Congrats to the 2019 SuperNova Award Winners

Aubrey Coggins

Constellation Research

Aubrey (Levinstone) Coggins manages Constellation's marketing and communications programs and is the producer of DisrupTV, a weekly Web series on innovation and the enterprise.  She previously served as a senior manager of corporate communications at Egnyte and held account roles at Access Communications and The Hoffman Agency. Born and raised in Texas, she received a bachelor's degree in Public Relations from The University of Texas at Austin – Hook ‘em!   ...

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Last night at the ninth annual awards Gala at CCE, our team unveiled the winners of the 2019 SuperNova Awards. This was one of our toughest years to choose just one winner in each category; the competition was top-notch.

As digital change continues to dominate the agenda of the global C-Suite, organizations across all industries and geographies must make deeper investments to create true impact. These case studies embody the necessary leadership and teaming talents needed as we move from the post-digital divide into winner-take-all networks.

If you missed the gorgeous gala last night, I’m happy to share the results with you. Congrats to the 2019 winners in each category.

2019 SuperNova Award Winners

AI & Augmented Humanity
Singapore Management University

Data to Decisions
GE Aviation

Data-Driven Digital Networks & Business Models
IBM Food Trust

Digital Marketing & Sales Effectiveness
Sportable Scoreboards

Digital Safety, Governance & Privacy
Sovrin Foundation

Future of Work: Employee Experience
Walgreens

Future of Work: Human Capital Management
Special Olympics
SiteOne Landscape Supply

Next-Generation Customer Experience
Posadas

Tech Optimization & Modernization
University of Pisa

The SuperNova Award winners were selected by a combination of industry judging and public polling. If you are interested in submitting a case study for our tenth anniversary, be sure to look at this year’s winners and prep your nomination for next year. The nominations open in Spring 2020.

For more information, visit https://www.constellationr.com/events/supernova/2019.

Are you prepared for the HHS fines?

David Chou

CIO
Harris Health Systems

Title: About David Chou David Chou is vice president and principal analyst at Constellation Research, covering the intersection of the healthcare systems’ stringent processes and regulations and the transformational power of technology. With nearly 20 years of experience as a senior IT executive across various health systems, David brings a CIO’s practicality to his advisory practice on emerging technologies and disruptive innovation. He offers guidance to healthcare institutions and practitioners navigating digital mega trends and how to strategically apply them to hospital operations to maximize their technology investments. David’s expertise is helping a health system transition toward becoming a digital enterprise in the healthcare setting. He has worked on building a digital…...

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The University of Rochester Medical Center (URMC) has agreed to pay $3 million to the Office for Civil Rights (OCR) at the U.S. Department of Health and Human Services (HHS), and take substantial corrective action to settle potential violations of the Health Insurance Portability and Accountability Act (HIPAA) Privacy and Security Rules.

#Chousangle - This is an area where, on the surface, it looks like an easy fix where every device such as a thumb drive and laptop should be encrypted. Encryption of the device is the easier part. The challenge comes down to setting up a security program, as stated in the correction action agreement.  

A third party consulting entity will conduct the risk analysis to identify the gaps. Once you have the gaps identified, the technical debt on the network infrastructure will be identified.  

• Will budgeted be allocated to support the next generation secured infrastructure or will the health system take 3-5 years on the infrastructure redesign and implementation, creating a constant state of hardware refresh? 
• Enterprise risk needs an organizational owner. Who is accountable for enterprise risk? This has to be the COO or someone leading operations and not the CIO.
• An important key area is to design a security program, including the right governance structure vs. checking off the box on the audit. 
• Will research institutions comply with the organization's security protocol?
• How will we hold employees accountable for failing to comply with the security guidelines?

These are just a few notes off the top of my head. What are your thoughts?

Why I'm a CDP Skeptic...And What We Need Instead

If you’ve read any of my other posts, you know how passionately I believe that customer experience should be the organizing principle and top priority of every business. Focus on customers is, to say the least, highly correlated with success. Customer understanding—knowing your customers and anticipating their needs even before they might themselves—makes it possible to design consistently good customer experiences that build successful businesses.

One of the most crucial resources for customer understanding is data. You might be forgiven, then, for thinking that I’d be a fan of customer data platforms (CDPs). After all, they promise to pull together all of a company’s customer data from any number of sources to help analyze, predict, and respond to customer behaviors in ways that improve marketing results.

But I’m not. Here’s why:

• We live in the real world, where customer data is imperfect, and continuously imperfect at that. The pace of change means that no customer data source can ever be perfectly up to date and consistent. And besides, although we’re getting better at prediction all the time, customers will still do things we don’t expect them to, or when we don’t expect them to. Implementing a CDP to fix that is believing in a fairy tale. Only a company in stasis is going to have a totally consistent view of customers…and a company in stasis is not likely to be a going concern for very long.
• There’s never been one consistent source of enterprise data for any length of time. Just as customer data is continuously imperfect, so is the structure of most companies. Mergers and acquisitions, expansion into new lines of business or geographies, new strategies, experimentation with new processes or technology approaches—all of these things mean that having only one source of data and insight is, and will likely forever be, an elusive goal.
• Marketing isn’t the only part of the business that needs a holistic view of customers. Analyzing and taking action on customer data only from a marketing perspective completely misses the bigger objective. Customer experience spans all aspects of a business. Anyone who interacts with customers needs to have access to a consistent view of those customers and insights that make each interaction better. Creating yet another data silo, especially when it purports to be a single source of customer information, is a short-sighted, ill-advised investment.
• Different departments and teams need different views of the same customer data. What’s most useful to customer service may be very different to what’s top priority for sales teams. What we need to know about customers varies with context. Insights that drive planning, strategy, or product development, for example, have a totally different scale and cadence to what informs effective real-time marketing efforts. Each part of the organization has its own lens or filters to get to the most valuable customer insights for them, but all need to work from a holistic, shared view. And while we’ll never achieve perfection, customer data should be highly consistent no matter who’s using it.

While the promise of CDPs falls short of the mark, these are fundamental challenges that need resolution. So what does getting it right look like?

Here’s my view on the key characteristics of enterprise-grade customer data management systems:

• Their design is driven by both a solid foundation of good data management practices and a clear set of use cases across different parts of the organization. In other words, they’re not just repositories, they’re also tools that flow seamlessly into all of the enterprise systems that require customer insight to operate effectively.
• They can adapt to support a wide range of needs and uses, beyond those that we have already identified today.
• They can accommodate both deterministic customer data (from systems of record) and probabilistic customer data (predictions based on observed behavior)
• They support different cadences, from real- or near-real-time to periodic, depending on the use case.
• They provide the ability to identify customer segments that meaningfully inform the most effective ways to interact and engage with individuals—regardless of the type of interaction (e.g., marketing, sales, service). That’s based on more than just analyzing behaviors from a marketing perspective.
• They ensure that personally identifiable information (PII) is only used where it needs to be and where it’s permitted, whether it’s a question of regulatory requirements or customer preferences.
• They draw from, coexist with, and feed into the tools that different teams use to get their work done.
• They help to establish the metadata structures that will improve effective data analysis and use over time.
• They’re straightforward to use across departments and functions.

This may all sound as much like a fairy tale as the promise of CDPs. The good news is that many companies and a whole lot of sharp minds are working on the problem. Those attempting to tackle it range from startups to established players (some of whom are already in the, ahem, CDP category) to large enterprise technology providers.

Part of the challenge is semantic. The term CDP has become muddied by undelivered promises and limited understanding of the need. Many of the more recently announced offerings in the area of customer data management have been unofficially described as “CDPs that aren’t just CDPs.” These are much more than just data management systems, however. They aim to provide intuitive tools for deriving and applying customer insights, not just consolidating customer data.

The pace with which players in this fast-evolving space are tackling the dual challenges of data management and applied use gives me hope that we’ll soon have some much more effective solutions to putting customer understanding at the heart of business operations. While I suspect we’ll never get to one single system for all customer data, there’s every reason to believe in practical solutions to this large-scale problem in the near future.

In the meantime, do some serious tire-kicking before you invest. Define the use cases that are most crucial for your organization to address immediately. Push hard to understand what a vendor's offering includes today, out of the box, and what's on the near-term roadmap.

Analytics For Applications: Three Next-Generation Options

Doug Henschen

Former Vice President and Principal Analyst
Constellation Research

Doug Henschen is former Vice President and Principal Analyst where he focused on data-driven decision making. Henschen’s Data-to-Decisions research examines how organizations employ data analysis to reimagine their business models and gain a deeper understanding of their customers. Henschen's research acknowledges the fact that innovative applications of data analysis requires a multi-disciplinary approach starting with information and orchestration technologies, continuing through business intelligence, data-visualization, and analytics, and moving into NoSQL and big-data analysis, third-party data enrichment, and decision-management technologies. Insight-driven business models are of interest to the entire C-suite, but most particularly chief executive officers, chief digital officers,…...

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Application-integrated cloud analytics offerings were announced by Oracle, SAP and Workday this fall. Here’s a closer look at when they’re a good fit. 

Applications and analytics have complemented each other for decades, but this fall we’ve seen a fresh wave of announcements about offerings built for the cloud.

The announcements, by Oracle, SAP and Workday, build on larger analytics initiatives they’ve each had in the works for years. At Oracle it’s the company’s next-generation Oracle Analytics (OA) platform, anchored by the Oracle Analytics Cloud. The SAP Analytics Cloud has evolved since its 2015 introduction to become the company’s next-generation product for BI, augmented analytics and planning. Now it’s complemented by the broader SAP Cloud Services platform and a new data warehouse service. At Workday, data and analytics investments have steadily increased from a partnership with Datameer to the 2016 acquisition of Platfora to Workday Rising announcements around its Prism Analytics platform.

Here’s a deeper dive into the announcements along with my take on how to size up the fit you’re your organization.

Analytics for Applications

Reporting capabilities have been tied to applications since the earliest days of the mainframe. Today’s demands for insight are far more sophisticated and, in the latest wrinkle, they must be built for the cloud. Tight integration with applications and cloud deployment are what these three announcements have in common:

Oracle introduces Analytics for Applications. Announced at Oracle Open World 2019 in September, Oracle Analytics for Applications are SaaS apps designed to complement Oracle Fusion cloud applications. First up is Oracle Analytics for Fusion ERP, which is now generally available. Oracle Analytics for Fusion HCM is expected in Q1 2020 and Fusion SCM and CX and NetSuite are on the roadmap. Under the hood, the the SaaS apps will combine Oracle Analytics Cloud with a bundled instance of the two-year-old Oracle Autonomous Data Warehouse service as well as supporting data models, dashboards and key performance indicators. The data pipeline from Oracle Cloud apps to the Autonomous Data Warehouse is managed by Oracle, and Oracle’s Integration Platform as a Service is also available to move data from third-party apps.

Oracle Analytics for Applications offerings will include instances of the Autonomous Data Warehouse and supporting data integration services.

The minimum per-user, per-month licensing level of OA for Fusion ERP required to get the bundled Autonomous Data Warehouse instance is 20 users. At this entry level the data warehouse instance includes 2 OCPUs (Oracle Cloud Processing Units) and 1 Terabyte of storage. Oracle says this is more than enough capacity for Oracle Cloud ERP deployments of that size. Oracle would, of course, be only too happy to add more users, storage and compute capacity as required, and data-integration services are available to bring in data from third-party sources batch ETL style.

SAP extends its analytics cloud. SAP and Oracle seem to be squaring off with similar offerings and even similar names: SAP Analytics Cloud (SAC) and Oracle Analytics Cloud (OAC). SAP embedded SAC into the user interface and user experience of S4/HANA ERP last year, and it’s currently working on embedding SAC into SuccessFactors. Oracle answered with its OA for Fusion ERP release in September and expects its OA for Fusion HCM release early next year. (One notable difference between SAC and OAC is that planning is an integrated aspect of SAC whereas Oracle offers planning through separate cloud services.)

SAP touts the elastic scalability and hybrid- and multi-cloud-friendly virtual data access of its new SAP Data Warehouse Cloud.

At the October 8-10 TechEd event in Barcelona, SAP announced the general availability of SAP Data Warehouse Cloud, a part of its broader SAP HANA Cloud Services platform, which also includes SAP Analytics Cloud and SAP HANA Cloud. The data warehouse service, which is based on HANA, of course, parallels Oracle’s “for Analytics” combinations with its Autonomous Data Warehouse service. Departing from traditional ETL, SAP Data Warehouse Cloud uses data virtualization and a customer-managed semantic layer approach touted as improving reporting flexibility and virtualized querying across hybrid and multi-cloud deployments without always requiring data movement.

Workday bundles dashboards, adds apps. Workday is taking a different approach, bundling a drag-and-drop, analytic Discovery Board tool with its Human Capital Management and Financial Management applications at no extra charge. The optional, extra-cost offering, announced in October at Workday Rising, will be augmented analytic applications, starting with Workday People Analytics. Part of the appeal is keeping potentially sensitive HR data and (once a finance app is available) financial data within the confines of the security and access-control framework of the related applications.

Workday People Analytics is an augmented analytic app that complements Workday HCM.

Workday’s included Discovery Boards and optional applications are all built on Workday’s Prism Analytics platform, which is a cloud-based data hub. This hub is built on Workday application data, but you can also load third-party data. Prism has big data (non-relational) roots (by way of the 2016 Platfora acquisition), but Workday is adding fast relational querying capabilities by way of columnar Parquet data storage.

My Take on Analytics for Apps

Should your application choices determine your analytics strategy, or are these choices best made independently? In my view it all depends the recency of your investments in analytical tools and platforms and the diversity of your applications and data management infrastructure.

Oracle and SAP both have long histories in data management and business intelligence, and with their respective announcements they’re clearly looking to expand from their beachheads in the cloud. The challenge is that many Oracle Business Intelligence Enterprise Edition and SAP Business Objects customers have already moved on to third-party alternatives. If a customer made the switch some time ago (as in, four or five years ago or more), those deployments may now be aging and vulnerable to replacement. Companies that have invested in rival analytics platforms more recently are far less likely to change course and start fresh on a new platform or split investments on two or more platforms.

The two other factors that might sway analytic investments are commitments to apps and investments in data management infrastructure. All-Oracle and all-SAP shops are less common than they used to be, but the more committed the organization is to Oracle or SAP apps and Oracle or SAP data management options, the more open they will be to OAC or SAC and related application content and warehouse services. If it’s an organization that went cloud years ago and is deep into using RedShift, Snowflake or other cloud-based data management options, I don’t see them going back.

This brings us to Workday, which, in contrast to Oracle and SAP, has a modest heritage in data management and analytics. Thus, it’s taking the sensible approach of giving away its app-integrated Discovery Board capabilities. That will give Workday customers – and particularly their analytics and data management teams — a start on using Prism Analytics. The hope is that these folks will gain familiarity and a comfort level with using Prism and will use it more broadly, perhaps even adding data from external sources. Meanwhile, Workday will be selling CHROs and CFOs on the integrated analytic applications it’s developing, starting with Workday People Analytics. These ancillary applications are designed around business questions and processes (reminding me of Siebel Analytic Apps, for those who remember those).

The Oracle and SAP offerings also have business appeal, too, with the inclusion of application-specific schema, key metrics and dashboards and the availability of industry content. But it’s a stack commitment verses an ancillary app offering. I see the Oracle and SAP offerings as a best fit for companies that are heavily invested in these vendors, and that will use these analytical platforms to meet most if not all of their analytical needs, not just those tied to ERP or HCM. Workday’s is a co-existence strategy with existing data management and analytics investments. What’s available and coming will complement HCM and financials, and it’s not built or intended to serve as broad BI, analytics and data management foundation.

Related Reading:
Oracle Reasserts Itself in BI and Analytics
Workday Unifies Its Approach to Machine Learning, Analytics and Planning
Augmented Analytics: How Smart Features are Changing Business Intelligence

How should we treat facial recognition?

Steve Wilson

Former Vice President and Principal Analyst
Constellation Research

Steve Wilson is former VP and Principal Analyst at Constellation Research, leading the business theme Digital Safety and Privacy. His coverage includes digital identity, data protection, data privacy, cryptography, and trust. His advisory services to CIOs, CISOs, CPOs and IT architects include identity product strategy, security practice benchmarking, Privacy by Design (PbD), privacy engineering and Privacy [or Data Protection] Impact Assessments (PIA, DPIA). Coverage Areas: -  Identity management, frameworks & governance-  Digital identity technologies-  Privacy by Design -  Big Data; “Big Privacy”-  Identity & privacy innovation Previous experience: Wilson has worked in ICT innovation, research, development and analysis for over 25 years. With double…...

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Various law makers -- such as the state of Illinois and the city of San Francisco -- are moving to restrain or prohibit facial recognition through specific legislation.  Evan Selinger and Woody Hartzog writing in the New York Times have called for a ban.  And now, the French privacy regulator CNIL appears to have found facial recognition trials in schools to be in breach of general data protection law. 

How should we look at face recognition and biometrics technology?  Does it need special policy & legal treatment, or is it already covered by general privacy law?  
In my view, most of the world already has an established legal and analytical framework for dealing with automated facial recognition.  We can see face matching as an automated collection of fresh Personal Data, synthesised and thence collected by algorithms, and along the way subject to conventional privacy principles.  Most global data protection laws are technology neutral: they don’t care how Personal Data ends up in a database, but concern themselves with the transparency, proportionality and necessity of the collection. 

Technology-neutral privacy legislation does not specifically define the term ‘collection’.   So while collection might usually be associated with forms and questionnaires, we can interpret the regulations more broadly.  

‘Collect’ is not necessarily a directive verb, so collection can be said to have occurred passively, whenever and however Personal Data appears in an information.  Therefore, the creation or synthesis of new Personal Data counts as a form of collection.  Indeed, the Australian federal privacy regulator is now explicit about “Collection by Creation”. 

So if Big Data can deliver brand new insights about identifiable people, like the fact that a supermarket customer may be pregnant, then those insights get much the same protection under information privacy law as they would had the shopper filled out a form expressly declaring herself to be pregnant. 
Likewise, if automatic facial recognition leads to names being entered in a database alongside erstwhile anonymous images, then new Personal Data can be said to have been collected.  In turn, the data collector is required to obey applicable privacy principles.  

Which is to say simply: if an organisation holds Personal Data, then unless it has the specific consent of individuals concerned, the organisation should hold as little Personal Data as possible, it should confine itself to just the data needed for an express purpose, refrain from re-purposing that data, and let individuals know what data is held about them.  

Data privacy principles should apply regardless of how the organisation came to have the Personal Data; that is, whether the collection was direct and explicit, or automated by face recognition.  If Personal Data has come to be held by the organisation thanks to opaque (often proprietary) algorithms, then the public expectation of privacy protection is surely all the more acute. 

Across cyberspace now, facial recognition processes are running 24X7, sight unseen, poring over billions of images -- most of which were innocently uploaded to the cloud for fun -- and creating new Personal Data in the form of identifications.  Some of this activity is for law enforcement, and some is to train biometric algorithms; I think it likely that other facial matching is being used to feed signals into people-you-may-know algorithms. But almost all facial recognition is occurring behind our backs, exploiting personal photos for secondary purposes without consent, and in stark breach of commonplace Data Protection rules. 
 

Cloud ERP is dominating the discussions with healthcare providers.

David Chou

CIO
Harris Health Systems

Title: About David Chou David Chou is vice president and principal analyst at Constellation Research, covering the intersection of the healthcare systems’ stringent processes and regulations and the transformational power of technology. With nearly 20 years of experience as a senior IT executive across various health systems, David brings a CIO’s practicality to his advisory practice on emerging technologies and disruptive innovation. He offers guidance to healthcare institutions and practitioners navigating digital mega trends and how to strategically apply them to hospital operations to maximize their technology investments. David’s expertise is helping a health system transition toward becoming a digital enterprise in the healthcare setting. He has worked on building a digital…...

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Workday had their #wdayrising event that brought together their customers, prospective customers, partners, and industry analysts.  In recent years, the healthcare provider industry has spent the majority of its technology budget on the electronic medical records. This has led to a neglect of back-office enterprise resource planning (ERP) software, and, as a result, almost every healthcare provider organization now is evaluating a cloud ERP solution.  Healthcare providers are evaluating ERP platforms based on new sets of criteria in search of a solution to drive digital transformation. These criteria could be described as "postmodern ERP requirements."

I am impressive by growth in workday's healthcare position and the workday healthcare summit event attendance has grown every year showing a lot of new customers in the community.

The primary concern among healthcare providers with workday during the last few years was that it had a robust HCM and accounting component, but it lacked a supply chain module.  Now the supply chain module is ready, and we have seen many new live clients along with the list of potential healthcare full cloud ERP clients growing.  Here is a view of the live and upcoming live clients on the workday supply chain.

Top of mind for healthcare CxOs

• Driving transformation and eliminating unnecessary expenses – The Workday supply chain module is designed for healthcare providers with inputs from many large healthcare provider organizations. Healthcare executives must utilize their ERP solutions as the platform to drive efficiency in supply chain. Areas of focus will be on optimizing their pricing structure with the manufactures, enhancing their charge capture capabilities, and creating a mobile-first experience for the front-line supply chain staff to streamline the stocking, par counting, and other areas of the operation.
• Organization talent and insight – The cost of external recruitment in a tight labor market that currently has a talent shortage is very tough and expensive. HR leaders want insights into their employees to increase staff retention while promoting growth internally. Analytics and insight into the workforce will be key to driving an engaged organization workforce culture.
• Grants management – One area that has always been tough for healthcare provider institutions with an extensive research operation is managing grants. The grants management feature in the financial management module will help capture the accurate information on tracking grants before it is awarded and post-grant for budgeting, staff utilization that is working clinically and on the research side, and reporting.
• Data-Driven – Healthcare organizations strive to be a data-driven organization, and they are focusing on providing the highest quality of care at the lowest cost. Enhanced ERP analytics will help CxO and department manager gain insight with better tools.

#chousangle

• Cloud ERP adoption will be just as hot as the electronic medical record (EMR) implementation wave as organizations focusing on their transformation journey.
• Early adopters of the cloud ERP have workday HCM while utilizing another system for finance and supply chain.  Will these organizations have the optimal data integration set up for driving operational efficiency, or will there be another wave of implementation to move towards an integrated platform?
• Healthcare providers have a choice to make in selecting their enterprise analytics platform since they have many different analytics systems in the portfolio.  Will they trust the cloud ERP analytics platform at the enterprise level or look at another solution?
• Workday credentials using blockchain technology can be a game-changer not for just HR on validating skills and credentials, but also for the clinical staff.  Workday credentials can be the source of truth for identity access management.   Healthcare organizations traditionally rely on their HCM system as the source of truth for employee information.  Now with the workday credentials, it is a prime opportunity to tie in roles and system access management, which is an immature area for healthcare providers.