If you don’t have the concept of OKRs implemented in your organization for master data management then the chances are that either there’s a maturity issue with this part of your business or your business really doesn’t see master data management as a pivotal aspect of your business’ data management.
In the end, having explicit OKRs is not essential, there are probably other ways that you likely measure the effectiveness and performance of your data management activities, however, if you think carefully about how you measure effectiveness and come up empty-handed then OKRs might well be a good place to start a data management improvement program.
Ultimately, objectives and key results (OKR) is a goal-setting framework, often used for financial objectives, but not necessarily so. This framework is used by organizations to define measurable goals for a particular area of the business and track outcomes.
The concept of an OKR and the development of the framework is generally attributed to Intel Corp. engineer and former CEO Andy Grove. Grove documented OKRs in his 1983 book “High Output Management”.
The objective of an OKR is qualitative and answers the question of what is to be accomplished in the context of the goal. Objectives are supported by key results that benchmark and monitor how the objective is achieved.
Key Results are quantitative, represented by data that tells you if you have attained your goal. Critical to Key results is the setting of some sort of outcome measure and knowing how far you are from achieving it.
For example, if you want to travel from Los Angeles to Hong Kong (6377nm) by boat (your objective), you could set up two key result areas such as average nautical miles travelled per day with a target of say 250 miles per day in order to arrive within say 26 days. Key Results (KRs) are therefore specific, measurable, and time-bound.
Because objectives are really company goals, you’ll define them for data management in the context of the direction in which you want to push your data management practice.
As an objective, the organization’s leaders need to answer the question as to what is most important for the short, medium and long-term horizons. This could be within the next quarter, the next half-year or the whole financial year. Ultimately it has to be something that is achievable in a reasonable time frame and not something with a projected completion that is too far out.
In addition, a typical OKR framework has only 3-5 Objectives. OKRs need to be significant, concrete, action-oriented and ideally inspirational, akin to something that Teresa Amabile of Harvard Business School describes as the “progress principle”. Like goal-setting, key results must be defined as S.M.A.R.T. (Specific, Measurable, Achievable, Relevant and Time-Based).
So now that you know understand what an objective is and what key results are. Let’s consider what might be most appropriate for customer master data management.
Business initiatives may drive objectives
As a data management organization you could consider quite lofty objectives but to be realistic you want your customer data management organization to choose objectives that have strong meaning to those responsible for the customer data.
Consider the following potential questions to inform you of potential objectives:
- Do you have an upcoming business initiative or area of business improvement that has a strong dependency on customer data?
- Is this initiative directly influenced or influential in what, how and where customer data is held, distributed and made available?
- Are there dependencies on the status quo with respect to customer data or is this a blue-sky initiative?
A new CRM or ERP implementation, a new product line, a business acquisition or an ambitious business initiative that will pursue additional market share could all be top-line objectives to drive more revenue. For the data management practice that may mean preventing the way the data management organization gathers, curates and disseminates customer data or makes it available.
An objective that might flow from this business initiative might consider a statement of intent for the data management function which might be as simple as “giving all business units appropriate controlled reliable access to accurate and timely, usable data without the risk of data leaks or data falling into the hands of bad actors“.
If we were to unpack that statement, it is made up of a number of important mini-goals or objectives.
giving all business units appropriate controlled ….access – this suggests that you’ll need centralization but centralization with appropriate access controls, perhaps a hierarchy of access with suitable roles and permissions.
reliable access – again implies centralization but with a continuous duty environment that is available to whoever requires the data, whenever they require it but imbues confidence in the recipients of the data.
accurate and timely, usable – this is a bit of a subjective one, but you can look at the data quality measures that you would have for your data, you can examine the speed with which data is gathered, prepared, curated and published and the suitability for the stakeholders in the pursuit of their individual business functions – all of these attributes imply that you need to set up some measures for accuracy and timeliness. (think SLAs).
without the risk of data leaks – is pretty categorical, this means that the data needs to be secured. It goes hand-in-hand with the “appropriate controlled access”
Controlled Access as an objective
We can reduce these objectives further to single words or we could stick with the general objective statement but now comes the need to define the key results and the measures that we would use to determine whether we are progressing or on track with meeting the objective(s).
A set of key results might be something like this:
95% correct data permissions assignment (in aggregate) at any point in time
You can see where we are going with this. Ultimately if this result is what you expect to have then the next step is determining how you will report them and what their meaning will have for your initiative.
Measures for this might be:
- Semi-annual data permissions review (audit)
- Data Permissions assignment review by at least one line supervisor
- Data Permissions remediation or documented concession within 30 days of a written audit
- Quarterly training review and remediation plans for those staff who are identified as misassigning permissions.
Data access is often a major problem for businesses. Either business users don’t have access to any data and have to get answers through negotiations with their colleagues or, the data that they have is uncontrolled, uncurated and potentially wrong. Some users have far more access and permission to manipulate data than they should.
So in the context of the objective, these four key results might be appropriate or adjusted according to the specific needs of your business. Consideration of these kinds of aspects might also be subject to regulation or compliance reviews to minimize business risk and maintain confidentiality and privacy.
The Pretectum CMDM provides you with a number of approaches to securing appropriate access to resources. Users are assigned to business areas where they may have certain levels of permission and business areas, in turn, have control over data schemas and data itself.
Even within the schema and data management areas, users have to be assigned explicit permissions in order to put into effect certain outcomes in the system or against the data. The platform works on the principle of least privilege to ensure that users don’t have access to things that they don’t need or should not have access to.