04.04.2022 Executive Data Bytes – Assessing Your Data Maturity

Welcome to another edition of Executive Data Bytes! This week, we discuss determining the data and analytics maturity of your organization. 

Focus piece: “How to measure your data maturity?”

Executive Summary

Data Maturity is the ability for a company to utilize data in strategy and decision-making. Once the highest level of maturity is achieved, data becomes the foundation of every business strategy and process, creating a significant competitive advantage.

This blog post from Telefonica Tech’s Think Big / Business explores the different organizational areas that comprise Data Maturity and offers a brief synopsis for executing a data maturity assessment.

Key Takeaways

According to Telefonica Tech, maturity can be measured in 6 key areas. NOTE: We at Data Products actually have 7 key dimensions for assessing D&A maturity. Contact us to learn how we assess maturity.

IT, platform & tools 

• Mature organizations have a clear strategy that determines whether systems run on-premise, in the Cloud, or using a hybrid approach that is rooted in their current processes and future goals regarding AI and analytics. 
• Mature organizations will also employ automation to run most of the daily processes for their platforms and tools.

Data Protection

• Data protection focuses on the security of the organization’s data. 
• Mature organizations are fully compliant with the GDPR and use privacy-enhancing technologies such as encryption, anonymization & pseudonymization, and differential privacy to minimize risk to personal information. 
• Mature organizations also have a clear policy on data access, with special attention given to administrator rights.

Data Governance & Management

• Governance measures how well data is managed as an asset. Managing data as an asset involves an accurate inventory of all data sources, a data dictionary, and a master-data-management solution. 
• Mature organizations have a data steward tending to availability, quality, updates, etc daily. They manage analytical models throughout their lifecycle and utilize external data to increase the value of their models. 
• Most mature organizations have a clear policy on Open Data, regarding use of Open Data, as well as when and where private data can be published as Open Data.

Organization (Logistics)

• This refers to how data professionals are organized within a company. 
• The power, structure, and purpose of dedicated positions or teams is a good benchmark for the company’s data maturity. 
• Consider distance from CEO/Executive Director, number of positions dedicated specifically to data (individual vs team vs department), and the purpose and focus of those roles (excellence/advancement vs operational/processes)

People

• This aspect focuses on acquiring and retaining personnel with the skills and profiles required for advancing AI and Analytics within the company. 
• Mature organizations give special focus to hiring or outsourcing data-specific profiles, including data analytic scientists, data engineers, data architects, “translators”, and AI engineers. 

Business

• Mature organizations have a comprehensive data strategy that details their plans and objectives for the six areas listed here. There is also a clear vision on how much needs to be invested in each of the dimensions for achieving the goals. 
• Mature organizations consistently track and report the economic impact of use cases to ensure that the value generated by investing in data is clearly understood throughout the company. 
• Organizations at the highest level of maturity also look for new ways to leverage their data to create business opportunities.

These 6 dimensions can be turned into a set of questions with predefined answers ranging from 1 to 5 to calculate maturity. The resulting questionnaire can be completed through interviews or as a self-assessment, with the collective score creating a basic picture of data-maturity. 

Focus piece: “How to select a Data Governance Maturity Model”

Executive Summary

Once you have determined your company’s current state of Data Maturity, it is time to develop a model to grow that. This article from LightsOnData outlines top things to consider when choosing a Data Maturity Model. 

Key Takeaways

Business Drivers should be the primary consideration when shopping for a data governance maturity model. These will feed into all other considerations. 

• What is the goal that you're trying to meet with this model? What does your organization hope? 
• These goals should be specific and purposeful. Once you know what you want to do, you can compare various data governance models and find which one is most likely to help you achieve those goals.
• Keep in mind that no maturity model will fully meet your needs. Flexibility and scalability are important factors to examine when comparing the proponents of a particular model to your organization’s goals.  

Resources are another highly important factor. This includes both budget and labor resources

• Most models cost money.  This could be the cost to purchase the model, but it should also include any costs required to implement the model.
• There are models that are simply guides you purchase for self-implementation. These typically have a lower direct cost but have a higher time/effort component that may require additional hiring or training in order to implement the model.
• More commonly, data maturity models are offered as a package that includes third-party assistance to assess your company’s needs and then scale and implement the actual system and processes. This option will require more initial monetary investment, but may well be worth it because it offers expert assistance to streamline the implementation and make sure the model is able to achieve your desired results.

Any current data governance management framework in place needs to be taken into account when deciding on a maturity model. 

• If your current framework is working well, you might not want to change it. This means you will require a maturity model that resonates with it.
• If you do not have a framework in place, or your current framework is problematic, the model you choose could actually help you determine what changes to make, and what to implement first.

Industry peers are a great resource for evaluating data maturity models.

• Identify peers that are ahead of your company in the area and reach out to see which models and industry partners they use.
• Be realistic and aware of how their organization compares to your own. 

Who We Are

Data Products partners with organizations to deliver deep expertise in data science, data strategy, data literacy, machine learning, artificial intelligence, and analytics. Our focus is on educating clients on varying aspects of data and modern technology, building up analytics skills, data competencies, and optimization of their business operations.