Put aside the data for now and think more holistically about your business objectives. A good business strategy that understands its dependence and relationship with data helps your business to understand what data you need to have and for what purposes. This in turn will inform your data strategy.
Answer these fundamental questions to guide your strategy:
- What is the problem you are trying to solve with data?
- What kind of data do you need, where and when?
- What is the data literacy of your organization and do you need to improve it?
- What is your data worth? Justify this proposition with CLV or other measures.
- How do you exchange, collate, disseminate and control the data you have?
A considered and well-formulated data strategy has a strong vision, clear goals, well-defined success metrics, and compelling business rationale.
Roles and responsibilities
All successful data governance and data management programs including those that only consider Master Data Management as a first step, have to be implemented by people. These could be stakeholders from the business, members of IT, a specialized group of people that form a data management organization (DMO) or they could be external consultants or service providers.
Answer these fundamental questions to guide your organizational definition:
- Will you have dedicated people for data management or will this be an integral part of the role of existing peoples’ day jobs?
- Have you committed to your initiative being a business led initiative as opposed to a technology and It led one?
- Do all the participants in the process understand their roles and responsibilities with respect to proper and appropriate activities and decisions around the data?
- Do you have assigned data owners?
- Do you have a decided escalation or triage process for contentious issues?
- Do subscribe to the concept of “Data Stewardship” ?
Recognizing that part of the responsibilities of people undertaking data stewardship is the creation and handling of the Data Management Body of Knowledge (DMBoK); defining, evaluating, and resolving data quality assessment; documenting policies and procedures ultimately help with transitioning to a more evolved state of organizational data governance.
Defining and measuring success
There is an old adage, often attributed to W Edwards Deming, statistician, and QC expert, that “you can’t manage what you can’t measure”. The same is true of your data management program. If you have not defined measures and are not evaluating those measures, are you in fact managing your data?
Very little data enjoy a static existence, more often than not, data evolves and changes according to the needs and usage of the business. Setting up monitoring of data is therefore one of the key tasks for the teams responsible for data management.
These are the common measures you might expect to see
- Assessment of Accuracy
- Evaluation of Relevance
- Completeness checks
- Consistency checks
- Rights and permissions
What you use to perform these assessments and checks is less important than ensuring that they are undertaken regularly and consistently and reported on.
Remediation plans can only be put in place and be executed upon if you are performing these checks.
Data quality measures can include different things such as information like the number of completeness checks, root cause analysis, recurrence relative significance of certain types of issues encountered. Your metrics may vary when compared with other organizations but in some instances, there are standard measures for industry sectors and your peers. What’s crucial is to establish key metrics for assessing data quality and follow through on leveraging them.
Communication and education
A well-implemented data governance program avoids being labeled as bureaucratic and instead demonstrates effective management and use of data through a comprehensive and elaborate communication program.
This means that those people with the assigned responsibilities of data governance need to bring strong communication skills to the table when dealing with people and process issues. Since data governance is often accompanied by business process change or digital transformation, many of the communication approaches will be common.
If this is a new initiative for the business there are a couple of critical communication touchpoints that might be beneficial.
- Develop a timeline and a schedule for your communication activities and channels.
- Introuce others to the initiative by way of an executive sponsor in town hall, internal newsletters or communiques.
- Make use of social media and print media on noticeboards, corporate screensavers and email banners.
- Clearly articulate business expectations in terms of outcomes – why are we doing this and what expected benefits will we or our customers all enjoy
- We talked about roles and responsibilities already but reiterate these so everyone understands especially those not a part of the team.
- Avoid buzzword bingo and jargon in conveying the message and building organizational understanding.
- Communicate on a regular basis.
The more others understand what you are doing and why you are doing it, the greater the likelihood of success.
Fitting MDM into the big picture
Master data management encompasses the processes, standards, and tools that are used to support the creation and management of master data.
MDM is a cornerstone to data management and data governance. At its core, MDM is the maintenance of the relationships between the master file and all other masters and transactions with the Masterfile for the customer in particular being a key point of reference.
A well-implemented and maintained MDM practice avoids duplicates, redundancy, and inconsistencies.
The long game
Things that are worth doing, often take time, and the same is perfectly true about data governance and the implementation of master data management as part of a more broadly focused data management program. It isn’t something you do once, and then you’re done.
Over time your organization will determine that the true value of data is often hidden in how it is used in ways that were perhaps less obvious at the outset.
Fast-evolving data and data needs mean that the job of data governance never really ends and so your business will need to keep pace with these changes both in terms of how the data itself is managed and in how teams think about the data.
The Pretectum C-MDM is a flexible and adaptable complementary technology to any data governance program with a special focus on customer master data management that can be of benefit to you in your data governance initiative.