Introduction

In a recent post inspired by Sunil Soares’ In-Depth Review of Data Governance Software Tools research report, Henrik Liliendahl Sørensen raises an important question: “Who needs a data governance tool?”

While Sunil’s report delves into the types of tools that can support governance, based on his expertise as a Data Governance consultant, Henrik’s post explores the broader aspects of data governance and poses thought-provoking questions. Let’s dive deeper into this topic and examine the significance of tools to support active data governance.

As data governance is very much about people and processes (bold added) and not so much about technology, do you need a tool at all? If you do, do you need a separate best-of-breed tool for the data governance part or will it be preferable to have it as an integrated part of the MDM solution?

Henrik Liliendahl Sørensen

The Role of Tools in Data Governance

In a comment on Henrik’s post, I suggested that you don’t need a tool for washing clothes – you can wash clothes by hand. It takes a lot of time and it doesn’t always get out the tough stains but it can be done.

A washing machine does the job better, and easier.

Similarly, appropriate data governance tools, correctly applied add tremendous value.

So what must be considered before investing in a data governance tool?

1. The Human Touch: Data governance is primarily about people and processes rather than technology. While tools play a crucial role, it is essential to consider the larger picture. Data governance initiatives must address the intricate interplay between individuals and workflows for effective implementation.
2. The Tool Paradox: While data governance tools can add tremendous value when correctly applied, there is often a disconnect between these tools and the real-life people and process challenges faced by data governance programs. Many technical tools focus on specific areas such as data discovery, data quality, business glossary, metadata, information policy management, and reference data management. However, relying solely on these tools without addressing the broader people and process issues may lead to incomplete data governance.

The Need for Automation in Data Governance

1. Complexity and Scope: Data governance initiatives across various industries have encountered challenges due to the complexity and vast scope of work involved. Manual processes without automation have proven to be inadequate, resulting in failed data governance programs. Automation streamlines workflows, reduces human error, and ensures the effectiveness and sustainability of data governance efforts.
2. Extending Beyond Master Data: Data governance should not be limited to Master Data Management (MDM) alone. While MDM certainly depends on data governance, the scope of data governance extends beyond managing master data. It encompasses compliance, and enterprise data quality, and creates a framework for big data analytics. Thus, data governance should not be solely reliant on MDM tools but should be supported by dedicated data governance centres that address the wider range of data governance complexities.

Choosing the Right Tools

1. Distinguishing Genuine Value: Buyers must discern between snake oil sales teams jumping on the data governance bandwagon and those offering genuine value. A dedicated data governance centre should be a crucial component of any enterprise’s roadmap, providing the flexibility to implement data governance operating models, report on compliance, manage maturity and ROI metrics, and ensure practical and process-driven management of master data.
2. Automating Complexity: Companies seeking ways to automate data governance complexities can benefit from resources like Sunil Soares’ report, Bloor Research’s Data Governance Market Update, and the MDM Institutes’ Field Report on the Data Governance Center. These sources provide valuable insights into managing data governance challenges effectively.

Let’s look at Henrik’s Question

Question 1: As data governance is very much about people and processes (bold added) and not so much about technology, do you need a tool at all?

Sunil’s report focuses on a reference architecture for data governance, proposing on the six data management areas most critical to support governance. In Sunil’s view (and I would largely agree) these are:

• Data discovery,
• Data quality,
• Business glossary,
• Metadata,
• information policy management, and
• Reference data management

Henrik’s posts point out the disconnect between these (largely technical) tools and the real-life people and process issues that plague most data governance programs. While each of these tools has a role to play, I agree that this is not data governance. We can have data quality without governance, we can have governance without reference data management, and so on.

A data governance tool must address the people and process issues

I have witnessed the birth (and in many cases the death) of multiple data governance initiatives in multiple industries – including financial services, telecommunications, government, and mining. In each case the deployments focussed on structures and processes, and, in each case, these deployments have been overwhelmed by the complexity and sheer scope of work required.

In the absence of automation the #datagovernance processes failed in every case.

Tweet

Question 2: If you do need a tool, do you need a separate best-of-breed tool for the data governance part or will it be preferable to have it as an integrated part of the MDM solution?

This question assumes that data governance is only required for Master Data Management. While successful MDM certainly depends on data governance, data governance must extend beyond master data.

Can a master data management suite’s data governance capabilities extend to manage all critical data?

In most cases, the so-called data governance workflows provided by MDM vendors focus on simple use cases such as approving the merge or deletion of master records.

In my opinion, these workflows do not address the data governance complexities of agreeing and enforcing the appropriate use of data, within context, across multiple business functions.

This is not data governance.

Assuming that the MDM suite enables proper data governance processes this would be normally bundled with a host of additional MDM-specific capabilities that add cost but no real value from a data governance perspective.

By definition, MDM tools focus on the management of master data. How does a master data-centric tool extend to address compliance issues, manage transactions, identify key requirements for BI, and so on?

Master data management needs data governance, but #datagovernance does not need #MDM.

Tweet

Data governance supports compliance, provides the framework for enterprise data quality, and creates the context for big data analytics.

Supporting the Data Governance Framework

To add value data governance platforms must provide the flexibility to implement commonplace Data Governance operating models to increase and sustain end-user adoption, report on compliance, manage maturity and ROI metrics, and make the management of master data practical and process driven.

This is not the focus of master data suites but rather the domain of the dedicated data governance tool.

This ttool may not be necessary at an early stage of your governance program, however, information and meeting overload will play havoc with even the best manual processes. A data governance centre is necessary to manage this overload and keep your data governance program alive.

Conclusion

Data governance is more than just laundry day. While tools have their place, they must be aligned with the broader aspects of data governance, encompassing people, processes, and automation. By understanding the complexities involved, distinguishing genuine value, and embracing dedicated data governance centers, enterprises can unlock the true potential of their data assets and ensure the success of their data governance initiatives.

Image sourced from https://c2.staticflickr.com/4/3315/3273394025_a6160676d5_o.jpg