IT’s role in building a Data Culture: Analytics agility, proficiency, and community

This is the final post in a three-part series about transformational analytics for the enterprise. In case you missed them, read the first on governance and data management that enables your digital business, and the second on modern analytics for fast decision-making.

Data-leading organizations see benefits like improved customer acquisition and retention, engaged employees, and operational efficiency. While analytics at scale promises many transformative business outcomes, most organizations struggle to build a widespread Data Culture that values and practices data-driven decision-making. Could it be that IT leaders are too focused on implementing technology and not enough on enabling their employees?

The massive acceleration of digital transformation has brought about new ways of working, and you’ve witnessed first-hand the critical impact your people can make when you empower them with trusted data. “Almost two-thirds (64%) of enterprises that have been the most successful with analytics projects in the past two years have increased spending on data management and analytics products and services as a result of the pandemic,” reports 451 Research*, part of S&P Global Market Intelligence.

Your employees are brilliant, creative, and hard-working—and they know exactly the questions to ask for valuable answers that can propel your business forward. As a trusted IT partner to these team members, you can spearhead a deliberate, holistic, and people-focused approach for an enterprise-wide analytics practice. Leading for success across three areas will empower your entire employee base to be more data-driven: 

1. Adopt an agile approach to managing your analytics environment
2. Support employees in growing their analytics skills
3. Foster community that builds and celebrates your Data Culture

Let’s take a closer look at each.

1. Adopt agile implementation practices

With modern BI, IT offers a governed model of self-service data for everyone, rather than continuing to gather requirements and design reports. Here are a few ways IT can help lead the business to greater outcomes through the deployment and ongoing management of an organization’s scalable analytics environment:

• Accommodate growth with an agile approach to analytics environment support: Analytics is increasingly mission-critical to organizations, particularly as adoption increases across the business. Because of this, you should consider reassessing server utilization more frequently than with other technology solutions—including changing your topology to scale with the organization’s evolving needs. If your analytics solution is flexible, you won’t be restricted to any specific architecture, data sources, or cloud deployments. Adopt agile practices for proactive planning and monitoring; a “set it and forget it” implementation creates the risk of inadequate resources that fail to support enthusiastic data users with their growing data workloads. 
• Help your teams take action in the context of their business processes: Incorporate data assets into employees’ existing workflows and applications by setting up email subscriptions, chat alerts, and @ mentions, or embedding analytics where employees are already spending their time—like internal portals and your CRM. This not only helps people engage fluidly with contextual analytics, in the moment, but can shorten the time to solve problems with automation and intuitive AI. Consider the impact across your customers' lifecycles when you help your enterprise employees ask questions of their data in natural language, get accurate responses, and increase cross-functional collaboration, ultimately helping people to act when the data is the most relevant.
• Ensure alignment to strategic goals and business outcomes: If you’re just beginning to build out your self-service analytics program, start with use cases aligned to priority areas. Some analytics solutions, including Tableau, provide department and industry-specific dashboard templates that can be customized to your needs, rather than starting from scratch. Focusing on high-priority use cases will encourage interaction with data and support your strategic goals. IT should define and socialize success metrics based on business outcomes to prove the program’s efficacy as more teams adopt and evolve their analytics use.

2. Support analytics proficiency across the business

Data literacy is the ability to explore, understand, and communicate with data—and it’s fundamental to organizations’ success with analytics for data-driven decision-making. IT plays a strategic role in facilitating a data-driven collaborative culture across the business in several ways.

• Establish a baseline for data literacy across the organization: Customers repeatedly tell us that developing employees’ data skills is a top challenge to effectively deploying their analytics platform. Whether it’s hiring new talent or reskilling existing employees, having a baseline for data literacy across the organization is critical in developing a Data Culture. What’s important for everyone to know when using your data? How does that baseline change by team, skill level, or type of data that people are accessing?
• Centralize support resources and learning documentation: Giving employees a centralized place to get their data questions answered, such as an internal portal or Wiki, will minimize the roadblocks on their journeys to being more data-driven. This also helps ensure standard processes and content quality across the company. The repository should include relevant examples of analytics content and documented learnings from your analytics initiatives so people can get up to speed quickly, as well as detailed information on avenues for help, collaboration, and training opportunities. Actively maintain these assets and refine practices as you strengthen your Data Culture.
• Develop learning paths to onboard newbies and sharpen others’ deep expertise: Whether you develop in-house training or rely on external resources and services, it’s important to provide comprehensive, self-paced, or guided training to get users up to speed at the appropriate level of skill and knowledge for their roles. Then, ensure a smooth transition from basic to more advanced analysis, matching the right technical capabilities and responsibilities to the right use cases for their job functions.

3. Foster community among data users

Building an internal community gives people a dedicated space to ask questions, share best practices, and celebrate achievements—all while having fun with data. These initiatives don’t have to be large programs right out of the gate; as engagement grows, you can formalize them with dedicated owners, leaders, and processes.

• Launch programmatic efforts to build community around data: Give people outlets to get inspired, seek answers, and learn new skills. This should include support-based programs, like one-on-one coaching, lunch-and-learns, and user groups, as well as competitions that encourage creative problem-solving or collaboration across teams. Ultimately, the goal is to get people to expand their perspective and increase their data skills through new connections.
• Formally communicate data wins to evangelize your analytics program: Don’t be afraid to toot your own horn—and encourage employees to do the same. Share victories and patterns of success to help create a virtuous cycle that expands and deepens engagement across your organization. Publicly identify data champions and reward them with opportunities to enhance their career. As your Data Culture develops, consider formal data leadership roles.
• Expand support for your internal users with a diverse, global data community: Looking outside your organization’s walls for inspiration and answers to shared problems can help accelerate your teams’ skills, encourage people to find creative solutions, and ensure lasting analytics engagement. In the Tableau Community, over a million data enthusiasts around the world—from analysts and academics to developers and data leaders—help each other every day to achieve personal and professional goals and realize the value that analytics has brought to them personally.

Start small to achieve success at scale—take the first step today

At Tableau, we work with organizations worldwide that have accelerated their digital transformation by evolving their data strategy. A huge success factor is whether the company can effectively establish a strong Data Culture where people are empowered to use data regardless of their skill level. And IT plays a valuable role in empowering everyone with a trusted environment for analysis, the data literacy needed to find insights and act on them, and to build a community that encourages and celebrates Data Culture. 

If this process seems daunting, you can take incremental steps to build these capabilities now, knowing that the steps you take today will have a huge impact on your business agility and resilience. And to help organizations everywhere, we’ve developed a proven methodology to scaling analytics and building your Data Culture: Tableau Blueprint. It helps organizations like yours benchmark their analytics maturity. From IT strategy and business alignment to agility, proficiency, and community, our approach offers a formal framework of questions to consider alongside step-by-step guidance. With it, you have a powerful tool at your disposal to improve the extensibility of your analytics program. You can get started right now—take our quick Blueprint Assessment.

Editor’s note: This article was originally published on CIO.com.

*Source: 451 Research’s Voice of the Enterprise: Data & Analytics, Data Management & Analytics 2020