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Analytics Maturity Assessment: F.I.R.E 4X4 Matrix

Analytics Maturity Assessment: F.I.R.E 4X4 Matrix

Bhasker Gupta

Analytics Maturity Assessment: F.I.R.E 4X4 Matrix

The speed at which analytics industry is evolving is almost awe inspiring. Yet, it’s evident that we are at the stage of maturity where standard practices and ripeness of analytics processes are far from reality.

Within the whole buzz of how essential analytics is today than ever for organizations, there is a huge confusion around different jargons that are currently floating around. The confusion limits the ability of organizations to view analytics as a single cohesive thread that binds all their business units with the charter of intelligent decision making.

This framework rationalizes how mature an organization is in its ability to incorporate the full potential of analytics. The model measure organization’s current strengths and weaknesses across 4 parameters essential for a robust analytics strategy. The model helps you take an honest look at how analytics is being utilized within your organizations and what steps can be taken to improve your analytics usefulness.

Focus: (Strategy & Vision)

Organizations today understand that analytics is a key factor to lasting competitive advantage, yet few organization go on to identify independent, well rounded use cases around analytics.


Most of the work around data science is done in piece-meal fashion; wherein management tends to look at data science as an experiment. Even where analytics shows promise, little advancement is made into the area.

While various reason can be attributes to why analytics does not become a part of how businesses operate; the biggest is absence of management focus. This often leads to a lack of sponsorship from stakeholders, stalling key analytics projects.

Often, management considers analytics initiatives in similar light as IT projects. Not enough focus is given to align analytics to organizational strategy.

Broad organizational introspections lead to an insight into management focus.

Integration: (Structure & Deployment)

Data Science initiatives starts in silos within various business units. This works well; and is even efficient; in cases where organizations are just getting started on their analytics journey.

Yet, as organizations move towards a robust analytics strategy, providing an efficient structure to the data science teams becomes critical. At Kruxonomy, we support both the centralized and de-centralized structure based on the maturity and needs of the organization.

A robust structure helps in reducing   disparate analytics processes and minimize sub-optimal utilization of resources. It also helps in sharing of best practices from the different functions and improve the way data scientists tackle various problem statements.

Not just how data science teams are structured, but this parameter also access how well knit is the data science team with the operational and management teams. This may include the organization-wide alliance of data scientists, an analytics center of excellence that fosters research and discovery in data science.

Rigour: (Competency & Governance)

The amount of new developments that is currently happening around data science and machine learning is almost overwhelming. The pace is at times incomprehensible by even seasoned data scientists and organizations that have remained on top of the analytics game.

See Also

Given your investment and conviction towards analytics, your current analytics initiatives and solutions will be assessed on the level of rigor against industry standards.

This would mean an assessment on the maturity of your analytics itself. Are your current analytics solutions effectively maximizing the return on your data? Are you data scientists spending more time on mundane reporting when there are opportunities to automate most of it? Are there opportunities for predictive modelling that are untapped?

Enablers: (Technology & Talent)

Few companies have organized their data with analytics as an end goal. Legacy systems often tend to create data that have operational and quality issues.

This has led to contemporary rant over massive centralized data stores i.e. data lakes often using technologies like Hadoop. Not all organization need big data infrastructure, yet centralized, quality data is key to analytics success.

This is true with analytics technology and talent within enterprises. Data science is a fast evolving area and organization need to quickly stay connected with the developments to be competitive.

Yet, skills and technology needs to upgraded at a much faster pace. Increasingly, there’s more that can be done in analytics from same amount of investment than it was possible just 2-3 years back. The cost of ownership in data science projects would continue to decrease over the years. It is best to utilize this to upgrade the technology and train your data scientists.

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