Education
It is essential that decision-makers outside of the data office are educated in what data means and how it is applied. Upskilling may take time to achieve as there are plenty of common misconceptions about data, such as that it means 1s and 0s in a spreadsheet or that it is just a measure of efficiency, but it is pivotal to your data success within an organisation.
Non-data professionals need to understand that data and analytics are often one or more degrees removed from day-to-day business operations in a traditional money-making sense; data itself does not make a business income, only the actions taken based on the data generate income. This can be hard for some to wrap their head around as businesses usually rely on positive or negative finance sheets to indicate success and value. A CDO must remember that to traditionally calculate ROI, the benefit of an investment is divided by the cost of the investment with the result is expressed as a percentage or a ratio. This highlights that proving the ROI of the earned part of the equation is difficult as data itself does not make an income.
This is where it pays to shine a spotlight on the things a traditional view of ROI cannot show and educate peers on the limitations of this calculation. Arguably the biggest example of ROI limitations is that the ROI calculation does not demonstrate time. If you can show that the business’s investment in data has sped up processes, that can be a way of proving ROI. If it used to take three months to run through certain forms of analysis, but now it can be done in three days thanks to new software, tools and architecture, that is a great ROI.
Storytelling
At every single meeting, DataIQ members share stories of their successes with each other, but this needs to be done to the organisation where that member works. Storytelling can be difficult in a couple of ways, but it is a skill that is necessary to showcase the work of a data office.
A hurdle that many data professionals find is that of language. It can be easy to use common terms and data-specific tech jargon with a group of data peers, but as soon as the audience changes to non-data professionals these words immediately lose the listener as they do not understand and cannot follow the story. When talking about progress and success, a CDO needs to take the audience into account and tailor the language of the story to match the education and data literate levels of the listeners.
When creating a story that will be shared widely or presented to leadership teams, there are some key questions to keep in mind:
- What do you want to prove or disprove?
- How and why has this set of data been collected for this cause?
- What is the goal of the story?
- This should be expressed as a single sentence to keep it simple and succinct for everyone to understand. If it not, revaluate the target and edit the scope.
- Why should the audience care?
- Keep pinpointing how and why this data development impacts the audience.
When it comes to presenting the story, are there any other tools at your disposal that would help make it an engaging experience? Make it visual and use clear, concise graphs and examples that show real-world applications of the data; make sure you are providing context and insight into the data and the problem it is aiming to solve; and always encourage questions from the audience. Staff will want to know how it will impact them positively and negatively (we all know about resistance to changes) and what is required from them.
Think like a stakeholder
Ultimately, money talks in businesses. CDOs need to appreciate this and be able to work it into their own agendas to connect with other areas of the businesses and maintain parallels with business objectives. Stakeholders hold the key to financial backing, so CDOs should identify the quick data wins that will highlight value and success from the data team.
Once there has been investment in the data operations, finding the early wins for stakeholders is imperative as the development stages of new programmes and architectures seldom show tangible financial benefits. To counter this problem, the first data sets produced by the new platform and tools should show data that was previously unavailable to stakeholders and decision makers. Although it may not have an immediate financial impact itself, being able to then demonstrate real world business applications of this new data will show the directions possible thanks to the investment.
However, a new problem arises as data requires continuous investment with new technologies and platforms to maintain peak capabilities. A CDO must be able to repeatedly ensure the efficient and competent analysis of data by the team to keep stakeholders and financial backing on side. For example, many industries would benefit from predictive analytics to efficiently plan operations for the year or years ahead in the safest way possible – this could be one angle a CDO could focus on to maintain stakeholder confidence.
There is no quick and easy way to demonstrate the value of data – if there was, there wouldn’t be an article about the challenges of proving data value – but it is possible. It will take time, patience and cooperation with multiple areas of a business, but the effort will result in increased investment, stronger relations with other departments and an improved business understanding of what the data department brings to the table.
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