Seven two. Let's walk the talks extremely countrywide on the Prime Media plus app. Chapman is past five. Good morning, welcome to the second half of the Early Breakfast Show. And we're looking at transforming organizations using data. So you might have heard about the conversation or the conversational trope digital transformation. What does it really mean for organizations and how can it be leveraged. We're talking to asang Amehana,
who's a business improvement specialist. No he didn't hear me hit correctly, because we will be talking to Asan to explain the real foundations behind it. She's a business improvement specialist. So what's digital transformation? Often sparking a rush to adopt new system but as business improvement specialist asang A Mayan explains, the real foundation lies in understanding and optimizing data and processes. First, we will look at how organizations can build sustainable digital
futures while still putting people at the heart of the transformation. Asana, thank you for joining us this morning, and how are you doing good, good good things inrighting me? So many organizations jump straight to adopting new digital systems. Why is starting with data and process optimization? Crucial. So if you
can explain this in the most simplest of ways. You know, we often get told if you're talking to your grandmother or like a child, how would you explain what digital transformation is and how important it is to businesses?
Okay, So to explain it to the most simplest way I can think of, digital transformation is basically a way in which we improve the manner in which you operate using digital software. Right, And why it's important to start with the process is that, in fact, if you get the process right, you'll actually start to experience efficiency before
you've even put the systems in. And it's commonly said that if you start to put in systems before you improve the process, you also risk the chance of multiplying the inefficiencies that sit within the process.
Okay, so we're taking it a step back to say systems before we actually talk about processes. Is there some strategic reasoning behind why you'd want to look at the systems first? Process first? Okay, process first, full systems.
And due to past experiences, my default is not to jump into a system solution system solution, right, And I'll give you an example. I'd worked for a company that wanted to develop all of its offices based on one key indicator is that they wanted to reach one industry KPI and there was KPIs in place. Processes were, you know, in place for us to start redeveloping. However, I went to look at the data and the information that informed that.
I looked at what is it that needs to be done in order to achieve this KPI and the result was that we found that we could improve or reach this industry industry target by simply changing the face of the company, painting maintenance, cutting trees, putting the product in front. And it had also had requirements to improve safety, so pedestrian walkways, painting signage, and also using the existing budget for marketing to put up particular branding in particular places.
And that was essentially where it kicked everything off in that we're able to achieve this without touching KPEX.
Just can you break down that k PIX Well, what does that mean?
So KPEX is using capital to improve something. So digital transformation requires heavy capital investment, right, and by just looking at the existing environment we found what you have already and looking at actually what the client wants. You can achieve that.
By just how do you define the gap between data collection and meaningful in amation and how can organizations bridge it? I often talk about data dums with collecting it and then we're leaving it. But it's not speaking to each other, it's not intelligible.
Data is a substance and must we build information and decisions are made, okay through good information? Right? So when companies want to use a process where they collect data first and see what the insights say and then kind of figure what to do next, I think that's backwards.
Okay.
The aim is to collect to first understand what we're trying to achieve, right, so what is the outcome? Then what is the information we need to be able to make those decisions? And then we collect the data that's relevant to making that decision. So you get data dumbs because let's collect data because we can. Let's then make sense of it and then see where it drives where it drives us, so it should be the other era out So.
What's that step of you understand to interpret the brief and what you want to achieve, and then you said you information and research and before you go into data the actual data collection, what's that step before?
So the step is first know what you're trying to answer. Okay, what decision are you trying to make? Then what information do I need to be able to make that decision? And then collect the data to be able to make that framework.
Yes, all right? And the common mistakes businesses make regarding data management and you know a lot of so we need data driven decisions, as you say, but what are some of the mistakes businesses make regarding data management?
It's collecting too much data with that purpose, so we've touched on that. This results in the data in the data dams and there are no insights in that right. And then it's also confusing systems for solutions, so a real focus should be on the workflow and clarity, not just the technology, right. And then it's also forgetting data must be trusted and used by people. And then I think a big one for me, it's the poor data capturing.
So usually there's no standard set beforehand, and the time taken to do that would would save so much in terms of askating to the information that we want so people, For example, when you're working with Excel Excel sheets entering January the forward and jan jan and twenty twenty four oh one and one twenty twenty four, and then you multiply all those inconsistencies.
Through like rows of data human errors.
Exactly, or you're multiplied by the number of entries that you're putting in. You spend more time cleaning the data. So that's exactly so that it can give information.
Gotcha. So just giving someone a spreadsheet really doesn't help to give those data driven insights. We hear a lot about, you know, data driven insight, but what does that look like? Who uses data driven insights and why is it so important?
So data driven insights are essentially used by leaders to make decisions, but the people who collect that data are the users of the processes, right, So the then is to collect the data in a clean manner so that it can be quickly analyzed. So it's that analyzing part that gives the information and the insights in order to make that decision.
And there's people involved here. So how do you balance the technical human elements to create sustainable improvements? Because you've got a data capture, as you've mentioned, can we use that as an example.
I don't lead with technology. Technology I feel is the last step, So I often say that it's it's optimizing the process that you currently have before you then transform. So if you can be sure that you've optimized what you have by optimizing, it means it's cleaning up the process, taking out the non value ads you'll often hear of that, and making sure that it's the value that you're trying to get out of the process that you maximize.
Sure, you don't want to tell someone that their workstream or task is a not value add but there's a way in which change management is key. What advice would you give organizations about asking the right questionestion before collecting data and even pushing back to say I need a bit more context so this has value.
Okay, I'm going to go back to that link.
Its right.
There's an example of the Boeing seven three seven right that there was an initiative to improve systems and they improved systems without the people involved, and the people are the pilots that actually end up using the system. And what happened there is that two Boeings crashed, and that was a devastating outcome because there was so much surety in that we fixed the system and the technology worked, but because you didn't train the pilots. The pilots weren't
able to use. So the technology fixing the technology without the people, it's quite dangerous.
So it also sounds like AI almost People say AI is going to replace us, and I think it's there's certain elements where it has existed, but we can go towards automation. So how all this kind of thinking impact
workers and listeners alike? Here? And in terms of just as a partying shot, if you've just joined us, we're wrapping up a conversation regarding transforming organizations using data with Asanga Malos a business improvement specially, so practically speaking for listeners out there as a takeout that is not just consultative, what would you say is an impactful workers and listeners alike can get from digital transformation?
And I'm going to start it for from my perspective. For me, data removes bias and subjectivity, right, I have the confidence to sit in any table. And I think this also goes way back to being young and black in a male dominated space. There's there's bias from outside because they're judging your age, maybe they judging your gender
or your race. But there's also your personal insecurities However, when I put something upfront and it's data editions, I'm sure that there's a saying that you must know, don't don't trust me, trust the data. It takes away that it gives you that confidence and there's also credibility. So that's that's where it's important for me, and that's where I can sit in a confidence position. But I think for workers it your voice doesn't get lost in rank
or personality. And I think for leaders, data helps you lead with clarity and not instinct, right so as leaders as so, I also want to say data shouldn't be used to justify one's decision, but it should be used to direct it.
As indeed, you know, there's the theory side of things and then there's a practical experience. But it's a pleasure talking to you this morning a sang Aman as a business improvement specialist. We've been talking transforming organizations using data. Sang I thank you so much for your time this morning and all the best for the future.
Thank you.