Featuring some of our experts around the world, from the U.S., Australia, South Africa, New Zealand, and Singapore. Through their digital business transformation, where business and data converge, the theme for our virtual summit. Their expertise span across industries including, but not limited to, manufacturing and distribution; life sciences; regulated industries; retail and a wide spectrum of business outcomes; navigating business processes such as supply chain, procure to pay, order to cash, HCM, PLM, and others. Be sure to use the chat feature on the bottom of your screen to submit your questions and exchange ideas with insiders. Enjoy your interactive session and thank you once again for joining our virtual summit.
Frankie Steel (02:05):
Hello, and thank you for joining us today. My name is Frankie Steel, and I'm the VP of sales in Australia and New Zealand for Syniti. I am very pleased to be moderating this session today. We have some very experienced experts on the panel today to share their knowledge with you. Please, can you help me to make this session interactive? Use the questions box below to post your questions, and we will do our best to answer them in this session.
I'd like now to ask the panel to introduce themselves. First off, joining us via the phone is Paul Deady. Paul, you've had a very interesting background. Can you tell us a little bit about it and your convictions when it comes to digital strategy and data? Right. I think we might be having few technical issues with Paul at the moment, I will flick on to Rohit. Rohit, please can you introduce yourself and share your areas of expertise?
Rohit Vattipalli (03:11):
Sure, Frankie. Hello, everyone. I am Rohit Vattipalli, Senior Consultant at Syniti, based in Singapore. I specialize in information governance and have experience working with clients from both governments and corporate entities in helping them manage data as an enterprise asset and for helping them leverage data for informed decision-making.
My personal favorite topic though, is in actually managing change after the implementation of information governance initiatives to ensure a successful high rate of adoption. Also, another area of particular interest to me and the area which I follow very closely is on the role of data and data governance and public policy and macroeconomics. Over to you, Frankie.
Frankie Steel (04:08):
Excellent. Thanks Rohit, and I'm sure we'll get a lot of questions today around the change management aspects, as I know that's a real challenge for some of our clients. Johannes, where are you joining us from today? Can you tell the audience please a little bit about you?
Johannes Lubbe (04:24):
Thanks, Frankie. Hi guys. My name is Johannes. I'm joining today from South Africa. I'm actually stuck here at the moment with no flights back to Singapore. But a little bit more about myself, I joined the South African National Defense Force as a Software Tester [inaudible 00:04:43] and worked on very interesting projects back in the day. We helped our customers, being the Navy, with tactical command and control systems.
A very important piece of my work back then was to ensure that the hexadecimal data pieces that we've seen across HF radios are actually correct for the symbols that gets displayed on the tactical command and control systems. Right then, I also understood the importance of having the correct data and thereafter would jump in problems if you do not send the correct data. In fact, we actually had people's lives at stake in our exercises of testing that software.
Straight from there, I had an interest in data and the importance of data in systems and enterprise-wide cross-harmonization of data as well. Since then, I've been in data-related projects. I joined an implementation consulting firm in South Africa. Since then, I've been in data-related projects all the way until I joined Syniti about three years ago.
Together with Syniti, we've helped customers in multiple verticals to manage their data as a strategic asset, and it stretches across all of the modern book knowledge areas in terms of data architecture, better quality reference to master data, and obviously linking all of that back into transformational journeys as well. I'm happy to join today and share some insights in what we've gained so far in the region.
Frankie Steel (06:17):
Wow, I love that. Using data to actually save lives, I think that's fantastic. We will try again to reconnect with Paul. Again, Paul, can you just give us a bit of understanding about your diverse backgrounds and your thoughts when it comes to digital strategy and data?
Paul Deady (06:36):
Sure, Frankie. Can you hear me clearly now?
Frankie Steel (06:39):
Yes, we can hear you. Can you speak up a bit please?
Paul Deady (06:43):
Sure no problem. Is that a little bit better?
Frankie Steel (06:43):
Paul Deady (06:44):
Great. Listen, and apologies for the technical issues and then connecting, but my relationship with Syniti actually goes back quite a while and BackOffice prior to Syniti. While I'm a relatively new member to the Syniti team leading delivery here in ANZ, my relationship with BackOffice and with Syniti as a customer and as a partner goes back probably 10 to 12 years.
And in my days as a customer at Rio Tinto, BackOffice and Syniti, we're a key enabler for us folks from a platform point of view and from a data strategy perspective. And in my days with AY and with SAP, they were a key partners for us in many transformation programs. And when I look at a data today, and being a member of the syniti team, what excites me is the history and the experience that Syniti have had as a data migration and platform partner.
But the drive that the organization has to know and have data is more of a strategic asset and the role that we can try with our customers to have data as a topic that we talk about upfront when we talk about transformation, as opposed to being more of a migration topic to the life cycle of transformation.
So my principles and drivers for data, Frankie are very much driven on and data being an upfront conversation, and that will use it to measure the maturity and readiness for other business to achieve the transformational outcomes. And probably more important post-migration of the data. We use it as a barometer to understand and assess the success of the transformation post the technical deployment. So for me, data has got a full life cycle relationship, when you look at transformation.
Frankie Steel (08:34):
I love it. Thanks, Paul. And it kind of leads me to my next question. Rohit, we've just heard this from Paul and we've heard it from the experts in the previous sessions. Data as an asset or more importantly data as a strategic asset. So our CDO is really honding in on this as a theme. To you, What kind of data matters?
Rohit Vattipalli (09:01):
Frankie, when we talk about any asset, any enterprise asset, the first classification, which we would normally think of is whether an asset is an appreciating asset or a depreciating asset. And this is also applicable to data. Right. The normal business processes, which we associate with assets, which is about stock taking and also valuation and housekeeping, which typically happen in plants and warehouses, this happens in data as well, and this should happen.
And in my observation, enterprises are doing those things though a little not more. This needs to evolve as a practice. What we can actually look at data as an asset is it is important to identify which one are those appreciating assets. Which means the historical data, which over a period of time provides more and more information for your analytics, for your predictive modelings, for your future machine learning algorithms to actually look at all the historical transactions and say, Hey, this is the kind of consumer behavior that you're expecting.
This is what the market is responding to a certain change in a product. Let me just give you an example of a bank I worked with. This bank has identified such an appreciating asset, which is 15 years of historical credit card transactions. What they have done is they have used these transactions to identify patterns of credit card fraud. When they actually looked at, they identified certain patterns in which those who steal credit cards spend the money.
They identified that typically within an hour of the credit card being stolen, the thieves actually before the owner of the credit card realizes that it is gone. They actually spend it in the outskirts of the city where there is less surveillance and coverage. That's an important thing. They use that in order to immediately alert certain suspicious transactions and after which they were able to actually get down the loss because of fraud by over 27%, which is quite significant.
That is the kind of data which really matters to enterprise. It is important that enterprises actually take a careful look at all the data that is available. Especially in these days, it's so much of data that is coming in through mobile devices, the IOTs and the marketing channels, or online bed, click streams data. It's important that you identify the one which is really appreciating in nature while also paying attention to the depreciating data asset, and make sure that you know could do the archival on time so that the processing power of your systems is actually optimized for the most important data assets.
Frankie Steel (12:11):
That's a really great example. Thanks for that. Yeah. Amazing to see the impact on fraud and I'm sure 27% [inaudible 00:12:18] was a lot of dollars. Right. Johannes can I ask you, I've heard the declaration, 20% of data drives 80% of the business. How has that evidenced itself in some of the data programs that you have led?
Johannes Lubbe (12:35):
Frankie, the 80-20 rule for me is actually a very important principle that can be applied beyond data. I think in general, it's the Pareto principle to say that 80% of results is usually driven by 20% of the effort. What you can do, is you can look at it from different dimensions in your data space. For example, if you just think about how you want to manage data as a strategic asset to start with in large organizations, they might have hundreds of systems.
And you do not necessarily see every system as being critical for the business to survive. When you need to apply formal processes, to govern your data, you really need to look at the first systems [inaudible 00:13:22]. Then within each one of those systems, we have certain data sets that's more important than others. In terms of identifying the importance to almost go through a formal process of justifying why a specific data element is important for the business.
And whether that's compliance or profitability or trust, because customer satisfaction is these various parameters that you can incorporate in that conversation to say, why do I need to govern specific elements of data to help my business to be better? And that's just one domain and do make it pragmatic, and operationalizable to really start small and pragmatic when you actually govern data. Certainly you get the most out of your important data for your business benefit, but you can also look at it from a business perspective.
One of our customers in the Philippines, at a marketing campaign, and they wanted to focus on the 20% most profitable customers. And the problem that they had, is they had no way of identifying that 20% of important customers are, because they had multiple systems, they had customers in a financial institution with loans and credit cards and home loans. They had all these data in separate systems and they had no way of really linking it all together to make a decision to say, "We want to talk to these 20% for our marketing campaign to do our upselling or cross-selling."
We were able to help them, for example, to really give them the data to support that argument, to really have a focused approach in their marketing segments. Again, applying the Pareto principle and focusing on your 20% most important customers, and then, using data to actually do additional work on that, really brings value out of your data. And I think you can go all the way down to data initiatives from a cleansing perspective.
If you have data quality problems in mining and manufacturing organizations, you might have thousands and thousands of errors in your material. For example, on how do you decide which ones to prioritize? We just start with that cleansing exercise, again, applying the Pareto principle and filtering your data on the most important data.
First, the material that you use most, and then cleaning those, or having governance controls around those, it's very important for organizations to apply. That's my thinking in terms of the Pareto principle. I think you can really apply it in different dimensions, but at the end of the day, first focusing on what's important and what's driving value for the business and using data to improve on that is what I would say.
Frankie Steel (16:14):
That sounds like a really pragmatic approach. Thank you. Paul during our break, you shared with me your observation, that data is a lead indicator. Can you tell us more about that?
Paul Deady (16:27):
Yeah, sure. Frankie. And if I follow the clock back a little bit, and I think of my days, and specifically with Rio Tinto and where we were going through a global transformation of the business over 10 plus year period and what we did and in Rio Tinto. And we used data very much as a lead indicator prior to deploying the Rio Tinto business solution. A product group level six months prior to the plan transformation initiative, we would do go in and do a detailed data quality health assessment. Now we did that as the lead indicator to understand the readiness of that product group for that business to continue the next wave of transformation.
So early in my career, I learned the value of data as a lead indicator and with a real focus on business readiness to transformation. As we deployed them through at the business connect solution currently attend to across product group, to the life cycle of the deployment.
We again, used data through the life cycle of the project as a lead indicator, to understand the status of the design and build completeness. We consciously use the trial conversions as an indicator of readiness to deploy and then post deployment and reviewed and data real use data, to assess whether or not the transformation has achieved the targeted business outcome.
For me, my real experience's the customer data was the lead indicator prior to deployment. It was the lead indicator during deployment, and then it's an indicator post deployment to see whether or not we've achieved the targeted outcome and subsequent to real. And as I look at my consulting career, and I've used data specifically again, when we look at multi-program or multi-project portfolios, where with the first iteration of the program, we establish a baseline year data.
Then through the life cycle of the portfolio, we again use data as the lead indicator to select which program or project within the portfolio we should go with next. For me, the other dimensions of data being a lead indicator, Frankie, that gives me a view to the full life cycle transformation, readiness, effectiveness to deploy and whether or not we're getting a return on investment post to transformation.
Frankie Steel (18:55):
I think that's a fantastic view because it's a very fact-based and consistent view through the journey. I think that's super important. Anyway, I want to talk now a little bit, growing on your guys' expertise on what are the lessons that you've learned? Johannes, what does metadata mean to you? How does it work? And how does it accelerate enterprise business processes?
Johannes Lubbe (19:20):
Recommended data is, is extremely important for organizations. If they do have an inspiration to be data-driven. The reason is... Let's start with what metadata means. Metadata is data about data. You have operational metadata, you have business metadata. When I talk about business metadata, you have business glossaries and what certain terms mean and what that impact is to the organization.
And the more data you add about data, the more context you have about all your data pieces, and that really becomes powerful for your decision making from a business perspective. And if you look at customer, for example, you might have a term customer, what does that mean? You might have a data set customer, what does that mean? Where does that customer data sets exist? It might exist in multiple systems. A customer might have a sales order.
It might have an address, is shipped to a boat. It might have account groups. There's a lot of different terms that can be linked all together. And when you also define what it means to the business, what impact it is, if you do have, for example, a rule associated to a customer, that in my system, I need to ensure that you cannot delete a customer if it's still as open accounts receivable. And that's a very important rule that you want to apply on your data, to ensure that your AR is still actually, and your revenue is still being collected and for you to actually implement controls and register information about all of these data sets is very important.
Because when you register the rule against this customer data set, it will affect certain policies. It affects certain KPIs, your customer satisfaction again. It might also improve your business processes to ensure that if you govern, this is a rule to say that I want to protect my customer data and not allow for you to delete it. If it's open at accounts receivable, that actually means you are improving your revenue collection as a business process.
But also metadata can be used [inaudible 00:21:26] As a company, we actually have a philosophy of being metadata driven way, we use data about data, to accelerate data initiatives. To give you an example, if you move from one legacy system to another target system, you want to be able to compare the metadata of your source system and of your corporate system and [inaudible 00:21:56] automation and intelligence software to auto-generate mappings and rules to ensure that you actually do migrate the data appropriately to TOG system.
And in fact, one of our solutions, that Syniti has is actually have a metadata driven approach to generate thousands of validation checks, to ensure you simulate your data set against that TOG with metadata model to ensure that whatever data you move across is actually in check. A metadata driven approach is really important for any organization. The power that you have of context to data is something that you cannot put a value against, it's invaluable.
Especially if you have to adapt in times like today, where you need to have data about data. If you think about supply chain and inventory management and stock not moving, you need to understand if you identify stock that's stuck. You need to identify what that stock is. What does it mean for the company? Can we get rid of it? Should we move it, should we not? What's the decision making that goes with that.
If you actually register all of your metadata appropriately, for all of your different pieces of your puzzle, you'll be in a position to make important decisions that will affect your business. From my perspective, metadata is the way that every organization should look at the data strategies going forward to ensure that they bring all of these pieces together for the much needed context.
Frankie Steel (23:28):
Thank you. That was very insightful. Paul, you mentioned that you used to be a Syniti customer, and now you're Australia, New Zealand's Director of Consulting and my partner in crime. What are the lessons that you can teach others given that you've sat on both sides of the fence?
Paul Deady (23:51):
Yeah, Frankie. I suppose probably for me, I can be the voice of the customer within a [inaudible 00:23:58] and from a regional point of view, from a durable perspective. So far If I laid that view, the fact that I've sat in both sides of the fence and devoted to the customer is an important dimension. And when I look at data specifically, and specifically being a customer of BackOffice Syniti and a deliberate partner with BackOffice Syniti and my ascertaining, where does. An understanding that data is an asset, and it's an asset, and probably the only variable, one of the key variables, when you go into an organization to deploy. The one variable that they bring to the table, is the data. Largely today it is quite a common or generic and technology or solution footprint. So what an organization brains or the differentiator tends to be the data element.
Understanding that data is a differentiator and understanding that data is an asset. When I look at my role within syniti, from a consulting point of view, it's to help our customers understand data as an asset, understand how to make it a differentiator for them within their business and in their market. And then through our tool set and solutions ensure that they get a maximum return from that data as an asset, and they get the opportunity to use it, to really differentiate themselves within their industry sector or within their market. That's my focus coming into the consulting leadership role at Syniti.
Frankie Steel (25:23):
Excellent. I know we've started some of these conversations already, look forward to them continuing. Rohit can you bring to life for us when it comes to driving and delivering data value, what's your experience thing?
Rohit Vattipalli (25:42):
In my experience, I've been a witness to a very drastic transition across the world where the business decisions are taken based on individual memory, to business decisions, being taken based on organizational memory. Data forms a very integral part of organizational memory. Any enterprise, which, which does not manage data as an asset, over a period of time of time will suffer from organizational memory loss.
This organizational memory loss actually results in decisions that are not very well informed in nature. Enterprises lose that particular competitive edge, which is supposed to be there. Personally, when I look at data, I would actually relate and I would always ask myself, what if data were a person? What if data were an individual? I would look at the personality of that individual as someone who's an extrovert who speaks a lot, but is very verbose in nature.
So it is important that actually organizations keep all their eyes and ears open, to what this data person or data individual is actually telling them. And another important thing is just like personalities have context behind them. Data has a context. And then, when you add that particular context to the data, it actually becomes valuable information.
Something that your data interprets today based on certain parameters may not necessarily be the same five years down the lane. Which means, the usage pattern say certain Nokia phones 10 years ago may not necessarily be applicable today. The market is changing. The dynamics are changing. The consumer behavior is changing and so should the way we interpret data.
Another important aspect I have noticed is that we are slowly moving away from statistical modeling the business data. Because in the days when data was not that easily available, businesses actually looked at engaging statisticians to get sample data from the markets and analyze that sample data. And usually the sample data sizes would never be more than a few thousand, to draw their own interpretations.
Statistics is always open to assumptions and interpretations. Probably the data is not data when you manage it properly. When it is properly groomed as a personality, it speaks loud and clear. Organizations can ignore it at their own peril. Having said that and looking back to what Johannes just said, managing data as an asset. He spoke also about metadata and how important it is. Just like the way you actually keep a stock of all the inventory of assets that you have. It's important that enterprises maintain what data they have and under what context the data has been captured as well. Right. That is also equally important so that you derive the real value of data.
Because, when you draw certain conclusions, it is also important to understand what are the premises, which have gone behind it. That is when it completes the full picture for business decision-making. I think, the good thing is that enterprises are on the right path. Most customers I have spoken to, they are all asking these questions. They're saying, how do we actually prepare for our journey? We want to implement the cutting-edge technologies of data analytics, of predictive analytics, especially in the service industry.
Recently, I was talking to an insurance customer who was saying, "We want to be able to, identify potential service disruptions, which cannot go. Is there something that we can actually do to leverage the historical data again?" Right. What they started doing is they started identifying all these, they started bookmarking data to see what are the service interruptions, which have occurred in the past, and how are they linked to people, the training of people, and also the organizational structure. Now that is putting all the pieces of the jigsaw together to form a complete picture. That way, we will be able to create full value out of data.
Frankie Steel (30:32):
I think that's super important the whole picture piece. I think that's a really good analogy. Thanks very much Rohit. I also think you described me as data, but that's okay. Just one question because we're kind of running out of time. One last question, before we go and Rohit, I'll direct this to you. It's kind of topical right now with COVID-19. Business continuity is really important. We're seeing massive disruption in businesses today. How are you seeing customers and clients tackle that?
Rohit Vattipalli (31:09):
Well, it's an important question. I think this is on everyone's mind. These days, everyone is asking this question to see what are the lessons that we can learn from this crisis? The good thing is, your human kind has always been learning lessons from each and every crisis, whether it is the Asian financial crisis or the 2009 crisis.
These things have actually changed the way financial institutions respond to credit risk or manage the credit risk. But this time around, the COVID crisis is creating a much larger disruption. And the first major impact it is going to create is on supply chain. The way we look, now there is a lot of talk. Governments are talking about it. Enterprises are talking about it. Because this crisis has demolished any sort of a separation that lies between governments and into price.
It actually talks about global responses to anything at a global visibility. While we might in the short term, see the supply chains actually moving away from a point to point delivery model to a hub and spoke model. What we will witness is a globalization of data. The kind of perceptions that people today have about data privacy about data sovereignty is actually going to change. What is going to replace in terms of policies or in terms of business strategies is to see what sort of data we can have to kind of get the visibility. Because let me just give you an example, we now have some kind of a benefit of hindsight on what we could have done better to manage the COVID crisis. One is, when the COVID crisis actually took us, everyone by surprise.
No one had data even Bureaucrats in the governments, or the C-level people in the enterprises didn't have data. Where are the supply chains? What are the alternatives that are available? If you look at healthcare, how many beds do we have? How many ventilators do we have? What is the supply of pharmaceutical industry? How it is happening.
If my main supplier is gone down, what are the next alternative suppliers? Do we have data about it? Everyone has like completely took off guard and they were like scrambling for data everywhere. I think this is what is going to change. Even just very recently, we OECD also actually published a paper, which says that I think that is a much larger necessity for not just governments, but enterprises across the globe to actually start sharing data between themselves.
Now, it's a different thing for policymakers to look at what data is shared and how much is shared and who has access to it. That's, a completely policy standpoint. But it is going to change the way we information management professionals look at it is are enterprises ready for this change that is happening and it is happening very soon. Because another major change in manufacturing, if you look at, the way just-in-time manufacturing used to happen is going to change.
Because now companies though do it is going to be expensive and look at having supplies, accounting for any disruptions. The just-in-time is not going to work anymore. With that kind of a thing, are really enterprises ready for such things? There is another change, diversification of supply chain. If your supply chain is diversified, are there uniform standards of data that you can actually use to exchange?
We will slowly be actually arriving at a stage where there will be protocols established for data sharing between enterprises, within countries and also between countries as well. And similarly, the pharma and healthcare sector, at least from a top government perspective, governments are looking at having real time access to data of how the hospitals are doing, what is the capacity? What is the utilization?
And secondly, even the patient health related data, are there any particular spikes of a certain unique disease which is actually happening in any certain locality? Can we do something to actually respond to such things? I think this is what is going to drive our future. Also I think this is the biggest lesson that this crisis has taught the entire world. In fact, the recent conversation, which is also happening between the business leaders of the G20 nations, is that there should be more exchange of data to see how the supply chains can be optimized. And also be optimized in such a way that even disruptions can actually be accounted for.
That way I think in a post COVID era, the way supply chains operate and also the way the data actually is used for decision making is going to completely change. Having said that, I think there is no right time than now where actually, the core business is at its leanest period for enterprises to start preparing. In fact, we are already getting inquiries from some of our customers are asking us, what is it that we can do now? Please tell us what is it that we can do to actually be ready for the changes that are happening globally?
I think that is a relevant question, which enterprises are asking. And we're also engaging with our customers currently to see, especially Johannes and I were recently in a conversation with a customer to see how we can actually improvise and actually get their inventory reporting much more accurate. To see actually how they can actually trace back the lineage of the data, to the source of truth, where it is coming from.
Also about standardizing the data that is coming from various suppliers to ensure that there is one uniform way in which you captured all your assets. These are all the relevant conversations that are happening globally, across everywhere in all regions. I think this is the future to come, frankie.
Frankie Steel (37:44):
Yep. I totally agree, and that it will be a new normal and let's hope there's some positives that are driven out of this pandemic. Anyway, on behalf of all our experts here in Syniti, we do want to say thank you for spending your time with us today. If you didn't get a chance to ask or answer any questions, please don't hesitate reaching out to us. We will be monitoring these sites to make sure that all questions are answered. So thank you again, everybody please stay safe and enjoy your evening or your day.