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.

The Nature of Measurement

Curiosity 

I have always been curious.  If curiosity killed the cat - I have been dead a long time! I now think that curiosity is linked to a desire to learn and I have been a proud life long learner.  It was with some excitement that a few months ago I signed up for Curiosity Stream as part of my Prime firestick subscription. Curiosity Stream is an online streaming service that specializes in documentaries from around the world.  This past week I was watching a BBC commissioned series on the history of measurement called Precision: The Measure of All Things.  It is a fascinating three part series on how we have developed greater precision in our core measurements and the tremendous impact such increasing precision has had on economic and social development. At one point the host says; "Without measurement, there is chaos."  It stopped me cold and may become the motto of this blog.  

Without measurement there is chaos.

What is so incredible about this thought is how true it is.  We need standards of measurement in all facets of life to be able to guide our activities, produce consistent outputs and contribute to outcomes that have impact.  Imagine trying to follow a recipe if we did not have standard measurements?  The recipe would not come out at all if we don't have consistent and standard messages. 

In Chaos there is Measurement

I shared this measurement quote with a colleague, who is a specialist in public sector performance measurement reporting, to get the reply; "In government it is the opposite: in chaos there is measurement!" How true in so many cases.  In the public sector we use a plethora of measurements to report on what we do.  Government is expected to be accountable and transparent - yet the measurement systems we use are anything but.  When we go to evaluate government programs and policies, how often does the report read that there is insufficient evidence or data to support any conclusions about the effectiveness or efficiency of an initiative?

Data, data where's the data?

The fact is that government is awash in data - it exists everywhere and is growing at an exponential rate. Government lags other sectors in the economy in understanding and analysing that data. Furthermore, because analysing data is sometimes hard to do, we tend to focus on analysing the data that is readily available and hope that will suffice.  Government is amazingly good at measuring outputs - the stuff we produce - but has greater difficulty measuring the impact on society and stakeholders an intervention is designed to achieve.  We need to do a better job and move from "in chaos there is measurement" to mastering the chaos through effective measurement and data analytics.

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

Challenge 1: Data is Hard To Find

 Ever had one of those days....

The other day I was looking for a report that had impressed me about a year ago.  I knew that I had downloaded it and kept it but I wasn't sure where to find it. I ended up doing searches through my computer hard drive, my external hard drive and my cloud storage.  I found it after about 10 minutes of work, but the experience reminded me of some statistics I have read about how much time employees spend looking for data. More recently the increasing use of datasets on cloud storage has created a whole new level of data access challenges.  I recently discovered that my data analytics tool could not connect to a Databricks cloud storage because the enterprise VPN would not let the connection go through.  I had to disconnect from the VPN and then connect to the data store through plain Internet.

This comes from a 2019 Forbes article: 

Numerous studies of "knowledge worker" productivity have shown that we spend too much time gathering information instead of analyzing it. In 2001, IDC published its venerable white paper, "The High Cost of Not Finding Information," noting that knowledge workers were spending two and a half hours a day searching for information.

Since then, we have seen the rise of the cloud, ubiquitous computing, connectivity and everything else that was science fiction when we were kids becoming a reality — including the imminent emergence of AI. Yet in 2012, a decade after the IDC report, a study conducted by McKinsey found that knowledge workers still spend 19% of their time searching for and gathering information, and a 2018 IDC study found that "data professionals are losing 50% of their time every week" — 30% searching for, governing and preparing data plus 20% duplicating work.

If approximately twenty percent of time our working time is spent searching for and gathering information that translates into one day out of five.  If you sum up the total compensation cost for your organization and take 20% of that total - that is the financial investment you (and your organization) are making to find and discover information. We need to do better than that.  

The causes for this include data silos - data being held by one office or individual with limited access by others.  Other causes are lack of integrated data inventories - we don't even know what we have so we cannot find it. Multiple data stores where we may have different information in different places.  Difficult to use document management systems.  It is great to have an enterprise document management system, but if the user interface and the document storage structure is too complicated - it takes forever to find a relevant document. Cloud data adds a whole other level of data silos. 

So what's the solution? Here are some things we can do:

1. Recognize that we have a data silo issue.
2. Evaluate the cost of finding data - check with your team members on their experiences in locating data. 
3. Identify the key data bottlenecks - where is it the hardest to find and retrieve data.
4. What data governance issues like metadata, data inventories, search tools are necessary to resolve the bottleneck?
5. Do it, fix it, try it:  change something, have a test or trial to see what works and then deploy. Often it is a small change that can make a big difference. 

Ultimately, data is one of our most strategic assets.  Helping our teams and analysts get to the data and documents they need quicker is going to help everyone do their job better.  We can do this!  Ultimately the fixes are not technical - often they are about better use of what we already have. 

What has been your experience in data bottlenecks and data silos?  What solutions have you seen work?  Feel free to share best practices in the comment section.

Feel free to contact me or connect with me on LinkedIn if you want more information on solutions and options. 

The Importance of Impact

Photo by Nadir sYzYgY on Unsplash

Impact is the recognition that our activities contribute to some enhancement of the community and the world around us. As humans, and within our organizations, there is a fundamental desire to make things "better".  We want to make a difference from our contribution to the world around us; at home, work and in the communities we belong to.  While getting paid is important - the ability to see that what we do contributes to the greater team effort is an important motivator.  

When I was in business school we talked a lot about the difference between efficient and effective.  Efficient was doing things right.  Effective was doing the right things.  Impact is all about doing the right things well.  Results-Based Management uses a tool called a Logic Model that articulates the effectiveness and efficiency connection really well.  The logic model is a hierarchy that takes Resources (people, money, data, etc.) and converts those resources into Outputs through Activities.  The outputs have a technical name; we call it "stuff".  That stuff contributes to Outcomes or Expected Results.  A Logic Model can have several levels of Expected Results.  

Efficiency is the capacity to convert resources, through activities, into outputs/stuff.  The better able we are to do that - the more efficient we are.  That being said effectiveness is more important.  It doesn't matter how efficient we are if we are not being effective! The Logic Model gives us the ability to understand both of those dimensions and how they connect. 

Impact is the correlation between our planned expected results and what we actually realize. Impact is a journey, not a destination.  Impact can happen at the personal, professional and organizational level. The role of data driven impact is the ability to use data to inform our capacity to be both efficient and effective. Data driven impact uses key metrics that help give us the insights we need to understand our impact. We need data to provide the feedback evidence to make informed decisions on our next steps, our strategy and activities.  We need data to help us understand our world. 

The good news is that there is data all around us and it is growing at an ever increasing pace.  The other good news is that there are now software and data analytical tools that allow virtually anyone to utilize that data without being a computer or data scientist.  The democratization of data is upon us.  On other words, all of us can experience data driven impact. 

Leveraging the Value of Your Data

Over the past year, I have been working on a special project to develop a Data Integration Strategy with a focus on leveraging the value of organizational data to promote data driven impact with both internal and external stakeholders. The project has been an amazing journey of learning and connecting with subject matter experts to absorb their best ideas and approaches. The project recently reached a major milestone and it was a good opportunity to reflect on what I have learned over the past year. So some thoughts that might resonate with others on a similar journey.

The project was designed to address the following three data challenges: Data is hard to find, hard to manage and hard to share. This situation is blocking innovation, adaptation and impact. In talking with other colleagues in diverse other organizations, it seems that these challenges are common. In looking to address these challenges, here are some of the things that I have learned through this project;

1. Digital transformation = data transformation. Digital transformation is fundamentally a data challenge that can effectively promote better data driven impacts and data driven decisions.
2. Data is a critical strategic asset and is often the second most valuable asset in any organization, the most valuable being people. Most organizations recognize the need to invest in our people, less so in our data management strategy. To our detriment.
3. Both of these assets are intangible and will not be found on the financial statements or on the balance sheet, yet both are critical to fostering organizational impact. During this project, we discovered innovative ways to measure and describe the value of our data assets to help focus attention on the magnitude of the issue.
4. Data challenges are 80 % people issues and 20% technology issues. The easy way out is to only focus on the technology - but the digital/data transformation comes from understanding the people-oriented data processes. In particular focusing on fostering a vibrant corporate data culture and investing in data literacy: the ability to understand, manage, share and manipulate data.
5. Data culture is a real thing. Solving the data challenges unleashes the potential for true innovation, agility and organizational responsiveness that contributes to organizational impact.
6. The solutions are a fraction of the cost of the value of the data and the potential disruption-losing mission critical data can cause. A number of studies by major consulting companies suggest that we spend approximately 20% of our time searching for data. That alone justifies the investment in better data management, data literacy and data culture.
7. Having an organizational data strategy has enormous corporate strategic benefits in providing; focus and direction, promoting data-driven decisions, enhancing the value of organizational data through integration, increased access, use and collaboration, all while promoting a culture of innovation, leadership and partnerships. All of which leverages the value of data and impact.
8. Lastly, is the recognition that data driven impact and the capacity to leverage the value of our data is not the job of the CIO and/or the CDO, but is a core strategic management responsibility for everyone in the C-suite on down. The CIO/CDO can provide support and guidance, but the day-to-day action comes from understanding, commitment and active investment of real resources (people and budget) from all of senior management.

Are there risks in undertaking the development of an organizational data strategy? Of course there are. There is a need to build commitment and consensus. To understand where you are and where you need to go. To create a sense of vision and mission that links data to culture and ultimately build community and excitement around the implementation. This is not new stuff - what is new for many organizations is focusing on data culture, data management and data literacy as core organizational capacities. We understand the need to focus on people to enhance organizational impact with our stakeholders; however we now need to understand and focus on how to leverage the value of our data to create truly remarkable data driven impact.

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I am sure there are a lot more lessons to be learned from this project - but these are the ones that come to mind as I reflect on the past year. I would not have missed this for anything in the world. To have come to an understanding of the intrinsic and extrinsic strategic value of data, the ability to leverage that value and contribute to data driven decisions and organizational impact has been transformative for me. Data Driven Impact - the next frontier!