Building a Data Culture

"I had come to an entirely erroneous conclusion, which shows, my dear Watson, how dangerous it always is to reason from insufficient data."  Sherlock Holmes (The Speckled Band - 1892)

Our goal here at Data Driven Impact is to promote the use of data to promote better policy decisions, program delivery and service to stakeholders. McKinsey suggests 70% of digital projects fail.  IBM says it is closer to 84 or 85% of digital projects fail.  Given that governments and public sector agencies are amongst the most active data collectors, we need to find better ways of using data as a strategic asset. In my experience, the biggest hurdle to effective data and digital transformation in government is the data culture. 

Data culture is the recognition that having data is not enough.  We need to transform that data into information, knowledge and insights that inform decisions, policy and impact. That requires a transformation of our organizational data culture. Data culture includes the values, behaviours and attitudes of executives and employees about the use (or nonuse) of data in our day to day activities. We already have an existing data culture that may dismiss data as a key strategic asset or as a critical input into decisions and discussions.  We need to transform our data culture into one where data is not just seen as a key strategic asset, but is used as a key input into creating effective policy, decisions, impact and outcomes. 

Tableau has created a framework to help organizations do just that. Having spent several years working with customers to improve their data strategy, the company identified five elements of a successful data culture: 

• Trust: Leaders trust their people and people trust the data.
• Commitment: People treat data as a strategic asset, and they fully commit to realizing the value of their data assets. 
• Talent: To effectively analyze data and use it to make better decisions, agencies must invest in their employees: a data culture, after all, is ultimately composed of ‘data people.’ 
• Sharing: Data requires collaboration: Most problems can’t be solved by one individual or team. They rely on collaborative teams to share ideas and insights with one another.
• Mindset: People prioritize data over intuition, anecdotes or rank. A shared, data-first mindset creates an environment where ideas lead to innovation and impact. 

Transforming a data culture is a key step in driving data driven impact in the public sector.  I believe that understanding and fostering a positive data culture is 80% of a public sector data strategy.  The technology is easy!  Building the culture takes time, listening and understanding - it is the people side of data transformation.  We will have lot's more to say on this subject in the future.

For more information on data culture see the Resources section at datadrivengovernment.ca

Please share your data culture experiences in the comments below.  

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Feel free to contact me by email or follow me on LinkedIn.  For further information on data driven impact and data driven government, visit my website at www.datadrivengovernment.ca.

Challenge 2: Data is Hard to Share

Recently, I was working on a data analytics project and needed to get confirmation on some of the data I was working on. I contacted my subject matter expert and we needed to exchange data.  He was using one kind of software and I was using another.  It took a few minutes to figure out how to export my data into a format that he could use.  Luckily, data formats are more easily exchangeable than in years gone by, but it highlights the ongoing issues of sharing data and the hurdles we need to go through to share our data. 

Sharing data is really important to the value of data.  Data is like money - in many ways data is the new currency - the value of data goes up the more it circulates. If I have a data set that works for me - that is good. However, the value of that data goes up exponentially the more it is shared and referenced.  This is a crucial component of creating impact, especially in public sector science based organizations.  

In the areas of climate change research, public health, food safety, and so many more, the ability to collect, analyse, publish and share data is a key success factor to increasing the impact of data to contribute to positive outcomes.  

The COVID-19 pandemic has been a perfect example of leveraging data and increasing the value.  Never before have so many scientists and health experts gathered so much data and shared it so quickly to be able to hasten the response to COVID-19.  The ability to rapidly develop vaccines was due to the rapid sharing of the virus genome.  The ability to rapidly enact social measures to limit spread was due to the ability to gather and share infection data and trend analysis.  As it seems that COVID-19 mutates and evolves quite rapidly - this ability to gather, analyse and share data will be essential in the ongoing management of the public health response over time.  

What are the key success factors that made the pandemic response so effective;

• data was gathered quickly
• data was shared in repositories that made it easy to access, download and use
• data was shared in open formats that allowed ease of use in different analytical packages and processes
• analysis results were also shared in open repositories for use by others
• data analytical processes were also shared in open repositories and the analytical methods were easily copied and replicated for use with local data

All these open data approaches continues to contribute to an enhanced ability to make data informed decisions and contribute to data driven impact.  

Imagine if we took the same open data approaches and applied them to the data we use every day in our organizations and with stakeholders.  The value of our outputs and the value of our impact would increase tremendously.  What's stopping you from sharing your data?

Let me know how you use these key success factors in the comments below, and any additional key success factors you have observed.  

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Feel free to contact me by email or follow me on LinkedIn.  For further information on data driven impact and data driven government, visit my website at www.datadrivengovernment.ca.

Driving Data Driven Decisions

So in the spirit of transparency; I snore.  Apparently, I have snored for a long time.  My snoring was the cause of some deep relationship issues in previous lives!  About 20 years ago I went to a sleep lab and started using a CPAP (continuous positive airway pressure) machine that keeps my throat open as I sleep and lowers the incidence of snoring and related medical side effects tremendously.  It has improved my personal relationships remarkably as well.  A positive impact any way you look at it!

Recently, I came to the conclusion that I need a new sleep machine.  Mine is now over 5 years old and the progress in sleep technology is amazing.  About a week ago I called my sleep machine provider and we met to review my needs.  The Ontario Government provides grants to offset the cost of these machines, but I need a prescription from the sleep lab to get a new machine.  

In the past, I would have had to book an overnight visit to the sleep clinic where they would monitor my sleep through several dozen electrodes and provide me with a diagnosis on my sleep patterns and behaviour.  It often took several months to get an appointment. With COVID there are no overnight sleep tests being done.  Instead they are using advance digital technology. This is where the data part comes in.

It seems that the current generation of sleep machines now have "smart" technology. The CPAP units now have a built in microchip that tracks your sleep patterns, an ability to automatically monitor and adjust air pressures and have a wireless cell phone chip to allow remote access to the data.  The machine has sufficient onboard memory to record your sleep data over multiple nights. 

To get my new sleep machine, I will be loaned one of these "smart" CPAP machines, use it for two weeks, have the data remotely accessed, analysed by the sleep lab and a prescription given for a new upgraded machine.  In other words, the prescription decision is being driven by remote data gathering without the need for an inconvenient overnight lab visit.  Furthermore, that data is aggregated into longitudinal studies on sleep patterns experienced by patients over time, contributing to better diagnosis and better health outcomes. 

The lesson here is that processes that used to take high levels of dedicated infrastructure and personal inconvenience, have been completely transformed through data and digital technology.  The ability to contribute to better health outcomes at the individual patient level has major implications for enhancing health in general.  How many processes do we interact with on a daily basis that could be enhanced through data collection and analysis that would contribute to more positive impacts?  What processes are we working with today that we need to examine and improve with data driven decisions?  

Next steps: look around your world and find three areas in your life and work that could benefit from better data analytics and contribute to better decisions and impact. 

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Feel free to contact me by email or follow me on LinkedIn.  For further information on data driven impact and data driven government, visit my website at www.datadrivengovernment.ca.