Most organisations fail to gain value from data, despite having heavily invested in data initiatives. But why?
In order to be impactful, a data strategy needs to integrate the resulting organisational change.
The value chain of data goes DATA > LEARNING > CHANGE > PROFIT
A data strategy that does not include applying the learnings to produce organisational (process) change, leaves the profitability of a data initiative to chance.
Many initiatives stop at the learning stage because they are only planned to go that far to begin with. They focus on the data side but not on the change management side. Companies hire data teams but do not give them the mandate or the help to manage change.
Is this happening to you?
A good way to tell if you are not managing change is to look at the result of your data initiatives. If you have funded a data initiative in your company but are only seeing limited value, take a look where the value occurs. Does it only occur in those areas where the “change management” part is not necessary or easier, such as CRM or acquisition? (There is usually no organisation in the way of changing a marketing campaign.)
Why does this happen?
Managers are not aware of the value data chain, so they expect the specialists to know.
The specialists do know, but the specialists’ field is data and not change management, so they can only plan so far.
What can you do about it?
Use change management. I will not go into how, but on a top level, for a change-managed data initiative
- Identify the stakeholder and their goals, and define the business case.
- Create a model for the change together with risks and benefits and track assumption model fit and progress against it, tune when needed.
- Communicate informatively: Why, the benefits (for you, for us, for me), the dimensions (when, who, where, how much)
- Staff process re-training plan. When process changes make sure everyone is on board in information and action.
- Manage employees resistance to change by aligning towards a united company goal. Provide counselling for managing fear of change.
How to fail:
- Wrong goal (build a data thing vs change the organisation)
- Wrong stakeholder/sponsor
- Start working on a solution before fully understanding the problem
- Half bake solutions and don’t validate them
- Ignore the people involved and don’t communicate upcoming changes
- Undefined, unclear outcomes
- Lacking dependency management (tracking those risks)
Successful data to value implementations do not stop at the data team, but in fact require the organisation to change. Data initiatives need to consider not only the staffing required but also the organisational commitment required for successful change.