Author: cooloperations

Data to dollars: Thoughts on valuing data as an institutional asset

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A core principle of data governance is that data, regards of who creates or has custody of it, is an asset of the institution. But what’s an asset? Some ground-breaking work in Canada and Europe might point the way to putting an actual dollar value on our data.

For the first time, Statistics Canada last year tried to estimate the value of the country’s stock of data. The total value of data, databases and software, and data science in Canada is between CAD $157 and $218 billion, the agency estimates. (1)

This is a first stab. Like every national statistical organization in the world, Stats Can is playing catchup with the explosion of digital data capture and processing and its importance to the economy. Data is clearly part of a country’s productive capacity, but it doesn’t show up in national accounting statistics such as gross domestic product (GDP).

The same for universities. Buildings are tangible assets recorded on the balance sheet. Data is intangible and, unfortunately, invisible to generally accepted accounting principles. What if the university were able to value data as a capital asset?

The value of software and analysis is more easily measured than the store of data. Subtract those two from the Stats Canada estimate, and the value of data by itself ranges from $105 to $151 billion for the whole country.

Using share of GDP for our sector, I estimate that publicly-funded institutions such as hospitals and universities might be holding $6.6 to $9.5 billion in data assets, assuming all subsectors have been equally productive. (2)

Drill down farther, and my back-of-envelope calculation finds that Dalhousie University’s data might be valued between $24 and $35 million. (3)

There are too many layers of assumptions to feel confident in the accuracy of that range. (For example, it is unclear whether or where research data is counted in the national statistics.) Better than trying to decompose the national figures, we might try valuing our data using Stats Canada’s methods.

Our university’s data is not for sale, so a direct market value cannot be determined. Stats Canada values data at the cost of producing it, plus an estimated return on capital. Data is produced by people engaged in data-related activities: Working with many years of labour-market surveys, the agency identified roles likely involved in the creation of data (data entry clerks, researchers, analysts and so on), estimated a likely percentage range for the amount of their job spent on data creation, and calculated the cost based on salaries plus associated costs.

Value based on cost of creation sets only the lower bound; as new uses for data are found, its market value could change dramatically. More work remains to be done. But as a starting point it might hold promise for institutions seeking to measure their data as a financial asset.

In the system of national accounts, data may come to form a whole new asset class. For businesses, non-profits and other organizations, adding data investments to financial statements would give it added prominence.

Which brings me back to data governance. The value proposition for data governance at a university is hard to define. Counting data as an asset — literally — might be a step in the right direction.

Notes

  1. Statistics Canada has produced two highly readable papers on this topic: “Measuring investment in data, databases and data science: Conceptual framework,” released 24 June 2019, and “The value of data in Canada: Experimental estimates,” released 10 July 2019.
  2. Economic production in the non-profit sector totalled $169.2 billion in 2017, representing 8.5% of Canada’s gross domestic product (GDP). In recent years, publicly-funded institutions such as hospitals and universities have accounted for about 6.3% of GDP, which leads me to conclude that our subsector might be holding $6.6 to $9.5 billion in data assets.
  3. In 2017, education institutions in Canada accounted for $46.5 billion, or 2.3% of GDP, suggesting total data assets of $2.4 to $3.5 billion. Education institutions in Nova Scotia accounted for $1.4 billion, or 0.07%, of GDP – implying data assets of $74 to $106 million. Taking the size of Dalhousie University relative to all the others in the province, our stock of data could be worth between $24 and $35 million. (Thank you to Juuso Vesanto, BI Analyst with Dalhousie Advancement, for pointing me to an Economist report on this topic which gave me the idea to use share of GDP.)

So your horoscope says you’re an introvert

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Have you ever been made to take a personality test as part of a job application? I have. And I learned a lot from it – although not about me.

The test was administered and interpreted by an associate professor of industrial/organizational psychology, consulting for the recruiting firm contracted to help fill the position. I was not surprised by my test results – introvert, detail-oriented, compromise-seeking – but I found the professor’s interpretive report a little on the negative side.

I got the job. I was offered the opportunity to meet with the professor. I showed up at his office keen to learn how I might compensate for deficiencies in my psychological makeup that would hold me back as a leader if left unaddressed.

You see, years of exposure to conventional wisdom about personality types had conditioned me to suspect that introverts don’t belong in leadership roles or collaborative work environments. Or maybe they can be admitted as long as they undergo the personality equivalent of conversion therapy.

For example, I once attended a team meeting on talent development in which one of the participants presented resource materials that essentially stated that introverts are best suited to working alone doing repetitive tasks. I should have objected but I said nothing.

(Introverts are well represented on my team. I can attest that no one in Advancement works in isolation doing solely repetitive tasks. No one.)

But I have an open mind, and I listened to what the professor had to say. Which wasn’t much. He seemed evasive, and I left with an empty notebook.

A few weeks later, I read a story in the local paper based on an interview with this same associate professor. He had co-authored a study on people who lie during job interviews. He said he had found a link between personality and deceptive interview answers.

Introverts, he found, tend to be less confident, and use deception to cope with the perceived difficulty of the interview, while extroverts tend to be more honest.

“What we found is individuals who are more extroverted, that are more conscientious and are more experienced and have better prepared for an interview (tend to) use more of the honest strategies,” he said. “While those who are more introverted, less conscientious, less prepared, and maybe less experienced, they go in the interview and apparently they tend to be less confident. They perceive the interview to be more difficult for them and then they use the deceptive strategy as a way to kind of cope with the anxiety that can arise because of the situation.”

I have not read the study, and the reporter may have mangled the message; the quote suggests the study included multiple variables. But it made me angry. I’ve kept the newspaper clipping for years, intending to write about it, but my anger has prevented me.

I’m still angry, but I write now because I fear that some hiring managers might actually believe this.

Personality testing has validity, and can be used for self-reflection and for helping diverse personalities work together. But barring people from higher levels of employment based on dubious interpretations of ambiguous data is wrong and harmful.

It is harmful to individuals, to teams, to organizations. We deny opportunities to talented people, or those talented people self-select out of the running. The psychological diversity of our teams that encourages good decision-making is impaired.

Personality is not irrelevant in assessing fitness for a role. But I know many extroverts – either I work with them or love them – and believe me, extroverts (as a group) are no better equipped to handle life and the world and leadership than introverts are. (And in some cases, I see people judged unfairly based on their extroversion — it can cut both ways.)

Personality testing is interesting the same way the starry sky is interesting: full of real and complex phenomenon. But if you’re a hiring manager or a potential job candidate, please don’t be swayed by astrology.

Effective data governance requires a leadership mindset

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The path to establishing effective institutional data governance might never be smooth, but some paths are smoother than others.

Success is more likely when people know their prime object is to advance the mission of the institution. Leaders recognize that data governance is the policy framework that establishes that key administrative data, regardless of where it is created or where it resides, is an asset of the institution, to be used to guide strategy and decision-making.

Success is less likely when people are primarily advocates for their area of custodianship or for a set of professional principles. Issues of confidentiality, security, data management, and protection of personal information are important, but do not define the core value proposition of data governance.

Advocates see policy through a lens that takes on the colour of their subject-matter expertise. Leaders appreciate the importance of these considerations, but strive to place those important pieces in proper relation to the value proposition.

The beauty is that advocacy vs. leadership isn’t about rank. You get to choose which role to play, and when. When you advise, you might do best to be an advocate. When you get to frame policy, you need to be a leader.

Data governance requires both mindsets, but ultimately leadership must prevail.

Figure out what you want to do, versus what you want to be

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What is a “job”? It seems a very solid thing. Often it isn’t. There’s a job description, some records in the HR database, a payroll arrangement, a title on a business card, a set of performance objectives, a workspace, and a lot of assumptions and expectations spoken and unspoken.

As a container, a job is less like a box with hard sides and more like an elastic bag. It changes. It can shrink in places, stretch in others.

Late last year, President Obama came to town. In his wake, a remark he’d made about advice to young people became widely quoted. He said, “Worry less about what you want to be and worry more about what you want to do.”

This advice translates to the workplace. Ambition expressed as a desired job title or income level is uninspiring and empty. Better to study how your organization’s strategic goals align with what gives you meaning, and think how your role could evolve to serve that alignment.

Your supervisor might not be asking you what you would like to accomplish, what gives you joy, or what purpose you want to pursue via work. But you should certainly ask those questions of yourself.

What universities can learn from the demise of newspapers

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The Newseum in Washington DC, struggling financially for more than a decade, closed its doors to the public on December 31. I was fortunate to visit a few years ago, and the loss makes me sad. The fate of this museum, dedicated to the history of journalism and the free press, seems symbolic of the fate of print journalism itself. Newspapers, once mainstays of democratic societies, have largely gone by the wayside.

Universities are like news organizations in that in the free world, they are institutions that have always defined themselves. I hope universities do not make the same mistakes committed by the business of journalism.

Technology has changed the way people consume news. But the demise of newspapers is not primarily due to technology. It’s due to the bungling ways media organizations responded to technology. They conspired in their own disruption.

Newspapers are dead because for decades they tried to be everything except what they were. They turned their backs on their strengths – context, judgement, authoritativeness, thoroughness, trustworthiness – while trying to imitate their supposed rivals.

Newspapers were already in trouble when I graduated from journalism school into a tight job market thirty years ago. And the roots go back much farther than that. When radio entered people’s homes, newspapers tried to become like radio, choosing speed and sensationalism over accuracy. In response to television, newspapers became more colourful, image-oriented, and with much shorter stories. By the time the internet was ascendant, in the public’s mind newspapers had no purpose distinct from the alternatives.

Online classified advertising erased corporate profits, but the newspaper itself was already irrelevant, easily displaced by the cheap substitutes it had come to resemble.

Microwaves were supposed to replace ovens, but ovens continued to do what they always did really well, and today microwaves are mainly used to rewarm our coffee. Newspapers cut their newsrooms, turned themselves into apps offering the same content as their rivals, and most of them got crushed.

It didn’t have to be that way.

Near as I can tell, universities are healthy. Online learning has hardly been a death blow. Not because online learning is bad, but because it’s a microwave oven. The university – that blend of social, physical proximity and shelter for solitude and quiet – that balance of conversation and contemplation – is very hard to substitute.

Universities do change, and must change. But to remain relevant, they must also tenaciously cling to what they are at their core, not try to imitate everything out there that they are not.

Data is an expensive tool. We should teach people how to use it.

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When you buy a tool you have to learn how to use it, or you’ve wasted your money. Our team understood this when we implemented a new CRM system: If frontline staff used it and used it well, the investment would deliver on the promise of facilitating advancement of the mission.

Data is also a tool. Managers and decision-makers will succeed if they know how to use data. The question is, what have we done to maximize on that investment?

During our CRM implementation, we had more than 50 working sessions with frontline staff – focus groups and training sessions that involved nearly everyone in configuring the software and applying it in their work. So many hours!

CRM was big, but our investment in data, spread over years, is much bigger. Like other advancement shops we have staff employed in the collection, creation, and management of data, staff who design and maintain the infrastructure for securely storing, assembling, and preparing the data, and staff who use the data to develop reports and business insights.

That investment far exceeds the cost of any CRM, yet has it been matched with an equivalent degree of training in its end-use by managers and decision-makers? For us and many other organizations, the answer is no.

Operations can get very good at translating between the data and the business, but staff across Advancement must be able to speak the language. Author and advisor Bernard Marr says, “… organizations that fail to boost the data literacy of their employees will be left behind because they are not able to fully use the vital business resource of data to their business advantage.” (1)

Organizations large and small, in every sector, are coming to this realization. A 2019 Gartner survey found 80 percent of organizations now plan to start developing staff competency in data literacy. (2)

Data literacy simply means the ability to understand data in the context of one’s business knowledge. It includes knowing where the data comes from, how it’s defined, the methods used to analyze it, and having a view to applying it to achieve an outcome.

You don’t need to be a mechanic to drive a car. You don’t need to be an analyst to make decisions with data. The next big leap forward in data-informed decision making might lie in helping more and more staff across the organization learn how to drive.

  1. “Why Is Data Literacy Important For Any Business?” by Bernard Marr (see also “What Are The Biggest Barriers To Data Literacy?“)
  2. “Design a Data and Analytics Strategy,” Gartner Inc., 2019

Managers and decision-makers in Advancement must learn to speak data

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Without the right culture, a great analytics team with all the computing power in the world is a brain without a soul. Large corporations are discovering that technology is not the obstacle to becoming data-driven. People, process, and culture are the obstacle.

Our shops are no different. Our business intelligence analysts are talented, our data is of high quality, and we’ve got the software and infrastructure. But analytics maturity is a whole-organization effort.

Analytics practitioners require three things of managers and decision-makers: Context, challenge, and action.

Context: Analysts learn about business context through the discovery process that precedes analysis. But decisions are owned by people who run programs. They need to approach findings with understanding, not blind faith.

Challenge: Donor behaviour is complex and not amenable to easy and definitive answers. Interpretations can and should be challenged. Analytics teams are not service desks – insert a question and out pops an answer. The process is a conversation, not a transaction. A conversation can only occur between different perspectives that nevertheless carry equal weight.

Action: Analysis, to be effective, is not merely informational. It leads to action. An analyst can’t act, only the manager or decision-maker can.

Operations can get very good at translating between the data and the business, but staff across Advancement must be able to speak the language.

Unmasking the imposter within

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One day last year I enjoyed a few hours in discussion with some of my counterparts from other universities. That evening in my hotel room I was in a reflective mood. I had spent a great day with people more experienced and knowledgeable than myself, and my feelings about that were not entirely positive.

Fortunately my misgivings didn’t last. After all, I had flown a thousand miles expressly to spend time with people who knew more than I did. This was what I came for, to learn. The comparison of self to others was an involuntary reflex and a distraction.

It felt like a tiny echo from a time years ago when I perceived that things at work were not going well. The challenges seemed larger than my capacity to deal with them. One morning during this period I was trudging to the office when I happened to pass a graffito scrawled on a Canada Post letter box. It said, “You Are Wrong.”

Thanks, I said to myself, that captures my feelings perfectly.

Bit of an odd thing to write on a mailbox, I thought. The next time I passed it, I looked more carefully and found that it didn’t say “You Are Wrong.” It said, “You Are Strong.”

I don’t believe in personal messages from the universe, but if you believe in messages from the universe, I won’t argue. It was what I needed at the time.

The way we see the world is coloured by the story we tell in our head. The story is about our talent or smarts or level of passion, and everything in sight becomes evidence for the prosecution. A question at a meeting is hostile, a colleague’s passing glance is dismissive, everyone else seems better qualified. “Strong” reads as “Wrong.”

A little imposter syndrome is a healthy sign that a smaller version of yourself is about to be eclipsed by a larger version, and is kicking up a fuss about it.

Advancement can learn from corporations’ failure to become data-driven

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Organizations large and small have invested heavily in data management systems, BI software, infrastructure, highly skilled data scientists, and tools to gather the data itself. Large corporations have spent like crazy on big data and artificial intelligence, and plan to spend more. Yet they are failing to become data-driven.

A majority of technology and business executives in a 2019 survey reported that they have yet to create a data-informed culture and an organization that competes on analytics. These are players like American Express and General Electric. (1)

What these corporations are discovering is that technology is not the obstacle. People, process, and culture are the obstacle.

Higher education advancement shops are not large corporations but most of us can relate. We are not data-first, evidence-based organizations.

We and other universities have done all the right things. We’ve improved the depth and quality of our data, we track and measure not only our own activity but engagement activities of our constituency, we have brought on talented BI analysts, and we have better tools for data staging, reporting, and analysis.

Like a large corporation, we’ve beefed up operations. But analytics maturity is more than technical capability. It’s time to consider the whole organization.

I define analytics maturity as consistently making strategic decisions that are informed by data. We succeed at working with individual teams on ad hoc, tactical decision-making, and that’s real progress toward maturity. Let’s keep going!

1. “Companies Are Failing in Their Efforts to Become Data-Driven,” by Randy Bean and Thomas H. Davenport, Harvard Business Review, 5 Feb 2019

Shiny objects, bright ideas, and your team

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Recently I read about a cool project in a magazine and shared it with one of my managers. Another university had had success with it, it was related to a challenge we were having that week, and honestly it was just cool. I had some level of self-awareness at least: I described it as a shiny object that I was just passing along for interest, and said I would not follow up. To her credit and mine, it has never come up since.

No harm done, I suppose. And ideas are good, right? But a supervisor’s ideas, even off-the-cuff ones, are hard to ignore. This manager might have moved my idea from pile to pile for weeks, unsure what to do with it but reluctant to throw it out. Like an appliance left plugged in that draws current in a steady trickle, it might have exacted a small but real cost in mindshare.

Better to jot the idea down and let it rest. I’ve always enjoyed musing aloud about cool things, but coming from the leader of a largish team, such talk may not read as blue-sky chitchat. Some people will give impulsively-shared ideas no more weight than they deserve; others will be alert for cues about what they should be doing. The latter will misinterpret notions as direction.

If you’re into brainstorming, it should be a planned event with ground rules and equal participation by all.