Is your in-house analytics center SLEAK?
The data sciences industry is growing at a rapid pace, making it seem like it has been around forever. It was only in 2012, that HBR claimed ‘Data scientist’ to be one of the sexist job of this century. In today’s world, it is very rare for large enterprises to outsource functions like sales or strategy and analytics is proving to be the next core element in how businesses function today. This is prompting a new wave – with a number of enterprises looking to setup their in-house analytics centres and take full advantage of the wealth of information at their disposal. With the industry not showing any signs of slowing down, it is important to understand what makes analytics centres and teams successful. The Analytics Powertrain There exists a popular myth, that an Analytics centre is deemed a success or failure solely based on the talent it is able to attract and retain. While talent is important, it is not the sole driver of success. The most critical element of the centre’s success is its integration with the organization and the value it drives. So how should this integration be engineered? Analytics teams and their processes are no longer ad-hoc. Enterprises often make the mistake of treating analytics teams as support and outsource them and as a result isolating them from the business and the decision-making process. Using analytics for pure reactive needs reduces the potential and the chances of it being successful. Rather, it should be a cycle that balances between proactive and reactive modes. Like a vehicle’s powertrain mechanism transmits the power from the original source of energy to the surface of the road analytics centres should drive all phases of the business lifecycle to propel the organization forward. For example, business Performance could be decoded using attribution models, which in turn determine the Strategy to be adopted. Similarly, scenario generators that provide an insight on how to gain a competitive advantage could be leveraged into fine-tuning an enterprise’s strategy. Once the strategy is finalized, a series of analytical experiments could aid the go to market Decisions and finally, operationalization on the ground occurs through Activities that are streamlined using systems that recommend the best action. It is therefore important to build analytics centres and teams as ‘Powertrains’ to ensure their success and relevance in the organization by integrating them in every step of the business process. SLEAK- the new paradigm For an Analytics centre to transform into a ‘Powertrain’, it is necessary to be SLEAK. Synthesize, to ensure the bridge between the business and analytics teams is strong. An ecosystem that allows for interactions that result in problem identification, translation and solution consumption with ease. Learn, to stay ahead of the industry curve. Technologies, methods, problems are becoming obsolete so fast that it has become a basic need continually learn to stay relevant in the current world. Experiment, to innovate and be a differentiator. Problems continue to evolve and hence long cycles for solutioning would not work. Quick experiments must be performed to stay ahead of the curve. Align, to be relevant in the organization. It becomes very important to streamline the efforts to ensure alignment with the organizational priorities. Any effort with no clear value adds is just a wastage of resources. Kultivate, – Knowledge Cultivation to create efficiency through collaboration. Creating a smart, collaborative environment prevents knowledge loss in the system and reduces the dependency on people through reusable assets. These cultural traits and characteristics are key to successfully setting up an in-house analytics center or enhancing an existing center’s offerings. SLEAK centers have a higher degree of success to realize their tremendous potential and act as innovation hubs for enterprises.
The data sciences industry is growing at a rapid pace, making it seem like it has been around forever. It was only in 2012, that HBR claimed ‘Data scientist’ to be one of the sexist job of this century. In today’s world, it is very rare for large enterprises to outsource functions like sales or […]
The data sciences industry is growing at a rapid pace, making it seem like it has been around forever. It was only in 2012, that HBR claimed ‘Data
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["data science", "Go", "API", "AI", "ML", "RAG", "Aim", "analytics", "GAN", "R"]
["AI", "ML", "data science", "analytics", "Aim", "RAG", "R", "Go", "API", "GAN"]
Ashwin Kumar S is a Partner with TheMathCompany. He has experience across several domains such as insurance, retail and e-commerce in the areas of Predictive modelling, Machine learning etc.
As part of TheMathCompany, he helps enterprises build and upgrade global analytics centres that are SLEAK, catering to their unique needs and challenges. This is enabled through playbooks and frameworks drawing on years of experience in analytics across multiple industry verticals.