The most considered data strategy will fail if it doesn’t define what value it’s looking to deliver. Monetising data shoud be a top priority. The value of data needs to be proven to your peers quickly. Organisations starting out with data projects often fail to gain clarity on its objectives. Failure to align your data strategy with wider business goals is a surefire set-up for disappointment. Analyst Nick Heudecker estimates that 85% of data projects fail. Not because of technology, but because organisations are too busy chasing the next big thing. Once it was big data, now it’s artificial intelligence (AI). We need to take a step back and look at the value of such plans.
By first proving the value of data, you vastly increase the likelihood of buy-in for future (and more ambitious) plans, not just at with your peers at board level, but across your entire organisation.
So what steps can you take to mitigate risk as you unlock the value of data in your organisation?
Key questions to ask
Although every data strategy is unique, there are some common areas to consider when prioritising your data projects and deriving their potential value.
- Does the proposed project align with short and long-term business goals?
- How much investment in resources, team members, and tools does the project require?
- Do you have all the required data for the project or will you need to collect additional sources?
- What outcomes do you expect from the project; how much impact will this have on your bottom-line, efficiency, productivity and processes?
If your organisation is completely new to using data, then prioritise ‘quick win’ projects. These are projects that require little initial investment or additional data, but which will have a significant impact on your organisation. It’s a way of quickly showing key stakeholders that the investment of time and effort in using data is worth it.
Start with the use case
Ensure your data projects solve a specific problem. Don’t start with tools: look at your use cases first. If you invest in a tool first, often you’ll try to use that tool in every scenario. In our experience, organisations often don’t know what the root of the problem is. The best way to counter this is to illustrate your problems via a use case before you think about investing in tools. For example, if you want to determine which customers are worth targeting, then you should ask yourself questions such as:
- What does an ideal customer look like?
- Which customer segments remain with our company the longest? Which ones leave?
- What behaviour does a good customer have?
Break your use case down into a granular level, with a lot of relevant business questions to answer. Use this information to design your solution.
In terms of project size, it might be a better idea to initially carry out several smaller projects (trials, prototypes and proof of concepts) across different departments. This demonstrates the strategic value of data to peers — who you may later rely on for buy-in.
Align with business strategy
Data projects that line up with your business strategy ensure that results will help the business achieve strategic goals. You won’t deliver value if your results are out of whack with what everyone else is working to achieve.
This also prevents certain projects being prioritised over others if, say, they are the pet project of someone who can shout the loudest in meetings.
Consider what opportunities are present in the business strategy, where it will benefit the most from using data. What problems are you trying to solve and how can data help this?
Monetise your data
There are several ways to derive and monetise the value of data. Data can help with customer retention or attracting new customers; it can uncover new revenue streams or business opportunities, either with products or alternate markets. This directly impacts your bottom line. Alternatively, it can differentiate your organisation, reduce running costs and decrease risk. Then there are other ways to gain value from data by bartering it for goods, services or partnerships, and also to improve business relationships.
Whatever your method, always link back to your objectives and the business case. It’s not wise to invest millions in bringing real-time analytics capabilities to your organisation when you could have achieved similar or better results through better use of existing data to improve your sales and marketing effectiveness.
Prioritise your data
Typically, start with the data you already have before investing in other data sources. That means you need to understand what data your organisation has available (which is harder than it seems). Many organisations have data tied up in silos across areas, departments, functions and different tools. Also, one individual rarely has a complete overview of all aspects of data. It’s worth consolidating that data and ensuring it can be used (and combined with different datasets).
Take action on insights
Value to a business will not happen if you don’t act on the insights uncovered through data. Even if the data is telling you something that isn’t in line with your gut feelings, act on it anyway. That could be running the analysis again with an improved data set or a larger range of data. Or it could be accepting that perhaps your instinct was wrong. It can be difficult to drop a product development project if the data tells you that it won’t perform well in your target market, but it’ll save you more money in the long run.
Create a data culture
Getting value from your data isn’t a one-team job. It requires the support of your entire organisation. Hence the recommendation of quick-win projects to make improvements across many departments. By getting everyone involved and showing them all the improvements made through data use, you help to shift their thinking towards a data-driven one. When doing these initial projects, find a few colleagues in the different departments who can inspire the rest of the organisation. Put a data leader in place who can lead cultural change and drive data projects.
Have a key contact
As your organisation moves towards a more data-centric culture, you’ll find people will begin to come up with other uses for data or where they can collect additional data sets to improve the company. When this happens, you should have a team who can handle all the requests, prioritise them and then help execute them in the different departments. Whether you have a distributed, central or hybrid model, there needs to be a key contact for everyone to place requests with. This will prevent departments going off-piste with their own data projects that don’t align with your data strategy.
Value at every stage
Every organisation will be at different stages of the data journey. Regardless of your data maturity, there are ways to derive the value of data now and not with lengthy long-term projects. A mix of short and long-term projects is a good idea for more mature organisations. For those in the early stages, prioritise smaller projects across multiple departments that show tangible value in a short timespan.
Jason Foster — Founder and CEO — Cynozure
