Competitive Organizations expand their Definition of Data Literacy

17.02.2023

Autor: Andreas Seufert

Unleash the power of AI – Findings and Implications: Part 2

The Article is a summary of some key findings conducted by our research [Set 22] on the AI ecosystem and its implications for companies. (provided with kind permission of the original source: „Unleash the power of AI – Findings and Implications. In: Seufert (Hrsg.) df&c – Magazin für #Digital #Finance #Controlling, Schwerpunkt Digitale Transformation. Heft 1-2022, Steinbeis Edition, Stuttgart 2022, (Seufert, A./ Nelson, M./ Setlur, V./ Turner-Williams, W./ Wright, K./ Myrick, N.“)

Mark Nelson is President and CEO at Tableau. He sets the vision and direction for Tableau, and oversees company strategy, business activities, and operations. Prior to becoming President and CEO, Mark was the Executive Vice President of Product Development for Tableau, helping broaden and deepen the company’s industry-leading analytics platform to support customers globally.

Vidya Setlur is the Tableau Research Director, leading a team of research scientists in areas including data visualization, multimodal interaction, statistics, applied ML, and NLP. She earned her doctorate in Computer Graphics in 2005 at Northwestern University. Vidya previously worked as a principal research scientist at the Nokia Research Center. Her research combines concepts from information retrieval, human perception, and cognitive science to help users effectively interact with systems in their environment.

Wendy Turner-Williams manages Tableau’s Enterprise Data Strategy, Data Platforms and Services, Data Governance and Management Maturity, Data Risk, and Data Literacy. She and her team are fuelling data-driven business innovation, transformation, and operational excellence at Tableau. Wendy has 20+ years of management experience across sectors, most recently leading the Information Management & Strategy Enterprise program at Salesforce.

Kate Wright is an analytics leader with 17+ years of development, product management, and leadership experience. She’s responsible for Analytics Engineering, Product Management, and overall User Experience for Tableau and Tableau CRM. Neal Myrick is VP of Social Impact for Tableau and the Global Head of the Tableau Foundation. He leads the company’s philanthropic investments to advance the use of data for a more just and equitable world. Neal is an active angel investor and sits on several global health and development advisory boards.

Andreas Seufert is professor at the University of Ludwigshafen and director of the Business Innovation Labs. Andreas leads the expert group controlling & analytics of International Association of Controllers.

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Introduction

During the last two years many organizations had to adjust strategies and adapt to a new world. Changes to the way we live, connect, communicate, and work has forced every person and organization to become even more digital and data-driven than ever before.

When many organizations transitioned operations online, it came with a huge influx of information because every digital interaction generates valuable data that can provide insights and support faster decision-making in this digital-first world.

To get deeper insights, we conducted research and spoke with experts, customers, and other thought leaders to learn what emerging forces continue to evolve how we work, the role data and analytics play, and what this means to the future of companies.

Following we briefly discuss some of our key findings:

• AI solutions will see greater success by reducing friction and helping to solve defined business problems.
• Competitive organizations expand their definition of data literacy, invest in their people, and double-down on Data Culture.
• There is growing recognition of data’s strategic value drives flexible, federated data governance techniques that empower everyone across the organization.
• Responsible organizations will proactively create ethical use policies, review panels, and more to improve experiences and business outcomes.

Findings Part 2: Competitive organizations expand their definition of data literacy, invest in their people, and double-down on Data Culture

In a market where data is the ultimate differentiator, data literacy is the key to unlocking the value of your data and technology investments.

And the key to data literacy is Data Culture. In the year ahead, competitive organizations will recognize the need to foster a shared culture and mindset that values and practices using data.

They’ll broaden the scope of data literacy beyond skills training to include a fundamental understanding of how data works and how it can be applied to the business.

As organizations invest in people development to future-proof the workforce, they’ll partner with third-party organizations to train and upskill. The development of statistical thinking is an imperative today. Every individual must be able to synthesize data to support decision making, make sense of our world, and prepare for the future [Dep 21].

Technology and AI investments are on the rise, and workforce development is essential for realizing the value of these data-intensive investments.

PwC expects AI to grow the world economy by $15.7 trillion by 2030 [Rao 17].

And the workforce is automating faster than expected, according to the World Economic Forum’s Future of Jobs Report [Rus 20].

Automation will displace 85 million jobs by 2025, while creating 97 million new roles. Half of those who stay in their current roles will need reskilling in the next five years.

There’s a growing demand for data skills in the workplace and in our society.

As public conversations grow increasingly data heavy, not everyone will need to be a data scientist, but everyone will need basic data fluency and analytical skills.

To realize the value of a data-literate workforce, however, we have some work to do. Not only is there a gap in data skills, there’s also a lack of data literacy programs, from the classroom to the workplace.

Despite 83% of CEOs wanting more data-driven organizations [Gop 21], only 43% of digital natives consider themselves data literate [For 21].

According to Forrester, less than half of academic institutions have data skills initiatives. And many corporations take a near-term approach by recruiting to fill immediate skills gaps rather than investing in data literacy and Data Culture.

The programs that do exist focus too heavily on tools and technology, failing to build a foundational understanding of how data is produced, used, and managed through the business.

Competitive organizations see the value in data skills and recognize that future-proofing the workforce is about more than just data skills and tools training. They will act to instil essential data literacy in their people.

Academia will infuse data literacy into curricula across disciplines. Employers will increase their data literacy investments.

A growing number of employers will recognize that teaching people how to use the tools and understand how the technology benefits the business is a critical piece of their technology investments.

They’ll look beyond tools and platform proficiency to focus on critical thinking and applying domain expertise to solving business problems. Culture is key to this mindset shift.

They’ll also accept that they can’t go it alone. Without the resources, internal expertise, and capabilities to run their own education programs—or keep up with the rate of change—organizations will view data literacy as a community effort. They’ll embrace agile, non-traditional approaches and partner with third-party training programs.

Recommendations – Part 2:

Their success is interdependent, so don’t overlook the value of investing in a combination of literacy training and a cultural shift. And remember—change won’t happen overnight. Be patient, keep at it, and recognize that it’s an ongoing commitment.

• Design a framework to set common goals and structure initiatives for sustainable success.
• Standardize terms, skill levels, success metrics, and processes across the business.
• Incentivize people. Get them excited about what they can do with data.
• Model and encourage data-driven decision making and demonstrate the value of data.
• Make space for discussion, learning, and development.

To meet this goal companies need to recruit, train, and incentivize a workforce and workplace where data is routinely sought, valued, and fluently utilized for decision-making at all levels and geographies. Here’s what that could mean for your organization:

• Evolve hiring practices and role expectations to require basic data skills.
• Partner with educational institutions with data skills initiatives and recruit data-literate students.
• Encourage and facilitate data upskilling in your existing workforce.
• Build data communities to encourage ongoing growth, development, and collaboration.
• Identify and recruit experts, or data champions, to inform corporate training programs. Build a culture of data-driven decision making to help you retain those experts.

For those in academia, it’s never too early—or too late!—to teach data skills and critical thinking. Infuse fundamental data skills into all stages of education and prepare more students to work with and understand data in their professional roles.

• Build analytics skill development and critical thinking skills into all courses. Reinforce that every future career can and will use data.
• Encourage students to bring data into their research and work.
• Make data fun! Explore how data shows up in the real world and bring data concepts to life for students.
• Communicate the value of data skills—from boosting career potential to using data to change the world.

Play the long game:

• Don’t just focus on your short-term needs by training for the tools and technology you currently have. Educate your people on tech-agnostic fundamentals, like how data adds value to the business.
• And don’t reinvent the wheel! Teach the basics in-house if you can and outsource the rest.
• Help employees develop more advanced skills by partnering with third-party organizations to adopt what’s already out there.

Literature:

[Dep 21] Department of Education – United States of America: Data Literacy, https://www.ed.gov/sites/default/files/documents/stem/20211015-data-literacy.pdf, October 15, 2021, (access 18.03.2022)

[For 21] Forrester: The Great Data Literacy Gap: Demand For Data Skills Exceeds Supply Need For Data Skills Are On The Rise, Can Academia Accelerate Learning To Meet Them? https://www.tableau.com/sites/default/files/2021-06/Tableau_Data_Literacy_Rep…, June 2021, (access 18.03.2022)

[Gop 21] Gopa: How Data Culture Fuels Business Value in Data-Driven Organizations, May 2021

[Pid 21] Pidsley/ Idoine: Maximize the Value of Your Data Science Efforts by Empowering Citizen Data Scientists, December 2021

[Rao 17] Rao et. al: Sizing the prize – What’s the real value of AI for your business and how can you capitalise? https://www.pwc.com/gx/en/issues/analytics/assets/pwc-ai-analysis-sizing-the-…, 2017, (access 18.03.2022)

[Rus 20] Russo: Recession and Automation Changes Our Future of Work, But There are Jobs Coming, https://www.weforum.org/press/2020/10/recession-and-automation-changes-our-fu…, October 20, 2020, (access 18.03.2022)