Roadmap to Analytics Maturity – Part 1
Abdul September 29, 2020
Analytics has the ability to combine data from numerous data sources in interesting ways to help organizations to uncover potential relationships that could possibly mark prosperity or extinction of any business in the digital era. Across industries, Analytics is helping businesses to become smarter, be more productive, be more customer-centric and be better at making predictions around every decision being taken within the organization. It is enormously powerful, but there is no getting around the fact that it is complex and still the leading organizations across the globe are struggling to truly understand the economic value of the data goldmine they are sitting on. Just imagine yourself owning a Lamborghini & it’s sitting in your garage lying unused? After all, it makes no sense to put in such a huge investment in such a car and then leaving it in your garage to rust, right? Drawing on the same analogy, think of Data Deluge businesses are grappling with as the great potential “asset” within the organization; data that remains pretty much silo’ed or locked away in the Data warehouses, and not doing nearly enough to maximize the use of effective analytics to unleash Analytics true potential.
The ever-growing complexity of today’s business environment hasn’t gone unnoticed by most of the IT executives. In a genuine attempt to make business meaning of a growing stream of unstructured/structured offline/online data, analytics is slowly seeping into the CXO’s business agenda and landed at the fulcrum of the business. The future belongs to organizations that effectively deploy contemporary analytics tools/technology/people and processes to understand their customers better, manage risks more effectively and transform the way they operations. Analytically-mature organizations concur that data is now the new source of sustainable competitive advantage, and businesses are constantly inventing new ways of creating economic value and heavily investing in new analytics infrastructure to glean meaningful insights from their data. No wonder, during annual conventions and on other forums, their CIO’s highlight data analytics on their corporate agenda, a transformational shift in the way they do business.
But the following dilemma still poses a question mark on the faces of most of the CXO’s across the globe: How can business navigate in this data-driven marketplace? What’s truly needed to transition from Analytics 1.0 to Analytics 2.0 (or even 3.0 for that matter)? And what are the key areas to focus on to be truly leaders in analytics space?
Pioneers in Analytics such as google, facebook, amazon, linkedin etc are already making use of data to advance a variety of business goals and to help consumers. While business across the globe, irrespective of their size or industry they operate in yearn to be leaders in analytics, excel at the art of predictive modeling, and making downstream data-driven decision-making happen is no cake walk. Most organizations are yet struggling to make to make data-driven decision-making part of their DNA, woven into the very fabric of the business, across functions, departments, LOB’s.
Nevertheless, it’s evident that business and IT leaders must have to rethink their business-as-usual approach —and get a more holistic view of the enterprise and the data residing with it. With the ongoing digital detonation, especially the stream of data from devices, sensors etc and with the advent of Internet of things a.k.a. connected devices, and era of machine-to-machine, machine-human interactions, the task becomes way more daunting and complex. Business owners need to step back a bit and re-examine the critical pieces of the business and look at data sciences with a fresh perspective. They need to chalk out a robust plan, have a strategy in place, define the analytics intent, draw a detailed road map for investing in various assets such as technology, tools, processes, people and data sets; and tackle the perennial intrinsic challenges pertaining to commitment from varied stakeholders and navigating the organizations through a major cultural shift.
Analytics is not just about generating insights, but getting those insights to the right business users. To sustain the long-term success of data-driven innovation, it is necessary to continually revise one’s analytical approach in order to generate insights that lead to new innovation and competitive advantage. Following key facets to Analytics should be kept in mind before the intent is set to invest in the Analytics arena:
Achieving Competitive Differentiation
Get overwhelmed by the enormity of the analytics challenge is commonplace, especially for SME’s, startups due to constrained budgets and IT laggards who need be justified on the ROI. Intent is to focus on the value analytics brings to the business, invest in robust analytics capabilities and more advanced analytics tools (beyond usual business reporting), hiring people who have the business knowhow and the acumen to see data conceptually in a different way. Most of the e-commerce and internet-based companies, are the torchbearers when it comes to creating competitive advantage and innovating fast with Analytics. Considering an example, LinkedIn is amongst the chosen few companies that leverages almost all of its data for exhaustive analysis and every transaction per se has data associated to it, which is of immense value if harmonized well with the other pieces of information. A team of analysts and data scientists analyze this data, create respective segments or groups, look at different kinds of numbers on different user subpopulations which are then gleaned upon for insights; hence making various linkedin products/services so targeted and personalized. Similar example could be of recommendation engines on e-commerce websites which on the basis of your past web behaviors, purchases etc. customize their website appeal and entice you with specific products which closely align with your immediate needs.
Integrating Data across Siloes for the Greater Good
Businesses need to closely look into all the analytics activities happening with an integrated mindset and ensure information aggregation is happening at all levels. Critical knowledge and insights are disseminated to all sales personnel, customer-facing employees, vendors, partners, suppliers and other business stakeholders. E.g., Nike recommends its stores as well as consumers on which store to purchase from based on location, pricing etc. It reaps the benefits with increased customer delight, avoids potential stock-outs and maintains optimal inventory levels across stores to rationalize inventory holding costs with effective demand planning.
Growing beyond the walls of IT
We already see a radical power shift happening with Analytics purview being expanded beyond the barracks of the IT organization. This has significantly proven to be disruptive and effected the operating contours of various C-Suite stakeholders in a big way. Chief Marketing Officers can now come up with more hyper-personalized campaigns which get them greater bang for the buck. Chief Risk Officers could proactively identify risks and get those early warning signs for effective mitigation. Chief People Officer now better know about the secret sauce behind high-performing individuals and could tweak their talent onboarding strategy accordingly or making engagement strategy with existing talent more effective. Those who have control over data, and the ability to analyze that data, move to the forefront in the organization. The new found power is helping various C-Suite stakeholders to drive executive meetings more effectively and thereby elevating themselves to enhance influence and improve personal brand within the organization.
Rethinking Real-time Business Operations
Operating in a real-time mode helps business make faster and more informed decisions is a well-known fact. When it boils down to effectively mine value from data, insights relayed at speed is imperative for lightning fast businesses of today. Latency in decision-making brings down the maximized economic value which could potentially be derived from the outcome. And unfortunately the ticker starts from the very moment the business event takes place.
Winners are the one who are able to set up robust governance structures for real-time responses to possible early warning signs, and also invest in the algorithms that give them actionable insights. It’s about understanding events, conditions and factors in a deeper and broader way and use data more proactively through predictive analytics and other tools. An example could be how retail supermarts track shopper movements within the store, give them real-time information about products, and generate real-time coupon/offers based on shopping behavior dynamically, past purchase patterns or plus data sitting within the CRM system(may be as part of a loyalty program).
Building a Data-driven culture
Embracing an Analytic-driven approach to decisions if critical to fact-based decision making, and should be adhered to all across the organization, from top to bottom instead of sheer gut feel. For any business, optimal Marketing Mix means appropriate allocation of advertising dollars is a top priority, and especially in an era where proliferation of device formats is the new norm and digital media channels are aplenty. Some of the leading Cosumer Goods organizations today exemplify best practices being followed in the industry when it comes to data-driven management. E.g. a leading CPG organization decided to bolster its traditional marketing mix channels with a new analytical approach— leveraging customer data sourced from disparate sources and a refreshed models repository for predicting behavior. To their surprise, especially in a digital era, TV seemed the most effective marketing medium for their set of target customers & eventually the decision divert investments from other print/digital media to TV proved beneficial.