And then the other piece of the training that I do is I work with senior analysts as they begin to step into their first leadership roles. Because the other thing that is hugely bought by I found very relevant to my world was that when you start leading, it's as if someone goes here, the keys to the car, if you go, you mean you want driving lessons to beat after school drive.

And a lot of us analysts types are super introverted and really fact oriented. And we like our processes. And we don't necessarily do the soft skills particularly well. Yet we all respond really well to those being done well, but they don't necessarily come naturally to us. And I include myself in that. But it's something that we need to learn and be aware of how do we communicate with people up the chain? How do we take analytics and when someone says, but I don't understand. As an analyst, our tendency is to double down on the detail. And when you know what you're looking for, you can literally see people lose the will to live because they don't understand and that isn't helping, and they're not number oriented. So just make it stop.

.I think it was partly due to a chance that we eventually built Taran DM because it was at the beginning of 2020 and we had two interesting projects. So it was like a lot of potential and one of them was a decision management platform, like some customised one with scorecards for a new fintech. And the other one was a platform for a large German automotive company, which was supposed to optimise their supply chain. Middle of February we were flying to the German company, to the exporter. And we had a really good session with the management team, getting a lot of ideas on how to move it further. We were super excited about it, but as I said, like it was February 2020.

So when COVID was spreading within a few weeks, those guys stopped answering our emails and then we got back to them later, they said sorry, but our supply chains have gone haywire because of Covid, we cannot do anything a few months or maybe even a few years!

So that project got killed. And then we ended up with the other second big project, which honestly was, I think, a better fit for us because most of the team was coming from the finance, we had like the experience with infrastructures doing like real time decisions, whether it was credit risk, whether it was the high frequency trading was a large part of the team came from actual credit risk teams that may have been using the tools such as FICO Blaze or Experian Power Curve. We know the the strong points, the weak points, so big up to the I would say drawing board and we thought maybe there is like a market opportunity here or let's you know, let's basically build something.

Whereby from the report we see that 7.5 million people in Tanzania do have bank accounts. And with the presence of mobile money operators, we have 24.4 million mobile money wallets currently opened by these telcos talking into the lending space.

In particular, the banks still dominate the biggest share in terms of value, but the market has seen a new narrative of digital loans, which is mostly dominated by MMOs and fintech players through their micro lending services, which still requires banks collaborations by funding for regulatory approval, as well as managing the provision side with the help of the FinTech players will bring onto the table, the scoring and big data capabilities.

CreditPy is including some functionalities regarding to develop credit risk scorecards, a PD model, basic data analysis, and it checks the informative variables in an automated way to determine which features is going to be passed to the predictive model. It also generates an automated model framework that is actually searching for the best predictive model across the different feature sets that potentially can be used during the model development.

And after this, there are actually many functions that has been defined to create the rating scale. And also, after creating the rating scale, its offers to do some validation, like univariate gini check, information value checks, basic multicollinearity checks, stability checks on the futures to see if there will be any drift on the predictions on the auto sample set bit applies a basic rate of evidence transformation on the data.

And finally, it allows the user to validate the created rating scale, predictive power of the model and the calibration.

But effectively, if you think about it, there is no company in the world, maybe just a few, whose whole businesses is data, noone really just creates data, noone really just handles data. However, every company, whether you're logistics, retailer, bank insurance, your mom and pop shops on a corner, they all deal with data - you've got prices, you've got sales, you've got measurements, if you are building a house.

However, as you said, often in organisations, they try to put a silo around data, they say, I have a Chief Data Office, I have a data function, I have specifically data processing, and data is a bit like the blood of an organisation. It goes everywhere, however, because it goes everywhere, you cannot just silo it somewhere. Of course, you need to have some patterns, some standards around data, but every part of the of a business has to be responsible for the data.

It's another excellent question, because I think there's a lot of discussion about big data and AI and machine learning. And they go together well, big data and machine learning, but they're not the same thing.

A lot of what gets done with machine learning in our industry is applying very nonlinear methods to the same old data. In fact, everything I've talked about so far has been more intelligent use of the data you've always had, Building Better models of your business and of your product.

If you have unique data, that's great. And often we find unique datasets in finance companies, where they're doing some kind of specialty lending. You know, one of my favourites for a long time was a group that was looking at point of sale loans for cruise ship tickets.