Whitepaper – The Analytics Translator
The Analytics Translator is the liaison between senior management, the business, and data experts. On some days, you are the gatekeeper of the project funnel, brainstorm ideas with executives, and work with the data experts to groom the backlog of viable ideas.
Data science teams often struggle to deliver business value with their models. This is the result of their work not being embedded in the organization. Working isolated from the business and suffering from ‘technology push’, instead of ‘business pull’ underly this issue. As a Product Owner (PO) you are in a great position to create business pull and increasing business value. Doing this effectively, means that you need to understand how you can leverage data science to build better products.
This blog was written by Rutger de Wijs, Product Management Consultant at Xebia.
Achieving Succes With Data & AI Is Hard
According to GoDataDriven’s annual Data & AI Survey, 79% of organizations see data as an essential part of their strategy. It is no surprise that executives have put data & AI on their agenda over the last decade and deployed data science teams to generate value. As a result, one of the leading global data trends for companies at the moment is putting predictive models into production.
The biggest challenge in any organization’s analytics journey is turning insights from data into valuable outcomes.
So, lots of new business value to look forward to then! Well, unfortunately not. According to McKinsey, the biggest challenge in any organization’s analytics journey is turning insights from data into valuable outcomes. And indeed, executives say they struggle with achieving measurable results from their data science investments.
Embedding Data Science in the Organization
How can companies overcome this struggle and unlock precious value from data science? You do this by embedding analytics into the decision-making processes that are part of the ‘insight-to-outcome-journey’. Or, in other words, enable decision-makers in all levels of the organization to regularly and naturally make decisions that are driven by data.
Product Owners, by nature, are such decision-makers. That makes them key to unlocking business value from data science.
Product Owners, by nature, are such decision-makers and therefore the key to unlocking business value from data science. Let’s unpack this a little further. One of your fundamental characteristics as a Product Owner is to be the ‘Product Value Maximizer’.
In addition to having a solid vision for the product, you weigh stakeholder input together with knowledge about the marketplace and prioritize all this with maximum value creation in mind. This process requires constant decision-making by you. More than anyone else in the organization you have the opportunity to be informed and steered by insights from data science. A similar point was made by data scientist Hilary Mason in a recent Train_Data podcast.
Data Science Product Owners & Analytics Translators
Some companies have a data science team that is steered by a ‘Data Science Product Owner’ to make data science part of the agile product management process. For example, Booking.com, CarNext.com and Sony PlayStation have recently advertised such roles. These POs often have a strong background in data science.
A Data Science PO makes the data science team operate effectively, but it’s not enough to maximize value creation out of data. To achieve that, ‘analytics translators’ are needed. McKinsey coined this term and they explicitly highlight that extensive data science experience is not required for this role. Rather, the profile is more all-round, a Product Owner profile, I would say. And that’s you!
Often companies employ multiple POs. When these POs all actively have data science in their toolbelt, the value-from-data-science-tentacles can reach further and wider into the organization thereby embedding it into the decision-making processes.
Imagine a Product Owner for a consumer-facing product that’s sold online. Naturally, this PO regularly looks at web analytics data and uses different ways of collecting user feedback to learn what to improve. But what about input on the optimal discount level for each consumer archetype? Or what about the best products or services to cross- and upsell to customers? Matching demand with the right supply is also important to keep internal stakeholders happy. What about demand forecasting that can be linked to manufacturing? Or finding optimal delivery routes to make a premium delivery service more efficient?
A ‘value translating PO’ not only knows how to ask the right research question to the data science team but also has the ability to oversee the model’s limitations, data caveats and alternatives to reach the end goal more efficiently as well as effectively cooperate with the data scientists.
Demystify the data science ‘magic black box’ by learning more about it...this ought to be part of the standard PO learning journey
Data Science as a Staple in the Product Owner Learning Journey
So, how do you become a ‘value translating PO’ that leverages data science to build even more valuable products? Demystify the data science ‘magic black box’ by learning more about it. One way to learn is to follow training in data science fundamentals, processes and pitfalls. This ought to be part of the standard PO learning journey. Imagine the value that you can unlock in your organization by being data science literate and incorporating it in your day-to-day decision making. It means lots of new business value to look forward to. All thanks to you!
Want to know more about the Data Science Product Owner role?
- Download the Analytics Translator whitepaper to get an in-depth overview of what the role entails and how to drive business value with data & AI.
Looking to advance your own data & AI skills?
- The Data Science for Product Owners course will teach you the skills you need to be a successful product owner for data & AI products.
- If you are looking to dive into data & AI yourself, the Python Essentials course is an excellent starting point.