Driving Business Adoption

Until now, we have focused on the technical skills essential to an analytics engineer’s work. However, the success of these professionals also depends on correctly identifying business requirements and ensuring the adoption of data projects.

Gathering requirements is the first step of any product development project. At this stage, soft skills such as clear communication, active listening, and project management are fundamental. By correctly identifying business priorities and quick wins, data teams eventually deliver relevant data products that the business embraces.

In this chapter, we will discuss the following topics:

• Defining analytics translation
• Scoping analytics use cases
• Ensuring business adoption

Defining analytics translation

In 2018, the consulting firm McKinsey introduced the analytics translator role as a new must-have for companies (https://www.mckinsey.com/capabilities/quantumblack/our-insights/analytics-translator). According to this article, analytics translators are essential to generate impact from data initiatives.

Analytics translators possess skills ranging from project management and business domain knowledge to technical literacy. They bring use cases forward from ideation to deployment and adoption, which makes gathering business requirements one of their main responsibilities. Ultimately, analytics translation is a set of practices that help companies develop valuable data products.

In Figure 12.1 (sourced from Xebia, https://pages.xebia.com/whitepaper-data-democratization), the responsibilities of analytics translators and analytics engineers are plotted in relation to the data supply chain; this time expanding the similar diagram displayed in Chapter...

Scoping analytics use cases

To identify the scope of analytics use cases, there are several key steps involved. The first important step is to identify the stakeholders involved in the project.

Identifying stakeholders

Identifying stakeholders ensures that the solution developed aligns with the expectations and needs of different parties.

First, let’s make the distinction between primary users and secondary stakeholders. Primary users, such as department heads or company executives, consume the data product directly. Secondary stakeholders, such as managers in other departments, impact the project indirectly.

Primary and secondary stakeholders require different levels of interaction and have different expectations. The stakeholder map allows us to map these in greater detail. It divides the relationship between a party’s influence and interest into four quadrants.

Figure 12.3 – The stakeholder map framework

As illustrated...

Ensuring business adoption

A data product only has value if its users successfully adopt it and make decisions based on it. For that, the analytics product should be known by its users and easily actionable.

Most data professionals are familiar with agile frameworks and know the importance of working incrementally, getting feedback, and documenting their work for better collaboration, transparency, and continuity. This section incorporates these techniques into analytics use cases to ensure business adoption.

Working incrementally

A typical struggle for analytics engineers, inherent to the fact that they stand between business and engineering, is balancing creating business value quickly while ensuring the quality of their solution. Developing quickly can come at the expense of a robust and future-proof solution, creating technical debt that will slow down the team in the future.

To ensure both the speed of delivery and the quality of the product, analytics engineers should...

Summary

In this chapter, we have discussed the value of analytics translation for analytics engineering. In particular, the data analytics value chain helps scope use cases by focusing on actionable, valuable insights. Finally, we explored practical ways to increase adoption and maximize the impact of data use cases.

In the next chapter, you will learn about a crucial practice to manage and protect organizations’ data assets: data governance. You will discover its objectives, methods, challenges, and how it intersects with analytics engineering.