March 29, 2022 - 2 min
Analytics Maturity Model - Helping Drive Insights That Matter
Extensive advancements in analytics have made companies struggle with data innovation. The most prominent market leaders in advanced analytics are still leaving value on the table. Ample research in operationalizing Artificial Intelligence complemented with the explosion of data is capable of revolutionizing business processes.
These data-driven competencies will underpin increased productivity and process optimization and improve overall business results with better decision-making, risk management, and critical competitive advantage.
We found that the missing link in numerous AI transformations is a well-planned Data Strategy. So, I have designed an analytics maturity model to aid and guide our clients in achieving their analytics ambitions.
Analytics Maturity Model—A look inward for a leap forward
Before companies can drive data eorts to value, they need to take a step back and focus on gaining a deeper understanding and baselining their present capabilities. The focus can then shift towards discovering the “art of the possible” and designing an analytics vision tailored and aligned with broader business strategy and goals.
I can break down analytic competencies into five maturity stages:
- descriptive analytics
- diagnostic analytics
- predictive analytics
- prescriptive analytics
- cognitive analytics.
Five simplified analytic maturities
Descriptive analytics implies having the ability to visualize data tailored to the viewer’s needs.
An emphasis is put on the customer-centric experience because it goes a long way in aiding the stakeholder’s decision-making process. It is principal to highlight important KPIs and not distract one’s attention with irrelevant numbers. The most distinguished data visualization tool is Tableau, with PowerBI being preferred by many.
Diagnostic analytics goes a step further. Beyond conveying historical data through various dashboards, organizations have far-reaching data-first interpretations of pivotal business events and metrics.
Seeking to provide these insights, data analysts turn to the analytic capabilities of the tools mentioned above.
With everyone striving for state-of-the-art solutions, it is possible to forget about the value diagnostic insights provide to the business.
Data-driven decision making process
Shifting from diagnostic to predictive analytics is where many organizations become liable to dawdling. This endeavor is a significant step and requires considerable commitment to be done properly. Predictive analytics eliminates a big blind spot in data-driven decision-making by allowing managers to anticipate future trends besides having the usual diagnostic, retrospective look at operations.
Prescriptive analytics is a natural improvement upon predictive. Rather than solely predicting metrics, these systems act as associates in the decision-making process by giving data-supported decision guidance.
However, low adoption can be attributed to the inability to use dashboarding tools and the requirement of significant upfront investments. As a result, stakeholders have often led away from developing these systems, not seeing the long-term value.
Cognitive analytics encompasses state-of-the-art solutions developed and used by primarily Fortune-500 technology leaders. Unfortunately, only a select few have been able to capture real ROI from these products. Regardless, learning about cognitive advancements helps managers envision what the future holds—having accurate vision aids in developing an appropriate data strategy to reap the benefits in the long term.
Guiding analytics eorts to value
Developing a data strategy is a crucial step toward becoming a data-driven organization. When done right, 80% say it was worth every penny. Helping you break down the complexity, the analytics maturity model will guide you in the process.
Feel free to reach out at firstname.lastname@example.org if you are excited to start your data journey with us.
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