Data Governance can be a hard sell. What can we do about it?
I was talking to a colleague the other day who was bemoaning the fact that Data Governance can sometimes be a really hard sell and I recognized what he meant. In some ways it’s similar to electricity. Or your wifi. When it doesn’t work, it’s immensely frustrating. However, when it does work, you don’t notice it and almost take it for granted.
When our data isn’t well organised, accurate or complete, we’re equally frustrated: it slows us down, stops us from answering certain questions or prevents us from generating the necessary insights. However, when our data is available, accessible, accurate and accompanied by the right context data, we rarely stop to appreciate it.
The Kano curve, captured in an article Professor Noriaki Kano wrote back in the 1980’s , describes this situation and provides some background as to why it occurs.
Although there are various interpretations of Kano’s graph, the basic theory describes a link between customers’ emotions and product success. Kano argues that a product or service is about more than just functionality: it is also about users’ reactions in response to the functionality and this reaction is dependent on the type of functionality delivered. It is this reaction which determines the success of the product or service and therefore we have to be clear about the types of functionality on offer and position information about the product or service accordingly. The basic premise is that there are (at least) three different types of functionality, or attributes:
- When a product only possesses “threshold” attributes, i.e. basic features that users expect a product or service to offer, the best we can expect is that the customer will indifferent. (The worst case is that they will be frustrated when the threshold attributes aren’t available.)
- If the product offers some performance attributes, users will begin to find the product or service attractive and this increases as the performance increases.
- When “excitement” attributes are present, surprise features that users didn’t expect but that they are absolutely delighted by when they discover them, that’s when users start to become really pleased with a product.
So how does this relate to selling our Data Governance efforts?
If we recognize that Data Governance primarily delivers Threshold Attributes and Performance Attributes, we can use this knowledge when selling our Data Governance efforts to sell more effectively. If we’re also clear that it is not the Data Governance itself that delivers Threshold or Performance attributes, but rather the benefits delivered by Data Governance, our selling becomes even more focused. If we then add in which benefits we miss out on when our Data Governance isn’t well organised, we have multiple options to promote our Data Governance efforts depending on the priorities and motivational bias of our different stakeholder groups.
We can split this approach into a number of concrete steps.
(1) Get clear which of the benefits we’re delivering are performance attribute benefits and stack them up
Kano’s theory proposes that no matter how many Threshold attribute benefits we deliver, we’re not going to make our users particularly happy. Therefore, we need to be clear what Performance attributes we’re delivering and promote these.
- If our data is easier to link, we spend less time joining data sets together to answer questions and generate insights, so we get much more done in a day.
- When a customer’s data is spread over various systems, if our master data management software pulls together an integrated view of the customer, we’re better able to help the customer when they phone our contact centre with a question.
- If we can easily find the definition of a term used in a new dashboard, we can more quickly understand and interpret the figures shown and pinpoint why we’re experiencing a certain problem.
If we can list enough of these performance attribute benefits delivered by our Data Governance projects, our stakeholders better understand how Data Governance helps them and therefore why they should support it.
(2) With threshold attributes, turn the message around
If some of our benefits relate to threshold attributes, we now know that the existence of these types of attributes doesn’t make users happy, but their absence does make users frustrated. Therefore, if we’re going to mention threshold attributes in our business case or sales pitch, we need to turn the message around. Instead of emphasising expected benefits, we need to get specific about the current problems, and their impact.
So rather than selling that we want to be able to compile a complete list of sales per product group or customers per region and the benefits this would bring, we can highlight how inefficient our processes are without these improvements. Rather than selling the benefits to be gained from having effective promotion data, we can make specific the problems we face when we can’t work out which promotions generate positive results and which promotions are failures, and how this affects our potential profits. Instead of promoting that with new customer channels we can increase the numbers of on-time payments, we can explain the risks we run when customers have unidentified poor credit records with us.
If we can explain the implications of our missing Data Governance, the current pain is felt more clearly. If we know we’re dealing with threshold attributes, we have to invert the message.
(3) Focus on audience specifics
If we want to make our Data Governance sell more effective, we can further tailor it to our particular audience. There are various obvious ways to do this based on relevance, so I won’t go into these here. However, when looking at performance and threshold attributes, we can also group benefits into “towards” and “away”  and focus on which best fits our audience.
Toward people are focused on their goals and what they need to do to achieve them. They are motivated by improving, growing and moving forward. This energizes them. Away people are more focused on what should be avoided or reduced. They are keen to minimise risk, reduce waste and are triggered when solutions will solve problems. They are more motivated by fixing things and preventing problems.
Both are valid points of view necessary in a balanced organisation. If we can recognize which trait is uppermost with a particular stakeholder or group, we can tailor how we present the benefits delivered by Data Governance more specifically, including whether we’re stacking up performance attributes benefits or inverting our threshold attribute benefits.
(4) Link benefits to the wider strategy to add weight
Once we’ve clarified the benefits of our Data Governance efforts, focused on performance and threshold attributes and then split them into towards and away buckets, we can add extra weight by linking benefits to the overall strategy.
- If our organisational goal is cost cutting, we need insights about which particular costs are most likely to spike so we can take preventative action to avoid this.
- If we’re in a highly competitive market and marginal gains are the key to gaining a tiny extra sliver of market share, we need razor sharp context for our data to be able to identify the effects of the smallest tweaks to our approach.
- If our goal is helping save the world by improving our record on sustainability, we need to be able to track our chosen metrics closely to see where we’re doing well and where we need to up our game.
In all cases, if the benefits of our Data Governance efforts can be linked to larger objectives, which in turn have a connection to the overall strategy, we have a stronger sell when attempting to persuade our stakeholders that Data Governance benefits are useful.
(5) Make it personal
A final point in terms of making our Data Governance effort an easier sell is: make it personal. Where possible, focus in on specific user groups and collect their Data Governance stories and experiences to add real resonance to the message.
One way to gather these stories is to use the Data Journey. In each specific situation there are data creators and data consumers. They’re frequently not the same people, or even in the same team or department, and this disconnect can understandably lead to a mismatch of priorities.
The priority of the data creator may be to deal with a customer, issue or transaction as efficiently as possible, so he or she can get through as many cases in a day as possible. Maybe this leads to shortcuts in data entry, misspelling an entry or entering fake data in a mandatory field when there’s no time to look up or track down the correct data. (Maybe think about getting an MDM solution?) However, when the data consumer then accesses this data later in the process, attempting to achieve their goals, he or she discovers the data is incomplete, inaccurate or context-less.
We can use the Data Journey to reduce the likelihood of this problem occurring. Specific pairs of creators and consumers are encouraged to share their work experiences with each other to develop a common understanding both of the situation which leads to the inaccurate data capture and to the consequences. Together they come up with suggestions to improve the situation and together they take responsibility for improvements. A series of follow-ups take place to evaluate the suggested improvements and where necessary amend the approach. The resulting benefits can be specifically linked to these users.
The end result is that both the Data Governance effort and the resulting benefits are more personal. Rather than being impersonal business cases, the value generated makes a concrete difference for actual users and therefore resonates in re-telling far more than general examples would do.
To sum up, yes Data Governance can be a hard sell. However, with an approach which focusses on actual value in combination with a more nuanced delivery of benefits, we have good chance of selecting the right cocktail of possible Data Governance benefits, and optimal positioning, to ensure maximum resonance with each and every stakeholder.
- Kano, N., Seraku, N., Takahashi, F. and Tsuji, S. (1984) Attractive Quality and Must-Be Quality. Journal of the Japanese Society for Quality Control, 41, 39-48.
- Charvet, Shelle Rose (2015, 2nd edition) Words that change minds: mastering the language of influence