Assess your organization’s analytics maturity to drive superior performance in your company
Learn how to measure and accelerate the enterprise analytics capabilities needed in your company to outperform your peers in today's competitive market.
In this article, you will learn about the importance of measuring and tracking the analytics maturity of your company. I will introduce the main industry-standard frameworks used to measure it, and finally, we will learn how to apply this in real life with a case study of a European wireless network operator.
This methodology is going to be helpful to identify and accelerate the analytical areas where the company is lagging to ultimately drive the most value out of analytics. The material covered in this article is mostly based on the excellent research done by the Internation Insitute for Analytics (IAA), founded in 2010 by Tom H. Davenport and Jack Phillips. Additionally, the case study was provided by Erik Strömgren, Sr Technical Consultant in Analytics at SAS Institute.
Measuring analytics maturity and its impact in company performance
Measuring analytics maturity in a company is becoming more relevant given the fact that recent studies have shown a positive association between analytics maturity and superior company performance.
Companies with high levels of analytics maturity are more likely to be included in and rank higher in “Top Company” lists from Fortune (Most Admired), Forbes (Most Powerful Brands, Most Innovative), Brand Finance (Top 500 Most Valuable Brands), and Boston Consulting Group (Most Innovative).
David Alles, Vice President of the International Institute for Analytics, illustrates in his research brief, “Analytics Maturity Powers Company Performance¹”, this positive association by implementing IIA’s maturity methodology.
In Competing on Analytics², Tom Davenport and Jeanne Harris cite several studies showing a “significant correlation between higher levels of analytical maturity and robust five-year compound annual revenue rates”. They also discovered that “high performers”, in terms of profit, shareholder return, and revenue growth, were 50 percent more likely to use analytics strategically compared to the overall sample and five times as likely as low performers.”
Many executives especially in the last couple of years have invested huge amounts of money to drive analytics adoption and performance across their enterprise. Nevertheless, many companies are failing in their efforts to become data-driven³. Therefore, it is crucial that companies start measuring their analytics maturity year-over-year to identify specific areas of improvement, start tracking the right metrics and invest in key priority areas to drive the correct analytical capabilities in their organization. To learn how to do this we will introduce two main frameworks that can be used to evaluate these aspects.
Frameworks and methodology
We will use two industry-standard frameworks to measure and understand the analytics maturity of a company. Both frameworks were updated by Tom Davenport and Jeanne Harris in their 2017 revision of Competing on Analytics.
The DELTA Plus Model Framework encompasses the five foundational elements of a successful analytics program with two new elements required for high performance. The model was first introduced in 2010 by Tom Davenport, Jeanne Harris, and Bob Morison in their book, Analytics at Work: Smarter Decisions, Better Results⁴.
To make real progress and become a data-driven organization, the capabilities and assets of these seven elements must evolve and mature over time. We will give a brief explanation of each element.
- Data: must be organized, integrated, accessible, and of high quality. It is important to identify what data is the most relevant to store and analyze to be cost-effective. Improving data quality and investing in secure cloud services platforms such as AWS or Azure will help the company in the long term to have a more flexible data architecture.
- Enterprise: an enterprise approach to analytics will greatly increase the organization’s competitiveness. Relying on enterprise-level organizational structures and plans will avoid organization silos, duplication in efforts, errors in analysis, and potential conflicts among different groups.
- Leadership: it is crucial that leaders in the company are fully committed to embracing a culture toward data-driven decision-making. This will benefit cultural acceptance of analytics and bring stronger support to those initiatives.
- Targets: it is difficult for a company to afford to be equally analytical in all parts of its business, therefore, companies must align their analytics efforts to their strategic targets to achieve corporate objectives. It is more important at the beginning to focus on a few initial but important use cases. Once the company is more mature, they will consider their analytics initiatives as business initiatives.
- Analysts: recruiting the right analytical talent will be crucial to advance the analytics strategy of the company. Having a balanced combination of analytics talent will be crucial to keep all aspects of an analytics project running smoothly. This includes having business analysts, data engineers, data scientists, translators, and UX/UI developers.
- Technology: creating an effective technology strategy for analytics is a critical prerequisite for success. The architecture must support experimentation and flexibility while making it feasible to integrate analytics with production systems and processes.
- Analytics Techniques: this aspect takes into consideration the main techniques that the company is implementing. This can start from simple regression analysis to more sophisticated techniques like machine learning.
Now that we have covered all the elements of the Delta Plus Framework, we will move on to the 5 Stages of Analytics Maturity model which was introduced in 2007 by Tom Davenport and Jeanne Harris in their book, Competing on Analytics: The New Science of Winning.
Organizations mature their analytical capabilities as they develop in the seven areas of the DELTA Plus Model. The 5 Stages of Analytics Maturity model enables an organization to assess which elements are strengths and which are weaknesses. For example, an organization may achieve Stage 4 in analytics leadership maturity, but achieve only Stage 3 in its management and use of data. This assessment enables targeted investment to mature analytics weaknesses based on the DELTA Model.
The graph above describes briefly each stage of the Analytics Maturity model. This will help the reader to know where to position its company in each of the seven elements described in the Delta Plus model.
To better understand how to apply these frameworks in practice, we will introduce a case study from a European wireless network operator to evaluate its analytics maturity level and suggest specific actions in each analytics area.
Analytics Maturity Case Study — Algebar Mobile
For our case study, we will work with Algebar Mobile, a fictive European wireless network operator. The company is a subsidiary of the fictive Orion corporation and it has about ten million customers, of which about 20% are business users. To read the complete case study, I invite you to visit the following link:
Case Study — Analytics Maturity — Algebar Mobile
Main tasks: 1. Score the analytics maturity of the company using the DELTA Plus Model, and 2. Suggest focus areas and actions
For this case, we would like to complete two main tasks:
- Score using the DELTA Plus model: score Algebar Mobile using the DELTA Plus model to identify where they are on the maturity index scale
- Suggest focus areas and actions: if Algebar Mobile is to advance in terms of analytics maturity, which of the seven areas would you advise they focus on developing, and what concrete actions would you suggest they contemplate
Task #1 — Scoring the maturity level of Algebar Mobile using the DELTA Plus model
In the chart that is shown below, you will see on the first column the seven elements of the DELTA Plus model. This includes:
- Technology, and
- Analytical Techniques
From columns 2 to 6, we have described common aspects for each of the 5 Stages of Analytics Maturity. For example, for the Leadership element in Stage 4, we can identify aspects like “Senior leaders developing analytical plans and building analytical capabilities.”
Next to that, we will identify the Score column where we rank the company on a scale from 1 to 5 for each element, and finally, on the right part, we have added some comments to support our score. As we can observe, Algebar Mobile has an average Maturity Index of 2.3 “Stage 2: Localized Analytics” based on the Five Stages of Analytics Maturity model.
By doing this exercise, we will identify, where Algebar Mobile is on the maturity index scale. I invite you to read the chart in detail to get a better idea of how to apply this framework in a real-life scenario.
Task #2 — Suggesting focus areas and actions
Once Algebar Mobile has calculated its maturity index, the next step would be to advance its analytics maturity by focusing on developing the key elements that bring the most value and ROI.
A useful exercise would be to rank these elements by the level of importance and suggest concrete actions as the next steps. Here is an example:
Improving the maturity index of a company will not be an easy task, since competing on analytics requires fundamental changes across the entire organization. Companies must create a data-driven culture, leaders need to develop new skills, legacy processes need to be changed and organizational inertia must be overcome.
This transformational process might take to company months or even years to complete, but in the end, it will bring economic benefits and operational efficiencies that will be worth the effort to gain a competitive advantage against their competitors.
: Alles, David. (2018). Analytics Maturity Power Company Performance. International Institute for Analytics. Retrieved from https://www.iianalytics.com/company-performance on May 1, 2021.
: Thomas H. Davenport and Jeanne G. Harris. 2007. Competing on Analytics: The New Science of Winning (1st. ed.). Harvard Business School Press, USA.
: Bean, Randy & Davenport, Thomas H. (2019). Companies Are Failing in Their Efforts to Become Data-Driven. Harvard Business Review. Retrieved from https://hbr.org/2019/02/companies-are-failing-in-their-efforts-to-become-data-driven on May 2, 2021.
: Davenport, Thomas H. & Harris, Jeanne G. & Morison, Robert. (2010). Analytics at Work: Smarter Decisions, Better Results. Harvard Business Review Press.