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.

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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!