Leveraging data to improve product performance | Manchester

Agenda

At our data event on Wednesday 27th March, we brought together leading experts including:

• Nina Dunwoody, Senior Delivery Manager at Pets at Home

• Tamana Bhambwani , Head of Regulatory Change Delivery at Barclays Partner Finance

• David Hoyle, Research Data Scientist Specialist at Dunnhumby

• Allan Lehan, Head of Data Science at Apadmi

Prioritise to meet business goals

Prioritisation is a huge part of getting any part of a project done well, and as our panellists agreed, data is certainly no exception. 

Pets at Home’s Senior Delivery Manager Nina Dunwoody said: “It can be easy to get flooded with requests for data points - you need to look at your overarching strategy, break it down into areas and strategic pillars and consider where does the highest value sit and how do you serve that?

“Once you get a centralised view you can understand what you should be focussing on, you can look at which action you are driving.”

Getting control of your data first is key, then you can start to think about what you should be focussing on.

Dunnhumby’s David Hoyle explained, “You need to decide if you are trying to collect data to operate a business or to optimise a business.” 

By considering what job you need your data to do, you can better prioritise to meet your goals and ambitions with your digital product.

Less is more - take stock of what you already have

There is often a lot of talk about what data we should be collecting, but our panel reminded us that we also need to consider what data we shouldn’t be collecting.

Barclays’ Head of Regulatory Change Delivery Tamana Bhambwani believes less is more when it comes deciding what to gather and store. She said: “Don’t capture the data if you don’t need it, because every time you capture something, you are paying to retain, maintain, archive and then delete it. If the data isn’t helping you to push your overall business objectives, then you don't need it.”

Something many organisations sometimes overlook is evaluating the information you already have, rather than taking on a flurry of new data points. You could be holding onto data you no longer need, and you can begin to clear out data, saving you time and money.

Slow down and save costs

Not trying to go too quickly at the start of a data project can also help to reduce costs in the long run.

“There’s a difference between rushing to build a piece of software that’s exhaustive versus building a piece of software that’s extendable, and data has a part to play in that,” David Hoyle explained.

“If you get this right, you’ll be prepared to make the changes and improvements you need in the future. If you get it wrong and don’t build the right foundations, you’re going to have to rip it all down and start again and it’ll cost you more in the long run. Sometimes going too fast can cost you.”

Creating an effective feedback loop

Data reporting is often something that is the last thought or priority due to budget and timing. It’s something that often comes back into data teams just a few months after a project kicks off.

“Suddenly, you realise you won’t know how the project’s delivering because you don’t have the insights for it,” said Nina Dunwody.

“At Pets at Home we engage as a team from the start of the project so that it mobilises. We explain where data is going to impact, and explore how it can be reported on - you might not need a fancy dashboard or report, but it's still something that needs to be managed.”

The panel all agreed that it is far easier to build in data and reporting from the start to create a more effective feedback loop that won’t get stalled or blocked. 

Apadmi’s Head of Data Science Allan Lehan added: “It has to be an ongoing process, you can’t measure something once - we are always looking at ways we can monitor key changes for our clients to directly attribute success to certain actions.”

AI and data - risk vs reputation

We’ve all seen the alarming headlines with details of dire data leaks, reiterating the importance of data protection when it comes to brand reputation.

So when it comes to experimenting with data and exploring new tools and technologies, there is some understandable caution and due diligence to be considered. 

“For Dumhumby, a strong data protection culture is an integral part of our business, if we had an incident we would lose our clients,” explained David. 

Meanwhile for the likes of Pets at Home and Apadmi, AI is something both are beginning to explore and understand when it comes to data and delivery. 

Pets at Home’s Nina shared that the organisation has started to explore the opportunities AI can provide; they have begun using AI to make some decisions when it comes to data, as well being an efficient option for data visualisation. 

Apadmi’s Group Content Director and panel host Jake Sargent reminded the audience: “Many are naturally AI curious, but it is important to have a proper use case and to do the due diligence when it comes to implementing AI, especially when it comes to handling sensitive customer data.”

Whether you’re looking for help collecting or managing data, or you’re hoping to improve your digital product performance, reach out to us today.