In this post, you will learn about data analytics maturity model which you could use to assess where does your business / organization stand on the path of using analytics to drive business value. If you represent decision-making stakeholders group and want to assess your organization readiness / capabilities to deploy analytics in order to create business value creation, you may find this post useful enough.
Here is a list of other articles I posted in the recent past in relation to strategic data analytics:
Here are the three broad categories / levels of data analytics maturity model:
- Analytically Challenged
Is your organization analytically challenged, analytical practitioner or analytical innovator?
Data Analytics Maturity Model
Here is the diagram representing the data analytics maturity model:
Analytically Challenged Organization
Here is how the analytically challenged organization would look like:
- Perform descriptive analytics: Analytics for analytically challenged organization would mostly mean descriptive analytics. Descriptive analytics is about using historical data to find patterns in the data in order to identify trends or extract actionable insights. For example, using historical sales data or spend data to identify or extract actionable insights in form of reports is descriptive analytics.
- Ad-hoc reporting: Reports are generated in ad-hoc manner based on demand from stakeholders representing business functions. These are not created based on some sort of analytics strategy.
- Cost-reduction is the key focus: In the ad-hoc reporting stage, the primary analytics goal is mostly the insights which can help in cost reduction.
- Data quality issues: Data quality remains one of the key issue for analytically challenged organization. Apart from data quality issue, the ability to access the data on-demand also remains as one of the key challenges.
- Lack of skilled professionals: In this stage, analytics organization does lack skilled professionals in the field of data analysis (analytical skills), usage of reporting tools, data management (sourcing / preparation / aggregation) skills etc.
The following represents some of the characteristics of organizations who could be termed as Analytics Practitioners:
- On the path of becoming data-driven organization: Ongoing initiatives to become data-driven organization; Management puts focus on laying down data analytics strategy to realize business value as a result of data analytics projects.
- Some data management practices can be found: The analytics team work to have data management practices in place in relation to sourcing data from different internal organizations / business functions, prepare & aggregate data for extracting insights.
- Descriptive & predictive analytics: Analytics organization is found to make use of both descriptive analytics and predictive analytics to find solutions to analytics problem. Predictive analytics make use of artificial intelligence / machine learning to create models which could make predictions based on the learnings from historical data set / past experience.
- Initiatives for skilling / hiring skilled professionals: Analytics team look out to hire skilled professionals or upskill existing employees to learn / acquire knowledge in the field of data management, descriptive analytics tools / frameworks usage, AI / ML engineers.
- Operational efficiency apart from cost reduction is key focus: Apart from cost-reduction, analytics focus expands its horizon to achieve operational efficiency in different departments related to different business functions.
The following represents some of the characteristics of organizations who could be termed as Analytics Innovators:
- Data-driven culture, data-driven products: Business stakeholders mandate it for data analytics team to work on data-based products. The key business decisions are made based on actionable insights extracted out of one or more data analytics projects. The approach to analytics from analytics innovators is found to be tied to the overall business strategy. And, this is how the analytics could provide competitive advantage to such organizations.
- ROI-based Analytics projects: Planned investment in the analytics projects with KPIs & value metrics in place to measure the ROI on analytics projects. Senior management does realise the value of data in terms of data-as-oil paradigm. Analytics team works with hypothesis-first-data-second approach for analytics projects.
- Well-established data management practices: Analytics innovators have well-established data management practices which includes some of the following:
- Get data from both internal & external sources
- Data preparation / aggregation / integration practices in place including usage of tools & frameworks
- Flexible data storage in place to facilitate ease access to data from different organizations; Presence of data lake is one of the key indicators
- Prescriptive, Predictive & Descriptive analytics: Analytics organization makes use of all form of analytics including prescriptive, predictive and descriptive analytics. Prescriptive analytics recommends actions to be taken which could impact the possible outcomes. While the predictive analytics predicts what is most likely to happen in the future, the prescriptive analytics recommends actions one could take to affect those outcomes.
- Skilled Professionals: Analytics team comprise of skilled professionals in the field of data management, AI / ML, reporting tools / frameworks, ETL methods etc.
Here is the summary of what you learned in relation to data analytics maturity model:
- An analytics organization can be assessed based on different parameters and a maturity level can be associated.
- An analytics organization can be termed as analytically challenged, analytic practitioner and analytics innovators.
- An analytically challenged organization mostly performs ad-hoc on-demand descriptive analytics by acquiring data present with internal organizations / business function unit. The key focus at this stage is cost reduction.
- The analytical practitioners apply both descriptive and predictive analytics with the help of decently skilled professionals and data management practices to realize the business value for both, operational efficiency and cost reduction.
- The analytical innovators innovate data-based, data-driven products / solutions which help make strategic decisions. All form of analytics including prescriptive, predictive and descriptive analytics are used to create analytics solution based on hypothesis-first-data-second approach.