Data Tips #15 - Three different angles in ideation
Olof Granberg
AI Strategist | D&A Mentor | Data Evangelist & Public Speaker | Executive Advisor | Data, AI & Privacy Tech
Today we are looking into Ideation and how to approach it. Throughout the years we have had many different hypes where new technology or new data has become available and has sparked a longing to utilize the new capabilities.
In Data & Analytics ideation there are many different ways to approach the ideation, here we will highlight three angles to approaching opportunities and discuss advantages and drawbacks to the different approaches.
Regardless of angle chosen, the idea must solve a customer or business problem/opportunity! And "implementing <Select hype of the year: AI / Big Data / Machine Learning / RPA>" is not a business problem. A much better business problem is "how can we automate invoice to time report matching to save one hour for each manager per week?"
I like the methodology to widen-narrow-widen-narrow in all phases of ideation, such as double-diamond. Regardless of methodology you can enter the Ideation from (at least) three angles:
1 - Business problem/opportunity: Start with looking at what are the biggest problems and largest opportunities within the business at the moment and in the future. Preferably tied together with your business strategy. Prioritise down to a few problems and then Ideate possible solutions to that problem using data, analytics and/or AI.
Pros: Focuses directly on the business and addresses your largest needs.
Cons: May be conservative in the ideas generated because you may be limited in your ideas by the current possibilities.
2 - New Technology available: Start by going through the capabilities of the new technology. Currently it is generative AI, in the past it has been other things. Then look at your largest problems and opportunities with the mindset of "what could we use our <new tech> to solve?". Prioritise down to a few problems and then discuss solutions using the new technology.
Pros: You are looking beyond your current capabilities and expanding the possibilities for your ideation.
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Cons: It is easy to get caught up in the idea that the technology has a value in itself rather than the possibilities it offers. It may also be hard to understand early on what can be achieved with the new technology.
3 - New (or not previously used) data available: Here we are starting by looking at the new data. It can be customer interactions, IOT, network or any other data that has not been widely used before. Return to the same thinking: "what could we use our new <data> to solve?" and move into ideation.
Pros: You are expanding beyond the current capabilities and looking at new information for your business. The new insights can lead you into operating your business in new and better ways.
Cons: This requires an analytical competence to understand and make sense of what the data can really tell us. Mixing that with the needed business expertise can be challenging, but very rewarding if successful.
So to summarize, three different angles to approach Ideation, all three have advantages and disadvantages.
The important statement still stands:
All ideas must solve a customer or business problem/opportunity!
Have fun.
Data Project Manager & Business Developer at Codepole
11 个月A great summary as always Olof, I'm reading your series with much interest. A practical issue I've stumbled upon multiple times, which almost reduces the choice of ideation method to a moot point, though, is speed of execution. I don't doubt there exists an elite few % of companies with sufficient data maturity and resources to tackle issues dynamically, but most orgs I worked or interacted with aren't that fast. Managers agreeing on what the biggest problem is, getting buy-in to test the new tech, onboarding new data (and running two-three iterations with the data producers before quality becomes OK) - all that takes time. Not to mention that overburdened data teams might not even be able to prioritize the new opportunity at once... or they do, but framework problems, legacy code, prod issues, etc. make the work take twice as long (at least) as originally planned. All those factors and more often cause that by the time the solution is released, the opportunity is gone or the problem has run its course. Obviously, longstanding issues are an exception, but many business problems & opportunities still have a strong time factor. Can one even account for that in the ideation stage, or does the solution lie elsewhere?