Helping Business Users Adopt Data Solutions

A data product is a tool, platform, or system that leverages data to generate insights, automate decisions, or enhance user experiences. They transform raw data into a functional asset, enabling both technical and non-technical users to extract meaningful benefits effortlessly. This could take the form of a dashboard, report, automated flag, or presentation. The problem with any tool is ensuring adoption and so here are a few tips...

Understanding End User Needs

Data products exist to answer questions and monitor key metrics. The ultimate goal is to support business decision-making, making end users key stakeholders in the planning and implementation phases. Engaging subject matter experts early helps data teams ensure that their solutions address real business challenges. It is useful to conduct structured interviews, surveys, and shadowing sessions to truly understand how users engage with data today and what pain points they encounter.

We find it is a good idea to engage people during your ideation phase and find out what a data product might look like in a ‘perfect world’ and then try to work backwards. Setting clear expectations early helps ensure alignment between data teams and business users.

Contextualising End User Needs

Once you identify the ideal solution, you need to assess current technological limitations and team capabilities to bridge the gap effectively. A data product must be accurate, timely, and reliable to be useful. Often, compromises need to be made between what is ideal and what is achievable in the short term.

Sometimes, working around existing constraints is necessary, but in other cases, it is better to delay implementation until a feasible solution is available. For example, LinkedIn once hesitated to launch a job recommendation feature due to the risk of suggesting inappropriate roles, which could harm the user experience and brand perception. The key takeaway here is that while ambition is important, it is equally crucial to recognise when a feature or solution is not yet ready for deployment.

Start with a Minimum Viable Product (MVP), collect user feedback, and track usage and ROI. A structured product roadmap ensures continuous improvement and scalability, helping avoid wasted efforts on solutions that do not meet user needs. The iterative approach also makes it easier to pivot based on feedback, rather than over-investing in a feature that users ultimately do not engage with.

Quality Assurance

Poor data quality leads to flawed insights and can negatively impact business performance. Data quality assurance should be an ongoing process rather than a one-time check. This includes implementing automated validation checks, establishing clear data governance policies, and routinely auditing data sources to ensure consistency.

It is also crucial to verify data accuracy from an end-user perspective. This means working closely with business users to confirm that the numbers they see align with their expectations and domain expertise. A strong data quality process should account for completeness, accuracy, and relevance.

Poor data can erode trust, making business users hesitant to rely on analytics solutions, so ensuring data is clean, validated, and up-to-date is just as important as building the product itself.

Making It User-Friendly

A seamless and intuitive user experience is critical for adoption. Reducing friction through integration with familiar tools, such as embedding analytics in CRM systems, can significantly enhance engagement. Making data products user-friendly is not just about aesthetics; it means making sure that they are easy to navigate, require minimal training, and are designed with end users in mind. One of the most effective ways to ensure usability is to test the product with very non-technical users. If they struggle to understand how to interact with the tool, chances are, others will too.

Additionally, incorporating tooltips, guided walkthroughs, and inline help options can make a huge difference in improving adoption rates.

Training and Support

Training should be interactive, allowing users to engage with the data product in real-world scenarios. Simply providing documentation or an onboarding video is rarely enough. Instead, hands-on workshops, live training sessions, and follow-up Q&A opportunities can significantly improve adoption rates.

A structured feedback loop ensures continuous improvement based on user needs. This can be as simple as embedding a survey link within a dashboard, but more importantly, it should involve regular check-ins with end users to see how well the product is meeting their needs.

Additionally, identifying Data Champions within teams can help drive adoption and ensure continuous support. These champions can serve as internal advocates who assist their colleagues and reinforce the value of data-driven decision-making.

Conclusion

Data projects fail if they lack user adoption. Equal emphasis must be placed on usability, integration, and training as on the technical development of the solution. A successful data product is not just technically sound but also intuitive, accessible, and aligned with the daily workflows of business users. Data teams should balance addressing immediate user needs with planning for long-term scalability.

If you are looking to create data products that your team will love and that drive real business growth, get in touch with the friendly 173tech team today!