integrations with Aster. Sri has more than 20 years of experience in advanced analytics and has had various senior data science and analytics roles in Investment Banking, Finance, Healthcare and Pharmaceutical, Government, and Application Performance Management (APM) practices. He has two Master’s degrees in Quantitative Economics and International Relations respectively from Temple University, PA and completed his Doctoral coursework in Business from the University of Wisconsin-Madison. Sri is passionate about reading fiction and playing music and often likes to infuse his professional work with references to classic rock lyrics, AC/DC excluded.