Five steps to building trust in data analytics solutions

  • January 16, 2023
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So, you’ve got your eye and budget aligned on new analytics tools. Your team is ready to go. Maybe you’ve hired an outside analytics partner to guide and deliver the implementation. It’s going to be smooth sailing to the delivery finish line. But are you prepared for what comes after implementation? How will you ensure your teams will use the new solution? That they’ll trust the data going into and out of it? The answer to all these questions is: You plan for the change.

At NTT DATA, our Analytics Enablement team uses a five-point framework to ensure a new solution becomes a widely adopted, trusted and valuable source of insight.

1. Communicate, communicate, communicate

You can’t communicate enough. Identify the people impacted and inform them of the changes coming. Be sure to reach all the specific end-user groups and tailor the messaging to what’s in it for them. Always include the why — such as support for the current tool is expiring; current system lacks key capabilities; data quality needs improvement or controls; timeliness of or time required to produce reports of deficiencies. Openly communicate the problems or issues the new solution will address and explain why.  

2. Establish credibility

Trust is built on transparency. Take the time to prepare data dictionaries with term definitions including field values, field sourcing and timing of changes. This allows end users to see what data is involved, how it’s pulled into the new tool and how it’s being used. The definition of terms may require adjustments as differences across systems or departments are identified. It’s also helpful to develop training materials that explain the shift and ease the transition. When the users know exactly what the data represent, they’re more comfortable using the new solution and have greater trust in the inputs and outputs. 

3. Engage users wherever you — and they — are

Ideally, you’ll create a communications plan that engages impacted users early and often through implementation and rollout. Even if you’re well underway or close to delivering the new solution into production, it’s not too late to communicate. Your project team can engage with end users to author training materials, test new reports and dashboards or help sell the solution internally. At any stage, involvement in the process gives the users a sense of ownership and allows them to become part of the change, instead of just a recipient.

4. Give it time

Building trust also takes time. Training or go-live shouldn’t be the first time end users see the tool and learn about its inputs/outputs. Leading up to and following the launch, schedule regular check-ins to create a partnership between the project team and your end users. In these sessions, ask for (and listen to) feedback, admit mistakes or shortcomings, and be ready to make adjustments along the way based on what you learn.

5. Measure results

Measuring the impact of your change management efforts provides valuable insights into what you’re doing right and what you could do better. Surveys are easy to conduct and allow you to measure the level of awareness of what’s changing. These surveys can include questions that address overall employee sentiment toward the changes. A more concrete measurement of how the change effort is progressing is the actual use of systems. There are a variety of tools to track login count and duration by user. If the old way of doing things is still available, tracking its continued use can help paint an accurate picture of the transition to the new platform. And finally, the volume of support calls — along with the nature of those calls — can inform the team where supplemental training is needed.

Managing change helps foster trust

For new data and analytics solutions to be effective, they must be adopted and trusted by end users to the fullest extent possible. Making this possible requires more than a successful deployment; it calls for a well-planned and consistently executed change management plan. To realize the intended business results of any new data and analytics solution or technology-driven initiative, the people who’ll be using it must be part of the process from beginning to end — and every step in between.

— By Susan Masters, Senior Manager, Analytics Enablement

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