Don’t fall into common traps when seeking to become data led! Here are 4 critical insights you should consider.
Data has been an increasingly valuable commodity within small and large organisations for quite some time. One can see this with the increasing evolution of online services, predictive algorithms to engage with customer groups more effectively online, smart supply chains — the list goes on.
It is therefore no surprise that digital transformation has been at the top of business agendas. Yet 72% of corporates surveyed in 2019 by Harvard Business Review stated that they have yet to forge a data culture, 53% even stated that they were not yet treating data as a business asset.
Today, Covid-19 is placing an even higher importance on data, particularly to identify and adapt to the changing needs of consumers and enable the workforce to maintain operations through this difficult time.
The fact is that despite the importance of data, many still feel that the cost-benefit associated with a data programme makes it not worth pursuing.
In this article, we want to share some of the key insights that we have learned when working with our clients, and provide some practical steps you can take to ensure you rapidly get results from your data programme, without falling into the pitfalls that others have experienced. These are based on real-world learnings and are all valid for any project: from publishing a single dataset to a full enterprise-wide strategy.
Insight 1: Always start by focusing on user needs, not technology. Consider processing your data to make it more valuable.
A common mistake that we often see is organisations focusing on how they can publish their existing data, rather than mapping out the needs of internal and external users, and how the data provides value to them.
Ask yourself: what value do I anticipate any dataset has, and to whom. If you can’t identify value, you shouldn’t be spending time on it.
You might accidentally hit nascent value by publishing a dataset at random, but without any consideration being given to how it will generate value for others, it’s likely to fail and could even be damaging to your business.
Most importantly, by processing data according to user need, you can generate significant additional value. For example, for a mobile operator and a retailer, we increased the value of a dataset from £10k to £150k by processing it for a target data use case, making it more valuable to the data consumer — in this case identifying where shops should be opened based on anonymised time of day, gender and age footfall.
Key recommendation: At the beginning of any data-focused project, we always start by mapping all of the ‘data users’. These could be individuals internal to your organisation who are responsible for generating value from data to facilitate operations, as well as external stakeholders, which are particularly important for ‘open data’ initiatives. Who needs your data and how can they use it to create new capabilities and value? How can these activities facilitate your goals and objectives?
Insight 2: Ecosystems are the catalyst to data value
It is essential to consider how data drives value across your enterprise activities. One of the most effective ways to do this is via an ‘Enterprise Ecosystem Model’. It describes how value is generated from data across the entire enterprise, including the people, process and technology components that are required to enable this. Often the limiting factor is not data itself: it is actually about the services you need to drive value out of the data.
Without understanding how data drives value across your organisation and other stakeholders, you’re essentially shooting in the dark — and you’re unlikely to hit your desired target.
Our motto here is that the ‘devil is in the detail’: only by mapping out how data and activities will drive value will you uncover critical challenges that, if not addressed, will cause the open data initiative to fail. This is particularly essential where many commercial and regulatory blockers can rapidly quash benefits if not addressed up-front.
This is particularly important for the increasing amount of enterprise data that is available, ‘big data’. It can be easy to get lost in the meaning and value of huge datasets — plotting the value and dependencies within an Enterprise Model makes it much easier to define, articulate and realise the value with partners.
Key recommendation: We recommend making Enterprise Ecosystem Models extremely visual so that they encourage engagement from a wide number of internal and external stakeholders. This drives validation, and provides a reason to believe that third parties, such as SMEs, are ready, willing and able engage with the initiative before investing further.
We successfully used this approach with the Royal Navy to engage the 12 principle global enterprise partners, validating and taking forward the Royal Navy’s future ecosystem for mission systems development.
Insight 3: Consider your Open Data Business Model to engage with others (and yes, this is also important for not-for-profit organisations!)
It is important to consider your Open Data Business Model at an early stage. It asks the question of how your data creates value for you and other stakeholders.
Business models are often associated with profit-making organisations, however regardless of whether your organisation is profit-making, income-regulated or not-for-profit, there must be a value-share exchange for others to engage with you and your data. SMEs and commercial organisations will also need to validate how data provides value to their shareholders, and therefore whether there is a valid business case behind the work involved. Value is not solely revenue.
The value-share aspect of an Open Data Business Model also extends across all commercial and legal aspects: you must be able to put yourself in the shoes of those you intend to engage with, and identify the key considerations that will be necessary to address In order to engage.
Key recommendation: Consider how your data is being used to generate value, for other organisations as well as your own. Pay attention to where the nascent value lies, and where efforts should be vested to deliver value early. Your Open Data Business Model should include:
- Target users of your data
- The value proposition of your data: how and why is it important to someone?
- Partners and other resources required to deliver your vision
- Key activities required to engage with others: this could include marketing, publicity, intellectual property considerations etc.
- Costs and resources required to deliver the data, now and going forward
Insight 4: Be realistic about what is achievable from an early stage, focusing on activities that will drive early value
The potential cost of data projects should never be underestimated. Quite often data stored within organisations is held within siloed applications, and the cost of making this data available externally, via a secure method, in a data format that others can use, can be significant.
We recommend addressing this, even conceptually at an early stage, to identify which pieces of data will be of most use and the key steps required to make that data available. This should include constraints regarding formats, redaction of sensitive data, security access and so on. You must then review how this aligns to your existing capabilities and identify the ball-park cost of completing the work before spending too much time and resource on it.
There are lots of ways to make your internal data safely and securely accessible. However, it is essential to understand your user needs, data constraints and requirements before you pick a technology, otherwise you may end up with an artificially high price for an over-specced solution, or too low a price for a solution that fails to meet needs.
Remember many organisations have their own technology products and may push these preferentially, so it’s desirable to choose a technology-agnostic advisor.
In the past, we’ve found that, quite rightly, organisations are very passionate about espousing a ‘digital by default’ agenda. However, without early realism of the cost associated with this, projects can be shut down when the real cost becomes apparent and before they deliver any value — resulting in a waste of time and money.
Key recommendation: It can sometimes to advisable to focus on activity that will deliver meaningful results in the short term which will also lead to long-term capability improvements. This can create momentum with stakeholders and provide sustained support for future activities. However in doing this, don’t get lured into providing short-term solutions that are ‘quick wins’ that are incompatible with long term objectives, or miss out all the ‘hard bits’ — doing this will simply result in the initiative being canned when stakeholders realise it cannot deliver meaningful long-term value.
How we can help
Successfully implementing a data-led strategy involves many aspects of your business that have nothing to do with data: your target users and stakeholders, alignment to existing capabilities and services, third party, SME business models and so on. We have spent over 10 years refining engaging approaches to deal with all of these aspects within a very short time in order for you to get on with implementing your strategy without losing momentum.
Our recommended approach covers three main areas:
· Assess your target vision. Who are your data users? What do they need? What value can you create for them as well as yourselves, and what is the overall size of the prize? And, most of all, how much it will cost you to implement the infrastructure you need to deliver. Remember partnering is always an option to remove barriers to delivery. We’ve often found immediate value can be engaged through our partnership network in order to bridge the capability gap — just because you don’t have capability now doesn’t mean you have to put your aspirations on hold: get others to help deliver your vision!
· Prepare your data strategy. We often refer to this as creation of a design blueprint, and it should address your needs already identified in the assess phase. We approach this by assessing the new capabilities and services that your organisation would need to deliver in order to create your new data-led ecosystem. Prioritise making the most of your existing assets to save money, as well as partner capabilities to avoid you having to create everything from scratch. Most important consideration should be given to the technology platform of choice, ensuring it meets your needs whilst not being too expensive. Finally, choosing a technology agnostic provider such as Sia Partners, can provide an objective view of the best solution for your needs, rather than potentially being limited to or steered towards an affiliated technology product.
· Action. Once your blueprint is articulated, and you have a clear view of the activities required, you can move on to building the capabilities and services as defined in your Design Blueprint. We believe it is important to constantly test and validate the build with your target customer during the process in an Agile manner, ensuring that key hypotheses made at the start remain true, and if not, the Blueprint is amended accordingly to continue to deliver value.
If you would like any further information about any of the topics raised or our approach, please do reach out to us at email@example.com, or myself at firstname.lastname@example.org.