Even the most considered data strategy will fail without a clear definition of the data value you need from it. Monetising data should be a top priority, and the data value needs to be proven to your peers quickly. Far too often, organisations in the opening stages of their data projects fail to gain clarity on their objectives.
Failure to align your data strategy with wider business goals is a surefire set-up for disappointment. Indeed, analyst Nick Heudecker estimated 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). In this case, it’s best to take a step back from the fray, and look at the data value of these plans.
By first proving the value of data, you greatly increase the likelihood of buy-in for future (and more ambitious) plans. This isn’t just 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?
Although every data strategy is unique, there are many common areas to consider when prioritising your data projects, and determining their value to your organisation.
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.
Try to make sure your data projects clearly solve a specific problem. Ideally, look at your use cases first, and avoid starting your journey with the tools. If you invest in a tool first, 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. To counter this, try illustrating your problems with use cases, before you think about investing in tools. For example, to determine which customers are worth targeting, then you should ask yourself:
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 and prioritise data value.
Likewise, 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 your peers; and you may later rely on them for project buy-in.
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?
There are several ways to identify and monetise the data value. Data can help with customer retention or attracting new customers, and it can uncover new revenue streams or business opportunities. Alternatively, it can differentiate your organisation, reduce running costs and decrease risk.
You can also gain data value by bartering it for goods, services or partnerships, or 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.
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 can be harder than it seems). Many organisations have data tied up in silos across areas, departments, functions and different tools. Similarly, 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 then combined with different datasets.
Value to a business will not happen if you don’t act on the insights uncovered through data. Even if the data tells you something that doesn’t match your gut feelings, act on it anyway. This could just mean running the analysis again with an improved data set, or a larger range of data.
On the other hand, it could be accepting that your instincts were wrong. It can be difficult to drop a product development project when the data tells you that it won’t perform well, but it’ll save you more money in the long run.
Getting value from your data isn’t a job for one team. It requires the support of your entire organisation, hence the recommendation of quick-win projects to make improvements across departments.
By getting everyone involved and showing them the value of data use, you help to shift their thinking towards a data-driven one. Maybe find a few colleagues in different departments who can inspire the rest of the organisation, or put a data leader in place who can lead this cultural change.
As your organisation moves towards a more data-centric culture, you’ll find people will come up with other uses for data, or where they can find additional data sets. When this happens, you should have a team who can handle all the requests, prioritise them, and help execute them across 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, which likely won’t align with your data strategy.
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 early, 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.
For more insights into building a data strategy, make sure to download our Data Strategy whitepaper.
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