The telecom world is changing, and organisations are challenged to not only grow, but to merely stay relevant. To cope up with the fast-changing environment, organizations need to be at a certain level of maturity where the decision-making process needs to move from gut feelings to number & reasoning-based practices. The mandate here is clear: Organisations need to become data-driven or risk being left behind. However, the reality is bleak considering that nearly 100% of enterprises want to become more data-driven, but only less than a third have accomplished that goal. The starting point to being data-driven starts with being able to assess where one stands, and which is the best way to move ahead to a certain objective, a notion which continues to remain challenging for organisations.
Almost all organisations have ventured into the woods of data analytics with a purpose to make a sense and get the best out of the huge volumes of data they have. Sometimes they ask what others (either in the same industry or across industries and academia) are doing and how they can replicate it, while at times they ask what new can be done that no one else has. To analogize the entire data analytics practice to the workings of an engine, an engine can be only as good as the sum of its parts. The parts must fit in well, the oiling mechanisms should help in friction reduction, the oil supply has to be timely and of course, the sparks need to be perfect. As many say, data is indeed the new oil and data analytics is fast becoming the engine (in this case, of growth).
Fine tuning this engine is the need of the hour. Assessing where we stand, which directions we can move towards and where would that lead us to, what would be the best enabler to move in each direction, and how to go about doing it, is mission critical. This, in itself, is an optimization problem where maximizing returns and minimizing costs given the multiple constraints is challenging.
Transformation is important, but to ensure true competitive advantage, organizations must transform themselves in a planned phase. The approach to analytics cannot be stochastic as it would result in more troubles than benefits. Organizations must traverse one stage at a time. Not only defining those stages is the need of the hour but also is knowing what steps one needs to take at a point of time to move from one stage to another. In this scenario, an Analytics Maturity Assessment becomes imperative.
We have been working with customers across the globe in helping them assess their Analytics Maturity, to define the business objectives and carve out an Analytics roadmap towards said objectives, through our Subex Analytics Maturity Models. Subex Analytics Maturity Models defines those stages and the steps between those stages, through an assessment across People, Process, Technology and most importantly Data.
To know more about why an Analytics Maturity Assessment is important, and how it can help your organisation, Schedule a Demo with us and our Subject Matter Experts will get in touch with you.
Dr. Sumit Singh is Senior Data Scientist at Subex Digital LLP. He has a PhD in Decision Science and is a Machine Learning researcher, practitioner and educator with over 10 years’ experience in academia and the industry. At Subex, he is responsible for statistical modelling and algorithm design for business requirements. As well he is involved in doing research activities to implement new ideas into telecom domain. His areas of interests are – Reinforcement Learning, Optimization and Stochastic Modeling. He can be reached at firstname.lastname@example.org