Why don't Data Strategy exercises meet their goals and how could Design Thinking help here?

Set targets and recognize your data maturity level

The purpose of a data strategy is to help an organization to achieve its goals (financial, compliance and other). IT driven strategy is seldom well linked with an organization's strategy and business targets. An important part of the data strategy work is target setting: what maturity level do we want to pursue? The difference between the current state and the target state has a cost, and typically the business has the money.

Acknowledging the facts is the beginning of all wisdom. If you don’t know what your current data maturity level is, objectively, it is hard or even impossible to figure out what you should do next, why, how, and what is the expected outcome of the improvement actions. Data maturity assessment is a good starting point for data strategy development. With good questions and maturity model one can identify the most crucial development areas. But relying solely on surveys and interviews often leads to identifying overly general development areas which have a low connection into the real world the customer is living in. This is where design thinking comes into play.

Use Design Thinking to step out of silos

Organizational boundaries prevent energy from flowing in the direction of the customer (or your peers). Many organizations are siloed, in terms of operations and data. By selecting one or more customer business processes or value streams that transcend operational boundaries, system-level problems can be caught. Utilizing design thinking methods for selected value streams can ensure the commitment of the different roles of the client and a concrete outcome in a reasonably short time.

Typically, when people from different functions start to draft their common value chain and discuss possible improvement areas, the value is already generated. People start to understand how their actions or careless operations affect others and ultimately the customer. In some cases, this kind of approach can also lead into process development work. This viewing angle is also very relevant from data and data quality point of view. It is very easy to say (even too easy) to say that our data quality is bad. This statement does little to help plan and direct remedial action. More useful is to identify e.g., in which part of the value chain the poor data quality is first identified, how it manifests, what IT systems are involved and what is the role of man in it. This can help to identify the root cause of the problems. It is much cheaper on the long run to fix the problem at the source.

Data strategy is a great tool for communication

Ideally, a data strategy serves as a common communication tool and enables business and IT discussion with the same concepts. Without a common language, it is pointless to wait for the commitment of the management. If you don't understand, you can't commit. Getting this joint understanding is one of the by-products of a successful data strategy process.

Good data strategy consists of guiding principles that are valid for the next 3-5 years (like “data as a product” approach) and more tactical items. Guiding principles are used to direct the development work on a high level and can be used even as a checklist when new development projects are initiated (“whether this idea meets the requirements of the principles?”). These should be shared and understood on an organization-wide level. Tactical items are the precision missiles that are targeted to fix a specific challenge and transfer the organization into the right direction on the maturity development.

Data Strategy is a process that needs leadership

Developing organization’s data strategy requires perseverance, systematicity and leadership. Developing data strategy is not a project but a process. Typically the needs for change identified in the data strategy work are not small and they may require changes at all levels (people – processes – technology) and this takes time. Because changes do not happen by accident, they must be managed continuously (like data strategy project portfolio management) and the progress must be monitored. This monitoring can be carried out with an annual survey, in which the management of the organization is asked e.g. the following questions:

• Does the data strategy guide the selection and prioritization of development projects?
• Do we understand what a data strategy is and why it is important?
• Does the data strategy allow for common direction and commitment?
• Is the development of a data strategy adequately funded and resourced?
• Does the implementation of the data strategy help us achieve our goals?

Based on the answers the needed shifts in focus can be carried out.

Making a successful data strategy that generates long-term value for the organization is a difficult task.

The 5 success criteria for a good Data Strategy

Based on my experience consisting of seven data strategy consultations for various industry sectors within last four years, I would summarize five success criteria for a data strategy:

• Data strategy is owned by business management.
• Realistic understanding of the current situation exists, and joint target setting is possible.
• Value chain thinking is used to complement general as-is understanding to better connect the data strategy to the real life of the customer.
• Data strategy includes both principles and concrete tasks.
• Follow-up and steering of data strategy development is in place.

If you can answer yes to all the above statements, you have a valid data strategy in your hands.

Written by Juha Kostamo