Democratization of Analytics
Abdul September 21, 2020
There can be little doubt in anyone’s mind that Analytics is becoming an ever more decisive element in all industries, ranging from Life Sciences, Healthcare to Energy, Utilities, Manufacturing etc. In developed economies, there are numerous business examples testifying how Analytics has played a critical role for a long time already, but as data velocity, variety, veracity, and volumes grow, so does the competitive pressure as organizations invent newer ways to differentiate and leapfrog others. Organizations today are ought to bank on fact-driven business as a way to gain the winning edge.
Thanks to the deluge of data and multiple form factors out there, we are already in the era of Analytics 2.0; an inflection point, with a radical paradigm shift we foresee in the way businesses are going to operate run in future. It’s time for businesses to unlock value at the very same shift in order to supersede newer thresholds. As Analytics grows more pivotal within any organization, this competitive advantage can rapidly lose its sheen & easily become a double-edged sword. Time is ripe for businesses now to answer the question, what more can be done with the data goldmine they are sitting on? How can they truly be an information-driven business?
As Tom Davenport says in his book Competing on Analytics, we are on the verge of Analytics 3.0, a data-driven economy where Data sits at the core of every business model, where Analytics is neatly woven into the fabric of the business; the organizational DNA, where Real-time & agile insight delivery (anytime, anywhere and via any medium) is commonplace, where Analytics is embedded at the point of decision & an integral strategic asset of differentiation and institutionalized decision-making happens at a scale beyond the imagination. Each and every atomic action in an organization will solely depend on the insights being generated from the data pile businesses carry, be it the next best product or feature, or getting foothold in some uncharted territories or even onboarding the right talent pool. A data-centric business model in its true sense!
But the challenge to industrialize Analytics still looms unleashed for many. How to make Analytics more pervasive within the organization, so that it’s an integral part of every transaction, there at the point of contact/delivery, and leveraged extensively in operational decision-making? How can it be tightly integrated into existing systems (CRM, ERP, marketing platforms, HR systems and/or financial systems) to bring additional awareness, business context, or targeted insights to support decision-making for specific business routines? How can the relevant information be used to support a decision or action in the context in which that decision or action takes place? How to make Analytics capabilities more accessible to the business users across the board?
Introduction of Analytics in true sense across enterprises is still lukewarm at best. Most organizations are yet to decipher the secret code to embed analytics deeper into day-to-day business operations. Analytics is still perceived to be a statisticians’ ball game; which I believe is overshadowing the real Analytics horsepower, and yet most businesses aren’t getting the most out of the capabilities a data-driven business can potentially deliver.
But if you delve a little deeper & try to unravel some of the contemporary trends or success themes cutting across most of the Analytically mature organizations, you would observe Analytics 3.0 already spreading its wings. We already see a wave of Analytics growing beyond the purview of CIO’s & CTO’s happening and going forward this trend would accentuate and become more mainstream. Analytics slowly is seeping into every organizational facet and fast becoming embedded within the business-as-usual processes. Push towards “consumption” of analytics embedded within the business process is trending up. Many clients across the globe, across industries, agnostic of size are embedding analytics within the business process and making it more “business user friendly”, easier for business users to “consume” the service directly and make most of the data trove available to the wider organization. Organizations (the Analytically mature ones) are already investing advanced systems to analyze unstructured data and understand the context, spot patterns within data, connect the dots and actually automate the business process. Hence, easier consumption of Analytics and its implementation across LOB’s or Business Functions is evident amongst the Analytics leaders today. Sooner or later, rest shall follow the suit!
As Tom Davenport says, before you take the plunge into the world of Industrialized Analytics, please be privy that the automated decision-making analytics brings to the table needs to be self-learning, flexible, and constantly scrutinized to ensure business alignment. Many a times, the analytics techniques or models employed get jaded with time and without appropriate vigil, they may prove to be fatal for the business. Make sure that the Analytics systems put in place are constantly monitored for business relevancy.
Getting the Analytics Game Plan Right
As a key proponent of Analytics, make sure you have a keen sense of what every department or LOB is grappling with and how Analytics could be a game-changer in addressing that. As an example, a CMO would be highly interested in running targeted promotions or hyper-personalized campaigns for improved conversion rate. It’s better to take down these business requirements, map point solutions or specific Analytics services which may deem fit for the cause and the typical benefit in terms of improved outcome (x% increase in lead generations, or y% increase in ROI). Such an approach ensures you get the appropriate attention you seek to put your case forward for discussion and give a sense to business on expected outcomes. A pilot program may get you going initially until there’s larger visibility to the Analytics initiatives.
Making Analytics Business User Friendly
Keep your analytics outcomes very consumable & business-user friendly. For example, location analytics employs advanced analytics techniques and models at the back-end to draw out tangible insights for the business but the same if packaged neatly into a mobile application, a jazzy UI to help a field representative create the best possible route to schedule customer interactions for the day, can literally make a very difference to usual business routine! A simple easy to use application leveraging GMaps API’s to chalk out the right route depending on diversions, traffic, no. of signals, customer availability et al can be deployed on every sales reps smartphone to revolutionize the way customer interactions are managed.
Prioritizing your Analytics Efforts
Last, but not the least, understand that it’s critical to prioritize your starting point. Firstly, gain insight into a business problem or pain-point that is most crucial to your business and that could really reap big benefits from analytics; a business challenge that truly affects the bottom line. Try narrowing down upon a problem which will drastically bring down operating costs, transform customer experience or has a direct bearing to the top-line. Understanding the organizational nuts n bolts, key metrics, short or long-term objectives and other key initiatives driving considerable impact, and thereafter aligning your analytics intent to the aforementioned areas, can help get maximum mileage in the initial pilot phase. In case you align it with some of the impeding challenges higher management is struggling with, garnering initial support and organizational visibility can be relatively easy. By crafting the right strategy to flag off your analytics initiatives, the chances are higher that you shall succeed in the budding phase itself, get the right business testimonials to establish credibility with various business owners and lay a robust foundation for running other analytics initiatives in the near future.
Be wary that looking up to Analytics Service Providers during infancy, ones who could handhold businesses and help navigate them through this data explosion can be truly instrumental. Their prior experience and expertise in setting up analytically-driven businesses can help in charting out appropriate data strategy, ensure widespread adoption of Analytics across the enterprise with credible success stories from past engagements and help make most of the Analytics investments.