Thursday, February 11, 2016

How New York Genome Center Manages the Massive Data Generated from DNA Sequencing

Transcript of a discussion on how the drive to better diagnose diseases and develop more effective treatments is aided by swift, cost efficient, and accessible big data analytics infrastructure.

Listen to the podcast. Find it on iTunes. Get the mobile app. Download the transcript. Sponsor: Hewlett Packard Enterprise.

Dana Gardner: Hello, and welcome to the next edition of the HPE Discover Podcast Series. I'm Dana Gardner, Principal Analyst at Interarbor Solutions, your host and moderator for this ongoing discussion on IT innovation and how it’s making an impact on people’s lives.

Gardner
Our next big-data use case leadership discussion examines how the non-profit New York Genome Center manages and analyzes up to 12 terabytes of data generated each day from its genome sequence appliances. We’ll learn how the drive to better diagnose disease and develop more effective treatments is aided by swift, cost efficient, and accessible big-data analytics.

To hear how genome analysis pioneers exploit vast data outputs to then speedily correlate for time-sensitive reporting, please join me in welcoming our guest.
Start Your
HPE Vertica
Community Edition Trial Now
We're here with Toby Bloom, Deputy Scientific Director for Informatics at the New York Genome Center in New York. Welcome, Toby.

Toby Bloom: Hi. Thank you.

Gardner: First, tell us a little bit about your organization. It seems like it’s a unique institute, with a large variety of backers, consortium members. Tell us about it.

Bloom
Bloom: New York Genome Center is about two-and-a-half years old. It was formed initially as a collaboration among 12 of the large medical institutions in New York: Cornell, Columbia, NewYork-Presbyterian Hospital, Mount Sinai, NYU, Einstein Montefiore, and Stony Brook University. All of the big hospitals in New York decided that it would be better to have one genome center than have to build 12 of them. So we were formed initially to be the center of genomics in New York.

Gardner: And what does one do at a center of genomics?

Bloom: We're a biomedical research facility that has a large capacity to sequence genomes and use the resulting data output to analyze the genomes, find the causes of disease, and hopefully treatments of disease, and have a big impact on healthcare and on how medicine works now.

Gardner: When it comes to doing this well, it sounds like you are generating an awesome amount of data. What sort of data is that and where does it come from?

Bloom: Right now, we have a number of genome sequencing instruments that produce about 12 terabytes of raw data per day. That raw data is basically lots of strings of As, Cs, Ts and Gs -- the DNA data from genomes from patients who we're sequencing. Those can be patients who are sick and we are looking for specific treatment. They can be patients in large research studies, where we're trying to use and correlate a large number of genomes to find the similarities that show us the cause of the disease.

Gardner: When we look at a typical big data environment such as in a corporation, it’s often transactional information. It might also be outputs from sensors or machines. How is this a different data problem when you are dealing with DNA sequences?

Lots of data

Bloom: Some of it’s the same problem, and some of it’s different. We're bringing in lots of data. The raw data, I said, is probably about 12 terabytes a day right now. That could easily double in the next year. But than we analyze the data, and I probably store three to four times that much data in a day.

In a lot of environments, you start with the raw data, you analyze it, and you cook it down to your answers. In our environment, it just gets bigger and bigger for a long time, before we get the answers and can make it smaller. So we're dealing with very large amounts of data.

We do have one research project now that is taking in streaming data from devices, and we think over time we'll likely be taking in data from things like cardiac monitors, glucose monitors, and other kinds of wearable medical devices. Right now, we are taking in data off apps on smartphones that are tracking movement for some patients in a rheumatoid arthritis study we're doing.
In our environment, it just gets bigger and bigger for a long time, before we get the answers and can make it smaller. So we're dealing with very large amounts of data.

We have to analyze a bunch of different kinds of data together. We’d like to bring in full medical records for those patients and integrate it with the genomic data. So we do have a wide variety of data that we have to integrate, and a lot of it is quite large.

Gardner: When you were looking for the technological platforms and solutions to accommodate your specific needs, how did that pan out? What works? What doesn’t work? And where are you in terms of putting in place the needed infrastructure?

Bloom: The data that comes off the machines is in large files, and a lot of the complex analysis we do, we do initially on those large files. I am talking about files that are from 150 to 500 gigabytes or maybe a terabyte each, and we do a lot of machine-learning analysis on those. We do a bunch of Bayesian statistical analyses. There are a large number of methods we use to try to extract the information from that raw data.
Start Your
HPE Vertica
Community Edition Trial Now
When we've figured out the variance and mutations in the DNA that we think are correlated with the disease and that we were interested in looking at, we then want to load all of that into a database with all of the other data we have to make it easy for researchers to use in a number of different ways. We want to let them find more data like the data they have, so that they can get statistical validation of their hypotheses.

We want them to be able to find more patients for cohorts, so they can sequence more and get enough data. We need to be able to ask questions about how likely it is, if you have a given genomic variant, you get a given disease. Or, if you have the disease, how likely it is that you have this variant. You can only do that if it’s easy to find all of that data together in one place in an organized way.

So we really need to load that data into a database and connect it to the medical records or the symptoms and disease information we have about the patients and connect DNA data with RNA data with epigenetic data with microbiome data. We needed a database to do that.

We looked at a number of different databases, but we had some very hard requirements to solve. We were looking for one that could handle trillions of rows in a table without failing over, tens of trillions of rows without falling over, and to be able to answer queries fast across multiple tables with tens of trillions of rows. We need to be able to easily change and add new kinds of data to it, because we're always finding new kinds of data we want to correlate. So there are things like that.

Simple answer

We need to be able to load terabytes of data a day. But more than anything, I had a lot of conversations with statisticians about why they don’t like databases, about why they keep asking me for all of the data in comma-delimited files instead of databases. And the answer, when you boiled it down, was pretty simple.

When you have statisticians who are looking at data with huge numbers of attributes and huge numbers of patients, the kinds of statistical analysis they're doing means they want to look at some much smaller combinations of the attributes for all of the patients and see if they can find correlations, and then change that and look at different subsets. That absolutely requires a column-oriented database. A row-oriented relational database will bring in the whole database to get you that data. It takes forever, and it’s too slow for them.

So, we started from that. We must have looked at four or five different databases. Hewlett Packard Enterprise (HPE) Vertica was the one that could handle the scale and the speed and was robust and reliable enough, and is our platform now. We're still loading in the first round of our data. We're still in the tens of billions of rows, as opposed to trillions of rows, but we'll get there.
We must have looked at four or five different databases. Vertica was the one that could handle the scale and the speed and was robust and reliable enough and is our platform now.

Gardner: You’re also in the healthcare field. So there are considerations around privacy, governance, auditing, and, of course, price sensitivity, because you're a non-profit. How did that factor into your decision? Is the use of off-the-shelf hardware a consideration, or off-the-shelf storage? Are you looking at conversion infrastructure? How did you manage some of those cost and regulatory issues?

Bloom: Regulatory issues are enormous. There are regulations on clinical data that we have to deal with. There are regulations on research data that overlap and are not fully consistent with the regulations on clinical data. We do have to be very careful about who has access to which sets of data, and we have all of this data in one database, but that doesn’t mean any one person can actually have access to all of that data.

We want it in one place, because over time, scientists integrate more and more data and get permission to integrate larger and larger datasets, and we need that. There are studies we're doing that are going to need over 100,000 patients in them to get statistical validity on the hypotheses. So we want it all in one place.

What we're doing right now is keeping all of the access-control information about who can access which datasets as data in the database, and we basically append clauses to every query to filter down the data to the data that any particular user can use. Then we'll tell them the answers for the datasets they have and how much data that’s there that they couldn’t look at, and if they needed the information, how to go try to get access to that.

Gardner: So you're able to manage some of those very stringent requirements around access control. How about that infrastructure cost equation?

Bloom: Infrastructure cost is a real issue, but essentially, what we're dealing with is, if we're going to do the work we need to do and deal with the data we have to deal with, there are two options. We spend it on capital equipment or we spend it on operating costs to build it ourselves.

In this case, not all cases, it seemed to make much more sense to take advantage of the equipment and software, rather than trying to reproduce it and use our time and our personnel's time on other things that we couldn’t as easily get.

A lot of work went into HPE Vertica. We're not going to reproduce it very easily. The open-source tools that are out there don’t match it yet. They may eventually, but they don’t now.

Getting it right

Gardner: When we think about the paybacks or determining return on investment (ROI) in a business setting, there’s a fairly simple straightforward formula. For you, how do you know you’ve got this right? What is it when you see certain, what we might refer to in the business world as service-level agreements (SLAs) or key performance indicators (KPIs)? What are you looking for when you know that you’ve got it right and when you’re getting the job done, based all of its requirements and from all of these different constituencies?

Bloom: There’s a set of different things. The thing I am looking for first is whether the scientists who we work with most closely, who will use this first, will be able to frame the questions they want to ask in terms of the interface and infrastructure we’ve provided.

I want to know that we can answer the scientific questions that people have with the data we have and that we’ve made it accessible in the right way. That we’ve integrated, connected and aggregated the data in the right ways, so they can find what they are looking for. There's no easy metric for that. There’s going to be a lot of beta testing.
The place where this database is going to be the most useful, not by any means the only way it will be used, is in our investigations of common and complex diseases, and how we find the causes of them and how we can get from causes to treatments.

The second thing is, are we are hitting the performance standards we want? How much data can I load how fast? How much data can I retrieve from a query? Those statisticians who don’t want to use relational databases, still want to pull out all those columns and they want to do their sophisticated analysis outside the database.

Eventually, I may convince them that they can leave the data in the database and run their R-scripts there, but right now they want to pull it out. I need to know that I can pull it out fast for them, and that they're not going to object that this is organized so they can get their data out.

Gardner: Let's step back to the big picture of what we can accomplish in a health-level payback. When you’ve got the data managed, when you’ve got the input and output at a speed that’s acceptable, when you’re able to manage all these different level studies, what sort of paybacks do we get in terms of people’s health? How do we know we are succeeding when it comes to disease, treatment, and understanding more about people and their health?

Bloom: The place where this database is going to be the most useful, not by any means the only way it will be used, is in our investigations of common and complex diseases, and how we find the causes of them and how we can get from causes to treatments.

I'm talking about looking at diseases like Alzheimer’s, asthma, diabetes, Parkinson’s, and ALS, which is not so common, but certainly falls in the complex disease category. These are diseases that are caused by some combinations of genomic variance, not by a single gene gone wrong. There are a lot of complex questions we need to ask in finding those. It takes a lot of patience and a lot of genomes, to answer those questions.

The payoff is that if we can use this data to collect enough information about enough diseases that we can ask the questions that say it looks like this genomic variant is correlated with this disease, how many people in your database have this variant and of those how many actually have the disease, and of the ones who have the disease, how many have this variant. I need to ask both those questions, because a lot of these variants confer risk, but they don’t absolutely give you the disease.

If I am going to find the answers, I need to be able to ask those questions and those are the things that are really hard to do with the raw data in files. If I can do just that, think about the impact on all of us? If we can find the molecular causes of Alzheimer’s that could lead to treatments or prevention and all of those other diseases as well.

Gardner: It’s a very compelling and interesting big data use case, one of the best I’ve heard.

I am afraid we’ll have to leave it there. We've been examining how the New York Genome Center manages and analyzes vast data outputs to speedily correlate for time-sensitive reporting, and we’ve learned how the drive to better diagnose diseases and develop more effective treatments is aided by swift, cost efficient, and accessible big data analytics infrastructure.
Start Your
HPE Vertica
Community Edition Trial Now
So, join me in thanking our guest, Toby Bloom, Deputy Scientific Director for Informatics at the New York Genome Center. Thank you so much, Toby.

Bloom: Thank you, and thanks for inviting me.

Gardner: Thank you also to our audience for joining us for this big data innovation case study discussion. I'm Dana Gardner, Principal Analyst at Interarbor Solutions, your host for this ongoing series of HPE-sponsored discussions. Thanks again for listening, and come back next time.

Listen to the podcast. Find it on iTunes. Get the mobile app. Download the transcript. Sponsor: Hewlett Packard Enterprise.

Transcript of a discussion on how the drive to better diagnose diseases and develop more effective treatments is aided by swift, cost efficient, and accessible big data analytics infrastructure. Copyright Interarbor Solutions, LLC, 2005-2016. All rights reserved.

You may also be interested in:

Monday, January 18, 2016

Procurement in 2016—The Supply Chain Goes Digital

Transcript of a BriefingsDirect discussion on the role of procurement as a strategic business force.

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

Dana Gardner: Hi, this is Dana Gardner, Principal Analyst at Interarbor Solutions, and you’re listening to BriefingsDirect.

Gardner
Our business innovation thought leadership discussion today focuses on the heightened role and impact of procurement as a strategic business force. We'll explore how intelligent procurement is rapidly transforming from an emphasis on cost savings to creating new business value and enabling supplier innovations.

As the so-called digital enterprise adapts to a world of increased collaboration, data access, and business networks, procurement leaders can have a much bigger impact, both inside and outside of their companies.

To learn more about the future of procurement as a focal point of integrated business services we’re joined by Kurt Albertson, Principal of Advisory Services at The Hackett Group in Atlanta. Welcome, Kurt.

Kurt Albertson: Thank you, Dana.

Gardner: We're also joined by Dr. Marcell Vollmer, Chief Operating Officer at SAP Ariba and former Chief Procurement Officer at SAP. Welcome, Marcell.

Dr. Marcell Vollmer: Thanks, Dana, for having me. Great being here.

Gardner: You must be seeing external forces having an impact on your company and on Ariba's customers. We're looking at mobile devices being used more and more for business. We have connected business networks. How are these trends impacting procurement, and why is procurement going to have a bigger impact as time goes on?

Vollmer: Thanks, Dana. That's a really good question. I see a couple of disruptive trends, which are very important and are directly impacting procurement.

Vollmer
We see how smartphones and tablets have changed the way we work on a daily basis, not to forget big data, Internet of Things (IoT), Industry 4.0. So, there are a lot of technology trends out there that are very important.

On the other side, we also see completely new business models taking off. Uber is the largest taxi company without owning a single cab. Airbnb is basically the same, the largest accommodation provider, but not owning a single bed. We see also companies like WhatsApp, Skype, and WeChat. They don't own the infrastructure anymore, like what we know from the past.

I could mention a couple more, like Alibaba. Everybody knows it was the highest IPO in history, with a market capitalization of around $200 billion, and they even don’t have an inventory. What we're seeing are fundamental changes, the technology on one side and then the new business models.

We now see the impact here for procurement. When business models are changing, procurement also needs to change. Companies intend to simplify the way they do business today.

Complex processes

We see a lot of complex processes. We have a lot of complex business models. Today it needs to be "Apple easy" and "Google fast." This is simply what millennials expect in the market.

But also, we see that procurement, as a function itself, is transforming from a service to function. And this is definitely one trend. We see a different strategic impact. What is asked of procurement from the lines of business is more important and is on the agenda for the procurement function.

Let me add one last topic, the evolution of the Chief Procurement Officer (CPO) role, by saying that seeing the different trends in the market, seeing also the different requirements indicated by the trends for procurement, the role of procurement, as well as the CPO role in the 21st Century will definitely change.

I believe that the CPO role might evolve and might be a Chief Collaboration Officer role. Or, in the future, as we see the focus is more and more on the business value, a Chief Value Officer role might be the next big step.

Gardner: Kurt, we're hearing a lot from Marcell about virtual enterprises. When we say that a major retailer doesn’t have an inventory, or that a hotel rooms coordinator doesn’t have any beds, we're really now talking about relationships. We're talking about knowledge rather than physical goods. Does that map in some way to the new role of the CPO? How has the virtual enterprise impacted the procurement process?

Albertson: Marcell brought up some great points. Hackett is a quantitative-based organization. Let me share with you some of the insights from a very recent Key Issues Study that we did for 2016. This is a study we do each year, looking forward across the market. We're usually talking with the head of procurement about where the focus is, what’s the priority, what’s going to have the biggest impact on success, and what capabilities they're building out.

Albertson
Let me start at a high level. A lot of things that Marcell talked about in terms of elevating procurement’s role, and more collaboration and driving more value, we saw it quite strongly in 2015 -- and we see it quite strongly in 2016.

In 2015, when we did our Key Issues Study, the number one objective of the procurement executive was to elevate the role of procurement to what we called a trusted adviser, and certainly you've heard that term before.

We actually put a very solid definition around it, but achieving the role of a trusted adviser, in itself, is not the end-game. It does allow you to do other things, like reduce costs, tap suppliers for innovation, and become more agile as an organization, which was in the top five procurement objectives as well.

Trusted advisor

So when we look at this concept of the trusted adviser role of procurement, just as Marcell said, it's about a lot of the procurement executives across multiple industries who are asking, "How do we change the perception of procurement within the eyes of the stakeholders, so that we can do more higher value type activities?"

For example, if you're focusing on cost, we talk a lot about the quantity of spend influence, versus the quality of spend influence. In fact, in our forum in October, we had a very good discussion on that with our client base.

We used to measure success of the procurement organization by cost savings, but one of the key metrics a lot of our clients would look at is percent of spend influenced by procurement. We have a formal definition around that, but when you ask people, you'll get a different definition from them in terms of how they define spend influence.

What we've realized is that world-class organizations are in the 95 percent range and 90 percent plus on the indirect side. Non world-class procurement organizations are lagging, in the 70 percent range in terms of influence. Where do we go from here? It has to be about the quality of the spend influence.
When we look out in the market, there are a lot of companies that don't have line-item level detail or they don't have 90 percent or 95 percent-plus data quality with respect to spend analytics.

And what our data shows very clearly is that world-class organizations are involved during the requirements and planning stages with their internal stakeholders much more often than non-world-class organizations. The latter are usually involved either once the supplier has been identified, or for the most part, once requirements are identified and the stakeholder already knows what they want.

In both cases, you're influencing. But in the world-class case, you're doing a much better job of quality of influence, and you can open up tremendous amounts of value. It changes the discussion with your internal stakeholders from, "We're here to go out and competitively bid and help you get the best price," to, "Let’s have a conversation with what you're trying to achieve and, with the knowledge, relationships, and tool sets that we have around the supply markets and managing those supply markets, let us help you get more value in terms of what you are trying to achieve."

We've asked some organizations how we become a trusted adviser, and we've built some frameworks around that. One of the key things is exactly what you just talked about. In fact, we did a forward-looking, 10-year-out procurement 2025 vision piece of research that we published a few months ago, and big data and analytics were key components of that.

When we look at big data, like a lot of the things Marcell already talked about, most procurement groups aren’t very good at doing basic spend analytics, even with all the great solutions and processes that are out there. Still, when we look out in the market, there are a lot of companies that don't have line-item-level detail, or they don't have 90 percent or 95 percent-plus data quality with respect to spend analytics.

We need to move way beyond that for procurement to really elevate its role within the organization. We need to be looking at all of the big data that’s out there in the supply networks, across these supply networks, and across a lot of other sources of information. You have PDAs and all kinds of information.

We need to be constructively pulling that information together in a way that then allows us to marry it up with our internal information, do more analysis with that, synthesize that data, and then turn it over and provide it to our internal stakeholders in a way that's meaningful and insightful for them, so that they can then see how their businesses are going to be impacted by a lot of the trends out in the supply markets.

Transformational impact

This year, we asked a question that I thought was interesting. We asked which trends will have the greatest transformational impact on the way procurement performs its job over the next decade. I was shocked. Three out of the top five have to do with technology: predictive analytics and forecasting tools, cloud computing and mobility, the global economy and millennial workforce.

Mobility, predictive analytics, forecasting, and cloud computing are in the top five, along with global economy and the millennial workforce, two other major topics that were in our forward-looking procurement 2025 paper.

When we look at the trend that’s going to have the greatest transformational impact, it's predictive analytics and forecasting tools in terms of how procurement performs its job over the next 10 years. That’s big.

Consider the fact that we aren’t very good at doing the basics around spend analytics right now. We're saying that we need to get a lot better to be able to predict what’s going to happen in the future in terms of budgets, based on what we expect to happen in supply markets and economies.
We need to put in the hands of our stakeholders toolsets that they can then use to look at their business objectives and understand what’s happening in the supply market and how that might impact it in two to three years.

We need to put in the hands of our stakeholders tool sets that they can then use to look at their business objectives and understand what’s happening in the supply market and how that might impact it in two to three years. That way, when you look at some of the industries out there, when your revenue gets cut in more than half almost within a year, you have a plan in place that you can then go execute on to take out cost in a strategic way as opposed to just taking a broad axe and trying to take out that cost.

Gardner: Interesting. So it's being much smarter, much more analytic, and being proactive, rather than reactive, to what’s going on inside your company. It’s interesting, Kurt, because I talk to a lot of IT people, and they're doing more with the data and the analysis and they want to elevate it to more people in the organization, like the CPO.

It seems to me that Ariba is in a position to bridge these groups between what IT or data providers can bring out in terms of analysis and what these CPOs are going to need in order to do their jobs differently.

Marcell, do you see that? Do you see SAP Ariba playing a role as the bridge between technology and business processes that the CPOs are going to be looking to have more insight from?

Vollmer: Absolutely, Dana. I couldn’t agree more what Kurt said about the importance of the top priorities today. It's very important also to ask what you want to do with the data. First of all, you need technology. You need to get access to all the different sources of information that you have in a company.

We see today how difficult it is. I could echo what Kurt said about the challenges. A lot of procurement functions aren't even capable of getting the basic data to drive procurement, to do spend analytics, and then to see that it really links this to supply-chain data. In the future this will definitely change.

Good time to purchase

When you think about what you can do with the data by predictive analytics and then say, "This is a good time to buy, based on the cycle we've seen is this time-frame." This would give you a good time to make a purchase decision and go to the market.

And what do you need to do that? You need the right tools, spend visibility tools, and access to the data to drive end-to-end transparency on all the data what you have, for the entire source-to-pay process.

Gardner: Another thing that we're expecting to see more of in 2016 is collaboration between procurement inside an organization and suppliers -- finding new ideas for how to do things, whether it’s marketing or product design.

Kurt, do you have any data that supports this idea that this is not just a transaction, that there is, in fact, collaboration between partners, and that that can have quite an impact on the role and value that the procurement officer and their charges bring back to their companies?
That helps procurement category managers raise their game and really be perceived as adding more value, becoming this trusted advisor.

Albertson: Let me tie it into the conversation that we've been having. We just talked about a lot of data and analytics and putting that in the hand of procurement folks, so that they can then go and have conversations and be really advisers in terms of helping enable business strategies as opposed to just looking at historical spend cost analysis, for example. That helps procurement category managers raise their game and really be perceived as adding more value, becoming this trusted adviser.

Hackett Group works with hundreds of Global 1000 organizations, and probably still one of the most common discussions we have, and even in on-site training support that we do, is around strategic category management. It's switching the game from strategic sourcing, which we view as an end-step process that results in awarding a competitive bid process, with aggregation of spend and awarding a contract, to a more formal category management framework.

That provides a whole set of broader value levers that you can pull to drive value, including supplier relationship management (SRM), which includes working with suppliers to innovate, impacting a much broader set of value objectives that our stakeholders have, including spend cost reduction, but not only including spend cost-reduction.

We see such a level of interesting category management today. In our Key Issues Study in 2016, when we look at the capability building that organizations are rolling out, we've been seeing this shift from strategic sourcing to category management.

Strategic sourcing as a capability was always number one. It still is, but now number two is this category management framework. Think of those two as bookends, with category management being a much more mature framework than just strategic sourcing.

Category management

Some 80 percent of companies said category management is a key capability that they need to use to drive procurement’s objectives, and that’s because they're impacting a broader set of value objectives.

Now, the value levers they're pulling are around innovation and SRM. In fact, if you look at our 2016 Key Issues Study again, tapping supplier innovation is actually a little bit further on down the list, somewhere around 10.

When we look at all the things that are there, it’s actually ninth on the list, with 55 percent of procurement executives saying it’s a critical and major importance for us.

The interesting thing, though, is that if you go back to 2015 and compare where that is versus 2016, in 2016, that moves nearly into the top three with respect to the significantly more focus on a key capability. SRM has been a hot topic for our clients for a long time, but this tells us that it’s getting more and more important.

We're seeing a lot of organizations still with very informal SRM, supply innovation frameworks, in place. It’s done within the organization, but it’s done haphazardly by individuals within the business and by key stakeholders. A lot of times, that activity isn't necessarily aligned with where it can drive the most value.
We have to rethink how we look at our supply base and really understand where those suppliers are that can truly move the needle on supplier innovation.

When we work with a company, it's quite common for them to say, "These are our top five suppliers that we want to innovate with." And you ask, "If innovation is your objective, either to drive cost reduction or to help improve the market effectiveness of your products or services and drive greater revenue, whatever the reason you are doing that, are these suppliers going to get you there?"

Probably 7 out of 10 times, people come back to us and say that they picked these suppliers because they were the largest spend impact suppliers. But when you start talking about supplier innovation, they freely admit that there's no way that supplier is going to engage with them in any kind of innovation.

We have to rethink how we look at our supply base and really understand where those suppliers are that can truly move the needle on supplier innovation and engage them through a category-management framework that pulls the value lever of SRM and then track the benefits associated with that.

And as I said, looking at our 2016 Key Issues Study, supplier innovation was the fastest growing in terms of its focus objective that we saw when we asked the procurement executives.

Gardner: Marcell, back to you. It sounds as if the idea of picking a supplier is not just a cost equation, but that there is a qualitative part to that. How would you automate and scale that in a large organization? It sounds to me like you need a business network of some sort where organizations can lay out much more freely what it is that they're providing as a service, and then making those services actually hook up -- a collaboration function.

Is that something you're seeing at Ariba, as well that the business network, helping procurement move from a transaction cost equation to a much richer set of services?

Key role

Vollmer: Business networks play a key role for us for our business strategy, but also on how to help companies to simplify their complexity.

When you reach out to a marketplace, you're looking for things. You're probably also starting discussions and getting additional information. You're not necessarily looking for paint in the automotive industry or the color of a car. Why not get an already painted car as a service at the end?

This is a very simple example, but now think about when you go to the next level on how to evolve and have a technology partnership, where you reach out to suppliers, looking for new suppliers, by getting more and more information and also asking others who have probably having already done similar things.

When you do this on a network, you get probably responses from suppliers you wouldn't even have thought about having capabilities like that. This is a process that, in the future, will continue to aid successfully the transformation to a more value-focused procurement function, and simplicity is definitely a key.
You need to run simple. You need to focus on your business, and you need to get rid of the complexity.

You need to run simple. You need to focus on your business, and you need to get rid of the complexity. You can’t have all the information and do everything on your own. You need to focus on your core competencies and help the business in getting whatever they need to be successful, from the suppliers out in the market to ensure you get the best price for the desired quality, and ensure on-time deliveries.

The magic triangle of procurement is not a big secret in the procurement world. Everybody knows that it's not possible to optimize everything. Therefore, you need to find the right mix. You also need to be agile to work with suppliers in a different way by not only focusing just on the price, which a lot of operational technical procurement functions are used to. You need what you really want to achieve as a business outcome.

On a network you can get help from suppliers, from the collaboration side also, in finding the right ones to drive business value for your organization.

Gardner: Another major area where we're expecting significant change in 2016 is around the use of procurement as a vehicle for risk reduction. So having this visibility using networks -- elevating the use of data analysis, everything we have talked about, in addition to cost-efficiencies, in addition to bringing innovation to play between suppliers and consumers at the industrial scale -- it seems to me that we're getting insight deeply into supply chains and able to therefore head off a variety of risks. These risks can be around security, around the ability to keep supply chains healthy and functioning, and even unknown factors could arise that would damage even an entire company's reputation.

Kurt, do you have some data, some findings that would illustrate or reinforce this idea that procurement as a function, and CPOs in particular, can play a much greater role in the ability to detect risk and prevent bad things from happening to companies?

Supply continuity risk

Albertson: Again, I'll go back to the 2016 Key Issues Study and talk about objectives. Reducing supply continuity risk is actually number six on the list, and it’s a long list, and that’s pretty important.

A little bit further down, we see things like regulatory noncompliance risk, which is certainly core. It's certainly more aligned with certain industries than others. So just from our perspective, we see this as certainly number six on the list of procurement 2016 objectives, and the question is what we do about it.

There's another objective that I talked about earlier, which is to improve agility. It's actually number four on the list for procurement 2016 objectives.

I look at risk management and procurement agility going hand in hand. The way data helps support that is by getting access to better information, really understanding where those risks are, and then being able to quickly respond and hopefully mitigate those risks. Ideally, we want to mitigate risks and we want to be able to tap the suppliers themselves and the supply network to do it.

In fact, we attacked this idea of supply risk management in our 2025 procurement study. It’s really about going beyond just looking at a particular supplier and looking at all the suppliers that are out there in the network, their suppliers, their suppliers, and so on.

But then, it's also tapping all the other partners that are participating in those networks, and using them to help support your understanding and proactively identifying where risk might be occurring, so that you can take action against it.
How do we manage and analyze all this data? How do we make sense of it? That's where we see a lot of our clients struggling today.

It’s one of the key cornerstones of our 2025 research. It's about tapping supplier networks and pulling information from those networks and other external sources, pulling that information into some type of solution that can help you manage and analyze that information, and then presenting that to your internal stakeholders in a manner that helps them manage risk better.

And certainly, an organization like SAP Ariba is in a good position to do that. That’s obviously one of the major barriers with this big-data equation. How do we manage and analyze all this data? How do we make sense of it? That's where we see a lot of our clients struggling today.

We have had some examples of clients that have built out an SRM group inside their procurement organization as a center-of-excellence capability purely to pull this information that resides out in the market, whether it’s supplier market intelligence or information flowing from networks and other network partners. Marrying that information with their internal objectives and plans, and then synthesizing that information, lets them put that information in the hands of category managers.

Category managers can then sit down with business leaders and have fact-based opinions about what’s going to happen in those markets from a risk perspective. We could be talking about continuity of supply, pricing risks and the impact on profitability, or what have you. Whatever those risks are, you're able to use that information. It goes back to elevating the roles of trusted advisor. The more information and insight you can put into their hands the better.

The indirect side

Obviously, when we look at some of the supply networks, there's a lot of information that can be gleaned out there. Think about different buyers that are working with certain suppliers in getting information to them on supply risk performance. To be frank, a lot of organizations still don’t do a great job on the indirect side.

There are opportunities, and we're seeing it already in some of these markets for supply networks to start with the supplier performance piece of this, tap the network community to provide insight to that, and get help from a risk perspective that can be used to help identify where opportunities to manage risk better might occur.

But there are a lot of other sources of information and it’s really up to procurement to try to figure this out with all the sources of big data. Whether it’s sensor data, social data, transactional data, operational data, partner data, machine-to-machine (M2M) data, or cloud services based data, there's a lot of information. We have a model that looks at this kind of these three levels of kind of this analytics model.

The first level of the model is just for recording things and generating reports. The second level is that you're understanding and generating information that then can be used for analytics. Third, you're actually anticipating. You have intelligence and you're moving towards more real-time analytics so that you can be quicker in responding to potential risk.
Procurement organizations need to ensure that they really help the business as much as possible, and also evolve to the next level for their own procurement functions.

I mentioned this idea of agility as being key on the procurement executive’s list. Agility can be in many things, but one of the things that it means with respect to risk is that you can’t avoid every risk event. Some risk events are going to happen. There's nothing you're going to do about them, but you can proactively make plans for when those risk events do occur, so that you have a well thought-out plan based on analytics to execute in order to minimize the impact of that risk.

Time and time again, when we look at case studies and at the research that’s out there, those organizations that are much more agile in terms of responding to these risks where you're not going to be able to avoid them, minimize the impact of those risks significantly compared to others.

Gardner: As we look ahead to 2016, we're certainly seeing a lot on the plate for the procurement organization. It looks like they're facing a lot more technology issues, they're facing change of culture, they're thinking about being a networked organization. Marcell, how do you recommend that procurement professionals prepare themselves? What would you recommend that they do in order to meet these challenges in 2016? How can they be ready for such a vast amount of change?

Vollmer: Procurement organizations need to ensure that they really help the business as much as possible, and also evolve to the next level for their own procurement functions. Number one is that procurement functions need to see that they have the right organizational setup in place. That setup needs to fit the overall organizational line of business spectra, what a company has.

The second component, which I think is very important, is to have an end-to-end focus on the process side. Source-to-pay is a clearly defined term, but it's a little bit different in all the companies. When you really want to optimize, when you really want to streamline your process, you want to use business networks and strategic sourcing tools, as well as running in a highly automated level of transaction to leverage the automation potential of what you have in a purchase order or invoice automation, for example.

One defined process

Then, you need to ensure that you have one defined process and you need to have side systems covering all the different parts of the process. This needs to be highly integrated, as well as integrated in your entire IT landscape.

Finally, you need to also consider change management. This is a most important component by which you help the buyers in your organization transform and evolve to the next level into a more strategic procurement function.

As Kurt said about the data, if you don’t have some basic data, you're very far away from driving predictive analytics and prescriptive guidance. Therefore, you need to ensure that you invest also in your talents and that you drive to change management side.

These are the three components that I would see in 2016. This sounds easy, but I've talked to a lot of CPOs. This journey might take a couple of years, but procurement doesn't have a lot of time. We need to see now in procurement that we define the right measures, the right actions, to ensure that we can help the business and also create value.
If you don’t have some basic data, you're very far away from driving predictive analytics and prescriptive guidance.

As was already mentioned, this needs to go beyond just creating procurement savings. I believe that this concept is here to stay in the future. I think the value is what counts, what you can create.

Gardner: I'm afraid we'll have to leave it there. You've been listening to a sponsored BriefingsDirect podcast discussion on the heightened role and impact of procurement as a strategic business force. And we learned how procurement leaders can make a much bigger difference in the organization as procurement itself transforms to become a focal point of integrated business services.

So please join me in thanking our guests, Kurt Albertson, Principal of Advisory Services at The Hackett Group, and Dr. Marcell Vollmer, Chief Operating Officer at SAP Ariba, and the former Chief Procurement Officer at SAP. And also a big thank you to our audience for joining this SAP Ariba-sponsored business innovation thought leadership discussion.

I’m Dana Gardner, Principal Analyst at Interarbor Solutions, your host and moderator. Thanks again for listening, and do come back next time.

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

Transcript of a BriefingsDirect discussion on the role of procurement as a strategic business force. Copyright Interarbor Solutions, LLC, 2005-2016. All rights reserved.

You may also be interested in: