How to institutionalise data-driven decision making
The amount of data circulating around the globe has grown at an astronomical rate in the last couple of years. In fact, 90 per cent of all the ‘zettabytes’ of data available in the world today have been created in the last two years, and demand to leverage this huge font of information for better business decision making has reached fever pitch.
Marketing departments, like others, are clamouring to harness the power of data to catalyse data-driven decision making.
While tapping into this trend for better customer insights is nothing new for marketers, the opportunities available are still not being fully utilised by many. In my opinion, too many marketers are stopping short in their processes and failing to turn data into smart data that can add true value to the brand. What do I mean by that?
Data analysis has evolved from traditional insights, garnered through surveys, which largely provided two-dimensional understanding of what your marketing needs might be. This level of understanding has improved dramatically through technology. The advent of the digital age is now providing marketers with far greater depth of insight by, for example, being able to monitor customer behaviour at the point of brand interaction, and in real-time. That said, there are a few requisites before setting the tone to making informed decisions:
Right skillset, toolset and mind-set
While data is the primer to the whole process, availability of this alone will not suffice in making better decisions. It has to be supplemented and corroborated by scalable tools, techniques and technologies – to derive meaningful insights. Also, organisations have to bring on board an amalgamation of convergent and divergent thinkers with requisite analytical skills to understand, translate and generate insights that can then be consumed effectively. These individuals must be wired to synergize the interdisciplinary tenets of maths, business and technology. But most importantly, embracing data to make business decisions is a radical shift in mind-set, which only forward-looking companies can inculcate in their business operations and strategy.
A culture of continuous experimentation and innovation
Companies looking for institutionalising “data-driven decision making” across functions, as a best practice, should employ analytical techniques in a ‘lab-like’ environment. Continuous experimentation, on the analytical approach, can lead to newer innovations and greater efficiencies. This has to be backed by a “learning over knowing” attitude, which we believe, is an essential pre-requisite if organisations have to scale and sustain the use of analytics.
Needless to say, creation, translation and consumption of analytics; developing a better art of problem solving and insight generation; and people, processes, tools, and learning technology platforms must unite seamlessly. These have to individually come together as bionics laying the foundation for institutionalising decision support via analytics.
The most essential ingredient to analytics equation is the human resource element- “the curious lot” with a quantitative bent and an ability to think from first principles. They should be able to integrate perspectives of business, maths and technology and should work closely with stakeholders in a white-box and collaborative manner helping them institutionalise analytics.
This encompasses the pioneering of structured frameworks for analytics and decision sciences. It includes, structured problem definition and insight generation methodologies, analytics governance and maturity assessment models, etc.
Platforms and Products
To support the entire analytical value chain, continuous and sustainable innovation has to happen. These result in bringing next-generation analytical solutions and platforms capable of catalysing problem definition, analytics methodology execution, right until the operationalisation of analytics.
The right mix of (different) analytics
Conventional thinking states that data analytics flows in unidirectional stages: a ‘Descriptive’, then an ‘Inquisitive’, followed by a ‘Predictive’ and finally a ‘Prescriptive’ analysis. It’s a four-stage journey that starts with understanding ‘what’s happened to the business or brand’, to ‘why something is happening’, to ‘what’s likely to happen’, to then looking for answers to the ‘so what?’ and ‘now what?’ questions. To get meaningful insights and a proper holistic view through these four areas, they need to be viewed in tandem, not moving or operating one stage at a time and in one direction.
The ability to address often complex business problems not only as a result of the dynamic nature of many businesses themselves, but also by an increasingly complex marketplace, requires an integrated approach to analytics. It requires an interdisciplinary approach that not only combines the application of maths and technology to analytics, as offered by data scientists, but also the additional layers of design thinking and behavioural sciences, offered only by ‘decision scientists’. It involves a cross-wiring between algorithmic processes and heuristics.
Most marketers will agree that data analytics is a key driver for brand innovation and competitive advantage. However, to fully harness the business benefits that data can offer, organisations will need not only the right tools and technologies, but also the right integrated processes, and people with right mind-set and skills.