Data Governance: Key to Data Management Cover Page

Data Governance: The Key to Data Management (Part 1), by Tom Marine

Part 1 of a four-part series on how to set up a data governance initiative

In Part 1, we discuss how to define a business case showing the benefits of a data governance initiative, and we provide guidance for creating the data governance charter. Part 2 focuses on reasons to change and capturing the current state. Part 3 delves into optimizing the future state. Part 4 covers how to develop various teams through gap analysis and cultural change.

Part 1: Facilitating Change and The Data Governance Charter

In today’s competitive and fast-moving business world, most organizations need to quickly adapt to new markets and opportunities, and to get the most out of their valuable assets like data. Data governance (DG) is an important approach to optimizing business processes and the data that flows through them, eliminating redundancies and creating accountability.

Data governance consists of:

  • redefining organizational principles,
  • changing decision-making,
  • increasing collaboration across the organization, and
  • cultural maturity (very important and typically overlooked).

While much of data governance involves people, practices and processes, software plays an important part in executing a worthwhile data governance initiative too.

Partnering business people with IT teams to choose and implement a software solution better ensures that the overall data governance program not only meets a range of business requirements, but can scale to handle future needs, provides cohesive data across all channels, and supports a consistent working environment.

But it’s still important to remember that while software provides the right tools, it’s not going to do the work to create an effective data governance program.

Facilitating Organizational Change

Starting the process for organizational change may require third party facilitation. This ensures an objective observer with no hidden agenda. Maybe it’s just having someone who can ask questions without fear of retribution. Or maybe it’s that your people are just too close to their processes, and without proper coaching, they won’t be able to handle change.

But it’s also about “first things first” – preparing for a project that may require change. And data governance, in many ways, is all about change. It changes decision-making responsibilities; changes accountability among those involved in the processes, work flows and processes; and inevitably, changes the way your business works.

A consultant can help you build the business case to highlight the benefits of implementing data governance. If you end up doing this on your own without a third party who has experience in the field, you may end up missing some important nuggets.

Business Case Development

The business case for data governance probably sits side by side with another data initiative, like a requirement for better product data management. If so, the development of a business case will, of course, reflect the needs of that initiative.

It’s important to bring IT and the business together at this point, to develop a collaborative base and to build alliances around what the business needs, not just to decide on a technology solution. In the business case, current challenges should be identified, along with any pertinent quantitative measurements to support the case.

Industry trends lend additional credibility to the report. The benefits should reflect a match between the trends and the challenges. A heuristic pricing model will help with the thoroughness, with some specific benefits wrapping up the report.

Building a Team

The right mix of people is a critical component of the data governance agenda. There are typically many kinds of personalities and job functions that need to be considered.

For instance, you can’t just bring in all the “yes men” and the people that have a positive outlook or want to see change. You’ll also need those who are contrary, don’t want to change, and may actually be a bit negative.

What you might find is that the ones you least expect can turn on a dime and become your most enthusiastic evangelists. Why is this? Because they feel they’re being asked for their input and they want to contribute.

Additionally, a DG leader will need to be appointed or elected. The responsibility of the leader will be to maintain the team’s integrity, through minutes, schedules and creating ongoing enthusiasm for the launch and maintenance of data governance.

The Data Governance Charter

Setting up a DG charter is the groundwork needed to develop any future data management goals, maintenance and change. The charter itself is nothing more than a document that outlines who to go to when changes to data management are required.

Providing the Definitions for Data Management

The company will continue to evolve, and so will the data, people and processes. The definition of how such evolution will work resides in the charter.

Here are some key areas that need to be addressed in a data governance charter:

  • Workflow Statuses – These are the steps to be followed as data makes its way through the enterprise. These need to be flexible, dynamic, and logical. But we’ve found that there’s no single workflow that serves a company through all of their data management steps. The obvious ones are “New Product Introductions” and “Product Data Maintenance.” But each of those could have multiple options, like New Product Introductions (NPI) that come from suppliers vs. manufacturers. Defining and maintaining these will be a key responsibility of the DG Team.
  • Responsibilities – With the workflow statuses comes the responsibilities of the people performing the work in each of those statuses. Defining those should stabilize after the first several months of a DG initiative, but as companies grow, management changes, and customer demands grow, so will the resources, table of organizations and marketing strategies – all of which can have an effect on who does what and when.
  • Security and Privileges – Part of a DG initiative will center not only on who is responsible, but also who is NOT responsible. Therefore, setting up security and privileges will come from that. An example of this would be data entry points. It’s not just important to enter data once, but it’s also important who enters the data, who can edit data, and where the data is entered. Many data management software providers preach the “enter once, use many” mantra within their solution – but it’s also about the single point of entry, which could (and probably should) be in multiple integrated systems.
  • Communication – There needs to be a standard time for the team to meet, and it should be treated as untouchable. Weekly minutes and action items need to be compiled and tracked. These meetings may start out more frequent, but they should never go away completely. Finding ways to make them engaging will be an ongoing challenge for the DG leader.
  • Integration Points – A diagram (Visio, Omnigraffle, white board) needs to be created depicting all the integration points and how they are managed. A graphical representation seems to not just help in understanding, but it also serves as a conversation starter. These are typically invaluable documents and continue to evolve as the project progresses.
  • Syndication Points – Similar to the integration point diagram, having a syndication map is also advantageous. This is typically more dynamic and may be a spin-off of the integration point diagram.
  • Data Quality – The key to tracking DQ is to align on with which normalizations to start, but also the considerations, approval and implementation of DQ changes, modifications or maintenance requirements. This can affect what appears in lists, how new data is captured, and new auto-fill content.
  • Data Stewardship – Concisely written guides for data stewards to follow will be a part of the charter. The first draft and approval cycle are best done as a part of the process design. After that, changes should be part of the data governance maintenance cycle.
  • Single Best Point of Entry – Having an entry point in one location may be the mantra of some “central source of the truth” providers. But a more logical definition is not only to have entry of data once, but it also to have it in the best point of entry. Therefore, a single piece of content may be in separate systems, but the key is to not re-enter content in multiple places (or systems). Integration of data between systems then becomes critical, along with the privileges of those attributes. DG provides the definitions of these points of entry, where they are and who will perform them – as well as any changes that need to happen as systems and data evolve.

RASCI Development

Identifying what groups and people fall into the following categories creates clarity and accountability. This typically becomes a widely used document:

  • Responsible – The person who is responsible for delivering the project/task.
  • Accountable – The person who has ultimate accountability and authority. They are the ones to whom “R” is accountable.
  • Supported – The person or team who are needed to do the “real work.”
  • Consulted – Someone whose input adds value and/or whose buy-in is essential for implementation.
  • Informed – The person or group who needs to be notified of results or actions, but are not needed for decision-making.

RASCI Matrix

Maturity Model

The development of a maturity model helps in identifying the priority factoring exercise, but more from a marketing implementation perspective. The “state of maturity” in a data management initiative may follow these descriptions:

Stages to Define

  • Product To Market – What is the maturity of getting products to market?
  • Data Management – What is the maturity of managing data?
  • Technology – What is the maturity of the technology being used?
  • Content Enrichment – What is the maturity of how content is enriched?
  • SEM – What is the maturity of search engine marketing?
  • Marketing – What is the maturity of the marketing group around using data?
  • Data Governance – What is the maturity of company around DG?

Maturity Level

  • No Strategy – redundancies, spreadsheets, outdated ERP, non-modified supplier data, no integration, no DG
  • Promise – some workflows, initiative under way, landscape understanding, recognition of content needs, understanding of taxonomies, understanding of multichannel benefits, some work around workflows
  • Development – some system synchronizations, demonstrations delivered, roadmap designed, content targeted but not centralized, SEO data entry roles developed, channel alignment planned, DG Charter in development
  • Coordinated – triggered workflows, solution implemented but not optimized, roadmap defined but not implemented, data centralized but not implemented, tools in place but not implemented, coordinated marketing for some channels, DG team and charter in place
  • Alignment – taxonomies alignment with workflows, solution utilized but not oriented to business requirements, roadmap aligned with some implementations, data stewards centrally handling content, SEO tools fully utilized, complex marketing messages across all channels, DG handling change request issues
  • Synchronized – All workflows synced to business requirements, data fully synched, roadmap operational, content synched, web content synched to business, marketing messages aligned will all channels, DG fully synched with business requirements

Data Governance Maturity Model

Priority Factoring

Part of determining an approach to implementation is setting priorities. There are eight factors we typically review to do this. At a high level, the exercise includes a matrix developed around these parameters:

  • Sequence – Does it make sense to proceed in a particular order?
  • Dependencies – Do the activities have dependencies on each other that create that sequence?
  • Resources – Do the resources have enough time to handle the implementation responsibilities? If not, what can be done to limit involvement in outside distractions?
  • Opportunities – What business opportunities will be gained OR missed during the implementation process?
  • Capacity – Is there capacity to handle the current load (technologically, knowledge expertise, infrastructure, etc.) and how can those be managed?
  • Demand – How does the demand on the business get affected and what is the demand to make the change?
  • Efficiency – Will the gains expected from the project outweigh the challenges of the project?
  • Productivity – Will the expected productivity improvement outweigh the challenges of the project?

Priority Factoring

Maintaining Data Governance

Keeping the DG team’s enthusiasm and momentum going can be a difficult challenge. Ongoing meetings should be kept sacred and the continuity of the team should be kept as consistent as possible.

  • Meetings – Don’t be tempted to have these as optional. Even if nothing is on the agenda, sparking a dialog may lead to pertinent conversations around the data. There’s always something going, it just needs to get facilitated into a conversation.
  • Communications – Follow the old rule:
  1. Tell them what you’re going to tell them (put out an agenda)
  2. Tell them (the meeting)
  3. Tell them what you told them (after meeting notes)

Don’t limit the notes to just the team – it’s good for other key players to hear what is going on, since that may lead to additional insights.

  • Charter Maintenance – The charter is a dynamic document. It is not an “etched in stone” guide. But there needs to be appropriate accountability on its upkeep. It will be looked at for reference. Consider the charter an agenda item for each meeting.

In the next installment (Part 2), we’ll talk about the challenges and drivers that businesses need to understand to make data management changes and how that fits into data governance. We’ll also talk about a fun exercise – capturing the current state.

About Tom Marine

Tom Marine Head ShotTom is a Principal Consultant for MDM at Liaison Technologies. He is an accomplished executive with more than 30 years of marketing strategy integrated with technology. Tom strongly embraces cultural-training techniques. He has facilitated change in many different Data Management environments, from workflows in highly technical e-Commerce/legacy integrations to creative productivity planning in marketing-advertising-product management suites.

Tom’s technical areas of expertise include MDM/PIM, e-Commerce and Multichannel (Omnichannel) marketing with additional experience in data governance initiatives, data quality engagements, integration roadmaps, and taxonomy/hierarchy development. Having been a user, provider and consultant on MDM, Tom brings a 360-degree approach to the specific needs of technology solutions with marketing.

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