Written by Pavel Kolesnikov |
Everybody knows that product managers need data analytics to be successful. However, far fewer people realize that, in turn, analytics need product managers just as much.
Back in 2008, one of Gartner’s studies noted that following the mantra of "If you build it, they will come" was the #1 flaw of business intelligence initiatives. Not surprising. This mindset is a common one, and it’s not unique to analytics and business intelligence. We’ve all seen examples of products or plans launched with great fanfare only to fail when it turns out that there’s no market for them.
What is surprising is that, 10 years later, not much has changed. Even though the "If you build it, they will come" approach has mostly been abandoned in favor of product management-driven development in most areas of software development, business intelligence initiatives can still be deeply flawed. In fact, Gartner has found that the percentage of data and analytics projects that fail is still over 70%, primarily due to low adoption.
Fortunately, there’s a strategy for correcting this trend. By putting a product manager in charge of overseeing your analytics and business intelligence efforts, you’re far more likely to see a successful implementation and higher user adoption rates.
What does a product manager do?
The role of a product manager is much more interdisciplinary than it may initially sound. They’re tasked with figuring out why a product needs to be built, what it needs to look like, when it needs to be built and most importantly, what should not have to be built. All of this work is crucial to enabling development teams to get from the initial idea to launching a product that is actually being used and delivers value.
They also act as a “bridge” between the technical teams and non-technical teams. A product manager usually has some degree of technical expertise and insight into the product and how it works. More importantly, they also have a firm grasp of the target personas, market conditions, and buying behavior.
This unique combination of skills makes product managers a valuable addition to the team. They’re there to get a complete understanding of the market or the target environment, guide the development of the product itself, and ensure the product’s ongoing success post-launch.
And yes, traditionally, the term “product manager” has been used to refer to people managing external-facing products, ranging from free services provided by Facebook or Google to complex enterprise applications.
However, more and more companies are looking to hire a “product manager” for their internal-facing products or various initiatives (few notes from Gartner on the topic here). And it makes perfect sense.
What value does a product manager bring to analytics and BI?
We’ve already established that analytics and BI efforts are not unique in their emphasis on the “If you build it, they will come” mantra. Considering this tendency and that one of the biggest problems that analytics initiatives face is low adoption, having a product manager on the team can be an enormous benefit.
Without a product manager, you risk building in features that aren’t useful for most—or worse, any—of your users. You may also overestimate the average user’s technical skill level and ultimately build a product that is too confusing to navigate, leading adoption rates to ultimately drop. But with a product manager on the analytics team, someone has been assigned responsibility for making sure the product is designed to meet the needs of the various personas while being intuitive for technical and non-technical users alike.
Our platform helps many ISV companies to enhance their external-facing products with analytical features. In fact, more than 50% of the Fortune 500 are using GoodData either directly or as a part of a product provided by a GoodData customer.
Over the course of these projects, we’ve seen that data and analytics initiatives, from business intelligence strategy to execution, require product management to be successful. Our experience in providing data and analytics for a number of products and industries has exposed us to the needs of end-users and product managers alike. And ultimately, it’s redefined how we approach projects to ensure that we’re doing all we can to support product managers—whether they’re working on an internal or external product.
One of the tactics we’ve taken is to form a separate product management team within our professional services organization.
This team has developed a methodology that combines the best practices of product management, agile development, and analytics and business intelligence. Over the course of the project, they’re on-call to support product managers as they bring a new analytic product to market or launch an internal product that delivers 360-degree analytics to all personnel.
The key steps involved in the methodology are:
- Mapping the current state
- Creating the business case to clearly articulate the desired business outcomes
- Crafting the product vision and defining personas, the jobs to be done, and what the first version of the roadmap looks like
- Developing a go-to-market strategy and launch plan (and yes, it’s worth doing this even for internal initiatives)
On a more granular and technical level, the key steps in each iteration of the roadmap include:
- More detailed user scenarios and wireframes that can be used for verification of the proposed solution
- Analytical (dimensional) data model to support the identified scenarios
- Map of available data sources to the model, identifying missing pieces or necessary cleansing or transformation tasks
- A working ETL setup, a working analytic model populated with data, and a working analytic layer, including interactive dashboards, data exploration, and front-end integration
- Automation including data integration and provisioning
If you’re starting a new data initiative, or even if you’re in the middle of an initiative, keep these points in mind. And feel free to contact us or request a demo of our platform if you think our experience could be of any help.
Written by Pavel Kolesnikov |
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