Definition of data-driven

Firstly, let’s define what we mean by ‘data-driven’. Technically, all businesses will already be using data to inform decisions (e.g. financial reports, operational information), but there’s a difference between simply using data and being driven by it. For me, a data-driven business is one that:

✓ Makes decisions only when the data supports them, not based on how things have always been done, or through instinct or ‘gut feel.’

✓ Has an executive level/board with an understanding of the importance of data and a healthy flow of information upwards (and downwards!) from other parts of the business.

✓ Considers data a critical asset or an arm of the business, where its use becomes a core metric you consistently monitor. From sharing or selling data externally, using it to optimise processes, or using it to inspire new workloads or streams of revenue, you see it as an invaluable tool.

Why your business should become more data-driven

Having data at the centre of your decision-making reduces risks, improves clarity and maximises business success. The recent IDC report, ‘How Data Maturity and Product Analytics Improve Digital Experiences and Business Outcomes’, surveyed digital experience decision makers to understand the maturity levels in the adoption and use of digital product analytics technology, culture, and practices. It found significant increases in revenues and profits, efficiency, higher NPS scores and lifetime customer value in those with higher data maturity.

More specifically, from an IT team’s perspective, that means fewer requests for changes with unknown outcomes or requests for information that doesn’t exist. Data gets you closer to the heart of the business and allows you to see the impact of decisions through demonstrable knowledge. You can take that back to other parts of the business and evidence examples such as the impact of online promotions on sales, the influence promotions have on traffic, and the knock-on effect on infrastructure. Democratising data and getting people across the business to speak the same language ultimately improves team decision-making and communication.

Barriers to becoming more data-driven

There are two significant challenges to strategically implementing data that, in my experience, come up repeatedly. Firstly, resources. By this, I mean anything from a lack of skills in the existing teams to not physically having enough people to look into data due to a lack of time or priority. Comparatively speaking, when considering it against our second challenge, this is relatively simple to fix – especially with the wide range of external consultancy services that can work flexibly to support you.

The second barrier is a lack of senior leadership buy-in. From a ‘why change what isn’t broken’ mindset, to a lack of understanding or a reluctance to invest, how you overcome this challenge depends on the individuals you’re trying to win over. I’ll go more into specific use cases to support your argument in the next section, but before we get to that, you need to spend time with those that have doubts to understand why they have them. When we visit businesses experiencing this challenge, we invest as much time as possible to resolve any doubts and get to the heart of their values and priorities. Once you understand the individual, you have a better chance of showing them a use case proving a data investment is vital to them and the business.

How can you identify and socialise use cases for investment?

Always start with a business problem you want to solve before determining which data to assess. Think about the different business areas and which key stakeholders are invested in them. Picking specific business areas means avoiding getting lost in the sea of challenges an entire organisation is working on. Businesses often want to solve something end-to-end, but this can be an overly complex place to begin as many factors from different areas of the company can influence the end goal. Having an identified business problem to solve with data gives you a focus. It means less temptation to add unnecessary complexity when your end goal is to show value to the right people as quickly as possible.

Start small and specific. As mentioned, trying to solve complex challenges can be tempting, but you want something you can gain insights into quickly. You’re looking for a clear lineage of information where you can see the impact input A has on output B. That doesn’t mean not having a vision or a longer list of challenges to solve in the future, but starting small gives you the best chance for success early on.

Look for recurring problems. When speaking to other parts of the organisation, find out what issues have arisen multiple times over the past 6-12 months. A repeated problem is a great use case as often that’s down to something systematic we can analyse and find a pattern within. Patterns are easy targets for optimisation and automation – two things we can use to make data more practically available.

Key takeaways to becoming more data-driven

You don’t need a data team or a huge investment to start. No Head of Data? No problem. Smaller businesses or those that are newer to data analysis should be more concerned about the output of a data project than building a large data team. Adding a new, complex data platform can be the financial equivalent of multiple new hires in terms of technology and tooling, so you must see its value before heavily investing. Use external resources, such as Netpremacy’s consultancy services, to create a minimum viable product to grow.

People and business challenges first, then data. Understand what is important to key stakeholders and the challenges that are impacting the business first before even starting to look at data. It can be enticing to investigate data off your own back. Still, without securing buy-in first and taking the time to understand what’s important to the people that matter, you’re setting yourself up for failure to get a decent data project off the ground.

Start small and look for patterns. Once you’ve identified the business area you would like to solve a problem for, look for challenges that have happened multiple times and, ideally, where there’s a clear line between two or so pieces of data.

If you’d like help with any of the above, I’d recommend booking a Data and Analytics Kickstarter session with us. Or, read how we helped Victoria Plum build a scalable data platform and answer a specific business question with data.