X marks the spot: how to map your data strategy in 6 steps


• Sep 27 2023

X marks the spot: how to map your data strategy in 6 steps

10 min read

A lot of businesses want to become truly data-driven, but it’s not always clear what that really means, or how you can get there. Here are the six things you need to do to create a dependable and successful data strategy.

  1. Have a specific measurable goal (with milestones)
  2. Ensure you have the right data quality (and quantity)
  3. Set up your data governance regime
  4. Create data literacy across the whole business
  5. Choose your tools wisely
  6. Plan the project delivery carefully

1. Have a specific, measurable goal (with milestones)

You wouldn’t plan your route before you knew the destination, but when it comes to data, that’s exactly what most businesses do.

Organisations devote a lot of time and money to developing their plans for data without first establishing the commercial goal. That’s a lot like opening a map and asking, ‘How shall we get there?’ without agreeing where ‘there’ is.

Your commercial goal is the X on the map. Without it, you’ll wander aimlessly.

TIP: ‘Being data-driven’ is not a goal in itself. Be specific — how will you know that you have become data-driven? Will a certain proportion of decisions be taken because of what your data has told you? Is there a percentage by which your data strategy will increase revenue or reduce costs?

Use milestones to create small goals on the way to the overall destination. Data transformation is a substantial project, and it is important that everyone involved gets to feel the satisfaction of tangible progress. Otherwise, it’s easy to become exhausted, demoralised, and burnt out.

Let’s say, for example, you wanted a successful Environmental, Social, and Governance (ESG) strategy. Data (and the strategy behind it) would be fundamental to success.

A business’s environmental impact is enormously complex, with three different scopes for carbon emissions, and some well-hidden environmental impacts that are easy to overlook. Data is fundamental to understanding and reporting your impact and your progress. However, because of the complexity of your emissions, you need a data strategy that can ensure you not only have the right data from the right sources at your disposal, but that it's accurate and reliable.

2. Ensure you have the right data quality (and quantity)

If your commercial goal is the destination, then data is your party’s provisions. Run out of food and water and the crew won’t make it — feed them with poor nourishment and they’ll slow, tire, and become ill.

The first questions you need to ask are:

  • Do we have enough data?
  • Is it of sufficient quality?

Those questions are harder to answer than they might seem. Knowing how much data you need and what constitutes good data quality takes a rare combination of data expertise and commercial awareness. It’s difficult to find people with both, but it remains essential.

If your data gives you half of the picture or a picture that doesn’t represent reality, and you make business decisions based on that data, those decisions cannot be commercially sound. Many businesses spend 10-30% of their revenue handling the consequences of bad data, often in correcting misconceived business decisions.

TIP: In your initial data assessment, you should check for the following:

  • Completeness — data sets have what they need and are linked between sources
  • Accuracy —data describes reality
  • Availability — data is accessible on demand
  • Timeliness — data is current 

Once again, that assessment can only be useful if it’s made by someone who understands business operations, commercial objectives, and data strategy — only they will know what is necessary, true, useful, and current and how to recognise it in data.

3. Set up your data governance regime

Since you will have spent a lot of time and energy cleaning, supplementing, and improving your data, you’ll want to keep it robust and reliable. Data governance puts in place the structures and processes to support that.

Eliminating and preventing silo

Individual departments can easily fall into bad habits or develop idiosyncrasies in their use of data.

When teams have specific data that they use day-to-day, it’s easy for them to develop their own culture around it. It’s gradual and unconscious, but they can start recording data according to their own tendencies and preferences. ‘Small’ things like data structure and naming conventions, or larger things, like where teams store and save data, can soon have enormous consequences for analytics, and therefore business decisions.

Ensuring proper use of data

‘Proper’ use applies to company data and to customer (or prospect) data.

The introduction of data errors is hard to reverse, and it can have a butterfly effect on the business, when a small error informs and affects increasingly significant decisions and processes.

Marketing lists, client databases, contracts, and proposals are just a few of the things that can contain highly sensitive material – whether that’s confidential information or personal data – and there are plenty of ways to misuse it, most of which will be accidental.

TIP: A data governance strategy is only as strong as its leadership. The increase of data in business over the last few decades has led to an increase in C-suite professionals with tangible data transformation experience, but they remain extremely rare.

4. Create data literacy across the whole business

On your journey, everyone needs to know their roles, their duties, and how those serve the collective goal. On a ship, the captain can’t be the only one who knows how to sail. In the same way, your data leaders can’t be the only ones who ‘get’ data. If they are, all they will do is fix mistakes — when the whole company is data-literate you can prevent mistakes in the first place, or at least notice early and nip them in the bud.

There’s no escaping that data literacy takes a significant investment, but it pays dividends.

You need to assess the current level of data literacy (in the whole business as well as in individuals), and then tailor an education programme to your team.

The greatest challenge for many companies is employee buy-in. Most people aren’t especially enthusiastic about data, and many can be resistant or even hostile to the idea of having to use it, either because of apathy or fear.

Again, this is where leadership comes in — most employees will need to be inspired to learn about data usage, and to think about it as part of their daily routine. Imposing new rules dictatorially won’t result in meaningful change. At best it will result in minimum compliance, which is not the relationship that you want your teams to have with data.

Training is not a ‘set and forget’ activity. You need to continually monitor behaviour (in a collegial, not authoritarian way) to ensure that training is having the desired effect, and that people don’t slip back into bad habits.

TIP: While it can be frustrating to business leadership to see employees failing to meet new standards, it’s important to provide support to those who struggle, not punish or single them out — negative reinforcement will only create or entrench hostility to the project.

5. Choose your tools wisely

You should never choose a tool without deciding what you want to achieve.

If you bought an expensive boat for a journey, it might be well-equipped and powerful, but if you bought it before choosing your destination, you risk finding out that the route is by road.

In data things like business analytics and business intelligence are different practices, so they require different technology.

Some businesses are tempted to invest in data technology first, then fit their data strategy around it. Those who aren’t confident with data, platforms, or strategy sometimes hope that technology will take that difficulty out of their hands. The trouble is, while they find themselves with a solution, it may not be the one they need.

Even those who are confident in researching data tools could find the water muddied by the process. You’ll inevitably speak to data technology providers, but of course, any vendor you speak to will have an interest in representing their solution as the perfect one for your specific needs. Having set out your commercial goal, you’ll be far better equipped to know the true pros and cons of any solution.

TIP: Strong commercial leaders will steer any conversations that you have with potential partners. They will be able to ask incisive questions that get to the heart of whether a vendor is able to help, or whether they’re simply trying to make a sale.

6. Plan the project delivery carefully

Related to governance and leadership, the rollout of technology will only work successfully with the right planning and project management.

TIP: Data is an IT project as well as a business one: it requires expertise across both, and collaboration between departments, between business people who understand data, and IT specialists who understand commercial drivers.

Here are the things to remember when planning and delivering your project.


Even if you know where you need to be, you may not know how to get there. With the sheer amount of data available to businesses, there’s always a danger of data overload. A visionary data leader will know when to focus on details, and when to look at the big picture — crucially, they don’t get overwhelmed by either.

Your leaders should always keep the objective in mind, as they plan every step along the way. In major transformations and projects, it’s easy to get lost in the detail and forget the goal, or vice versa. Those with a firm grasp of business and data find it far easier to keep themselves and their teams on track.

Willingness to adapt

Having a vision should not mean a refusal to adapt or compromise. By always remembering that the project is to serve the business, and not the other way around, you and your experts will make sure you don’t become wedded to a course of action or a tool simply because it was a first or long-standing preference.

If something changes in the business or in the wider commercial landscape, it may be that you need a change of direction in your plan or project. Remaining sensitive and receptive to that will keep the project heading towards the right business outcomes.

Creating a data culture

Your project is not just a matter of installing software — it’s the start of a new data culture. Part of transformational leadership is communicating changes to the wider company, making sure they have the tools and instruction they need to play whatever part is theirs, and most importantly that they buy into the project.

The whole company needs to be on board — if even a few people aren’t convinced of its merits, they are likely to use it incorrectly, whether that’s conscious or unconscious. At that point, the quality of data will be compromised, the butterfly effect above will occur, and the project is in serious danger of failure.

The destination is where the data journey begins

Arriving at the X on your data map will have taken serious investments in time and energy. However, you already know that it’s not the end. As soon as a business has achieved its goals, it will set more — there’s always another journey.

There are always new opportunities with data, and new projects to plan, implement and complete. The need to discover new projects, identify opportunities, and manage transformation means that you will always need the skills in-house to deliver them.

The combination of commercial vision and data expertise is a rare one, even in the most experienced candidates — those with both are highly sought-after. RPI specialises in finding those in-demand specialists globally and matching them to the business where they can spark true data transformation.

Get in touch today and find out how we can transform your data culture, tech projects, data literacy, and governance by placing the talent that your organisation needs.


RPI provides access to the top leadership and technology talent globally