This blog series unpacks everything from best practices to best philosophies when it comes to delivering the best analytics experiences.
At InterWorks, we spend a lot of time working in the intersection of sales teams and technical consulting. In practical terms, this means we’re interpreting and translating requirements—listening carefully to what clients want, asking the right questions, mapping those requirements back to appropriate technology choices and defining the best way to achieve the outcome the client wants. This combination of technical knowledge, experience and curiosity is the foundation for something we strive for: consulting excellence.
Leveraging Experience to Avoid Data Product Pitfalls
One component of consulting excellence is being open, honest and candid about something not often talked about: failure. When projects go bad. When budgets overrun. When bad choices are made.
The core function of any consultant is to “provide expert advice.” Our view is that sharing our experience of risks, problems and potential failure is just as important as sharing our success. In the world of data monetisation where planning and product demand is often speculative, this becomes crucial.
So, as the data and analytics ecosystems continue to evolve, here are a few examples of patterns and approaches we repeatedly see where successful outcomes become difficult to achieve.
Pitfall #1: Prioritizing Technical Development over Delivery Leadership
Data monetisation products are complex. Tight and effective collaboration between teams is crucial, expectations must be clear, and progress (or not) must be visible to project sponsors.
However, when presented alongside detailed technical delivery plans, it’s often easy to disregard the importance of effective project management and delivery leadership as being optional or somehow supplemental to the project. It’s also often the first item to come under scrutiny when agreeing on budget and scope.
In our experience, delivery leadership is, in fact, fundamental to project success—and not just from a pure BAU project management perspective. Our advice? Recognise that dedicated resource to own project outcomes, handle tough questions and set expectations is just as important as the resource to actually build the solution.
Pitfall #2: Undervaluing GTM & Marketing Activity
In early stages of product planning and ideation, it’s easy to get excited. You’re optimistic about the possibilities. You’re doing something new to the market, you’re ahead of your competitors. And now (given some advice from InterWorks, perhaps), you know more about the technical possibilities the solution might offer. You’re already imagining the first successful pitch; thinking about the number of sign-ups you’ll get in Y2. Things are going to be great!
Here’s the thing: data and analytics products are often re-inventions of more rudimentary services. Manual exports from Excel become interactive dashboards. Time-consuming custom analysis becomes user-driven. Manual bulk exports become real-time data shares from a high-performance data warehouse. The underlying components are similar, but the mechanism for delivery, interaction and customer engagement are often very different, and no assumptions should be made that customers will engage without careful product positioning, considered pricing and a defined, well-constructed approach to sales and marketing.
So, don’t put all your trust in the technical capabilities of the product. Start thinking about GTM strategy early, build small, iterative and scalable POCs and, if possible, garner feedback from existing and prospective customers. Pragmatism is just as important as optimism.
Pitfall #3: Skipping the Gap Between Vision and Deliverables
For many organisations, data and analytics is still new. Reporting responsibility may have sat within finance teams in the past, CDO / CAO functions have yet to be established, and data monetisation represents a potential new source of revenue.
It’s not surprising that for an organisation at this stage, there can be a lack of senior direction and management to bridge the gap between high-level C-suite aspirations and the technical reality of building complex analytics products within delivery teams.
We often fulfill this function for our clients during the early stages of an engagement: translating vision-setting to technical requirements and working with internal teams to drive adoption and understanding. But unless the vision is consistently shared and understood across the organisation, the ability to execute will suffer. Whilst it’s tempting to hand this responsibility to an existing team, organisations that don’t consider that dedicated resources may be needed for this “hearts and minds” function will often fail before the first customer signs up.