Implementing self-service analytics

Authored by Matthew Morehead and Eric Lesser

The power of self-service analytics

Workforce analytics teams often can fall victim to their own success. As they demonstrate the value of their work, they can quickly become deluged with additional in-house requests for services that may outstrip their ability to deliver. To deal with that reality and to extend the reach and scale of their analytics efforts, many organizations turn to self-service tools as a way to democratize access to data and place the power of insights into the hands of internal business partners, center of excellence (COE) teams and even front-line managers.

Providing self-service platforms for intracompany use may seem like a logical step in the evolution of a people analytics organization. However, many organizations often struggle with the development, rollout and ongoing sustainment of these resources. Companies need to anticipate the time and capital necessary to:

• Customize, innovate and evolve the tools to the needs of their end users
• Develop an agile approach that addresses new user requirements and upgrades
• Provide ongoing education and coaching to build a level of understanding and comfort for users

When those do not occur, end users are more likely to revert to previous approaches to accessing data. (Back to the overworked analytics team.)

How can leading companies increase the value of their workforce analytics self-service investments? Below are key steps that are frequently overlooked before, during, and after implementation.

Before implementing self-service

1. Build your product management capability. Establishing an end-to-end product management capability promotes a user-centric focus that drives accountability, innovation and alignment with long-term goals. An effective product management team combines technical and functional expertise to address user issues and meet evolving business needs. The team's technical proficiency and functional knowledge are pivotal for navigating stakeholder requirements and ensuring the product resonates with users and aligns with business objectives.
2. Conduct market research. Conducting thorough market research is essential—even with internal customers whose true needs may not come to the surface through typical requirement-gathering exercises. Utilizing tools such as surveys, focus groups and in-person observations can help assess the feasibility of self-service analytics for user personas and inform the development of the business case. By analyzing existing business processes, organizations can better determine if the tools address real needs and/or add tangible value and then if investing in them is justifiable.
3. Address regulatory compliance. Compliance is not only about legal adherence but is also crucial for building trust with users and stakeholders and influencing product design and operational processes. It’s essential to engage with legal experts early to navigate the specific regulatory landscape of your domain, data and geographic scope. Related, it’s important to distinguish between actual regulatory requirements and long-established organizational norms that often masquerade as such.

During development and implementation

1. Focus on user experience (UX) design. Teams typically center their focus on back-end technical challenges, such as data inaccuracies, integration and security during the development and implementation of self-service analytics. But while it’s crucial to tackle these technical issues, it’s equally important to devote attention to the design and testing stages with a focus on UX—how the tool meets user needs and addresses their most critical questions.
2. Leverage an agile approach. The use and acceptance of minimally viable products (MVPs) followed by incremental enhancements based on user feedback can decrease time to value and increase the frequency in which users identify product improvements. MVPs set a manageable expectation, making the incremental improvements very apparent to users—which in turn increases user advocacy.
3. Test usability with diverse groups. Testing is often a priority for analytics teams when implementing any new platform, solution or product. Where successful analytics teams differentiate themselves is in who they involve in the testing. Some people analytics units keep the testing entirely within their development teams to protect end-user time and goodwill. Other teams may involve champions or super users. However, these individuals are often the most facile with the technology and can result in a skewed view of the larger user population. The most effective usability testing often involves a diverse user set with varying functional focuses, tech savviness, geographies, ages and even cultural backgrounds.

After go-live

1. Leverage the power of internal marketing. While it’s true that companies rarely forget the importance of change management during the launch of self-service tools, they often underinvest in it. For many, change management defaults to the delivery of user training. However, promotion for self-service analytics should also include the development of a comprehensive launch campaign that identifies key stakeholder groups, their needs and desired behaviors—along with tailored actions needed to build awareness, fluency and acceptance. Leading organizations get creative and leverage internal marketing channels that will generate interest and excitement (such as personalized videos, contests and awards). They focus on not only the tool’s functionality but how the tool will improve capability and job performance.
2. Invest heavily in user support. Successful organizations deploy a larger-than-usual user support team that can provide extensive, in-depth assistance after implementng a self-service analytics platform. This team not only reacts promptly to inquiries, but also incorporates proactive outreach strategies such as follow-up calls, personalized check-ins and tailored tutorials. The team incorporates a community-based approach to provide guidance to users, holding office hours and capturing and disseminating innovations. The support team finds ways to encourage super users to demonstrate how they realize value from their tool usage. Finally, this team clearly communicates a product vision, road map and release cadence to the overall user population.
3. Incorporate real-time user feedback mechanisms. Developing an approach for collecting user feedback is important to understand the adoption of existing features and to capture ideas for new releases. Gathering feedback can include actions such as using in-app feedback tools, beta user forums and dynamic usage analytics. But collecting data is only part of the process: Successful organizations analyze the data on an ongoing basis and develop corrective action(s) based upon what they learn.

Self-service can be a powerful tool to boost the analytical prowess of an HR organization, democratize internal access to data and enable a workforce analytics team to better focus its already limited resources and attention. However, simply implementing a self-service platform isn’t enough to guarantee success. Leading organizations take a user-centric approach that motivates stakeholders and builds capability before, during and after implementation. Just as organizations invest in marketing, education and usage analytics to help them maximize their customer-facing investments, they should also use similar approaches to build internal capability within their organizations.

Authors:

• Matthew Morehead
• Eric Lesser