Idea Generation For Data Projects
How can you ensure that your data initiatives go beyond simple reporting and provide real value for your business users? How do you involve business stakeholders on that journey, get them excited about the possibilities of data but also gIve them realistic expectations on outcomes and timelines? Here are a few steps.
Step 1 - Why Data?
It seems an obvious question, but why does your company need data? If we were to ask most businesses they would undoubtedly say to make better decisions, but we see 5 distinct advantages:
Holistic View: Tying together your different systems, channels and tools in order to create a clear picture across your business whether that’s in operations, finance, marketing, procurement etc.
Agility: The earlier you see the signs of change, the sooner you can be on the right side of it. Many data initiatives are started with a mind that a positive first step in becoming more agile would be to react quicker to change, and then later on maturing to the level where you are anticipating and predicting the change.
Customer Closeness: The right insight can help you understand not only who your customers are, and the channels they come through, but also which customers will go on to have the greatest lifetime value, which are at risk of churning etc.
Streamlining: Identifying areas of waste, automating systems, and refining of processes through data. Data can also help you to narrow your focus, spending money in smarter ways or on areas with a higher return.
Automation: Making choices easier, either through automated workflows or through powering of AI.
A quick, useful exercise you can undertake is to take these 5 key benefits and ask stakeholders to score them in terms of importance, using a percentage for each benefit adding up to 100%. This later allows you match your data ideas to the priorities of the business, and ensures that you don’t get tied to one particular benefit. (For example having to find a cost reduction with every initiative.)
As an example:
10% Holistic view
50% Customer Closeness
In this example the number one priority is getting closer to customers, with greater agility around decisions coming second.
Step 2: Idea Generation
At the idea generation phase we would recommend that your data project has internal support from both management and that you involve heads of function. But it should also be about the people who will actually be using the data. We have found that in order for people to embrace change they need to feel ownership of that change. If data is just a mandate from management, it won’t get the buy-in it needs to be successful.
There are no ‘wrong’ answers at this stage and it’s important to encourage contribution. We have found though, that in order to come up with ideas, people will need a basic level of understanding of how a data pipeline works and the areas it can help with.
The point of the exercise is to get people excited about data, map out different areas/ processes where it might be useful & uncover areas for integration. Try and foster conversations around data touchpoints, sources and where data can be activated.
This might be a little scary for you if you’re a data person. Free reign on ideas can seem dangerous if you’re the one who has to implement them! This is a good opportunity to set expectations and gives you the opportunity to have a more strategic role in narrowing down these ideas.
Step 3: Establish Priorities For Further Review
All ideas should go through a robust process of analysis and feasibility review before being added to any roadmap. As a first lighter step, you should narrow down your list of ideas – here is a few ways to consider doing that:
Hone The Idea – Try and centre your ideas around decisions and actions and not problems. Customer churn is a problem and identifying at-risk customers is a good use of data, but how will your team intervene? What channels will they use? What actions can they take (for example offering a discount)? This can help you flesh out an idea into 4 key questions instead of a nebulous approach where you need to understand the customer’s entire history to make a decision.
Popularity – It is always good to take note on which ideas are popular with the team and what gets people excited about the possibility of data. While these may be bigger ideas for tomorrow, you can start working towards them today and creating data products that teams will enjoy using should always be a priority.
Relation To Business Priority – Earlier, we did a little task around business priorities and it is useful to match up your ideas to this. Thinking of predicting customer churn, it would match highly to customer closeness and agility – the two priorities in our previous example.
Impact To Business – While it is advisable to flesh out your business case, you will probably have a good top-level understanding as to which ideas are more likely to see ROI and how that can be measured.
Implementation – Likewise, you will probably have a high level of understanding on which data initiatives are easier than others, or at the very least which may need to come first in order to lay the foundation for the other later projects.
It is important that you give feedback to the team involved in the idea generation session on which ideas will be taken forward for further consideration, and which will not. There is nothing more disheartening than coming up with an exciting idea and then never hearing anything about it again.
With your initial brainstorming done, you should now have a smaller list of ideas in which to take forward to a more robust feasibility review.
If you’re not sure which data ideas will bring long-term value to your business, how long they would take to implement or the costs involved, why not book an informal call with the 173tech team today?