Introducing Data & Analytics Maturity Assessment of “SAP Data and Analytics Advisory Methodology"

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In the last blog, I announced the new release of the “SAP Data and Analytics Advisory Methodology”. One of the improvements is the introduction of a Data and Analytics Maturity Assessment used in phase IV. This tool helps to evaluate data governance and organizational aspects that are essential for a successful realization of the value proposition defined for the business outcomes in phase II.
Thus, this blog provides guidance how to apply the maturity assessment to identify improvement actions that are considered in the roadmap afterwards.

Overview of the Data and Analytics Maturity Assessment

The “SAP Data and Analytics Advisory Methodology” focuses on data & analytics architecture development. But even the best architecture is ineffective when critical data governance processes, policies, key roles, and competencies are not established within the organization. Therefore, the methodology provides a general investigation of data governance-related topics to identify critical gaps that impact the architecture’s ability to deliver effective value.

This maturity assessment offers a total of 15 data governance and organizational focus topics from five dimensions, which can be assessed first from the current perspective and then for the desired future state. The assessment comprises five maturity levels, ranging from “initial” to “data-driven”.

Not all governance topics in context of the architectural investigation are equally important. It is sufficient to select and analyze the most relevant aspects.
The maturity assessment is used in phase IV of the methodology and is organized in two parts:

• Part 1 covers topics related to critical data governance topics like data strategy, data quality & KPI’s, data architecture and processes.
• Part 2 addresses typical data governance and management roles, their responsibilities, and interactions in an organization.

Let’s have a look into the two parts of the assessment in more detail.

Part 1: Assessing data governance focus topics

Data governance is a crucial framework that outlines the rules, processes, and accountabilities necessary for organizations to effectively manage their data. This comprehensive approach treats data as a product, ensuring that it meets specific standards and delivers value to both internal and external stakeholders. When data is handled in this manner, it is referred to as a "data product".
The key components of data governance are:

• Rules: include standards, policies, and guidelines that set the foundation for data management.
• Processes: the mechanisms that facilitate data management and decision-making on relevant topics.
• Roles & Accountability: defines the responsibilities and decision rights for data management.

Managing "data as a product" involves overseeing its availability, usability, security, and integrity to deliver business value. The goal of data governance is to ensure the right data reaches the right person at the right time, and at the expected quality, enabling informed decision-making that drives business outcomes.

The first part of the maturity assessment starts to investigate the data strategy as a baseline to establish data & analytics as a core competency. This is followed by looking into KPI’s to track data strategy execution and assess data quality. Then data architecture is analyzed that is important to provide a common understanding of data. Finally, we investigate data governance processes to ensure sufficient data quality and deliver tailored reporting, analytics and business AI solutions.

Part 2: Assessing organizational focus topics 

Part two of this maturity assessment focuses on the dimension “organization”. Here you evaluate the readiness of the organization regarding roles and teams contributing to the data governance and identify improvement potential.

In a mature data-driven organization, data governance and management teams consist of several roles, each with distinct responsibilities and skills.
Examples include:

• Chief Data Officer: responsible for maintaining data quality, security, and compliance, as well as promoting innovation and data-driven decision-making within the organization.
• Data Steward: enforce data governance policies and procedures and ensure compliance with data regulations.
• Data Domain Owner: manages a specific area or domain of data within an organization to ensure accuracy, integrity, security, and availability.

Such roles are part of the data governance, which is recognized as an overarching responsibility within the organization. Nevertheless, it is crucial to define the structure of governance boards and teams to establish and enforce data governance rules and policies effectively.
This structure includes:

• Data Governance Council: provides strategic guidance of data governance programs, prioritizes data governance projects and initiatives, and approves organization-wide data policies and standards. Data Governance Council: provides
• Data Governance Team(s): define Data Governance policies, roles, methods, processes, tools etc. (Framework), enforcing data standards and collaborating with operational teams to resolve governance issues
• Data Domain Teams: apply data governance policies, rules, methods, tools etc. on daily basis, manage data product demand & requirements and ensure their data quality and security.

The "organization" dimension of the maturity assessment evaluates in how far such roles and teams are established in the company.

The following five focus topics are assessed:

Procedure of maturity assessment

Although the maturity assessment is divided into two parts the assessment can be executed in one session, e.g. in a workshop with relevant stakeholders. To perform the maturity assessment, the following steps are carried out:

• Select data & analytics governance focus topics from the five dimensions relevant to the scope of the assessment and target architecture.
• Assess current maturity for selected topics, either collectively or by the individual stakeholders.
• Assess future maturity by discussing whether the current level of maturity is sufficient or whether a higher level is required.
• If the current and required maturity levels diverge, then necessary actions must be agreed upon and incorporated into the final roadmap.
  The Data & Analytics Advisory Methodology provides a template with a detailed description of each focus topic and its maturity levels.

Here is one example where four relevant focus topics were assessed:

 

In case current and desired maturity levels are different you should document what the gap is and what actions need to be taken to get to the to-be maturity level.

In the example above a data strategy was not existing but was considered as essential to provide the guardrails and guidelines to improve overall data & analytics governance. Also, the skills and knowledge in the organization required to work with data and create business value (data literacy) was not sufficient. Thus, follow up actions were defined to organize a data strategy development initiative and define a training plan to improve data literacy.

If this general assessment leads to the conclusion that data governance needs to be investigated in more detail, please refer to related frameworks from SAP (SAP Data Management Framework) or SAP partners.

In the next blog I will explain phase IV of the “SAP Data and Analytics Advisory Methodology” in its entirety with a focus on developing the architecture roadmap.