Speaker 1: Seven two. Let's walk the talks extremely countrywide on the
Speaker 1: Prime Media plus app. Chapman is past five. Good morning,
Speaker 1: welcome to the second half of the Early Breakfast Show.
Speaker 1: And we're looking at transforming organizations using data. So you
Speaker 1: might have heard about the conversation or the conversational trope
Speaker 1: digital transformation. What does it really mean for organizations and
Speaker 1: how can it be leveraged. We're talking to asang Amehana,
Speaker 1: who's a business improvement specialist. No he didn't hear me
Speaker 1: hit correctly, because we will be talking to Asan to
Speaker 1: explain the real foundations behind it. She's a business improvement specialist.
Speaker 1: So what's digital transformation? Often sparking a rush to adopt
Speaker 1: new system but as business improvement specialist asang A Mayan explains,
Speaker 1: the real foundation lies in understanding and optimizing data and processes. First,
Speaker 1: we will look at how organizations can build sustainable digital
Speaker 1: futures while still putting people at the heart of the transformation. Asana,
Speaker 1: thank you for joining us this morning, and how are
Speaker 1: you doing good, good good things inrighting me? So many
Speaker 1: organizations jump straight to adopting new digital systems. Why is
Speaker 1: starting with data and process optimization? Crucial. So if you
Speaker 1: can explain this in the most simplest of ways. You know,
Speaker 1: we often get told if you're talking to your grandmother
Speaker 1: or like a child, how would you explain what digital
Speaker 1: transformation is and how important it is to businesses?
Speaker 2: Okay, So to explain it to the most simplest way
Speaker 2: I can think of, digital transformation is basically a way
Speaker 2: in which we improve the manner in which you operate
Speaker 2: using digital software. Right, And why it's important to start
Speaker 2: with the process is that, in fact, if you get
Speaker 2: the process right, you'll actually start to experience efficiency before
Speaker 2: you've even put the systems in. And it's commonly said
Speaker 2: that if you start to put in systems before you
Speaker 2: improve the process, you also risk the chance of multiplying
Speaker 2: the inefficiencies that sit within the process.
Speaker 1: Okay, so we're taking it a step back to say
Speaker 1: systems before we actually talk about processes. Is there some
Speaker 1: strategic reasoning behind why you'd want to look at the
Speaker 1: systems first? Process first? Okay, process first, full systems.
Speaker 2: And due to past experiences, my default is not to
Speaker 2: jump into a system solution system solution, right, And I'll
Speaker 2: give you an example. I'd worked for a company that
Speaker 2: wanted to develop all of its offices based on one
Speaker 2: key indicator is that they wanted to reach one industry
Speaker 2: KPI and there was KPIs in place. Processes were, you know,
Speaker 2: in place for us to start redeveloping. However, I went
Speaker 2: to look at the data and the information that informed that.
Speaker 2: I looked at what is it that needs to be
Speaker 2: done in order to achieve this KPI and the result
Speaker 2: was that we found that we could improve or reach
Speaker 2: this industry industry target by simply changing the face of
Speaker 2: the company, painting maintenance, cutting trees, putting the product in front.
Speaker 2: And it had also had requirements to improve safety, so
Speaker 2: pedestrian walkways, painting signage, and also using the existing budget
Speaker 2: for marketing to put up particular branding in particular places.
Speaker 2: And that was essentially where it kicked everything off in
Speaker 2: that we're able to achieve this without touching KPEX.
Speaker 1: Just can you break down that k PIX Well, what
Speaker 1: does that mean?
Speaker 2: So KPEX is using capital to improve something. So digital
Speaker 2: transformation requires heavy capital investment, right, and by just looking
Speaker 2: at the existing environment we found what you have already
Speaker 2: and looking at actually what the client wants. You can
Speaker 2: achieve that.
Speaker 1: By just how do you define the gap between data
Speaker 1: collection and meaningful in amation and how can organizations bridge it?
Speaker 1: I often talk about data dums with collecting it and
Speaker 1: then we're leaving it. But it's not speaking to each other,
Speaker 1: it's not intelligible.
Speaker 2: Data is a substance and must we build information and
Speaker 2: decisions are made, okay through good information? Right? So when
Speaker 2: companies want to use a process where they collect data
Speaker 2: first and see what the insights say and then kind
Speaker 2: of figure what to do next, I think that's backwards.
Speaker 1: Okay.
Speaker 2: The aim is to collect to first understand what we're
Speaker 2: trying to achieve, right, so what is the outcome? Then
Speaker 2: what is the information we need to be able to
Speaker 2: make those decisions? And then we collect the data that's
Speaker 2: relevant to making that decision. So you get data dumbs
Speaker 2: because let's collect data because we can. Let's then make
Speaker 2: sense of it and then see where it drives where
Speaker 2: it drives us, so it should be the other era
Speaker 2: out So.
Speaker 1: What's that step of you understand to interpret the brief
Speaker 1: and what you want to achieve, and then you said
Speaker 1: you information and research and before you go into data
Speaker 1: the actual data collection, what's that step before?
Speaker 2: So the step is first know what you're trying to answer. Okay,
Speaker 2: what decision are you trying to make? Then what information
Speaker 2: do I need to be able to make that decision?
Speaker 2: And then collect the data to be able to make
Speaker 2: that framework.
Speaker 1: Yes, all right? And the common mistakes businesses make regarding
Speaker 1: data management and you know a lot of so we
Speaker 1: need data driven decisions, as you say, but what are
Speaker 1: some of the mistakes businesses make regarding data management?
Speaker 2: It's collecting too much data with that purpose, so we've
Speaker 2: touched on that. This results in the data in the
Speaker 2: data dams and there are no insights in that right.
Speaker 2: And then it's also confusing systems for solutions, so a
Speaker 2: real focus should be on the workflow and clarity, not
Speaker 2: just the technology, right. And then it's also forgetting data
Speaker 2: must be trusted and used by people. And then I
Speaker 2: think a big one for me, it's the poor data capturing.
Speaker 2: So usually there's no standard set beforehand, and the time
Speaker 2: taken to do that would would save so much in
Speaker 2: terms of askating to the information that we want so people,
Speaker 2: For example, when you're working with Excel Excel sheets entering
Speaker 2: January the forward and jan jan and twenty twenty four
Speaker 2: oh one and one twenty twenty four, and then you
Speaker 2: multiply all those inconsistencies.
Speaker 1: Through like rows of data human errors.
Speaker 2: Exactly, or you're multiplied by the number of entries that
Speaker 2: you're putting in. You spend more time cleaning the data.
Speaker 2: So that's exactly so that it can give information.
Speaker 1: Gotcha. So just giving someone a spreadsheet really doesn't help
Speaker 1: to give those data driven insights. We hear a lot about,
Speaker 1: you know, data driven insight, but what does that look like?
Speaker 1: Who uses data driven insights and why is it so important?
Speaker 2: So data driven insights are essentially used by leaders to
Speaker 2: make decisions, but the people who collect that data are
Speaker 2: the users of the processes, right, So the then is
Speaker 2: to collect the data in a clean manner so that
Speaker 2: it can be quickly analyzed. So it's that analyzing part
Speaker 2: that gives the information and the insights in order to
Speaker 2: make that decision.
Speaker 1: And there's people involved here. So how do you balance
Speaker 1: the technical human elements to create sustainable improvements? Because you've
Speaker 1: got a data capture, as you've mentioned, can we use
Speaker 1: that as an example.
Speaker 2: I don't lead with technology. Technology I feel is the
Speaker 2: last step, So I often say that it's it's optimizing
Speaker 2: the process that you currently have before you then transform.
Speaker 2: So if you can be sure that you've optimized what
Speaker 2: you have by optimizing, it means it's cleaning up the process,
Speaker 2: taking out the non value ads you'll often hear of that,
Speaker 2: and making sure that it's the value that you're trying
Speaker 2: to get out of the process that you maximize.
Speaker 1: Sure, you don't want to tell someone that their workstream
Speaker 1: or task is a not value add but there's a
Speaker 1: way in which change management is key. What advice would
Speaker 1: you give organizations about asking the right questionestion before collecting
Speaker 1: data and even pushing back to say I need a
Speaker 1: bit more context so this has value.
Speaker 2: Okay, I'm going to go back to that link.
Speaker 1: Its right.
Speaker 2: There's an example of the Boeing seven three seven right
Speaker 2: that there was an initiative to improve systems and they
Speaker 2: improved systems without the people involved, and the people are
Speaker 2: the pilots that actually end up using the system. And
Speaker 2: what happened there is that two Boeings crashed, and that
Speaker 2: was a devastating outcome because there was so much surety
Speaker 2: in that we fixed the system and the technology worked,
Speaker 2: but because you didn't train the pilots. The pilots weren't
Speaker 2: able to use. So the technology fixing the technology without
Speaker 2: the people, it's quite dangerous.
Speaker 1: So it also sounds like AI almost People say AI
Speaker 1: is going to replace us, and I think it's there's
Speaker 1: certain elements where it has existed, but we can go
Speaker 1: towards automation. So how all this kind of thinking impact
Speaker 1: workers and listeners alike? Here? And in terms of just
Speaker 1: as a partying shot, if you've just joined us, we're
Speaker 1: wrapping up a conversation regarding transforming organizations using data with
Speaker 1: Asanga Malos a business improvement specially, so practically speaking for
Speaker 1: listeners out there as a takeout that is not just consultative,
Speaker 1: what would you say is an impactful workers and listeners
Speaker 1: alike can get from digital transformation?
Speaker 2: And I'm going to start it for from my perspective.
Speaker 2: For me, data removes bias and subjectivity, right, I have
Speaker 2: the confidence to sit in any table. And I think
Speaker 2: this also goes way back to being young and black
Speaker 2: in a male dominated space. There's there's bias from outside
Speaker 2: because they're judging your age, maybe they judging your gender
Speaker 2: or your race. But there's also your personal insecurities However,
Speaker 2: when I put something upfront and it's data editions, I'm
Speaker 2: sure that there's a saying that you must know, don't
Speaker 2: don't trust me, trust the data. It takes away that
Speaker 2: it gives you that confidence and there's also credibility. So
Speaker 2: that's that's where it's important for me, and that's where
Speaker 2: I can sit in a confidence position. But I think
Speaker 2: for workers it your voice doesn't get lost in rank
Speaker 2: or personality. And I think for leaders, data helps you
Speaker 2: lead with clarity and not instinct, right so as leaders
Speaker 2: as so, I also want to say data shouldn't be
Speaker 2: used to justify one's decision, but it should be used
Speaker 2: to direct it.
Speaker 1: As indeed, you know, there's the theory side of things
Speaker 1: and then there's a practical experience. But it's a pleasure
Speaker 1: talking to you this morning a sang Aman as a
Speaker 1: business improvement specialist. We've been talking transforming organizations using data.
Speaker 1: Sang I thank you so much for your time this
Speaker 1: morning and all the best for the future.
Speaker 2: Thank you.