Drive Stakeholder and Analytics Team Collaboration and Engagement Early and Often

By Pacific Northwest Data Analytics Leadership Board members including: Chris Lomax, Jesse Lynch, and Jennifer Klemisch with case study contributions from Chris Lomax, Jesse Lynch, Thuy Le, and Chris Nagano

When you hear “team collaboration and engagement” related to data analytics projects, do you immediately think of your internal customer stakeholders? Perhaps not. And yet fostering ongoing collaboration between these stakeholders and your hands-on working team can make a significant impact on analytics adoption. Stakeholders should be at the forefront of collaboration and engagement throughout analytics development. All too often, they are brought in at the beginning of a project for requirements development or project kickoff, then not again until the final report-out and delivery. This method leaves opportunities on the table and frequently leads to poor adoption of the analytics. We have outlined four best practice strategies that help develop stakeholder-team collaboration and engagement to improve adoption and deliver more business value:

1. Start with a project data map to identify and involve the right parties

2. Define what success looks like

3. Maintain an open solution mindset

4. Use storytelling to gain adoption

Leveraging these adoption strategies, organizations can help ensure their teams are approaching project solutions with a broad mindset that is inclusive of the impacted teams, which leads to better analytics adoption.

But first, the risk of not collaborating

Before we cover the strategies to drive engagement, it is worth considering the risk of not using higher levels of collaboration. If business stakeholders are not included throughout an analytics project, there is a significant risk of lack of relevance of the analytics to business needs or inaccuracies in the output, thus damaging the reputation and credibility of the analytics team. When these concerns exist, business teams are more likely to create shadow “in-house” analytics teams that might use conflicting data to produce competing insights. Thuy Le, a Senior Manager for Sales Operations and Analytics, provides a notable example of this situation at an online real-estate marketplace company. A division within the company struggled to identify operational and promotional improvement opportunities due to the confusion caused by conflicting dashboards and reports. Business stakeholders in operations, marketing, and sales needed more accurate insight on rental property transactions, the impact of promotions on sales, and tracking of rental cancellations. Operations, data science, and finance teams each frequently provided reports and insight, however each team used different data sources leading to differing data, analysis, and interpretation. In addition, the disparate data sources, parameters, and assumptions used to develop reports and analytics lacked explanation. At first, the stakeholders in operations, marketing, and sales who made decisions for the business initially had low trust in the insights they received, once the broader team was engaged during development of a single dashboard each team trusted and adopted the analytics they received.  To learn more, visit the case study at Thuy Le Real Estate Case Study (pacnwdataanalytics.net).

Start with a project data map to identify and involve the right parties

Analytics project kickoff typically looks something like this: a few people write up a charter, determining the project scope, potential solutions, insight needs, and deliverables. They share it with a few agreeable, in-the-know people, along with the key leaders who are paying for the project. This approach may get the project started quickly, but it skips over a valuable process that uncovers useful collaborators. Instead, start by creating a project data map. A project data map identifies how data flows through systems and the human touch points (cleansing, loading, etc.) from data acquisition to analytics insights, then identify who may have an interest in the data or analysis. An interest may include involvement in the acquisition, preparation, and/or use of the data. It’s helpful to identify teams and leaders outside of the intended audience that may be impacted and provide early awareness of the project to these teams to give them an opportunity to engage. Adding stakeholders and leadership representation on the team upfront can save time by preventing project delays or adoption issues later.  This early engagement develops the foundation for trust and understanding that encourages adoption of analytics. Chris Nagano, the Director of Data Science and Analytics at a global travel company, demonstrates how he engaged with a Vice President with whom he socialized the value proposition for analytics on their products. The VP’s support was crucial in the success of the project focused on aligning on metrics and leading indicators to business outcomes like partner bookings and sales. He had this to say about the engagement: “The combination of business and product team engagement along with production of analytics focused on the most important metrics for product improvement helped to increase analytics adoption at the company.” To learn more, visit the case study at Chris Nagano Online Travel Case Study (pacnwdataanalytics.net).

When engaging with various business stakeholders, seek to understand what insights would be valuable for them. Leaders and stakeholders from different organizations will have a unique perspective on what is valuable to them and sometimes seemingly opposing perspectives or goals. Seek to understand those perspectives so they can be addressed and communicated. A key example of this was shared by Chris Lomax, a Digital Media VP, at a company that provides advertising-supported video streaming services to customers. The streaming services’ product and sales leaders had opposing opinions about the volume of ads to be served to customers during breaks in content. The sales team’s approach was to increase ads to drive revenue while the product team sought to reduce ads to deliver a positive user experience and engagement. The impact of these opposing approaches was significant not only financially but also in terms of reduced morale on the sales and product teams due to conflicts on the optimal ad volume. With the curious engagement on their respective needs, the sales and product teams were more open to the insights than they were previously and soon adopted the recommendation for optimal number of ads for the best revenue growth. To learn more, visit the case study at Chris Lomax Media and Entertainment Case Study (pacnwdataanalytics.net).

It is valuable to engage IT to support quick turnaround of infrastructure, data access, and data engineering requests during development. Jesse Lynch, the technology director at a telecommunications company, created a tiger team of data engineers that applied best practices in generating high quality, reusable data supported by data modeling, published schemas, and documented metadata. To build on this success, the tiger team was set up with capacity for new, urgent analytics requests to support future analytics projects. The combination of relevance, quality, and speed helped to increase trust and adoption. Supply chain, marketing, finance, and credit teams all benefited from more curated data and easily analyzed insights. To learn more, visit the case study at Jesse Lynch Telecommunications Case Study (pacnwdataanalytics.net).

Define what success looks like

Once a comprehensive team is formed and potentially impacted business leaders are engaged, the collective team can determine the charter, scope, deliverables, and outcomes. The key deliverable out of the planning stage should be a noticeably clear response to “What does success look like?”. It should also be clear what various team members are expecting to get out of this project. This is a key opportunity to define and highlight the various perspectives that business leaders have expressed. For example, in Thuy Le’s story above, they understood that “executive leaders wanted to be able to see the real and accurate numbers to better manage the business.” The expected value and when it will materialize should also be clearly identified. Have the technical and business sub-teams work collaboratively to define:

-         the business insight needs

-         why business wants the insight

-         the analytic use case and solution

-         the expected business actions

Create defined checkpoints throughout the development process. For each “checkpoint” identify what additional information is needed, reviews, and user acceptance tests to be completed. Thresholds to continue or stop the project should be defined upfront. Consider that analytics is never “done.” It is a continuous learning journey where business value can be extracted along the way.

Maintain an open solution mindset

To foster collaboration, it is important to set ground rules for keeping an open mind and approaching discussion topics from a place of discovery, rather than “knowing an outcome” or “looking to prove something.” As with any team, learning to work together takes time and understanding. The project success requires learning from the various team members and decision makers their pain points and their roadblocks.  Allowing each person, the space to vocalize their concerns and desires for the project will allow for collaboration throughout the project execution. Back to Chris Lomax’s story, his teams’ analysts started by understanding the various business personas, including what they thought was most important to meet their own business goals. They took a curious and listening-based stance when asking questions. They set expectations for the type of insights and timing of those insights and received broad buy-in to the upcoming analyses. The business teams were naturally integrated into the process and contributed as team members. The curious approach extended further as the analysts dug into the data to identify challenges with the data processing logic that differed from existing documentation.

Using these strategies for Thuy Le’s team resulted in “much more trust in the data and reports from stakeholders. Data coming from the operations, data science, and finance teams now agreed with each other. The company could now more confidently understand their rental sales, marketing, and operational progress and make decisions and changes with greater confidence. Ultimately, the group was able to grow their business based on the more trusted insights.”

Chris Nagano shared “The combination of business and product team engagement, along with production of analytics focused on the most important metrics for product improvement, helped to increase analytics adoption at the company. The data-driven decision-making helped to increase bookings and revenue for the partners and company and, helpfully, commissions for market managers.”

Use storytelling to gain adoption

After the development of the analytics, it is imperative to go back to the project data map to revisit the stakeholders and communicate to them in a way that is easily absorbed. In the case Chris Lomax shared, the insights were turned into a story that incorporated an understanding of their audiences’ personas in the sales and product teams, uncovered gaps, identified what is most important, shared insights in a visual and digestible way, and provided clear recommendations for next steps. With the storytelling-based approach, the insights were better adopted. Similarly, Chris Nagano, shared that to drive the adoption took continued effort such as regular newsletters, supportive messages from leaders, scheduled discussions, promotion over Slack, and publication of a wiki that described the metrics. The content was targeted to the audience and utilized storytelling to engage them. Through these efforts the use of the dashboard his team created dramatically increased.

Summary

By starting with a project data map to identify and involve the right parties, defining what success looks like together, maintaining an open solution mindset, and using storytelling to gain adoption together you can establish a collaborative and engaged environment across business and analytics teams. Fostering this environment ensures project teams are set up to be successful and the organization is ready to adopt the resulting analytics.

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Pacific NW Data Analytics Leadership Board

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Many data analytics teams fail to engage their stakeholders sufficiently early and often to ensure full alignment and adoption. In this fourth article of the PNW Data Analytics Leadership Board's (www.pacnwdataanalytics.net) article series, we share best practices in stakeholder engagement that help drive adoption of your analytics output. These practices include developing a project data map to identify and involve the right parties, defining what success looks like together, maintaining an open solution mindset, and using storytelling to gain adoption together you can establish a collaborative and engaged environment across business and analytics teams. Click on this article to learn more! #dataanalytics #analytics #team #business #data #leadership P.S. If you are a data analytics leader in the PNW and would like to build your network and expand your expertise by sharing knowledge with other experts, visit the PNW Data Analytics Leadership Board website Contact Us page at https://lnkd.in/gMjen5gX . The Board is hosted by Unify Consulting www.unifyconsulting.com with two events each year.