There are four key steps to improving the reputation of the data function within the organisation:
- Building out a strong data team.
- Select the right problems to support.
- Managing the day-to-day versus encouraging innovation.
- Capitalising off the future of data.
Building out a strong data team
Having a reliable data function that can deliver what the company needs is the first step to building up that reputation. Hiring graduates and apprentices or building a data academy is a great way to start as it is a quick way to demonstrate what data can do, straight out of their education.
However, no team can function without the right senior support and balance. Bringing in more senior people in the right ratio is important; previous members have found a 4:1 ratio of graduates and juniors to seniors to be the sweet spot.
Once the data team has been built, you can start to secure those little wins to prove the value of the data function. A good suggestion for how to do this is to act as a consultant in the rest of the business. Understand what the problems other departments face are, and then assess how data can help solve them.
Of course, the data team cannot support on every problem the business has, nor can every problem be easily solved.
Select the right problems to support
It is imperative to select the right problems to showcase the value of data and the data team when building the reputation of the data function.
Conversations with DataIQ Members have provided insight into a framework for this very problem:
Step 1: Find your cheerleader
Establish an engaged stakeholder, who understands the importance of data and the task at hand, to drive work requests and solutions to the data team.
Step 2: Data quality
Ensure the data is appropriate to address the problem. Does the right data exist? How accessible is the data required?
Step 3: Implementing the outcome
Knowledge must be applied effectively and efficiently to create a solution. This will demonstrate the key data-led insights, drive the organisation to further data-driven successes and ensure a suitable use of resources.
Step 4: The severity of the request
Is this request something those asking for assistance are invested in, or is this just something that would be nice to have and a part of a hype train? Usually, a tell-tale sign of investment in a request is if they are willing to hand over someone from their team to support on the request.
Step 5: Do not be afraid to say no
Data teams cannot take on every problem a company faces, and it would not be efficient to do so. If you cannot help, say no.
Similarly, if the data team attempt a solution and realise part-way through the process that it is not going to work or there is not have enough buy in, do not be afraid to pause the project until buy in is achieved.
Following this framework will help ensure that the data team is working on appropriate projects and solutions to support the company, which will help increase and improve the requests coming in.
Managing the day-to-day versus encouraging innovation
Once the above steps are implemented, it is likely that ad hoc requests pulling on the resources of the data team will increase. This is positive in developing data culture for the organisation, but it is important to remember that as members of the data team focus on these new requests, there is less time for innovation and experimentation.
One suggestion to ensure innovation is to place restrict the analysts’ time for the wider business. Some businesses have developed and launched a self-service tool to help others solve their data requests before requiring the resources of the data teams.
There are other tools that can be developed to support self-service, including an application that gives people access to the data, but not access to the data platforms. This would provide external teams with the information they need to solve the problem themselves without risking the quality and integrity of the data.
Capitalising off the future for the data function
The support and innovation from the data teams will improve the efficiency across the company, and this is set to continue with the new opportunities presented by artificial intelligence (AI).
With AI, predictive analytics can identify problems teams and customers may come across before they even exist. Applications can be developed to help customers identify and solve these problems themselves. All of this will serve to continue to improve data’s reputation internally and externally.
Of course, not all data teams, companies, and customers are the same, but by following the insights of DataIQ members, your data function can receive the 90s rom-com montage makeover for your happily ever after.
