The title CDO started out as a joke

Are they mandated to?Usama Fayyad: Its almost getting there.

I think many banks fear getting in trouble if they dont have a CDO now.

The industry basically demands an accountable individual, who is looking and seeing whether that organization is using the data properly, how theyre using it, do they have the right limitations on its use.

I used to call my role at Barclays the responsible use of data, but before you can use it responsibly, you need to make sure that its usable, and available, and can be actually applied in a lot of these innovative applications as well while staying on the safe side.

So one of the big programs I pushed there was I called it KYC, which is in banking, is know your customer, a big area of spend.

I called it The Journey from KYC to UYC, where UYC stood for understanding your customers, which actually shows the business value rather than just doing it for a regulatory reason, which is what KYC is all about.

Kate Strachnyi: You mention a skills gap, which is validated by The Data Literacy Project led by Qlik, who says that only 24% of the global workforce is data literate.

What is the first step a CDO should take to drive data literacy and data culture within their companyUsama Fayyad: Thats a great question and one of the things that Ive done personally with a lot of our engagements as Open Insights, say with some of the largest organizations in the world, we actually quickly realized that data literacy and data culture is a big part of making the stuff useful and usable.

To that end, we actually launched something we call Data Academy in a lot of these big companies that are targeted at basically achieving data literacy.

Theres literacy at two levels.

There is what an average employee should know about data, why its important, why its important to be safe with it, why its important to be sensitive to it, why its important to make sure its kept safe.

Reference: https://www.


com/us/bi/data-literacy-reportThen there is the part of whats possible with it, which goes more into the analytics.

A lot of data analysts and a lot of business analysts dont even know the art of the possible on what you can do with data mining algorithms, with machine-learning algorithms.

I mean, a great example in monetization, when I joined Yahoo within two years, but putting in the right machine-learning system, data systems, data management regimes, and some of the big data technology, we were able to generate, without much work, $800 million of additional revenue derived from targeting for Yahoo.

Basically, they were selling the same ads at 10 to 20 times the price that they used to sell at before.

Because now, they could actually do targeting that they could prove to their customers, “Well, this well-targeted.

This is reaching the right audience, which resulted in a much better dynamic!” both with the advertisers willing to pay more, and the consumers being slightly happier.

Nobody loves to see ads, but if you see ads that are relevant, its a better experience than ads that are completely irrelevant or at the wrong time.

That created a good dynamic there that actually allowed us to create value.

So its very important to have that data culture, and that awareness and part of that is both the data literacy as well as the data science literacy.

Kate Strachnyi: Okay.

Lets say you have a big company like Barclays.

How do you actually implement something, where lets say the average admin or somebody who might touch data once in a while, but is not really a data analyst or really specialized to work with data, how much do you teach them?.How much should they know?Usama Fayyad: Yeah.

Its different levels.

When we ran these data academies -we did that at Barclays.

We did that at Barclays Africa, as well, we did that at MTN, which is Africas largest telecom, we did it at many companies in the U.


and in Europe- The way you do it is you create different tiers for different levels of awareness and different concepts that you want to emphasize.

So somebody who doesnt touch a lot of data, we probably want them to be aware enough to understand that KPIs matter, which KPIs they should pay attention to.

Our philosophy is every KPI, or key performance indicator, should have a version that goes all the way from the board to the CEO, all the way down to the lowest level in the company.

If you dont have that, then something is broken in the way youre –Kate Strachnyi: You mean it should be the exact same KPI or –Usama Fayyad: No, no.

Its at the different level of granularity, right?.The person in the operations probably wants to see a lot more detail.

The person on the board doesnt want to see any detail, but wants to see the big signal and where should they pay attention to whats going wrong, like which region is veering off forecasts and so forth.

That insistence, culturally, on saying, “Every report has a version that goes all the way from a board to the lowest level, even though it may not go to the board ever.

” Is instills the culture of thinking about it that way.

Most of the training is about awareness, and as you get down to specialized roles, it becomes more technical and more around whats possible because those are the people who can actually make a difference, and help you make stuff happen.

Ive been involved personally in many data science projects, where we would work hard to come up with an amazing predictor of something.

One example that comes to mind was working with one of the very large car manufacturers where we were trying to predict the sales by car and by model in different micro-markets.

The problem was very hard.

We cracked it in a very innovative way, and in the end, we discovered that the executive team was unable to do much with those predictions, even though they were super accurate.

In those years, incentives on automobiles were a big deal.

We figured incentives, where you pay consumers money back for buying a car, was the easiest thing to reprogram, right?.You could change them by market, by demand, etc.

It took, basically us working with the executive assistant, who prepares the.

So we worked with the Executive Assistant to take the spreadsheet that goes to the executive team, and just allow us to colour code certain cells as in these are red because youre probably overpaying.

These are green, theyre great.

And these need attention or change.

Just doing that color-coding, went from great predictions, where people were just watching them as a spectator sport to like, “Oh, now, I can act on it.

In this market, I need to change it up for down.

“Now, it became actionable.

Thats the importance of basically involving everybody because the whole supply chain of data and the consumer chain is reliant on a lot of people doing a lot of different roles.

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Reposted with permission.

Bio: Kate Strachnyi is an author, Advisory Board Member of IADSS, Udemy instructor, and host of the Datacated Weekly, a project dedicated to helping others learn about various topics in the data realm.

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