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.

It’s time to “Niche Down.” Find your Different at CCE 2019

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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Most of us are tricked into believing that achieving personal and professional success means fitting in. What it really takes is the courage to stand out. How do you become legendary and make a big impact? By being different.

Christopher Lochhead and Heather Clancy, authors of “Niche Down,” will take the main stage at Connected Enterprise in a few weeks and share inspiring stories and themes from their new book. How do we exploit the exponential value of what makes us different vs. the incremental value of what makes us better? We will be challenged with new ways of thinking and problem solving. Whether you are an entrepreneur, moving up in your career or shaking things up, there’s something for everyone in this. I personally can’t wait to watch their keynote. 

Connected Enterprise is just a few weeks away – November 4-7 at the Half Moon Bay Ritz-Carlton! This year’s lineup of keynotes, fireside chats, panelists and attendees is one not to be missed. Here’s a quick snapshot:

• Vint Cerf, "Father of the Internet" and Chairperson at The People-Centered Internet
• Annie McKee, Author, Speaker, Senior Fellow at the University of Pennsylvania
• John Hagel, Management Consultant, Speaker and Author
• Alan Beaulieu, Principal at ITR Economics
• Marie L. Wieck, General Manager at IBM Blockchain
• Tricia Wang, Co-Founder at Sudden Compass
• Aneel Bhusri, Co-Founder and CEO at Workday

Beyond the main stage and official sessions, CCE offers many opportunities to network during the dinners, golf, receptions and other breaks before, during and after the sessions. I’m always blown away by all of the lessons I personally take away from the non-structured portions of the event. If you are new this year to CCE, be sure to look for one of the Constellation team members. We’ll be happy to make introductions. 

A few highlights to mark down on your schedule:

• The welcome reception happens on the first evening of the event. Come ready for a mocktail or cocktail and networking!  
• The agenda is almost finalized. All sessions are on the main stage, and while we’d love for you to make every session, we get that things come up. Try to schedule meetings and unofficial networking talk around the panels and talks. There’s plenty of time to squeeze it all in. 
• BT150 winners will be inducted for 2019-2020 at the conference. 
• The results are in, and hopefully I won’t spill the beans before the SuperNova Awards Ceremony. We had so many great case studies this year.
• Golf and spa options are available for attendees. 

Be sure to register as soon as possible since space is filling up fast. Check out the agenda, and get ready for a fast and mind-expanding experience.

Save the date for next year - Oct 26-29, 2020. Our 10th anniversary is going to be awesome! 

As Barney" Stinson in “How I Met Your Mother” would say, it’s going to be “Legen… wait for it…dary… Legendary." Let’s explore our differences at the conference and Niche Down. See you in a few weeks! Looking forward to meeting/catching up with you.  

Order out of chaos

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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Why is blockchain the way it is? Why should any storage system ? no matter how distributed it is, or “immutable” ? need to consume such a lot of the world’s electricity?  The answer is more political than technological.

I don't mean party-political, but more basically blockchain is always about power. Blockchain is rooted in the rejection of fiat currency and centralised administration. Cryptocurrency advocates generally oppose the way that traditional money is organised, and favor an unorthodox and expensive way of organising electronic cash. And most other blockchain applications seek essentially to change the way that shared data is managed; that is, they want to alter power structures. 

By better understanding its unusual objectives and starting assumptions, you’ll improve the chances of success when planning new blockchain applications (experience shows at least 90% of blockchain pilots are failing to proceed to production).  The reality is that most proposed extensions of blockchain beyond cryptocurrency are not good ideas, as I hope to show.

Can we generalise from cryptocurrency to data management?

The Bitcoin blockchain takes an exorbitant approach to solve the well-understood Double Spend problem in cryptocurrency.  Bitcoin started from a desire to remove some of the management structures of conventional money systems (structures which happen to simplify transactions and save costs). When considering non-currency applications for blockchain platforms, we have to first check whether the same approach is warranted, and whether the same assumptions are even sensible. Most real-life data management is much more complex than the rarefied world of cryptocurrency.

Thousands of nodes are needed to support Bitcoin’s blockchain and the like ? not because storage of transaction logs really needs to be distributed, nor because account holders don’t trust other computers ? but in order to crowd-source a special decision-making process which could be done by a single system administrator if it were trusted to do so.  The goal of the original blockchain was to arrive at a chronological order of accepted transactions, as a means to stop Double Spend.

The importance of order

A core feature of all practical cryptocurrency systems is cryptographic private keys, possessed by account holders. Private keys are used to digitally sign transactions, to prove that someone is in control of an account and that they consent to move some of their balance to another account. The Double Spend problem arises simply because using the same private key to sign the same transaction (e.g. “Transfer 10 Clams from A to B”) results in the same cryptogram.  So if we see the same transaction more than once, how can we tell if it’s real before anyone takes advantage of a bogus balance? There needs to be a mechanism for watching each transaction and deciding if it’s legitimate or an attempted Double Spend.

For decades it had been assumed that the oversight of cryptocurrency movements would have to be done centrally, by a digital reserve bank or some scheme operator (akin to the private credit card systems).  The idea of a non-fiat cryptocurrency seemed a fantasy ? until Satoshi Nakamoto invented Bitcoin and the blockchain. 

Arguably the most ingenious aspect of Nakamoto’s blockchain is the way it crowdsources the oversight.  The outcome of the blockchain algorithm is a public history of accepted transactions, which is updated periodically and shared across many nodes. The famous “consensus” process underlying the blockchain delivers agreement about just one thing: the time order of transactions ? for that is enough to detect and reject attempted Double Spends.

Cryptographic security systems usually require key lifecycle management to ensure certain private keys are in the hands of certain registered users.  But blockchain was designed on the basis that no authority registers the account holders.  And yet it pretty much guarantees that Bitcoin will move from one account to another, with nobody knowing anything about who is in control of the essential private keys.  Blockchain is the only cryptographic system I know of that provides such certainty without centrally organised key management.  It literally produces order out of chaos.

Is there anything (else) like cryptocurrency?

So there’s really only one question to ask before qualifying a potential application for most blockchains: Is your use case truly devoid of key lifecycle management?  Do you want a situation where nobody knows which private keys go with which user accounts? 

Foregoing key lifecycle management is highly unusual, which explains why beneficially generalising blockchain to regular data management is so rare. Most business systems have an intrinsic order: there’s a scheme by which users, accounts and/or devices all go together.  Strangely, some of the strongest interest in blockchain lies in environments that are otherwise highly organised, such as supply chain, asset management, and the Internet of Things, yet in these settings, key management and user or device registration are normal. 

If Key Lifecycle Management is necessary, rethink blockchain

If there are other reasons for needing key lifecycle management, then you should rethink blockchain or crowdsourcing consensus. IoT and supply chain are widely touted blockchain use cases, which fall into this problematic category.  

Some futurists envisage swarms of autonomous robots self-organising to achieve some sort of outcome, but all industrial IoT today is far more orderly.  Devices come with serial numbers and IP addresses!  There is no uncertainty whatsoever where any given cryptographic keys are located.

Likewise, there’s no crypto-chaos in any supply chain. Growers, makers, distributers, agents, warehouse personnel and couriers are all employees or contractors; they’re all formally registered, and they interact with supply chain records via specific accounts and terminal equipment. There’s nothing about this sort of environment that requires you crowd-source consensus as to the order in which transactions take place.

Encryption for confidentiality also creates erodes the blockchain proposition.  Almost everybody agrees that personal information should never be held on a public blockchain, but many pundits presume that encryption will make it OK.  But encryption for confidentiality requires key lifecycle management: you need assurance of which key pair goes with which intended recipient of the encrypted data, so it doesn't fall into the wrong hands. And if you can organise that, then decentralizing any other aspect of the ledger is futile. 

Blockchain brings a heady basket of benefits: extreme tamper resistance, decentralisation, synchronisation, and ultra-high availability.  But none of these are unique to blockchain.  Blockchain’s only special trick is it delivers security in the absence of key lifecycle management, and that’s the explanation for its weird and wonderful and inefficient architecture.  In the real world, most use cases require user registration, and then blockchain loses its reason for being.

Constellation Releases Q3 ShortList Updates: Who’s New to the List?

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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With more than 70 lists to choose from, the Constellation Research ShortList library offers an invaluable (free) resource for technology buyers.

What vendors should be on your short list when considering technology purchases? The Constellation Research ShortList offers our take on that very question, and we now offer 74 lists across enterprise software and services. These ShortLists are updated twice yearly, in Q1 and Q3, ensuring fresh insight for your upcoming purchase decisions.

What sets ShortLists apart? For starters they’re free to all, as are our AstroCharts on that latest trends in business and technology. The second differentiator of ShortLists is that they’re focused on emerging technologies and capabilities that pioneers and early adopters are looking to exploit to drive innovation.These are spelled out in "Threshold Criteria" included with each list.

To share one example, among the 14 ShortLists that I personally oversee, the Constellation ShortList for Smart, Augmented BI and Analytics looks at computer-aided features that help users with data-preparation, discovery and analysis, natural language interaction, and trending, forecasting and prediction. Aided by heuristics, machine learning, natural language processing and automation features, these emerging augmented features can be helpful to novice business users and advanced data analysts alike.  

Which vendors and products made it onto our Q3 Constellation ShortLists? There are dozens of new names across all our lists, but here are a handful of examples from my own ShortLists:

• Qlik and SAP are new to my Smart, Augmented BI and Analytics ShortList for Q3. Qlik Sense was added in the wake of Qlik’s January 2019 acquisition of Crunch Data and subsequent integration of its CrunchBot and natural language understanding capabilities. Qlik also added an Associative Insights feature that spots correlations, patterns and outliers related to selected measures. The SAP Analytics Cloud has a range of smart capabilities, including ML-assisted data prep; Smart Insight, Discovery, Grouping and Predict features; Conversational Analytics; and Forecasting and Value-Driver Tree analysis for planning.

• Stibo Systems is new to our Q3 Master Data Management ShortList on the strength of its multi-domain MDM platform and growing cloud partnerships, including, most recently, MuleSoft, Tenovos and Tenzing.

• Dataiku made our Q3 update of the Self-Service Advanced Analytics and Machine Learning ShortList on the strength of growing customer adoption and continued development of Dataiku DSS. The DSS platform addresses the needs of data-analyst “clickers,” through a visual user interface, as well as data scientists and data engineers, who can code and use a range of notebooks, languages, ML libraries and APIs.

• The Q3 2019 Self-Service Data Preparation ShortList added Informatica on the strength of cloud data lake, prep, data quality, collaboration and autoscaling advances in Informatica Data Preparation 10.2.2 along with maturation of its suite-based approach, supporting catalog and CLAIR AI engine.

There aren’t just new vendors to consider. A few of my colleagues introduced entirely new ShortLists in Q3, like Holger Mueller’s DevOps ShortList and R “Ray” Wang’s Innovation Services and Engineering ShortList. Be sure to check out the entire collection and consider the threshold criteria for inclusion in each ShortList. These are the leading-edge capabilities that should be on your buying radar.

DisrupTV: Diving into Data Science to Build Trust and Innovation

Sharon Lee

Marketing Intern
Constellation Research

...

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“Data science isn’t new. What we’re doing with it is.” 

On episode 160 of DisrupTV our host Vala Afshar and guest host Dion Hinchcliffe interviewed Anushka Anand, Senior Product Manager at Tableau Software; Xiao-Li Meng, Author and Data Scientist at Harvard; and Jon Reed, Co-Founder at Diginomica to discuss the growing possibilities in the data science frontier. Here are a few takeaways from the episode:

Artificial Intelligence and Machine Learning will Transform the Data Analytics Space

“Analysts spend 80% of their time preparing data and only 20% analyzing it,” according to Anushka Anand. Currently, the most challenging part of data analytics is data cleaning. Anand said machine learning has the potential to clean data to allow people to perform more meaningful analysis- a major opportunity. It won’t replace the old task but rather complement the analyst’s job interacting with data. 

Machine learning and artificial intelligence can also be used to build trust with customers who use smart products. Customers are often skeptical when there are automated functions in a system, such as an information filtering system that gives recommendations, as they pertain to individual privacy. The ability to explain these functions behind the algorithm is key to building trust with customers and ensuring success of the product.

2019 Conference Focus on Data Quality in an Enterprise Context and Other Hot Topics

With Fall conference season just around the corner, there are multiple enterprise tech trends to keep an eye on. Jon Reed gave tips on how to approach these trends by turning it into a fun interactive game. Vendors are expected to hype up 5g, Cloud, and DevOps. When vendors promote 5g, you ask “What are the use cases?” When vendors promote the cloud, you ask “What about cloud security?”. Reed reminded us that with each of these trends, it is important to look at each industry specifically and remember that all trends have a dark side we must discuss. Be sure to check out his full interview for hilarious and educational takeaways.

Data Science has Innovative uses in Producing Governmental Data

Xiao-Li Meng joined the show to share his work on governmental data and the unexpected law of large populations. His primary teaching method attempts to demonstrate how one can quickly learn a large amount about a topic using statistical analysis. Meng explains the power of representative sampling. All current statistical techniques are based on the idea that data is mixed well enough. The real problem is that the natural population, especially in self reported polls, do not provide randomly reported samples.

Meng’s work focuses on how small data actually is, and he explains this in the context of the 2016 elections and how the negative effects were exacerbated because the data was not well mixed. It’s much easier to measure the quantity rather than the quality of data. Quality depends on which questions you ask. Data quality is crucial, and this is where AI and data cleaning comes in to aid the process.

As we make our way further into the data science space, there are countless new discoveries and challenges that arise. The solutions aren’t certain, but what is certain is that enterprises must use data science to stay relevant.

This is just a small glimpse at the great insight shared during the show. Please check out the full discussions in the video replay here or the podcast.

DisrupTV Episode 160, Featuring Anushka Anand, Xiao-Li Meng, Jon Reed from Constellation Research on Vimeo.

DisrupTV is a weekly Web series with hosts R “Ray” Wang and Vala Afshar. The show airs live at 11:00 a.m. PT/ 2:00 p.m. ET every Friday.