Outside Insight, Leveraging on Big Data for Smart Decisioning

Timothy Oriedo, Executive Coach and Data Scientist Strathmore Business School [email protected]

The annual Actuarial Summit themed Harnessing Opportunities in East Africa, was marked recently and besides concerns of the disruptive implications of the International Finance Reporting Standard 9 that is soon coming into force, the next major area of focus was emerging technologies. A significant proportion of speakers including Shaun Bennet (FIA) of QED and James Norman of KPMG centered their presentations on Analytics, Machine Learning and the Automated Actuary. My address to the summit on Big Data application on the Actuarial practice pivoted on how the actuarial professions need to start building statistical models with appreciation of outside insight. Indeed one of the chief guests, the incoming Governor of Bungoma county alluded to the fact that his campaign strategy was lent impetus by his actuarial practice, although he had to adjust to the reality on the ground by engaging with the target communities in an analytical mindset. 

In building of statistical models, majority of the data that is put under consideration is organization’s operational data, which includes transactional records and customer metrics, those are yesterday’s news. Amidst the fast-paced change of digital transformation and the world at large, external data is likely to hold the insights you need to understand your environment and navigate the market of tomorrow. Speaking to most of the actuaries on the sidelines of the summit they mostly admitted they don’t really use external data in a systematic manner – its more anecdotal.

Internal data, traditionally powers much business intelligence and analytics. Companies spend a lot of time and effort mining their internal infrastructure and operations. It’s only about the organisation -lagging performance indicators one ends up only seeing the shadows of opportunities that they had in the past more often falling into the paralysis by analysis trap. By so doing there is a massive lost opportunity in looking into external data which is one of the biggest blind spots in executive decision making today. External information contains so much forward-looking information. For instance from the job listings one can find out if your competitors are hiring, and from social listening using natural language processing technologies one can ascertain if ones customers are changing their behavior, those are external forces influencing the organization’s future performance, and it can be found in online ‘breadcrumbs’- social media and external data.

Gartner defines big data as the three Vs: volume, velocity, variety information assets. While all three Vs are growing, variety is becoming the single biggest driver of big-data investments. This trend will continue to grow as firms seek to integrate more sources and focus on the “long tail” of big data. The convergence of IoT, cloud, and big data has also created new opportunities. As more and more devices have sensors, IoT is generating massive volumes of structured and unstructured data, and an increasing share of this data is being deployed on cloud services.

Its becoming increasingly easier to access external data from different sources to help build analytical frameworks. Government for instance is driving democratization of data through opendata.go.ke a move that seeks to make accessible public government datasets accessible for free to the public. The portal provides data sets ranging from Agriculture, Education, Finance, Health, Energy, infrastructure among others. All organization’s need to do is create APIs that can fetch the targeted data set and stream it to a data lake that will enable ingestion of the data alongside other data sets to arrive at a robust model. A data lake is like a man-made reservoir. First you dam the end (build a cluster), then you let it fill up with water (data). Once you establish the lake, you start using the water (data) for various purposes like generating electricity, drinking, and recreating (predictive analytics, etc.). Up until now, hydrating the lake has been an end in itself. That however will change as the business justification for analytics tightens. Organizations will demand repeatable and agile use of the lake for quicker answers. They’ll carefully consider business outcomes before investing in personnel, data, and infrastructure. This will foster a stronger partnership between the business intelligence and other organisational departments.