Most organizations use analytics to improve their operations to enhance business performance and growth. However, through our experience of working with telecom organisations across various geographies, we have witnessed that analytics in many cases is done in an ad-hoc manner rather than in a planned phase. This results in organisations not witnessing the kind of return they were expecting from their analytics investments. An Analytics Maturity Assessment (AMA henceforth) helps in identifying such pitfalls and avoiding them. Few of the pitfalls that AMA helps in identifying are:
- Data Issues & trust: The root of any analytics project is the Data element. It is important that the end user does not have any form of data trust issues when it comes to their data. Proper data capturing, storing, infrastructure design, validation etc. are important dimensions which AMA investigates with proper checks and balances based on industry standards.
- Lack of Clarity: The first step to having clean data comes from having a good understanding of the data. There are basic graphs, queries, questions that every data analysis requires at the start of any project. For companies to be truly data driven, a Data Analysis pack consisting of the above elements and beyond is necessary. AMA checks these points to help figure out whether the first cut is robust enough. This understanding, the results of such a pack, is essential not only for the analytics team but helps business users as well gather lots of useful insights.
- Lack of Business Understanding: Analytics is a means to an end and not an end in itself. The intention is to help the organization improve on its top line, bottom line and operational efficiency. This is not possible unless the analytics team has a good understanding of the business as well the business team clearly understands what analytics can bring to the table. Synergy between the two is needed for implementable outcomes. The analytics solution should answer the questions that the end user wants to know. AMA helps in avoiding this pitfall or checks the status quo by having multiple checklists such as SMEs, trainings, meetings, presentations on this dimension.
- Improper Implementation: After what is to be done is clear, how it is done is essential for effective implementation. Ad hoc project work and repetition of same mistakes are cost centers for organizations. There must be standard practices for project implementation.
- Information Asymmetry: Having to constantly reinvent the wheel is another cost factor which takes the essential time of resources. This issue is usual the result of teams working in silos and not functioning as a larger team. Often similar analytics projects are undertaken in different verticals such as say finance and operations. This is another grey area which AMA helps in identifying and avoiding.
- Missing dollar accountability: Many organizations do not have long term vision in place. In these cases, analytics becomes good to have but it remains just a cost center. The vision and purpose of analytics needs to should come from the top and be clearly identified and communicated. Each project’s expected outcomes should be predefined and once implemented, its ROI calculated. This is essential for the integration of analytics into the DNA of the organization for it to be able to make a string contribution to the growth story.
It is essential for every organizations to take stock of the situation after certain intervals. It is even more important when trying to inculcate something very different and new within the organisation to bring about a mindset/cultural change. Most of organizations are investing in analytics but find using it effectively difficult. AMA helps in streamlining this change. It helps in asking pertinent questions and figuring out the change methodology. As noted above, there are quite a few pitfalls of not having an AMA for an organization, and not having an AMA in place can be expensive considering the associated risks.
To understand more on how you can adopt an AMA within your organisation, view a recent webinar we had conducted on the topic.
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 email@example.com