The explosion of data and analytics, has led to much hue and cry over building and sustaining profitable analytics capabilities. While organizations have realized competitive advantage through leveraging data and analytics the right way, there is a need to assess their status as an analytically driven organization to prepare themselves for an environment which is constantly in flux.
In assessing their analytical capability, organizations usually get lost in tangibles like data and technology leaving out the softer aspects of building a successful analytics center – culture, governance, change management and impact generation. A holistic view of an organization’s analytical maturity will give a more accurate assessment of its standing as a data-driven force in the industry. This analytical maturity can be assessed based on the following dimensions –
As a noun:
- Governance & Leadership – Organization of analytics function with well-defined roles, responsibilities and accountability
- Investment on infrastructure – Synergy of the right tools, technology and team
- Execution sophistication – Level of delivery excellence with the right design of execution framework
- Types of problems solved – Alignment of analytics solutions with business goals
With varying levels of sophistication for each of the dimensions above, organizations fall under distinct categories of analytical maturity – Traditional, Transitional, Translational and Transformational
A sizeable proportion of organizations today approach analytics in the way they always have – a bunch of Statistics majors churning out spreadsheet over spreadsheets of indecipherable numbers which are hardly ever used for any decision-making exercise. Such organizations that continue to work in traditional ways are characterized by their use of elementary data management platforms, adherence to age-old tools and techniques and inclination to stick to obsolete processes all of which stem mainly from an overall lack of vision of where analytics figures within their organizational setup.
Although there is little dispute on how analytics can play a critical role in increasing revenue, cutting operational costs and enabling a better customer experience, companies struggle to devise a suitable governance structure and combine it with the right leadership to generate sustainable impact through analytics. With no governance structure and lack of sponsorship from the top, organizations are unable to realize the full potential of analytics. They end up wasting time and capital on unsuitable talent, expensive technologies, attempts to solve problems irrelevant to organizational objectives and plugging of gaps in a patchy execution framework. According to the Chief Analytics Officer at the world’s largest Gaming & Hospitality firm –
“You have to start with the charter of the organization. You have to be very specific about the aim of the function within the organization and how it’s intended to interact with the broader business. There are some organizations that start with a fairly focused view around support on traditional functions like marketing, pricing, and other specific areas. But alignment around how you’re going to drive the business and the way you’re going to interact with the broader organization is absolutely critical. From there, everything else should fall in line. That’s how we started with our path”.
In order to evolve as a data-driven company, such organizations need a paradigm shift in their view of how analytics can turn into an innovation hub from a back-office function. While a firm may experiment with a centralized, de-centralized or a hybrid governance for analytics, a solid roadmap of where their analytics needs to be in the future is critical to set things in motion. Without such a top-down push, these organizations die a slow death, losing competitive edge and becoming irrelevant in their industry. Even the biggest players in Real Estate in India, for example, have been unable to come up with a concrete analytics mandate which has allowed niche data analytics organizations like Nestaway and Commonfloor to establish their dominance in the industry.
In the transitional stage of analytical maturity, organizations get some of their basics right. They have a well-defined governance structure and a dynamic leadership. They invest more to acquire and train the right talent. They also invest considerably on tools and technologies as they gear up to pursue pertinent business problems. However, they fail to inculcate a data-driven culture within their organizations which paralyses their ability to execute to plan and churn out analytics solutions.
In one such instance, one of Australia’s largest insurer began its journey to be analytically self-sufficient – underwent re-organization to setup governance solely for analytics, discarded traditional data lakes to adopt cloud-based data management systems and adopted latest analytics tools and technologies.
Despite such substantial changes, it struggled to deliver quality solutions consistently. Desperate to show results to business, the insurer resorted to working with a host of vendors, with different expertise, across all its lines of business. According to the Chief Science Officer at a top multi-national insurance and financial services firm –
“The biggest challenge of making the evolution from a culture that largely depends on heuristics in decision making to a culture that is much more objective and data-driven and embraces the power of data and technology—is really not the cost. Initially, it largely ends up being imagination and inertia”.
Without a suitable analytics change management strategy in place, such organizations lose out on the opportunity to combine their invaluable domain expertise with in-house analytics talent to generate richer business insights. Moreover, the lack of an agile execution framework aligned to their own organizational goals and values results in slow progress owing to the hassles associated with vendor management. In the long term, the return on investment on external vendors continues to drop as problem-solving know-how becomes siloed and hinders the organization’s sustainable transition to become data-driven.
Some organizations get stuck at the translational stage of analytical maturity wherein they begin re-inventing themselves to fully realize the potential of analytics. Such organizations invest in the best-in-class analytics talent, mix of licensed and open-source tools and use the latest techniques to solve business problems. They have a sophisticated execution framework which takes a holistic view of analytical problem-solving, bringing together insights from all lines of their business. However, they struggle with translation – identifying the right business problem to pursue and turning numbers into actionable and reliable business decisions. This results in poor consumption of analytics across the organization and individual business units lose confidence in their ability to deliver relevant solutions. Even with high investment on talent and tools, they are unable to generate demand to sustain their analytics center.
In one such instance, a major industrial products company made a huge predictive analytics commitment to preventive maintenance to identify and fix key components before they failed. Halfway through the extensive and expensive data collection and analytics review, a couple of the repair people observed that, increasingly, many of the subsystems could be instrumented and remotely monitored in real-time. In other words, preventive maintenance could be analyzed and managed as part of a networked system. This completely changed the design direction and the business value potential of the initiative. The value emphasis shifted from preventive maintenance to efficiency management with key customers. Essentially, the predictive focus had initially blurred the larger vision of where the real value could be.
In another instance, a hotel chain used some pretty sophisticated mathematics, data mining, and time series analysis to coordinate its yield management pricing and promotion efforts. Almost five months later, after the year’s financials were totally blown and HQ’s credibility shot, the most likely explanation materialized: The modeling group-the data scientists of the day-priced against the hotel group’s peer competitors. They hadn’t weighted discount hotels into either pricing or room availability. For roughly a quarter of the properties, the result was both lower average occupancy and lower prices per room.
Such organizations need to make tactical changes – improve data reliability to build trust with stakeholders, hire an optimum mix of business and analytical talent to enable internal selling for demand generation and ensure solutions which make sense to the business as a whole. A multinational financial services firm spoke about its success story –
“The first change we had to make was just to make our data of higher quality; to make sure that the data has the right lineage, the right permissible purpose to serve the customers. The second area is working with our people and making certain that we are centralizing some aspects of our business. We are centralizing our capabilities and we are democratizing its use”
Rarely will a firm reach the transformational stage of analytical maturity. These organizations are the torchbearers of the data-driven industry. They have moved away from one-off initiatives in disparate units to more holistic analytics solutions that have enterprise-wide impact. They create their own platforms specifically tailored to their business needs. They bridge the talent gap through customized training programs. They approach highly complex real-world problems using state-of-the-art techniques. They acquire and support start-ups to expedite growth by collaboration. One such firm, AT&T talks about talent as one of the most important pre-requisites for reaching this stage –
“You have to have the data, But without talent, it’s meaningless. Talent is the differentiator. We’ve helped contribute in part to the development of many of the modern technologies that are emerging in the open-source community. So, we’ve delivered over 50,000 big data related training courses just this year alone. We want to make sure that they can develop their skills and then tie that together with the tools to maximize their productivity.”
With their highly sophisticated analytics setup, these organizations are able to diversify into multiple verticals and disrupt traditional business models. Google, Amazon and Netflix have revolutionized the analytics industry with their niche solutions. Even with high investments in R&D, one of the major challenges faced by such organizations is maintaining industry lead. The constantly changing environment keeps them on their toes to experiment and innovate for leading the analytics race.
It is apparent that every organization faces different challenges during the four stages of analytical maturity. Even with suitable governance and investment, some organizations could still have low maturity due to lack of talent and execution capability. And some could forever be stuck in transitional stage due to their inability to design their analytical roadmap. Without traversing the translational stage of maturity, organizations may never be able realize profits through analytics. Moreover, there is no one-size-fits-all solution to overcome the challenges pertaining to each of these four stages. Therefore, it is imperative for organizations to assess their analytical maturity and identify steps necessary to take their analytics capability to the next level.
If You Have an Interesting Story of Building Analytics Capability or Want to Know The “t-value” of Your Organization, Reach Out to Us At TheMathCompany.
MathCo was founded by Sayandeb Banerjee, Aditya Kumbakonam and Anuj Krishna with the mission to build “global in-house analytics centers”. We aim to make large enterprises self-sufficient by helping them build or enhance global analytics centers that are unique to their needs and challenges. An analytical center rooted in an organization’s culture and values can seamlessly integrate with other global capabilities and unlock value from data & information. Our goal is to build a self-sustaining center and get out
*t-statistic: a ratio of the departure of an estimated parameter from its notional value and its standard error
