Data is no longer just a competitive advantage. It is critical to the health—and often the survival—of an organization. This playbook is intended for executives and data leaders who want to build resilience with data, starting with their workforce. Fostering a Data Culture equips every individual in your organization with the insights they need to tackle your most complex business challenges.


Introduction: Measuring the business impact of Data Culture


The COVID-19 pandemic showed that an agile, strategic use of data greatly impacts an organization’s ability to react to market changes. But getting the most out of your data requires more than just technology. It requires a commitment to promote data-driven decision making at every level of the organization.

Despite the trillions of dollars invested in data and analytics in recent years, leaders are still failing to create a data-driven culture.

According to a NewVantage Partners’ 2019 Big Data and AI Executive Survey comprised of 64 C-level executives:


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72% of survey participants reported that they have yet to forge a Data Culture.

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53% stated that they are not yet treating data as a business asset.

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52% admit that they are not competing on data and analytics.


This global crisis puts a spotlight on these failures and reminds us that organizations need to cultivate behaviors and mindsets that support a Data Culture—a shared mission to put data at the heart of every decision.

Even before this crisis, data-driven companies were reaping business benefits. Tableau commissioned global market intelligence firm IDC to conduct a study* to identify the key visible and invisible elements that influence a Data Culture. Surveying employees from large enterprises across the globe, they examined the drivers that separate data-leading organizations from those in earlier stages of adoption.



Data Culture Scale

Source: IDC InfoBrief, sponsored by Tableau, Why You Should Care About Data Culture, April 2020.


Compared with data-aware companies—those at the least mature end of the Data Culture scale—18 percent more of data-adopting companies saw revenues increase and 20 percent more saw profits increase. Yet the most successful companies are the data-leading organizations—those with the most successful Data Cultures.


Data-leading companies see benefits like:


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41% greater improvement in production time to market compared to data-aware entities.

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89% improvements to customer retention and acquisition compared to data-adopting entities.

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45% greater improvement in employee retention compared to data-aware entities.


Data Culture is even more critical now. McKinsey highlighted three areas that continue to evolve out of the COVID-19 crisis:
  1. In the United States, 75 percent of people using digital channels for the first time indicate that they will continue to use them post-crisis.
  2. Business and analytics leaders are rallying around new analytics solutions to support critical business areas in increasingly digital ways.
  3. Demand recovery will be uneven and historical data forecasting models will be of little use to predict where pockets of demand will emerge. Rebuilt analytical models will be essential to steer operational decisions.
 

Are you leading with data?

Find out by asking team members these questions:

Do people know how to interpret data?

Can people get help from colleagues with analytics or data-related questions?

Do we give people access to the data they need?

Are people accountable for the data they access and create?

Do we require data to support decisions?


Creating a Data Culture may seem daunting, even in the best of times. It takes a commitment from every level of the organization to influence how people think about and act on data insights. But the reality is, you can take incremental steps to build these capabilities now, knowing that the steps you take have a monumental impact on your resilience in the near-term and in the future.

How to use this playbook


This playbook lays out a simple, but effective roadmap for building a Data Culture. It contains four chapters, each with a specific focus area.


For each area, we outline how to make it happen—recommendations on how to build, run, and when you’re ready, expand and mature these capabilities.


Building Running Expanding Image

Building

Foundational steps that make it possible to derive value from data.

Running

Programs and activities that create measurable value across an organization.

Expanding

Actions to optimize and future-proof your data efforts for years to come.


Keep in mind that Data Culture isn’t linear—it’s a living organism that continuously evolves. We urge you to take a phased approach that makes sense for your organization’s goals and needs. The steps in this playbook can be executed and repeated, both as a Data Culture comes to life and after it is thriving.

Chapter 1:

Align leadership metrics to business priorities

 

Goal:

Leaders from across the organization align on the most pressing business needs to determine where the organization should be focusing resources around data.

What it looks like:

Leadership buy-in ensures that individual leaders are all working toward the same goals and assigning data resources to areas that have the biggest impact. In times of rapid change, agile organizations assess and re-evaluate key priorities as conditions shift and learnings arise.

To understand how the business is performing against priorities, a data leadership committee creates a key set of metrics and works with the right people—typically an analyst team—to locate, create, and align data sources to support these metrics. In this stage, data sources help provide a snapshot view at the leadership level. Ideally, these sources are updated on a regular or real-time basis so leaders can define expectations on how a metric should perform. This affects how you prioritize more in-depth data sources for later analyses.

Abercrombie & Fitch creates alignment with real-time data

Abercrombie & Fitch uses near real-time data to guide a quarterly alignment meeting between executives, business group leaders, and product teams. These quick insights help leaders map out goals, align on intent, and determine where they want to focus their efforts.

Read More


Exec Dashboard

An executive dashboard brings your top business metrics together in one view. This example monitors profitability, product, shipping, performance, and order detail all within the same workbook.

Making it happen:

Building

Create a data leadership committee accountable for driving value with data. Include stakeholders across the business and analytics functions.

Assess and prioritize the highest-value business problems that should be the focus for data-driven transformation.

Define a set of key guiding metrics to understand the health of the business, using at most, 10 key indicators.

Running

Develop data to support guiding metrics at the leadership level, composed of a few high-level data sources and visualizations to foster a shared source of truth.

Analyze metrics against historical performance to understand how the business is fairing relative to past business conditions and current forecasts.

Track metrics on a regular cadence against expected performance to promptly identify unexpected trends and proactively address business challenges.

Expanding

Redeploy and focus data resources on the most urgent and high-potential projects. Don’t be shy about discontinuing long-standing efforts if they aren’t serving pressing needs.

Assign executive sponsors to monitor metrics at the senior levels of the organization to ensure early warning of successes and failures and continue to refine.

Expand visibility into metrics through regular, organization-wide communications so executives regularly evangelize strategic use of data.

Chapter 2:

Build data sources to address
critical decision points

 

Goal:

Business metrics guide prioritization of data efforts and teams build data sources to address the most critical business questions.


Chapter 3:

Grow value with specific
use cases

 

Goal:

Create immediate value and engagement for priority use cases, sharing key data insights through dashboards and data visualizations.


What it looks like:

Create use cases aligned to priority areas to encourage interaction with data. These use cases take the form of data assets—visualizations, reports, dashboards, and/or workbooks—that are useful, engaging, and offer insights to help solve immediate business needs. Teams across the organization can evolve these assets to suit their own needs and identify other areas that could benefit from additional data assets or data sources. As these assets evolve, ensure that teams refer back to the definitions outlined in leadership metrics, so everyone is speaking the same language. Share victories and patterns of success to help create a virtuous cycle that expands and deepens engagement across the organization.

Generating organic excitement and trust at Red Hat

Red Hat’s Enterprise Data and Analytics team developed key performance dashboards, working closely with business leaders across all functional areas. This created synergy and trust that grew their data community and tripled their Tableau user base.

Read More


Sales Pipeline Dashboard

This sample sales pipeline dashboard shows how deals were affected by COVID-19. This example shows how you can adapt existing dashboards for different use cases.



Making it happen:

Building

Identify subject matter experts in each department that can provide quick feedback and ensure that data and analytics teams have the business context they need to develop data assets.

Identify use cases where teams could benefit from access to key data sources and engage the tiger team to address specific needs.

Outline requirements for data assets to determine supplemental data needed to expand relevance to other audiences. Ensure customized metrics and dimensions can be mapped back to a standardized definition.

Running

Create purpose-built data assets like interactive visualizations, addressing key business processes and decision points. Focus on approachability, tailoring assets to specific audiences.

Bring data assets into important meetings with stakeholders, executives, and board members to encourage data-based approaches to prevailing views and to showcase executive sponsorship.

Launch programmatic efforts and assign champions to offer support and coaching through formal meetings or informal communications like chat groups or company portals.

Expanding

Incorporate data assets into employees’ existing workflows and applications by setting up email subscriptions, chat alerts, or embedding in workflow applications like your CRM.

Search for and incorporate new data into data sources and dashboards that support predictive and prescriptive analytics for more advanced use cases.

Support development of data knowledge by adding definitions, explanations, notes, and metadata to data assets, gathering feedback from users along the way.

Chapter 4:

Promote widespread
data discovery

 

Goal:

People at all levels have the confidence and the knowledge to follow the data discovery cycle on their own with as little intermediation as possible and then use data insights to drive business decisions.


What it looks like:

Once people are confident with data, they will want to ask richer questions and create new data assets for themselves. In practice, this means that data must be well-described, well-governed, and accessible. It also requires widespread data literacy—the ability to explore, understand, and communicate with data. At this stage, organizations benefit from data literacy training programs to teach fundamental data skills. Fostering community programs gives people a dedicated space to ask questions, share best practices, and encourage engagement. At the beginning, these programs don’t have to be large efforts. They can take place where conversations are already happening, and as engagement grows, you can formalize efforts with dedicated owners, leaders, and processes.

JPMorgan Chase prioritizes community and data literacy

To deepen engagement across a 30,000-person community, JPMorgan Chase used a gamified structure with skill belts that guide people through different levels of data training depending on their experience.

Listen to the presentation

Making it happen:

Building

Prioritize collaboration in department-level goals and initiatives, empowering individuals at every level of the organization to own decisions in their purview and take action based on data.

Expand data exploration by making data sets and assets available through a common BI platform, with governance that balances empowerment and control. Enable ad-hoc analysis through access to natural language and visual analytics tools.

Start innovation and problem- solving initiatives like data competitions to propose new hypotheses that challenge established notions about how the business works.

Running

Focus on enabling rather than creating content by expanding training initiatives. Open opportunities for all skill levels to build confidence and data literacy. Provide relevant examples so people can get up to speed quickly.

Institute community-building programs like lunch-and-learns, user groups, or competitions that set the stage for larger programs. People can ask questions, get help, and increase their data skills.

Invest in robust data lineage, the key to sharing data and building trust. Use your BI platform to identify and address data quality issues in sources with the highest usage.

Expanding

Formulate a method and a repository to capture learnings, such as an internal portal or Wiki, and allocate employee time to this function.

Document leading practices for data discovery to capture successful methods and to provide inspiration to others. Actively maintain these practices and refine as your Data Culture develops.

Publicly identify and celebrate data champions and reward them through promotion cycles, career growth, and leadership opportunities. As Data Culture develops, consider formal data leadership roles.

Conclusion

Future-proofing your organization
for whatever comes next

 

Future-proofing your organization for whatever comes next

The COVID-19 pandemic is widening the gap between the leaders and the laggards: those who are actively embedding data and analytics into the fabric of their company culture and those who are hesitant to invest in the programs and the technology that help them get there. Data-leading organizations pivot when necessary, innovate constantly, and refine consistently, giving them a distinct competitive advantage when times are tough.

Creating a Data Culture isn’t a matter of flipping a light switch. Now is the time to make incremental changes, starting with your workforce. Build out focus areas that lay the foundation for individuals and teams to expand their use of data. Taking these steps helps you move in the right direction, future-proofing your Data Culture for whatever lies ahead.


Meet the Tableau Platform: Flexible technology that supports a scalable Data Culture

Tableau is the world’s leading analytics platform. Powerful, secure, and flexible, the Tableau platform is designed for the individual and scaled for the enterprise. As a trusted advisor to the world’s largest organizations—including Honeywell, Charles Schwab, Nissan, and many more—Tableau helps customers successfully establish a culture of data built on trust and a strategic commitment to data.


Looking to accelerate your Data Culture? We wrote the Blueprint.

Tableau Blueprint outlines Tableau best practices based on the expertise of thousands of customers to help you turn repeatable processes into core capabilities. It helps you see the big picture—your analytics strategy—or zoom in on a specific area to fine-tune and improve.

Explore Tableau Blueprint



*Source: IDC InfoBrief, sponsored by Tableau, Why You Should Care About Data Culture, April 2020.

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