How to Promote a Corporate Data Culture

Based on my recent experience in exploring and testing Data Culture methodologies the following five themes and actions are a critical part of promoting a data culture. 

1. Promote curiosity and exploration

Encourage employees to ask questions and explore the data that is available to them. Promote a data-driven culture by rewarding employees who take the time to examine data and ask questions. This will help employees feel comfortable exploring data for insights and solutions.

2. Foster a collaborative environment

Encourage employees to work together to share insights and solutions. Collaborative problem-solving can lead to new ideas and solutions. When employees feel comfortable sharing data and working together, they can create a data-driven culture.

3. Make data available to everyone

Make data available to all employees, regardless of their role or level within the company. When everyone has access to data, they can work together to find solutions and insights. Data should be accessible in a way that is easy to understand, so everyone can use it to make better decisions.

4. Emphasize the value of data

Make sure employees understand the value of data and how it can help them do their jobs better. When employees see the value in data, they will be more likely to use it to make better decisions.

5. Encourage experimentation

Encourage employees to experiment with data and test new ideas. When employees are encouraged to experiment, they can find new ways to use data and solve problems. This can help create a data-driven culture within your company.

In the next two weeks what could you do to use these ideas to foster a more data oriented culture in your organization. 

The future of government is data-driven

 From an article I recently submitted for an industry newsletter. 

The future of government is data-driven

In an era of big data, governments are turning to data-driven methods to make better decisions. By analyzing data from previous government actions and by tapping into data from multiple sources, governments can identify patterns and trends that might not be evident from intuition or anecdotal evidence.

Data-driven government is not a new phenomenon—in the early 1800s, the US government began publishing statistics about the American economy. However, data-driven government has taken on a new urgency in the past decade, as policymakers have come to realize the potential of data to help them make better policy, program, strategy and management decisions.

Today, data-driven government is being used in a broad range of areas, including taxation, public health, education, and management. For example, in the area of public health, governments are using data to track outbreaks of disease, to monitor the effectiveness of public health campaigns, and to identify risk factors for disease. In the area of education, governments are using data to track students’ academic progress, to identify schools that are struggling, and to evaluate the effectiveness of different educational programs. In management, data analytics is essential to measure outcomes and impact.

One of the biggest challenges for data-driven government is to make sure that the data is accurate and timely. In many cases, the data is drawn from multiple sources, including administrative data (data from government agencies), survey data, and data from private companies. Sometimes, the data is messy and it takes effort to clean it up so that it can be used for meaningful analysis and insights.

Another challenge for data-driven government is to ensure that the data is accessible to the people who need it. In many cases, the data is stored in databases that are difficult to access. Moreover, the data is often presented in complex formats that are difficult to understand.

Despite these challenges, data-driven government is starting to have a big impact on the way that governments operate. By using data to make better decisions, governments are able to improve the quality of life for their citizens and achieve better outcomes for their economies.

Data is essential to inform public policy and data-driven decisions are informed by data analysis insights. 

There is a wealth of data available to inform public policymaking, and data-driven decisions are more informed as a result. Data can show which policies are working, where resources are best allocated, and what interventions are most effective in specific situations.

However, data is only useful if it is used effectively. The first step is for policymakers to recognise the importance of data and to make it a central part of their decision-making process. They need to be willing to analyse data and to question preconceived notions.

Once policymakers have access to data, they need to be able to interpret it correctly. This means understanding the limitations of data and using it in conjunction with other forms of information. It also means being aware of the potential biases that may be present in data.

Data-informed policymaking is not just about using data to make decisions. It is also about using data to assess the impact of decisions and to learn from the results. It is also about using data to assess the impact of decisions and to learn from the results. This allows policymakers to adapt their policies as needed and to ensure that they are having the desired effect. This allows policymakers to adapt their policies as needed and to ensure that they are having the desired effect.

Policymakers around the world are beginning to recognise the importance of data and the benefits of data-driven decision-making. Data is only useful if it is used effectively. The first step is for policymakers to recognise the importance of data and to make it a central part of their decision-making process. They need to be willing to analyse data and to question preconceived notions. Once policymakers have access to data, they need to be able to interpret it correctly. This means understanding the limitations of data and using it in conjunction with other forms of information. It also means being aware of the potential biases that may be present in data.  

Conclusion

This article discusses the advantages of data-driven government, which include increased transparency, smarter decision-making, and improved public services. While the benefits are clear, there are key challenges, such as the need to protect privacy and ensure data accuracy and data literacy across organizations. However, the advantages of data-driven government are so great that they are likely to outweigh the challenges. The future of government is data-driven.

Ascent of Man - Knowledge and Certainty

Recently, I was invited to attend an online workshop hosted by Data Leaders in the UK.  The workshop discussed The Culture Change Challenge: Effectively Overcoming the Common Barriers.  It was a really good workshop - timely for me with the work I am currently doing leading a Data Culture Enablement Project with the Canadian Forest Service and supporting the development of a roll-out plan promoting data literacy and data skills across all of Natural Resources Canada.  

One of the presentations looked at the way humans make decisions and the role of noise and bias in data-driven decision processes.  The presentation reminded me of an amazing TV series from the 1970's called The Ascent of Man, hosted by Jacob Bronowski. It was one of the early blockbuster series from the BBC. One episode in particular had a deep and lasting effect on me; Episode 11: Knowledge or Certainty.  The subject is Germany between the two world wars.  At the time (1920's) physicists were defining the principle of uncertainty, the idea that no matter how much you know - there will always be a degree of uncertainty that can be statistically measured.  For science it implied that we need to take all results with a degree of skepticism. Not that scientific results are wrong - but that the degree of "rightness" is never 100%.  At the same university were social scientists who were proposing theories of anthropological certainty that promoted theories of racial superiority. These ideas were adopted by the Nazis to justify the superiority of the white, Nordic race and concentration camps. 

At the conclusion of the episode, the host Jacob Bronowski visits Auschwitz and relates the story of how members of his family died in the Halocaust.  He walks into a stream, reaches down into the stream to pull up a handful of sand and states that this is a lesson in what happens when a society assumes certainty. It was a powerful moment that I will never forget. It reminds us that we need to be humble about the knowledge we have and how we use it. 

Data science is no different.  It is a powerful tool that can help us understand the world we live in and give us insights that contribute to inform policies, decisions and actions.  We must be wary of the noise and biases that may be in the models and work hard to eliminate them as much as possible.  In the end, however, there is always a statistical factor of uncertainty that we need to acknowledge and work with. Data science can never give us certainty.  The role of humans is to apply wisdom and common sense to the results of any data analysis.  We need to be open to the opportunities and insights data analysis can give us, but understand the limits of what technology can give us.  

Data Culture Enablement Project

 

About eight months ago, a proposal for a Data Culture Enablement Project was granted funding from a Natural Resources of Canada departmental special projects fund. The objective of the project is to develop a series of tools to promote a vibrant data culture and to enhance data literacy and skills within the Department. The project was sponsored by the Canadian Forest Service with the CFS acting as the pilot site. I have the privilege of being the project lead.

 

Over the past eight months a number of core deliverables have been created including:

1.      a draft corporate data values statement as a foundation for a vibrant data culture

2.      four unique Data Personas that can be applied to any position within the organization

3.      a data culture/skills assessment tool based on the Open Data Institute Data Skills Framework

4.      analysis of where the CFS is on a data maturity framework

5.      a matrix to crosswalk combining data personas with a corporate data maturity model, including characteristics, capabilities, recommended data skills and associated sources of training.

 

As we started analyzing the data from the assessment survey, some preliminary observations have emerged.

 

1. Promoting and developing a data culture is more complex than it originally appeared. The technology part is easy - it is the people part that requires thought, patience and perseverance.
2. If you bring 10 people into a room and ask them to define what data is, you will get 15 answers.
3. Data literacy and skills is the operational side of data culture. You cannot promote broader team data literacy and skills without a supportive data culture, hence the need for a corporate data skills values statement.
4. Ultimately, promoting a supportive data culture requires senior leadership buy-in and a strong commitment to change management and ongoing communications

 

There is no question of the positive organizational value that comes from promoting a corporate data culture and the affiliated data literacy and skills. It is not, however, a quick fix. Data culture is a journey, not a destination.

 

More to come, as we work our way through this project. 

Comments and thoughts appreciated....

A Data Culture Values Statement

 Recently, I have been reading Mark Carney's book Value(s).  It got me thinking about the need for core/key values as an essential tool to guide the implementation of an organizational Data Culture/Literacy program. Based on my current work in developing a Data Culture/Literacy change management process, I drafted the following organizational Data Culture Values Statement.  Based on any feedback I get, I plan to convert this into poster for complimentary download.  Feel free to comment below. 

A comment on the concept of Data Culture.  If you put 10 people in a room and asked them to define "data" you would come out with 15 answers!  I am using the term Data Culture to refer to the attitudes, practices, and behavioural norms an organization applies to their data.  Data is often a by-product of processes, and as an asset never appears on a balance sheet.  Data is intangible and ephemeral.  Yet, we know from countless studies that data is now a key strategic asset.  Data Culture is the way data is seen, managed, and recognized in an organization.  The purpose of this values statement is to provide a foundation for building a positive Data Culture in any organization.

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A Data Culture Values Statement

1.  We believe that the basis of a being an excellent and data-driven organization starts with promoting a positive data culture.

2.    We recognize that every organization already has a data culture. The foundation of this Values Statement is built on understanding the nature of that culture, and having passionate conversations about where we are and where we need to be with that data culture.

3.     We believe that promoting a positive data culture is the work of everyone from executives to the newest hires.

4.     Data is a core strategic asset.  We will manage our data infrastructure/assets with the same intentionality as we manage people, strategy, and stakeholder engagement.

5.     Every individual in our organization is already a data worker, we commit to helping each of them understand the value and contribution their data work brings to the organization.

6.       We will provide everyone with the tools, training and opportunity to match their data skills with the requirements of their roles and responsibilities.

7.       We actively promote the use of data to inform decisions, strategies, policies and planning.  As W. Edwards Deming put it; “Without data you’re just another person with an opinion.”

8.       We encourage every member of the organization to explore, analyze, and communicate data insights that contribute to innovation and continuous improvement.

9.       We will promote active sharing of data products, datasets, and data workflows to enhance the value of our data through collaboration.

10.   We will foster data governance policies and procedures that promotes the ethical use of data, reduce bias, and reflect the highest core values of our organization.

11.   Ultimately, data, data analytics, data models, and data products are only an enabler.  We will be open to what the data tells us that we may not have seen before; to which we will apply common sense and wisdom in the use of data, in informing decisions, strategies, policies and our actions.

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Data Literacy- the why

I was recently asked to do a briefing note on the what, why and benefits of promoting data skills and data literacy.  This post is based on some of that briefing note.

Background/Considerations

There are three major trends influencing global development that impact organizations of all types:

1.  The Fourth Industrial Revolution – broad scale technological innovation and disruption
2.  COVID-19 Pandemic Recovery – building a global health care system and local economic recovery
3.  Global Economic, Social and Environmental Sustainability – to meet the competing issues of global climate change, democratic challenges and a sustainable economic system that meets the needs of all within a net-zero climate.

Data is the lubricant in all of these areas that contributes to the ability to manage these trends with insights that contribute to informed policy making and operational decisions.  Data is now a key organizational strategic asset, second only to people. Leveraging the value of data is now a core strategic advantage.

Today’s largest and most successful organizations use data to their advantage when making high-impact decisions. Corporate investments in data skills and literacy is essential to increase the organizational ability to manage data, derive meaningful and reliable insights, and communicate results to decision makers and stakeholders.  Companies that are more data literate experience 3 to 5% greater enterprise value, which translates to $300 to $500 million greater total market value (The Human Impact of Data Literacy, Accenture, 2020). The higher the enterprise data literacy level, the greater value the business can generate (Deloitte, 2020).

As we come to the end of 2021 and look into the future of 2022; how do the three major trends influence what you and your organization is doing?  What is the role data can play in that?  How can you leverage data as a core strategic asset to discover the insights you need to innovate?

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Feel free to contact me by email or follow me on LinkedIn.  

Digital Transformation

I recently had a discussion with a colleague on the concept of digital transformation and what a successful digital transformation process would look like.  My email reply suggested the following:

Successful digital transformation focuses on the people issues.  The technology (hardware and software) is just the enabler. Successful digital transformation starts with leadership - the C-suite has to understand and support the digital transformation journey - and it is a journey.  Secondly, you need to look at your data culture:

• How are you using data now?  
• How do you need to use data in the future?  
• What are your near term and longer term goals for the transformation effort?  
• How are you going to communicate the process?  

Start with one key business process where digital transformation would make a major difference.  Work on that project alone to demonstrate that digital transformation can work.  Make lots of little adjustments - not a big moon shot.  Engage the participants that have a stake in the digital transformation as key enablers and team members.  The key here is to help them understand the benefits of using digital tools to do their work better.  

According to the experts (Deloitte et al) 80% of transformation projects fail.  The number one reason for failure is lack of C-suite support and ignoring the people side of the equation.  There are lots of successful digital transformation projects.  Look for the smaller examples - not the big huge ones.  Good Luck.

If you are engaged in a digital transformation project - you might want to ask yourself the same questions and focus on one project that can move the needle in a significant and positive fashion.  Don't try to boil the ocean - make incremental changes over time.  This is really a form of digital Kaizen.  

Feel free to contact me with questions or comments.  Sharing is caring!  To see my recent presentation to the Open Data Institute on Leveraging Data for Impact see the video here.