Piece by piece
It is easy to forget that people who are not submerged in data on a daily basis do not have immediate frames of reference that data professionals have. There needs to be a step-by-step explanation as to why something is required within a data strategy, how it can be achieved and then its success needs to be demonstrated.
In its simplest form, three key points to highlight why a data strategy is so pivotal are:
- To align data with the business strategy by explaining how data will support key goals for the organisation.
- To create a focus for investment, improvement and impact.
- To harness all data-related activities into a unified programme and avoid overlaps, duplication or irrelevant projects.
The onus here is on the data leader to heighten their storytelling abilities and to train the data team in storytelling to make sure the key points are explained as simply and succinctly as possible. The DataIQ community frequently discusses issues regarding storytelling and different ways in which individual members have approached and succeeded in demonstrating value to decision makers, but it is a constantly evolving battle, particularly in an era of fluctuating financial resources.
As an example of a practical step that most businesses take early on, data professionals often create dashboards to provide something tangible to highlight value to business stakeholders. However, it can be very easy to get sidelined creating new and exciting dashboards for dozens of different teams and projects to demonstrate the impact data is having – but a data strategy ensures that these are released at the correct time. There is no point unveiling a new dashboard that the organisation simply is not ready for. This can also lead to ignoring root problems with data in the organisation rather than analysing them and addressing them effectively.
By installing a good data strategy that can work step-by-step for the data team and wider organisation it leads to clear goals and definable actions that with improve business. It will assist with avoiding distractions and progressing the data journey of the business into the next era. Too many businesses claim to have a data strategy which turns out to be a data vision and there needs to be more groundwork done to upgrade it to a full strategy.
Data as an asset
There is a shortfall when those with data strategies fail to recognise and utilise data as an asset. A data strategy needs to demonstrate how data will be used as an asset to improve the business and achieve objectives. There needs to be an element of exploiting data’s value and explaining why data-driven decisions are so valuable.
One common hurdle for data practitioners is proving return on investment (ROI), which can be difficult for data teams as there is often a diluted path from the investment presented to the data office and the financial results being seen. For example, a heightened data capability can lead to big improvements in productivity for different departments. The difficulty here is examining the types of productivity that have been impacted by data for each different team and then calculating what that then means as a quantifiable net benefit for the organisation to be highlighted. Team members that have access to higher quality data and relevant information about market trends and customer preferences are better equipped to perform their tasks and enhance their business roles.
The act of storytelling to non-data audiences for data practitioners becomes exponentially easier by having a clear data strategy that is understood by stakeholders. Once there is an understanding of what the data office is looking to achieve, the disconnect between investment in data as an asset and proving ROI becomes less complicated.
Ultimately, a clear and well-understood data strategy helps businesses identify gaps and take corrective actions to improve operations, enhance customer experience and drive growth. By fuelling data-driven decision-making opportunities for business leaders, data strategies are a cornerstone of success for data teams and their value must be appreciated by the wider organisation.