Artificial Intelligence

Plex Systems Accelerates Push Into IoT With DATTUS Acquisition

Customers: Prepare for a Tsunami of Data

On July 31, 2018 Plex Systems, a cloud ERP and MES solution provider for manufacturing, announced it had completed the acquisition of DATTUS, Inc. a leader in Industrial Internet of Things (IIoT) connectivity technologies. This is an important step in executing on its product strategy, which includes connecting to more IIoT data for more actionable insights, along with enhancing manufacturing and business processes.

As a solution provider of enterprise resource planning (ERP), the Plex Manufacturing Cloud provides the operational and transactional system of record of your business. But looking beyond business transactions, Plex’s goal has always been to connect the shop floor to the top floor. But that is often easier said than done.

While sensors, machines and equipment on the shop floor have been collecting vast volumes of operational data for decades now, that data has not always been “connected” or accessible for decision making. Indeed the very fact that this data collection has been happening for decades contributes to the problem. Many of the machines and software put in place decades ago pre-date the Internet and therefore have no ability to connect to a network. Retrofitting equipment or replacing it is expensive and most of these machines were designed to last a lifetime. Expensive custom integration projects are beyond the expertise and budgets of all but the largest manufacturers. So what’s the alternative?

Providing an alternative is what DATTUS is all about. DATTUS solutions connect manufacturing equipment and sensors to the cloud. Think of it as the bridge between you and your machines. The platform is a hardware/software combination, which collects data from PLCs, VFDs, industry protocols like MTConnect, and popular enterprise applications including Salesforce, SAP and (of course) Plex.

In addition to this plug and play connectivity, DATTUS also brings IIoT data management and industrial analytics. The data management and analytics capabilities previously offered by Plex were sufficient for managing the volumes of data within ERP. But as customers are empowered to bring almost any data stream into the Industrial Internet of Things, they now need to be prepared for a tsunami of data.

Giving Manufacturers a “Leg Up”

The IIoT is just one of several inter-related digital technologies we continue to watch, and what we most often see is limited progress being made in terms of leveraging these technologies. Our 2018 Mint Jutras Enterprise Solution study explored plans and investments in selected digital technologies normally associated with Industry 4.0. We find very low rates of adoption (Table 1) and many have no plans to change that. In spite of all the hype around all these technologies we confirmed many are still sitting on the sidelines of the latest manufacturing revolution.

Table 1: Digital Technologies Plans and Investments in Manufacturing

Source: 2018 Mint Jutras Enterprise Solution Study

*Includes those that expect vendors to deliver at no additional cost

Running legacy solutions based on outdated technology forcibly sidelines some. And others are hamstrung by decades-old equipment on their shop floors. Plex Systems’ acquisition of DATTUS can’t help with the first unless those running legacy solutions are willing to trade up to a more modern, technology-enabled solution. But it can help in connecting those disconnected machines.

While all adoption rates are quite low, we do find IoT has the lowest percentage of manufacturers with no plans and no activity and close to the highest percentage of those that have already made some investment (second only to 3D printing). This tells us manufacturers have at least a grasp of its potential. Indeed manufacturers have been collecting vast volumes of data from sensors on the shop floor for decades. And yet that data has gone largely underutilized because manufacturers fail to connect the data back to the enterprise applications, and the business decisions. And this is where DATTUS can open new doors.

Instead of retrofitting equipment or developing custom connections, the DATTUS platform provides “out-of-the-box” direct connectivity for machines using cellular capabilities. It can capture data from non-networked, discrete industrial assets while remaining agnostic to data type, machine protocol, and infrastructure. It is a hardware-agnostic IIoT solution that can reliably collect and manage data and make it available for further analysis and open doors to several other of the technologies listed in Table 1.

The availability of more data increases the need for analytics in order to make sense of it. The data within an ERP solution lends itself to historical reporting and perhaps even ad hoc queries. Both are designed to answer questions you already have. But where do you turn when it is not intuitively obvious which questions you should be asking in order to optimize production or grow your business?

Therein lies one of the primary differences between reporting and analytics. While reporting answers a series of pre-defined questions, the discovery process and the iterative nature of analytics helps you ask the right questions. Reporting helps you identify a problem. The right kind of analytics helps you avoid it. Reporting seldom helps you recognize an opportunity. Analytics help you seize it.

But as volumes of data start to grow exponentially, you eventually reach a point where the human mind is no longer able to assimilate and cope with that volume. This is where machine learning can add a level of intelligence that is simply not possible without technology. Data sets have grown rapidly in recent years, thanks, at least in part, to information-sensing devices such as those to which the DATTUS solutions connect.

And the shop floor provides us with some of the most often cited use cases for artificial intelligence and machine learning. The ability to constantly scan data collected by machinery and equipment on the shop floor, searching for patterns that have previously led to failures, have saved manufacturers countless hours (and costs) associated with preventive maintenance. By predicting failures, you only need to bring production to a halt to perform maintenance when it is really needed.

Similarly, in environments regulated by strict adherence to specifications, by monitoring sensor data continuously, machine learning can alert operators before out-of-spec product is made. While shop floor supervisors are only able to scan, monitor and cope with a limited amount of data, machine learning knows no such limitations. Machine learning can recognize patterns and correlate data points that a human does not recognize as relevant. And as more data is gathered, it keeps on learning. That is what continuous improvement is all about.

DATTUS adds capabilities for analytics on data-in-motion, quickly providing insights in support of decision making on the shop floor. This includes:

  • Anomaly detection (quality control)
  • Custom event rules
  • Real-time production and efficiency reports
  • Performance forecasting
  • Predictive analytics
  • Machine learning

As part of Plex Systems, we also see the potential of applying these industrial analytics capabilities to the business side of the equation within ERP for supply chain planning, financial planning and budgeting, forecasting and more. The possibilities are endless.

Mint Jutras believes these digital technologies are destined to be absorbed into the enterprise in general, and manufacturing in particular, in much the same way as technologies like artificial intelligence (AI) and natural language processing (NLP) have insinuated themselves into our personal lives.

Think about it. As consumers, we didn’t loudly voice our desire for AI or NLP. But that didn’t stop Apple from delivering Siri on an iPhone. Pretty soon Microsoft delivered Cortana on Windows 10; Google delivered Google Now; Amazon delivered Alexa and now Bixby is on your (newer) Samsung Galaxy. We see these digital technologies being absorbed into the manufacturing landscape in much the same way, as long as solution providers like Plex and DATTUS continue to innovate and push them into the mainstream.

Conclusion

While the technologies in Table 1 are typically outside the scope of ERP, in order for them to be truly transformative, they must interoperate and/or integrate with the enterprise applications like ERP in the front and back office. When purchased separately it is often a daunting task to connect back to ERP and in turn, the business itself. But without this connection, factories don’t get any smarter and neither do the leaders making business decisions. And that’s the real goal of digital transformation in manufacturing: a smart factory and smarter business decisions. And therefore this acquisition makes perfect (and practical) sense.

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Infor: Building Innovation From the Inside Out

Realizing Potential through AI, Analytics, Network, Cloud, and Industry

Infor has a mission: to “build beautiful business applications with last mile functionality and insights for select industries, delivered as a cloud service.” Behind this mission is a solid strategy to deliver industry-specific functionality to a growing number of specialized micro-verticals, through “Cloudsuites” that leverage the power of Internet-based networks, analytics and artificial intelligence (AI). Over the past several years the privately held company has spent billions of dollars acquiring and developing technology to execute this mission, providing a steady stream of innovation along the way. In the last 12 months alone the company has delivered 176 new products.

And yet, Infor is still one of the largest enterprise application solution providers of which you may never have heard. Even some of its customers (those running acquired legacy solutions) are not aware of how innovative Infor is. This is, at least in part, because of its approach. In the enterprise application market it is not unusual for a vendor to pre-announce its latest, greatest, most innovative idea with a big splash. Then as it begins to execute on this idea it realizes just how much foundational work needs to be done to deliver on it. In the meantime it grows quiet (or announces its next big idea) and (often several years) later when (and if) the first deliverables are finally ready, it makes another big splash.

Infor has taken an entirely different approach, building innovation from the inside and working its way out. It too had a vision of tremendous new innovation driven by advanced technology, but it also had the foresight to clearly see that much of the work needed to deliver on this vision was foundational. And therefore, while others were “splashing,” so to speak, Infor was building that foundation behind the scenes, but with a clear vision of the possible. It is now time to emerge from under the covers as the potential is being realized.

A Smarter Approach

This approach is even smarter than it might appear to be on the surface. Having grown through acquisition, Infor has a very broad portfolio of enterprise applications, including multiple Enterprise Resource Planning (ERP) solutions, some more modern and strategic than others. But Infor’s portfolio also contains other applications that extend the capabilities of ERP, such as Customer Relationship Management (CRM), Enterprise Asset Management (EAM), Human Capital Management (HCM), Supply Chain Management (SCM) and more. The different strategic ERP solutions can benefit from these complementary applications, some more so than others. So how can Infor integrate all of these applications and also individually bring them to their full potential without a lot of duplication of effort? The answer lies in taking a two-pronged approach.

Infor has invested in building a strong foundation, which has evolved into the Infor OS (operating service). Infor OS provides a common set of shared services to augment its applications (Figure 1). Rather than reworking each individual user interface, for example, Infor developed a common user experience (UX), which further serves to unify the experience when working across different but complementary applications.

Figure 1: Infor OS Augments the Cloudsuite(s) with Common Shared Services

Source: Infor

But before these strategic enterprise applications could take full advantage of those shared services, they had to be transformed. The transformations took time and effort in parallel with the development of Infor OS. But these efforts proved to be invaluable. Once complete (and even to a certain extent during the process), rather than working on security, connecting to the Internet of Things (IoT), country-specific localizations, or a host of other elements of the infrastructure, the individual enterprise application development teams could focus on delivering features and functions specific to their target markets.

Infor OS: The Journey to Microservices Architecture

While the name (Infor OS) is fairly new, the development of this foundation has been evolving for almost 10 years. First introduced as the Intelligent Open Network (ION), it was based on the same premise as Infor’s prior Open SOA (Service Oriented Architecture) (circa 2006 to 2009). That premise: to provide an environment that enables new functionality to be developed once and shared by multiple products in the Infor portfolio. However, unlike Infor’s Open SOA, which had become very heavy and took years to develop, ION was kept lightweight and simple. Over the years the name has changed and it has evolved to support what is commonly referred to today as a microservices architecture.

Never heard of microservices? You’re not alone. For the reader with a technical background, a microservices architecture, is defined (by Wikipedia) as an architectural style that structures an application as a collection of loosely coupled services. Unfortunately the reference to “loosely coupled” often conjures up the old argument of an integrated suite versus “best of breed.” But this is not that.

For those nontechnical readers, think of it as constructing a solution from a set of Lego building blocks. Think about how you build a structure from Legos. Each Lego block is made of the same kind of material and is attached (connected) to the other Lego blocks the same way. In many ways they are interchangeable. But by choosing different colors and sizes, and connecting them with a different design, you can make a structure that is very unique. And once constructed, if you want to change it, decoupling some of the blocks and replacing them doesn’t destroy the parts that are not affected. There is far less disruption introduced than if you had constructed it with timber, a hammer and nails.

Infor needed to transform existing strategic solutions by refactoring the underlying code to introduce microservices. Again, for the nontechnical reader, think of it as restructuring the code without changing the behavior or the functionality. You might be wondering, why bother to change the code if you aren’t changing what it does? There may be any number of reasons, including enabling the solution to take advantage of those common shared services. But Mint Jutras feels the most valuable by-product of refactoring is to make it more “extensible.” In the context of ERP: to make it easier for Infor (and possibly its partners) to add specialized features and functions to a solid code base, with minimal disruption.

This is really the (not so) secret sauce behind Infor’s ability to deliver “last mile” functionality, not just for major industries like manufacturing, or even verticals like food and beverage, but also micro-verticals like dairy, beverage, bakers, confectionary, ingredients, prepared/chilled foods and meat/poultry/fish. While some features and functions might be the same across all manufacturing, food and beverage manufacturers and distributors also must deal with lot and sub-lot traceability and recall. Many within food and beverage must also deal with catch weights.

Catch Weight is a food industry term that means “approximate weight” because unprocessed food products (particularly meats) naturally vary in size. A retailer might order a case of 12 turkeys. The manufacturer (food processor) will estimate the price of the order by the approximate weight (e.g. 15 pounds per turkey), but will then invoice for the exact weight shipped. This can wreak havoc in an ERP solution not well-prepared to handle it.

But catch weight doesn’t affect all food industries in the same way in. It is also used in the cheese industry to manage shrinkage as the cheese ages. So handling catch weight varies for different types of food. By handling all the different types of catch weights in a single line of programming code, you add a level of complexity that adds little or no value to the customer beyond the single problem it is facing. A cheese processor doesn’t care if you can satisfy the needs of a butcher. Having different “Lego blocks” of code to insert depending on the needs of the specific micro-vertical preserves simplicity without sacrificing very specific functionality.

Beyond Features and Functions

But there is more to be gained than industry-specific features and functions from this foundational approach. Most companies today are forced to undergo a digital transformation. Two years ago our 2016 Mint Jutras Enterprise Solution study found that 88% of participants felt that digital technologies were necessary for survival and 80% agreed that digital technologies are truly transformative in the way they connect operations to systems such as ERP. And yet at the time almost half still relied at least partially on spreadsheets and/or manual processes for maintaining their operational and transactional systems of record (i.e. conducting business). Our latest 2018 study shows at least half of companies still rely at least in part on spreadsheets to satisfy needs of various departments. So obviously those transformations are still a long way from being completed.

One strategic acquisition by Infor could go a long way in supporting these digital transformations. In 2015 Infor acquired GT Nexus, a cloud-based global commerce platform. This acquisition represents a marked shift in acquisition strategy. In its formative years Infor aggressively acquired its competitors with more of an eye to growing market share than filling gaps in its portfolio. By comparison, the acquisition of GT Nexus is quite strategic.

As we noted a year ago in Infor Ushers In the Age of Networked Intelligence:

More and more of the communication, collaboration and business processes of any company are likely to extend beyond the four walls of the enterprise. Focused on the supply chain, GT Nexus largely applies to those industries that must manage the movement of materials, but also has an impact outside of traditional manufacturing and wholesale distribution. The procurement of supplies in industries like healthcare and hospitality has not changed in decades and are ripe for innovation.

Whether you deal with a physical product or services, the value chain has lengthened and become more complicated. Yet expectations of response time and delivery performance have risen dramatically. Hence the need for an added level of intelligence in dealing with this new digital, network economy.

In addition, it is worth noting that last year Infor also acquired Birst, Inc. a pioneer of cloud-native Business Intelligence (BI), analytics and data visualization tools. The addition of Birst’s analytical tools was also a step forward, but Mint Jutras sees it more like another investment in infrastructure and shared services rather than a true differentiator. While the executives that came along with the acquisition might argue Birst is better (the best?) in terms of capability and speed of data discovery and easy to use analytics, most of the existing Birst customers are running enterprise applications that are not part of the Infor portfolio and it is still sold as a stand-alone tool. So you don’t have to run an Infor application to benefit from them.

That said, the tools were made immediately available to Infor customers as a like-for-like trade-in. Since then Infor has been working to replace any existing data cubes and content (previously Cognos-based) and also build out additional applications, content and migration tools.

Enhanced Data Management

Birst allows Infor customers to draw from all sorts of data sources for analysis. But the better story is what Infor has done in terms of data management in general, and to understand that you need to look across several different components “inside” Infor, including artificial intelligence, which requires you to select algorithms, train models and deploy data science. Because we’re talking about advanced technology, this can get very technical very quickly.

A business decision-maker seldom knows the difference between linear regression, neural topic modeling, K-means clustering and a boosted decision tree. Nor should they have to. From a business decision-maker’s point of view, it is more important to understand the potential, and that is quite simple. It’s all about answering these questions:

  • What happened?
  • Why did it happen?
  • What should I do?

To Infor’s credit, this is exactly what it is offering, even though it often falls into the trap of offering TMI (too much (technical) information) to nontechnical business folks.

What happened?

This is all about collecting data. It might be structured data from enterprise applications (yours or your trading partners’), semi-structured data like XML or CSV (maybe you get orders or payments from customers in XML files or streams of IoT data) or entirely unstructured data from social media or other community-based data. You need a common place to put all this data and Infor’s answer to this is its Data Lake. A data lake is a storage repository that holds raw data (usually vast amounts of it) in its native format. Yet while the data is in its native format, Infor also provides a catalog that can be used to determine connections between the different data elements (e.g. an order is connected to a customer, a dollar amount is connected to a key performance indicator).

But you need to consume that data in order to determine what really happened. Figure 2 (provided by Infor) is a bit on the technical side. The key takeaways from it: You might use Birst for analysis of the data; you might use the data in universal searches within the Infor applications; or you can develop your own applications using Infor’s Mongoose development platform.

Figure 2: Infor Data Lake: How to consume data from the data lake

Source: Infor

Why did it happen?

For the “Why?” question, Infor leverages the different connections within the data and does a correlation analysis, looking for causal factors. Did sales go down because prices went up? Or did they go down because sales reps were on vacation or left the company? Was the weather to blame? Or a sluggish economy? For some of these questions you need massive amounts of data, not all of which resides in your enterprise applications.

Infor claims to have no shortage of insights to offer across customer relationship management (CRM), financials, human capital management (HCM), procurement and more. An example of the types of financial insights are shown in the sidebar to the left.

What should I do?

This is where the real data science comes to play. Since announcing the Coleman AI Platform Infor has been developing its first AI data science applications. These are generally predictive in nature, drawing on deep machine learning for forecasting, optimization and decision execution. Some examples include patient demand forecasting for hospitals, a predictive framework to predict asset failure, inventory optimization across a number of different industries, predicting estimated time of arrival for logistic providers and benchmarking performance across industry. Benchmarking of course requires access to large quantities of external data.

And don’t worry if you don’t have data scientists on staff. Infor has over 100 of them ready and waiting to help.

Conclusion and Recommendations

For a company of its size Infor has been exceptionally quiet over the past several years. In the software industry staying quiet often means there is little or no new innovation to share. In the case of Infor, this could not be farther from the truth.

Infor is led by a group of executives with both the vision and the expertise to understand the true potential of advanced digital technologies today. Oftentimes before you can ever hope to take full advantage of this advanced technology you must lay a strong foundation, and this might go largely unnoticed as it is being developed behind the scenes. But Infor’s executives were not afraid to dig in and lay that foundation.

Infor is now starting to reap the rewards of these efforts. It is time to share them with the world, not quietly, but loudly and proudly. Even many of its own customers remain unaware of all that Infor has developed. There are over 90,000 Infor customers and many are still running on old versions or older, non-strategic products. They seem to think none of this new technology is for them.

Mint Jutras would caution them (and other companies running non-Infor legacy applications) against this train of thought. If not for you, then who?

To those running these old solutions: Don’t expect massive (any?) innovation for your old products. They aren’t going to get you where you need to go in order to compete effectively in the global digital economy. For decades ripping and replacing ERP solutions was avoided at any and all cost. Those days are over. If you are running an old, outdated solution, it is unequivocally time to rip and replace. You’ll be happy you did.

To Infor: You’ve developed a lot of great stuff. Get on your bandwagon and shout!

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Introducing Aera’s Cognitive Technology

Enabling The Self-Driving Enterprise

Cognitive capabilities are highly valued in human beings. They make people smart, and smart is good. According to the Oxford Dictionary, cognition is “the mental action or process of acquiring knowledge and understanding through thought, experience, and the senses.” Yet as automation becomes more and more prevalent, we expect more and more functions and processes to be performed without human assistance. Can technology really imitate human cognition? Why not? After all, we live in a world where self-driving cars, although not yet ubiquitous, are a reality. And in a world where terabytes of data are being replaced with zettabytes, is it even possible for a human to process data at the speed and granularity necessary for timely, data-driven decisions?

Enterprise applications have been used to streamline and automate transactional processes for several decades now, particularly where simple and straight forward rules can be applied. When inventory falls below safety stock, order more. But how do you know when to change safety stock? How do you balance inventory across your distribution network or work off excess inventory? How accurate is your forecast? Is it possible to automate the cognitive functions that understand (recognize patterns and learn from the past), predict the future, and not only make recommendations, but also take action? Aera Technology not only thinks it is possible, it is delivering on that promise today to enable the Self-Driving Enterprise.

Aera is quite a unique kind of company. Headquartered in Mountain View, California, it serves some of the world’s largest enterprises from its global offices located in San Francisco, Portland, Bucharest, Cluj-Napoca, Paris, Munich, London, and Pune. Using proprietary data crawling, industry models, machine learning and artificial intelligence, Aera’s goal is to revolutionize how people relate to data and how organizations function. It offers what it calls a “cognitive operating system.”

The Self-Driving Enterprise

Aera starts with the premise that if built-in intelligence can drive a car, then it should be able to drive a company. Like a self-driving car, a self-driving enterprise must connect all the different data points both inside (engine, accelerator, steering wheel, brakes) and outside (roadways and road conditions, other vehicles, pedestrians). It must do all this in real-time, because speed and direction changes must occur immediately as any of those conditions change. And it must be always on and always thinking. No snoozing at the wheel allowed. It also must be able to operate autonomously. With no driver, a self-driving car has to take action without being told what to do.

A self-driving enterprise will still have humans at the helm. Aera is not setting out to eliminate the decision-makers, but it is trying to make them smarter and more effective, able to use all the data available, not just the usual subset contained in an enterprise resource planning (ERP) solution.

If this has you curious to learn more, click here to read the full report.

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Workday: Getting Smarter and Smarter

Enter the Age of Intelligence

In a recent Mint Jutras report, “How Smart Are Your Enterprise Applications?” we outlined some of the different ways solution providers are adding a new level of intelligence to their offerings… or not. While “intelligence” has become the holy grail of enterprise applications of late, not all vendors are delivering on the promise of smarter applications. For some, it’s just the latest buzzword added to their marketing collateral and some are simply playing catch up to current next generation applications. Others are taking their first baby steps, but a select few are truly entering the “age of intelligence.”

Where is Workday along this progression? Since its inception in 2005, it has never been a company that over-inflated its capabilities with bravado and marketing spin. Born in the cloud and built on a next-generation platform that continues to evolve, Workday also never had to play catch-up. And the first steps it took in moving into the age of intelligence were not baby steps, but instead bold ones, including some strategic acquisitions.

Workday’s acquisition of Identified in 2014 was an important step in incorporating predictive analytics and machine learning into its portfolio. In 2015 it acquired Gridcraft and last year it acquired Platfora. With both of these acquisitions, Workday sought to build insights [read intelligence] directly into its applications. More recently its benchmarking capabilities take insight and intelligence to another whole level by putting Data as a Service (DaaS) in the context of your business performance, in comparison to your peers. And Workday has opened the doors to more innovation from a broader community by making its Workday Cloud Platform available beyond its own development team.

It is clear Workday is getting smarter and smarter with each new release.

Smart, Smarter, Smartest

So, what does it take to make an enterprise application smart? In our previous report we distinguished different levels of intelligence:

  • Smart: We concluded any enterprise application is smart in that it’s not dumb. It can follow instructions – instructions like, IF <this condition> THEN <do this> ELSE <do that>. Business applications have been built on IF THEN ELSE statements since the earliest computer programs were developed. Workday applications are no exception and indeed, they can now go beyond simply following specific instructions. They are starting to learn to take some simple rule-based actions on their own. For example, the recruiting module is smart enough to decline any outstanding applicants once a position is filled, and yet keep them on file to review when other vacancies open up.
  • Smarter: To make an application smarter, you need to make it easier to use and better at communicating. Progressive releases of Workday have made the user experience very compelling while also adding more and more insights. Workday has also borrowed concepts from consumer technology, putting more power in the hands of users using mobile devices, not only alerting managers to exceptions, issues and required approvals, but allowing them to take immediate action. Workday Talk provides a “chat” capability modeled after social media. Participants can follow conversations attached to business objects like sales orders, customers or products. Groups and teams can be assembled to foster collaboration. When people are better informed, they can make more intelligent decisions, faster.
  • Smartest: But the smartest applications today combine the pattern recognition capabilities of machine learning to produce artificial intelligence (AI) and predict the future. The highest level of intelligence will be achieved in combining a variety of technologies together: AI, deep machine learning, Natural Language Processing (NLP), image recognition and predictive analytics are all at the forefront of this movement. And Workday has all these technologies in its kit bag. It has already taken some initial steps in leveraging them. For example, it has embedded machine learning capabilities into its Talent Insights to identify retention risk. Look for more use cases to be delivered using data from both inside and outside of Workday.

It is quite clear that Workday’s Human Capital Management (HCM), Financial Management, Student Management and Planning solutions are smarter than your average enterprise applications. Let’s dig a little deeper into some ways they will get even smarter.

Building Insights In: Prism Analytics

Good reporting is a necessary backbone of applications like HCM and financial management. Reports provide a historical perspective, help you assess your current position and answer questions you have about your performance. But analytics provide a deeper level of understanding and help you ask the right questions. Analytics are iterative by nature. You start with a question, issue or problem: Sales are down. Reports might tell you what regions or products are problematic, but you won’t really know why until you drill down, and you are never quite sure what path you need to take until you find out more. And you won’t even be prompted to investigate until you already have a problem.

Predictive analytics help you anticipate conditions, prompting you to investigate a situation before the problem rears its head. You would like to be able to conduct this kind of investigative work right in the familiar environment of the solution running your business. But it is even more powerful when you can look beyond the structured data that resides within your enterprise applications. Workday has woven the technology acquired from Platfora, into the fabric of its solution, rather than bolting on components. And yet Workday Prism Analytics will not be limited to Workday data, but will also bring in non-Workday data, which can then be presented through Workday reports, scorecards, and dashboards for analysis.

Typically this type of mix of data requires data preparation to be done by a data administrator with the technical skills needed to load the external data, cleanse and prepare it and then create reports, queries and/or dashboards. This activity doesn’t go away with Workday Prism Analytics, but it is simplified enough for a technical business user to perform – and perform quickly enough to be of value. And the data can be blended with, transformed and enriched by your transactional system of record (Workday data). In doing so Workday has struck a nice balance between having a super powerful tool on the back end but also super easy to use on the front end, avoiding the usual trade-offs.

Workday is in the early stages of delivering this, and also has plans down the road for data discovery. Data discovery typically goes after big data in search of patterns that may not be intuitively obvious. Using the right visualization tools, it helps you understand which data is most relevant to your problem, even if you don’t know exactly what to ask for.

Benchmarking Performance with Data as a Service (DaaS)

It takes a different kind of intelligence gathering to understand your business performance in relation to others in similar roles or industries. As a multi-tenant SaaS solution provider, Workday is in a unique position to provide you with access to this kind of comparative data. But of course, you must be willing to give, in order to receive. Workday needs permission to use this data, but paraphrasing the words of Workday leadership: We don’t take customers’ data. They give it to us.

Workday sits on a large volume of data collected from hundreds of customers subscribing to its software. This is data that can be invaluable to the entire Workday community for benchmarking against peers. Customers must opt in to contribute secured aggregated data. In turn, they receive benchmarks. Today this Data as a Service (DaaS) is available for customers to explore Workday usage and HCM results, including workforce composition, diversity, turnover, etc. Financial management data is coming soon. Within the first three weeks of this service being available, Workday reported 100 customers had opted in and contributed data. Obviously, as this number grows, so will the value of the data.

Expect more from Workday along these lines in the future, including data from other sources (private and public) not included in Workday.

Machine Learning and AI

Of course the availability of a growing volume and diversity of data opens the door for machine learning and therefore artificial intelligence. Workday’s acquisition of Identified in 2014 was an important step in incorporating predictive analytics and machine learning into its repertoire of capabilities. Identified’s patented SYMAN (Systematic Mass Normalization) technology mines Facebook for social data and then uses artificial intelligence to transform that data into professional intelligence. The “learning” comes from continued use, validating predictions with outcomes from Workday employee data on performance and retention.

Workday released Workday Talent Insights in 2015, identifying retention risk and delivering a talent scorecard. Through this introduction Workday learned that customers prefer an embedded experience, not a standalone application and that the overall user experience is paramount, along with access to data for training algorithms.

The Power of a Platform

Since it was founded in 2005, Workday has always insisted it was (and is) an applications company, rather than a technology company. It has always offered cloud-based business solutions. While it built these applications on a solid and modern platform, it always resisted the urging of pundits and industry observers to become a “platform” company. Until now.

The Next Chapter for Workday

Now it will be both a “platform” player as well as a business solution provider. The Workday Cloud Platform was soft launched a few months ago with selected service partners. Built on the principles of openness, Workday will provide the tools needed to manage the complete application life cycle, with data modeling and a single Application Programming Interface (API) point of integration.

So how does this make Workday applications smarter? Of course there are no guarantees, but by opening up the platform, along with all the presentation services, conversation services, and analytics Workday uses to make its solutions smarter, the level of intelligence is more likely to deepen. The Platform will include both Workday Talk (NLP) and BOT for anomaly detection.

So, what are developers building on the platform? Here are a few examples:

  • Talent Mobility, allowing employees to visualize career opportunities and connect with employees across globe.
  • ID Services to manage security badges
  • Supplier requisitioning that allows suppliers to directly populate data in Workday
  • Safety services management

Summary

The Innovation Keynote at the 2017 Workday Rising Event was entitled “The Age of Intelligence.” The Keynote was presented by Mike McNamara, the CEO of one of Workday’s largest customers, Flex (a contract manufacturer formerly known as Flextronics). In his opening remarks, Mr. McNamara summed up this new age by saying, “Today it’s not about controlling land and resources, but rather about applying intelligence.”

In many ways, intelligence is a new currency in the global, digital economy. And yet, when most solution providers today talk about intelligent applications, they often simply mean new ways of interacting with the solution and analytics that help you derive more and better insights from the data. But this is the minimum you should expect today. Workday has aggressively taken steps towards real intelligence, through acquisition and its own development efforts. Workday Prism Analytics, Benchmarking and DaaS, machine learning, natural language processing and the Workday Cloud Platform all combine to provide powerful insights and intelligence, not through separate bolt-on tools, but embedded in a single solution.

If your current solutions are not headed down the path towards intelligent applications, if you are starting to look for new, smarter ones, Workday is a good place to start.

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