Qlik’s Top Researcher Describes New Ways for Human Cognition and Augmented Intelligence to Join Forces

Transcript of a discussion on how the latest research and products bring the power of people and machine intelligence closer together to make analytics consumable across more business processes.
 

Listen to the podcast. Find it on iTunes. Download the transcript. Sponsor: Qlik. 

Dana Gardner: Hi, this is Dana Gardner, Principal Analyst at Interarbor Solutions, and you’re listening to BriefingsDirect. Our next business intelligence (BI) trends discussion explores the latest research and products that bring the power of people and machine intelligence closer together.

Gardner

As more data becomes available to support augmented intelligence -- and the power of analytics platforms increasingly goes to where the data is -- the next stage of value is in how people can interact with the results.

Stay with us now as we examine the latest strategies for not only visualizing data-driven insights but making them conversational and even presented through a form of storytelling.

To learn more about making the consumption and refinement of analytics delivery an interactive exploit open to more types of users, we are now joined by Elif Tutuk, Head of Research at Qlik. Welcome to BriefingsDirect.

Elif Tutuk: Thank you. It’s a great pleasure to be here.

Gardner: Strides have been made in recent years for better accessing data and making it available to analytics platforms, but the democratization of the results and making insights consumable by more people is just beginning. What are the top technical and human interaction developments that will broaden the way that people interact differently with analytics?

Trusted data for all

Tutuk: That’s a great question. We are doing a lot of research in this area in terms of creating new user experiences where we can bring about more data literacy and help improve people’s understanding of reading, analyzing, and arguing with the data.

Tutuk

In terms of the user experience, a conversational aspect has a big impact. But we also believe that it’s not only through the conversation, especially when you want to understand data. The visual exploration part should also be there. We are creating experiences that combine the unique nature, language, and visual exploration capabilities of a human. We think that it is the key to building a good collaboration between the human and the machine.

Gardner: As a result, are we able to increase the number and types of people impacted by data by going directly to them -- rather than through a data scientist or an IT department? How are the interaction elements broadening this to a wider clientele?

Tutuk: The idea is to make analysis available from C-level users to the business end users.

If you want to broaden the use of analytics and lower the barrier, you also need to make sure that the data machines and the system are governed and trusted.

Our enterprise data management strategy therefore becomes important for our Cognitive Engine technology. We are combining those two so that the machines use a governed data source to provide trusted information.

Gardner: What strikes me as quite new now is more interaction between human cognition and augmented intelligence. It’s almost a dance. It creates new types of insights, and new and interesting things can happen.

How do you attain the right balance in the interactions between human cognition and AI?

Tutuk: It is about creating experiences between what the human is good at -- perception, awareness, and ultimately decision-making -- and what the machine technology is good at, such as running algorithms on large amounts of data.

As the machine serves insights to the user, it needs to first create trust about what data is used and the context around it. Without the context you cannot really take that insight and make an action on it. And this is where the human part comes in, because as humans you have the intuition and the business knowledge to understand the context of the insight. Then you can explore it further by being augmented. Our vision is for making decisions by leveraging that [machine-generated] insight.

Gardner: In addition to the interactions, we are hearing about the notion of storytelling. How does that play a role in ways that people get better analytics outcomes?

Storytelling insights support

Tutuk: We have been doing a lot of research and thinking in this area because today, in the analytics market, AI is becoming robust. These technologies are developing very well. But the challenge is that most of the technologies provide results like a black box. As a user, you don’t know why the machine is making a suggestion and insight. And that creates a big trust issue.

To have greater adoption of the AI results, you need to create an experience that builds trust, and that is why we are looking at one of the most effective and timeless forms of communication that humans use, which is storytelling.

To have greater adoption of the AI results, you need to create an experience that builds trust, and that is why we are looking at one of the most effective and timeless forms of communication that humans use, which is storytelling.

So we are creating unique experiences where the machine generates an insight. And then, on the fly, we create data stories generated by the machine, thereby providing more context. As a user, you can have a great narrative, but then that narrative is expanded with insightful visualizations. From there, based on what you gain from the story, we are also looking at capabilities where you can explore further.

And in that third step you are still being augmented, but able to explore. It is user-driven. That is where you start introducing human intuition as well.

And when you think about the machine first surfacing insights, then getting more context with the data story, and lastly going to exploration -- all three phases can be tied together in a seamless flow. You don’t lose the trust of the human. The context becomes really important. And you should be able to carry the context between all of the stages so that the user knows what the context is. Adding the human intuition expands that context.

Gardner: I really find this fascinating because we are talking not just about problem-solution, we are talking about problem-solution-resolution, then readjusting and examining the problem for even more solution and resolution. We are also now, of course, in the era of augmented reality, where we can bring these types of data analysis outputs to people on a factory floor, wearing different types of visual and audio cue devices.

So the combination of augmented reality, augmented intelligence, storytelling, and bringing it out to the field strikes me as something really unprecedented. Is that the case? Are we charting an entirely new course here?

Tutuk: Yes, I think so. It’s an exciting time for us. I am glad that you pointed out the augmented reality because it’s another research area that we are looking at. One of the research projects we have done augments people on retail store floors, the employees.

The idea is, if you are trying to do shelf arrangement, for example, we can provide them information -- right when they look at the product – about that product and what other products are being sold together. Then, right away at that moment, they are being augmented and they will make a decision. It’s an extremely exciting time for us, yes.

Gardner: It throws the idea of batch-processing out the window. You used to have to run the data, come up with report, and then adjust your inventory. This gets directly to the interaction with the end-consumer in mind and allows for entirely new types of insights and value.

Tutuk: As part of that project, we also allow for being able to pin things on the space. So imagine that you are in a warehouse, looking at a product, and you develop an interesting insight. Now you can just pin it on the space on that product. And as you do that on different products, you can take a step back, take a look, and discover different insights on the product.

The idea is having a tray that you carry with you, like your own analytics coming with you, and when you find something interesting that matches with the tray – with, for example, the product that you are looking at -- you can pin it. It’s like having a virtual board with products and with the analytics being augmented reality.

Gardner: We shouldn’t lose track that we are often talking about billions of rows of data supporting this type of activity, and that new data sets can be brought to bear on a problem very rapidly.

Putting data in context with AI²

Tutuk: Exactly, and this is where our Associative Big Data Index technology comes into play. We are bringing the power of our unique associative engine to massive datasets. And, of course, with the latest acquisition that we have done with Attunity, we gain data streaming and real-time analytics.

Gardner: Digging down to the architecture to better understand how it works, the Qlik cognitive engine increasingly works with context awareness. I have heard this referred to as AI². What do you all mean by AI²?

Tutuk: AI² is augmented intelligence powered by an associative index. So augmented intelligence is our vision for the use of artificial intelligence, where the goal is to augment the human, not to replace them. And now we are making sure that we have the unique component in terms of our associative index as well.

Allow me to explain the advantage of the associative index. One of the challenges for using AI and machine learning is bias. The system has bias because it doesn’t have access to all of the data.

With the associative index, our technology provides a system with visibility to all of the data at any point, including the data that is associated with your context, and also what's not associated. That part provides a good learning source for the algorithms that we are using.

For example, you maybe are trying to make a prediction for churn analysis in the western sales region. Normally if you select the west region the system -- if the AI is running with a SQL or relational database -- it will only have access to that slice of data. It will never have the chance to learn what is not associated, such as the customers from the other regions, to look at their behavior.

With the associative index, our technology provides a system with visibility to all of the data at any point, including the data that is associated with your context, and also what’s not associated. And that part that is not associated provides a good learning source for the algorithms that we are using. This is where we are differentiating ourselves and providing unique insights to our users that will be very hard to get with an AI tool that works only with SQL and relational data structures.

Gardner: Not only is Qlik is working on such next-generation architectures, you are also undertaking a larger learning process with the Data Literacy Program to, in a sense, make the audience more receptive to the technology and its power.

Please explain, as we move through this process of making intelligence accessible and actionable, how we can also make democratization of analytics possible through education and culturally rethinking the process.

Data literacy drives cognitive engine

Tutuk: Data literacy is important to help make people able to read, analyze, and argue with the data. We have an open program -- so you don’t have to be a Qlik customer. It’s now available. Our goal is to make everyone data literate. And through that program you can firstly understand the data literacy level of your organization. We have some free tests you can take, and then based on that need we have materials to help people to become data literate.

As we build the technology, our vision with AI is to make the analytics platform much easier to use in a trusted way. So that’s why our vision is not only focused on prescriptive probabilities, it’s focused on the whole analytics workflow -- from data acquisition, to visualization, exploration, and sharing. You should always be augmented by the system.

We are at just the beginning of our cognitive framework journey. We introduced Qlik Cognitive Engine last year, and since then we have exposed more features from the framework in different parts of the product, such as on the data preparation. Our users, for example, get suggestions on the best way of associating data coming from different data sources.

And, of course, on the visualization part and dashboarding, we have visual insights, where the Cognitive Engine right away suggests insights. And now we are adding natural language capabilities on top of that, so you can literally conversationally interact with the data. More things will be coming on that.

Gardner: As an interviewer, as you can imagine, I am very fond of the Socratic process of questioning and then reexamining. It strikes me that what you are doing with storytelling is similar to a Socratic learning process. You had an acquisition recently that led to the Qlik Insight Bot, which to me is like interviewing your data analysis universe, and then being able to continue to query, and generate newer types of responses.

Tell us about how the Qlik Insight Bot works and why that back-and-forth interaction process is so powerful.

Tutuk: We believe any experiences you have with the system should be in the form of a conversation, it should have a conversational nature. There’s a unique thing about human-to-human conversation – just as we are having this conversation. I know that we are talking about AI and analytics. You don’t have to tell me that as we are talking. We know we are having a conversation about that.

That is exactly what we have achieved with the Qlik Insight Bot technology. As you ask questions to the Qlik Insight Bot, it is keeping track of the context. You don’t have to reiterate the context and ask the question with the context. And that is also a unique differentiator when you compare that experience to just having a search box, because when you use Google, it doesn’t, for example, keep the context. So that’s one of the important things for us to be able to keep -- to have a conversation that allows the system to keep the context.

Gardner: Moving to the practical world of businesses today, we see a lot of use of Slack and Microsoft Teams. As people are using these to collaborate and organize work, it seems to me that presents an opportunity to bring in some of this human-level cognitive interaction and conversational storytelling.

Do you have any examples of organizations implementing this with things like Slack and Teams?

Collaborate to improve processes

Tutuk: You are on the right track. The goal is to provide insights wherever and however you work. And, as you know, there is a big trend in terms of collaboration. People are using Slack instead of just emailing, right?

So, the Qlik Insight Bot is available with an integration to Microsoft Teams, Slack, and Skype. We know this is where the conversations are happening. If you are having a conversation with a colleague on Slack and neither of the parties know the answer, then right away they can just continue their conversation by including Qlik Insight Bot and be powered with the Cognitive Engine insights that they can make decisions with right away.

Gardner: Before we close out, let’s look to the future. Where do you take this next, particularly in regard to process? We also hear a lot these days about robotic process automation (RPA). There is a lot of AI being applied to how processes can be improved and allowing people to do what they do best.

The Qlik insight Bot is available with an integration to Microsoft Teams, Slack, and Skype. We know this is where the conversations are happening. They can just continue their conversation by including the Qlik Insight Bot and be powered with the Cognitive Engine insights that they can make decisions with.

Do you see an opportunity for the RPA side of AI and what you are all doing with augmented intelligence and the human cognitive interactions somehow reinforcing one another?

Tutuk: We realized with RPA processes that there are challenges with the data there as well. It’s not only about the human and the interaction of the human with the automation. Every process automation generates data. And one of the things that I believe is missing right now is to have a full view on the full automation process. You may have 65 different robots automating different parts of a process, but how do you provide the human a 360-degree view of how the process is performing overall?

A platform can gather associated data from different robots and then provide the human a 360-degree view of what’s going on in the processes. Then that human can make decisions, again, because as humans we are very good at making decisions by seeing nonlinear connections. Feeding the right data to us to be able to use that capability is very important, and our platform provides that.

Gardner: Elif, for organizations looking to take advantage of all of this, what should they be doing now to get ready? To set the foundation, build the right environment, what should enterprises be doing to be in the best position to leverage and exploit these capabilities in the coming years?

Replace repetitive processes

Tutuk: Look for the processes that are repetitive. Those aren’t the right places to use unique human capabilities. Determine those repetitive processes and start to replace them with machines and automation.

Then make sure that whatever data that they are feeding into this is trustable and comes from a governed environment. The data generated by those processes should be governed as well. So have a governance mechanism around those processes.

I also believe there will be new opportunities for new jobs and new ideas that the humans will be able to start doing. We are at an exciting new era. It’s a good time to find the right places to use human intelligence and creativity just as more automation will happen for repetitive tasks. It’s an incredible and exciting time. It will be great.

Gardner: These strike me as some of the most powerful tools ever created in human history, up there with first wheel and other things that transformed our existence and our quality of life. It is very exciting.

I’m afraid we will have to leave it there. You have been listening to a sponsored BriefingsDirect discussion on the latest research and products that bring the power of people and augmented intelligence closer than ever.

And we have learned about strategies for not only visualizing data-driven insights but making them conversational -- and even presented through storytelling. So a big thank you to our guest, Elif Tutuk, Head of Research at Qlik. Thank you very much.

Tutuk: Thank you very much.

Gardner: And a big thank you to our audience as well for joining this BriefingsDirect business intelligence trends discussion. I’m Dana Gardner, Principal Analyst at Interarbor Solutions, your host throughout this series of Qlik-sponsored BriefingsDirect interviews.

Thanks again for listening. Please pass this along to your IT community, and do come back next time.

Listen to the podcast. Find it on iTunes. Download the transcript. Sponsor: Qlik.

 

Transcript of a discussion on how the latest research and products bring the power of people and machine intelligence closer together to make analytics consumable across more business processes. Copyright Interarbor Solutions, LLC, 2005-2019. All rights reserved.

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Financial Stability, a Critical Factor for Choosing a Business Partner, is Now Easier to Assess

Transcript of a discussion on new ways companies gain improved visibility, analytics, and predictive indicators to assess financial viability of partners across global supply chains.

Listen to the podcast. Find it on iTunes. Download the transcript. Sponsor: SAP Ariba.

Dana Gardner: Hi, this is Dana Gardner, Principal Analyst at Interarbor Solutions, and you’re listening to BriefingsDirect. Our next digital business risk remediation discussion explores new ways companies can gain improved visibility, analytics, and predictive indicators to better assess the financial viability of partners and global supply chains.

Gardner

Businesses are now heavily relying upon their trading partners across their supply chains -- and no business can afford to be dependent on suppliers that pose risks due to poor financial health.

We will now examine new tools and methods that create a financial health rating system to determine the probability of bankruptcy, default, or disruption for both public and private companies -- as many as 36 months in advance.

To learn more about the exploding sophistication around gaining insights into supply-chain risk of a financial nature, I am pleased to welcome Eric Evans, Managing Director of Business Development at RapidRatings in New York.

Eric Evans: Thanks, Dana. I appreciate being on the podcast.

Gardner: We are also here with Kristen Jordeth, Go-to-Market Director for Supplier Management Solutions, North America at SAP Ariba. Welcome, Kristen.

Kristen Jordeth: Hi, and thank you very much.

Gardner: Eric, how do the technologies and processes available now provide a step-change in managing supplier risk, particularly financial risk?

Evans

Evans: Platform-to-platform integrations enabled by application programming interfaces (APIs), which we have launched over the past few years, allows partnering with SAP Ariba Supplier Risk. It’s become a nice way for our clients to combine actionable data with their workflow in procurement processes to better manage suppliers end to end -- from sourcing to on-boarding to continuous monitoring.

Gardner: The old adage of “garbage in, garbage out” still applies to the quality and availability of the data. What’s new about access to better data, even in the private sector?

Dig deep into risk factors

Evans: We go directly to the source, the suppliers our customers work with. They introduce us to those suppliers and we get the private company financial data, right from those companies. It’s a quantitative input, and then we do a deeper “CAT scan,” if you will, on the financials, using that data together with our predictive scoring.

Gardner: Kristen, procurement and supply chain integrity trends have been maturing over the past 10 years. How are you able to focus now on more types of risk? It seems we are getting better and deeper at preventing unknown unknowns.

Jordeth: Exactly, and what we are seeing is customers managing risk from all aspects of the business. The most important thing is to bring it all together through technology.

Within our platform, we enable a Controls Framework that identifies key areas of risk that need to be addressed for a specific type of engagement. For example, do they need to pull a financial rating? Do they need to do a background check? We use the technology to manage the controls across all of the different aspects of risk in one system.

Gardner: And because many companies are reliant on real-time logistics and supplier services, any disruption can be catastrophic.

Jordeth

Jordeth: Absolutely. We need to make sure that the information gets to the system as quickly as it’s available, which is why the API connect to RapidRatings is extremely important to our customers. On top of that, we also have proactive incidents tracking, which complements the scores.

If you see a medium-risk business, from a financial perspective, you can look into that incident to see if they are under investigation, or if things going on where they might be laying off departments.

It’s fantastic and to have it all in one place with one view. You can then slice and dice the data and roll it up into scores. It’s very helpful for our customers.

Gardner: And this is a team sport, with an ecosystem of partners, because there is such industry specialization. Eric, how important is it being in an ecosystem with other specialists examining other kinds of risk?

Evans: It’s really important. We listen to our customers and prospects. It’s about the larger picture of bringing data into an end-to-end procurement and supplier risk management process.

We feel really good about being part of SAP PartnerEdge and an app extension partner to SAP Ariba. It’s exciting to see our data and the integration for clients.

Gardner: Rapid Ratings International, Inc. is the creator of the proprietary Financial Health Rating (FHR), also known as RapidRatings. What led up to the solution? Why didn’t it exist 30 years ago?

Rate the risk over time

Evans: The company was founded by someone with a background in econometrics and modeling. We have 24 industry models that drive the analysis. It’s that kind of deep, precise, and accurate modeling -- plus the historical database of more than 30 years of data that we have. When you combine those, it’s much more accurate and predictive, it’s really forward-looking data.

Gardner: You provide a 0 to 100 score. Is that like a credit rating for an individual? How does that score work in being mindful of potential risk?

Evans: The FHR is a short-term score, from 0 to 100, that looks at the next 12 months with a probability of default. Then a Core Health Score, which is around 24 to 36 months out, looks at operating efficiency and other indicators of how well a company is managing the business and operationalizing.

We can identify companies that are maybe weak short-term, but look fine long-term, or vice versa. Having industry depth -- and the historical data behind it -- that's what drives the go-forward assessments.

When you combine the two, or look at them individually, you can identify companies that are maybe weak short-term, but look fine long-term, or vice versa. If they don’t look good in the long-term and in the short-term, they still may have less risk because they have cash on hand. And that’s happening out in the marketplace these days with a lot of the initial public offerings (IPOs) such as Pinterest or Lyft. They have a medium-risk FHR because they have cash, but their long-term operating efficiency needs to be improved because they are not yet profitable.

Gardner: How are you able to determine risk going 36 months out when you’re dealing mostly with short-term data?

Evans: It’s because of the historical nature and the discrete modeling underneath, that’s what gets precise about the industry that each company is in. Having 24 unique industry models is very different than taking all of the companies out there and stuffing them into a plain-vanilla industry template. A software company is very different than pharmaceuticals, which is very different than manufacturing.

Having that industry depth -- and the historical data behind it -- is what’s drives the go-forward assessments.

Gardner: And this is global in nature?

Evans: Absolutely. We have gone out to more than 130 countries to get data from those sources, those suppliers. It is a global data set that we have built on a one-to-one basis for our clients.

Gardner: Kristen, how does somebody in the Ariba orbit take advantage of this? How is this consumed?

Jordeth: As with everything at SAP Ariba, we want to simplify how our customers get access to information. The PartnerEdge program works with our third parties and partners to create an API whereby all our customers need to do is get a license key from RapidRatings and apply it to the system.

The infrastructure and connection are already there. Our deployment teams don’t have to do anything, just add that user license and the key within the system. So, it’s less touch, and easy to access the data.

Gardner: For those suppliers that want to be considered good partners with low financial risk, do they have access to this information? Can they work to boost up their scores?

To reduce risk, discuss data details 

Evans: Our clients actually own the subscription and the license, and they can share the data with their suppliers. The suppliers can also foster a dialogue with our tool, called the Financial Dialogue, and they can ask questions around areas of concern. That can be used to foster a better relationship, build transparency, and it doesn’t have to be a negative conversation to be a positive one.

They may want to invest in their company, extend payment terms or credit, work with them on service-level agreements (SLAs), and send in people to help manage. So, it could be a good way to just build up that deeper relationship with that supplier and use it as a better foundation.

Gardner: Kristen, when I put myself in the position of a buyer, I need to factor lots of other issues, such as around sustainability, compliance, and availability. So how do you see the future unfolding for the holistic approach to risk mitigation, of not only taking advantage of financial risk assessments, but the whole compendium of other risks? It’s not a simple, easy task.

Jordeth: When you look at financial data, you need to understand the whole story behind it. Why does that financial data look the way it does today? What I love about RapidRatings is they have financial scores, and it’s more about the health of the company in the future.

But in our SAP Ariba solution, we provide insights on other factors such as sustainability, information security, and are they funding things such as women’s rights in Third World countries? Once you start looking at the proactive awareness of what’s going on -- and all the good and the bad together -- you can weigh the suppliers in a total sense.

Their financials may not be up to par, but they are not high risk because they are funding women’s rights or doing a lot of things with the youth in America. To me, that may be more important. So I might put them on a tracker to address their financials more often, but I am not going to stop doing business with them because one of my goals is sustainability. That holistic picture helps tell the true story, a story that connects to our customers, and not just the story we want them to have. So, it creates and crafts that full picture for them.

Gardner: Empirical data that can then lead to a good judgment that takes into full account all the other variables. How does this now get to the SAP Ariba installed base? When is the general availability?

Customize categories, increase confidence 

Jordeth: It’s available now. Our supplier risk module is the entryway for all of these APIs, and within that module we connect to the companies that provide financial data, compliance screening, and information on forced labor, among others. We are heavily expanding in this area for categories of risk with our partners, so it’s a fantastic approach.

Within the supplier risk module, customers have the capability to not only access the information but also create their own custom scores on that data. Because we are a technology organization, we give them the keys so an administrator can go in and alter that the way they want. It is very customizable.

It’s all in our SAP Ariba Supplier Risk solution, and we recently released the connection to RapidRatings.

Evans: Our logo is right in there, built in, under the hood, and visible. In terms of getting it enabled, there’s no professional services or implementation wait time. So once the data set is built out on our end, if it’s a new client that’s through our implementation team, and basically we just give the API key credentials to our client. They take it and enable it in SAP Ariba Supplier Risk and they can instantly pull up the scores. So there is no wait time and no future developments to get at the data.

Once the data set is built on our end, we just give the API key to our client. They take it and enable it in SAP Ariba Supplier Risk and they can instantly pull up the scores. There is no wait time.

Jordeth: That helps us with security, too, because everybody wants to ensure that any data going in and out of a system is secure, with all of the compliance concerns we have. So our partner team also ensures the secure connection back and forth with their data system and our technology. So, that’s very important for customers.

Gardner: Are there any concrete examples? Maybe you can name them, maybe you can’t, instances where your rating system has proven auspicious? How does this work in the real world?

Evans: GE Healthcare did a joint-webinar with our CEO last year, explained their program, and showed how they were able to de-risk their supply base using RapidRatings. They were able to reduce the number of companies that were unhealthy financially. They were able to have mitigation plans put in place and corrective actions. So it was an across the board win-win.

Oftentimes, it’s not about the return on investment (ROI) on the platform, but the fact that companies were thwarting a disruption. An event did not happen because we were able to address it before it happened.

On the flip side, you can see how resilient companies are regardless of all the disruptions out there. They can use the financial health scores to observe the capability of a company to be resilient and bounce back from a cyber breach, a regulatory issue, or maybe a sustainability issue.

By looking at all of these risks inside of SAP Ariba Supplier Risk, they may want to order an FHR or look at an FHR for a new company that they hadn’t thought of if they are looking at other risks, operational risks. So that’s another way to tie it in.

Another interesting example is a large international retailer. A company got flagged as high risk and had just filed for bankruptcy, which alerted the buyer. The buyer had signed a contract, but they had the product on the shelf, so it had to be resourced and they had to find a new supplier. They mitigated risk, but they had to take quick action, get another product, and some scrambling had to be done. But they had de-risked some brand reputation damage by having done that. They hadn’t looked at that company before, it was a new company, and it was alerted. So that’s another way of not just running it at the time of contract, but it’s also running it when you’re going to market.

Identify related risks 

Gardner: It also seems logical that if a company is suffering on the financial aspects of doing business, then it might be an indicator that they’re not well-managed in general. It may not just be a cause, but an effect. Are there other areas, you could call them adjacencies, where risks to quality, delivery times, logistics are learned from financial indicators?

Evans: It’s a really good point. What’s interesting is we took a look at some data our clients had around timeliness, quality, performance, delivery, and overlaid it with the financial data on those suppliers. The companies that were weak financially were more than two times likely to ship a defective product. And companies that were weak financially were more than 2.5 times more likely to ship wrong or late.

The whole just-in-time shipping or delivery value went out the window. To your point, it can be construed that companies -- when they are stressed financially – may be cutting corners, with things getting a little shoddy. They may not have replaced someone. Maybe there are infrastructure investments that should have been made but weren’t. So, all of those things have a reverberating effect in other operational risk areas.

Gardner: Kristen, now that we know that more data is good, and that you have more services like at RapidRatings, how will a big platform and network like SAP Ariba be able to use machine learning (ML) and artificial intelligence (AI) to further improve risk mitigation?

Jordeth: The opportunity exists for this to not only impact the assessment of a supplier, but throughout the full source-to-pay process, because it is embedded into the full SAP Ariba suite. So, even though you’re accessing it through risk, it’s visible when you’re sourcing, when you’re contracting, when you’re paying. So that direct connect is very important.

We want our customers to have it all. So I don’t cringe at the fact that they ask for it all because they should have it all. It’s just visualizing it in a manner that makes sense and it’s clear to them.

Gardner: And specifically on your set of solutions, Eric, where do you see things going in the next couple years? How can the technology get even better? How can the risk be reduced more?

Evans: We will be innovating products so our clients can bring in more scope around their supply base, not just the critical vendors but across the longer tail of a supply base and look at scores across different segments of suppliers. There could be sub-tiers, as a traversing with sub-tier third and fourth parties, particularly in the banking industry or manufacturing industry.

We will be innovating so our clients can bring in more scope around their supplier base, not just the critical vendors but across the longer tail of a supply chain and examine the scores of different segments of suppliers. It could be third tiers and fourth-parties.

And so that coupled with more intelligence or enhanced APIs and data visualization, these are things that we are looking into as well as additional scoring capabilities.

Gardner: I’m afraid we will have to leave it there. You have been listening to a sponsored BriefingsDirect discussion on new ways that companies can gain improved visibility, analytics, and predictive indicators to better assess the financial viability of their partners across their global supply chains.

And we have learned about new tools and methods create a Financial Health Rating system to determine the probability of bankruptcy, default, or disruption for public and private companies.

So a big thank you to our guests, Eric Evans, Managing Director of Business Development at RapidRatings in New York. Thank you so much, Eric.

Evans: Thank you, I appreciate it.

Gardner: And we have also been here with Kristen Jordeth, Go-to-Market Director for Supplier Management Solutions, North America at SAP Ariba. Thank you.

Jordeth: Thank you.

Gardner: And a big thank you as well to our audience for joining us for this BriefingsDirect digital business risk remediation discussion. I’m Dana Gardner, Principal Analyst at Interarbor Solutions, your host throughout this series of SAP Ariba-sponsored BriefingsDirect interviews. Thanks again for listening, and do come back next time.

Listen to the podcast. Find it on iTunes. Download the transcript. Sponsor: SAP Ariba.

Transcript of a discussion on new ways companies gain improved visibility, analytics, and predictive indicators to assess financial viability of partners across global supply chains. Copyright Interarbor Solutions, LLC, 2005-2019. All rights reserved.

 

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