A Brief Guide to Providing Insights as a Service

The data product, which is, in most cases, a report, either static or interactive, is the vehicle that delivers value to the end-user. We can use an analogy from architecture by following the Vitruvian rules for buildings and translating it to our context:

• Sound: Is the analysis mathematically and statistically correct?
• Useful: Can the client generate insights from it?
• Aesthetic: Is the data product well-designed and, as such, easy to understand?

This previous article about strategic data science discusses this model of good data science in more depth, which is also explained in detail in my book on the topic.

The value of a service product is in simplest terms the difference between benefits and cost. The Product provides the benefits of the value equation, visualised below. While the product delivers value to the customer, there is also a cost, which is the next dimension in the framework.

Whether you are an internal data science team (B2E, Business-to-Employee) or a commercial service provider that assists other organisations with creating value from data (B2B, Business-to-Business), all development has a price. However, the monetary cost of a data product is only one dimension of the total Price equation.

Firstly, consumers of services incur a time cost. The time cost of a service is the amount of time a consumer (in our case, the user of a data product) spends using the tool. The time we spend to use a service is an opportunity cost because we could be doing something else.

The time cost also extends to the development phase as the input from end users is essential. A data professional relies on the detailed, contextual knowledge of the end-user to create insights.

An extension of the time cost to use a service is the mental cost. How much mental effort does an end-user need to use to understand the output of your data product? The cognitive cost of consuming insights can be minimised in two ways. Firstly, the design of the report needs to be aesthetic. Not in the sense of beatification but in making the end product easy to understand.

The mental cost to users of a report depends a lot on their data literacy. The advanced specialisation of data science has created a knowledge gap between subject-matter experts who use the report and the advanced statistical methods used by analysts. Reducing this knowledge gap requires the service provider to educate their customers, which falls under the next dimension of the IaaS framework.

When working in a B2B environment, the necessity for promotion is easily understood. However, promotion is also essential for internal teams working in B2E.

A data science service provider is often invisible to the consumer of its services. Being invisible is mostly a good thing because it means our data products are working flawlessly, but this great uptime also comes at a cost, as your services might be taken for granted. The most effective way to combat invisibility is Promotion.

For teams providing services to external entities (B2B), promotion is an essential business activity. However, this also applies to internal data teams. I my workplace, we ensure that our colleagues have regular visibility of the tools we develop, through intranet articles and other internal communisation means. These promotional activities ensure that management understands the relevance of the team, and improve data literacy across the organisation.

To promote data science skills in my industry, I wrote a textbook for water professionals who want to move away from their spreadsheets, the content of which I also deliver as a course. One objective of this course is to provide water professionals with skills to communicate with data scientists. Increasing data literacy among the people who work with data scientists is essential to ensure the full value of our services is transferred.

Another important aspect of providing valuable data services is that your client knows the team. Don't hide your experts behind the veil of technology. This thought segways nicely onto the next dimension of the framework.

Enough has been said and written about the technical requirements of being a data scientist. The Insights as a Service Framework focuses on the so-called soft skills. Especially with the steady advancement of AI developments, non-technical skills will become increasingly crucial for a data scientist to thrive in this industry.

The importance of domain knowledge cannot be underestimated. The time price of a service suggests that the client needs to invest time in explaining the context of the data problem to the developer. However, the development team also needs to have sufficient domain knowledge to advise the client. I have been in many situations where an end-user knows what problem to solve but is unable to clearly articulate what they want. It is then the task of the service provider to understand their customer and provide them what they need.

The well-worn "faster horses" marketing cliché also applies to providing Insights as a service. A data service provider needs to be able to provide the client with a vision (in this analogy, a car) that solves or prevents problems.

The process of service delivery relates to your pipeline from ideation to production. Each organisation will have their own governance model to decide what projects are executed and how they are implemented.

From a service perspective, your process needs to be seamless and easy to understand while also minimising the time the client has to invest in development and testing.

Service Blueprinting is a commonly used technique to map a service. The main difference between this approach and the common process maps is that in a service blueprint, the perspective of the customer is also taken into consideration. A good service blueprint add equal weight to the inputs of both the service provider and the service beneficiary.

Last but not least is the technology platform that you use for developing the data product. This choice is mainly a technical one, which goes beyond the scope of this article.