Why Building Substantial Internal Analytics Expertise is Core to Performance

Why Building Substantial Internal Analytics Expertise is Core to Performance | Jigsaw
Author Image Sarita Digumarti

The importance of analytics and data driven decision making is very evident to most companies, and most senior management executives have a mandate to investigate and understand how to leverage data for advanced analytics within their own organizations.

What is less clear is how exactly to leverage this data? Does it involve working with specialist providers of analytics expertise who can identify how to unlock value from all the data that resides across different parts of an organization? Or is it better to invest in building an analytics team internally that can provide decision making support to management? If the approach is to build internal capability, which organization within the company should manage the analytics team – is it IT? Strategy? Or should analytics capability work independently as a support function for multiple functional teams?

In many instances, especially in large companies, the answer is usually a combination of all of the above. However, each of these approaches will not be completely effective unless there is one key factor considered: how well the top leadership of the company truly embraces the potential and importance of data driven decision making.

This is different from the identification of analytics expertise as a driver of growth. It involves a fundamental transformation of company culture to one that expects and respects the role of data in making decisions – both big and small. This in turn requires an overhaul of existing data collection and storage systems to be able to make data available to employees in easy to use formats. And it requires enabling all employees across different functions and teams with data understanding, analysis and analytics skills so they can use data to test hypothesis, validate outcomes, and analyse past performance for understanding, insights, and future planning.

The path to building an analytics culture is not easy. But it is critical. An MIT Sloan study from 2016 that investigated the most common reasons for analytics not succeeding as much as desired across multiple companies listed the lack of planning and preparation for a sustained change in culture as one of the 5 top reasons for under-achieving with analytics. Multiple other studies emphasise the idea of an analytics culture, where all levels of people from top management down to managers and employees are comfortable with the idea of including data as part of their everyday decision-making processes.

Therefore, as companies increasingly look to employ data driven insights and analytics predictions in their businesses, it is very important to include plans for a corporate program in Big Data and corporate training in Machine Learning by:

  1. Building a data oriented culture that emphasises the importance of generating data backed decisions as much as possible
  2. Enabling data accessibility across multiple teams and levels of employees (with appropriate levels of access), and
  3. Providing employees with the right set of data analysis and analytics skills so they can use data meaningfully

Otherwise, companies run the risk of essentially ending up with very superficial or very narrowly applicable outcomes, and the potential of analytics and Big Data is never truly realized.

To learn more about how to build a data-centric team, CLICK HERE