US East and Latin America

01. Introduction

In business today, many organizations are researching, designing, or implementing strategies for digital transformation. However, in a world increasingly dominated by data, it seems fewer organizations have data governance at the core of their transformations. Often a lack of buy-in from senior leaders is cited as the cause.

It’s common sense to start with the end in mind, identifying business goals, optimization opportunities, and new business models. Data is often the output that helps to achieve the outcome, whilst in many cases, data is also the input, supporting process and enabling automation. That’s why having a robust organization-specific data governance strategy should act as the book ends to any digital transformation.

02. What is it, and why do we need it?

When devising a ‘Digital Transformation House,’ with its traditional foundations underpinning the cores of capability, data is a primary foundation, sitting adjacent to technology and process. Holistically, the three enable new capabilities and solutions while optimizing existing ones. Consider a project management system, for example, requiring robust processes to manage the work, technology to support decision-making, and data to feed the machine. Process and technology are generally easier to rationalize as they are more tangible, and a typical conversation on data governance often leads to frustration and consternation, a feeling of not knowing where to start.

 Data is often the output that helps to achieve the outcome, whilst in many cases, data is also the input, supporting process and enabling automation. 

Many stakeholders identify ‘not being able to find information’ as a primary pain in their role. Another is using technology that gives inaccurate insights because ‘the data isn’t right.’It is acknowledged that data is a challenge worth solving – and needs a distinct strategy – aligned to the business need and the process and technology ecosystems. Hence data governance becomes the business strategy for our data, with the initial and simple question of, ‘what do you need to do your job?’. A simple but methodical approach to data governance is key to avoiding the risk of boiling the ocean and not really achieving anything.

03. What knowledge can we use?

A comprehensive body of knowledge exists through DAMA International. With data governance at the center, there are ten other knowledge areas, ranging from data architecture to document and content management and metadata to reference and master data. Collectively, it is a framework that provides a structure for developing an organization-specific data governance strategy.

To make sense of it, it’s worth asking, ‘what does it mean for us?’ and ‘how well are we doing this now?’. To that end, breaking down the complexity of each area into simple-to-understand statements is important. Take data architecture, for example, where we ask, and ‘What data do we need to do our jobs? How is this data produced in a format that we can use? ‘. A closer investigation often uncovers a low level of maturity, with a lack of detailed specifications routinely used on every project causing inconsistent data capture. The future state would provide a full list of data dictionaries covering every dataset, backed by a policy, clear roles and responsibilities, and appropriate appointments of consultants and contractors.

04. Implementing it

As mentioned, a framework that captures the knowledge areas and is aligned with a business is crucial. For project management, a simple series of buckets, also acting as a cyclical process, could be:

1.      Information definition and specification

2.      Information management, storage, and security

3.      Information quality assurance and control

4.      Information analysis and use.

5.      Information migration and archival.

A framework that comprises policies, standards, and procedures based on these five buckets would improve data accessibility, usability, accuracy, and timeliness. It would also support an organization’s digital transformation, with data governance as a fundamental component.

Nonetheless, taking small steps is generally recommended. To use an analogy, consider a warehouse with a thousand rooms and a thousand racks in every room containing millions of jars. If the jar is not labeled and stored in the correct location, how will you ever find it again? The real value isn’t necessarily in the jar itself but in its sweet jam. Ensuring the jam is sweet and tasty doesn’t matter if you can never find the jar. It’s the same with our files and our data.

05. Takeaway

Making sense of data governance is challenging, getting buy-in from leaders more so. However, the next time you hear, ‘we don't have the data; we can't find the information,’ respond with, ‘Oh, maybe we can help with that.’