There are plenty of tools and point solutions that address bits and pieces of the challenge of delivering artificial intelligence (AI) and Internet of things (IoT) applications. C3.ai’s focus is on delivering an end-to-end platform for developing, deploying and running these applications in production at scale.

Whether customers use every aspect of the C3.ai platform or not, big enterprise-scale companies seem to be attracted by that promise of quickly developing and running innovative, data-driven applications at scale. There was plenty of evidence of that fact at C3.ai’s February 25-27 Transform conference in San Francisco, where customers including Bank of America, Shell, 3M and Engie detailed their deployments.

C3.ai’s cloud-first platform is comprehensive, addressing the needs of developers, data engineers and data scientists, and the operational teams challenged with bringing applications into production at scale. Most of the components are based on open source software, such as PostgreSQL, Cassandra, Kafka and Hadoop. Yet the platform is also designed to be modular and open, so customers can swap in preferred tools and components, whether those might be integrated development environments, data platforms, AI and machine learning (ML) frameworks and tools, or DevOps components (see integrations slide, below).

The core, differentiating aspect of the C3.ai platform is its “type system” architecture, which ensures development speed, repeatability and scalability. The type system uses metadata to represent everything in the end-to-end development-to-deployment process: all data and data sources, underlying storage technologies, data science models, data-processing services, applications and application services. Everything is represented in a consistent, abstracted way that hides the complexity of behind-the-scenes technologies and that simplifies application development, deployment and ongoing optimization.

C3.ai offers its platform as a service running on Microsoft Azure, but it can also run on private clouds or other public clouds. It’s also possible to straddle hybrid and multi-cloud deployment, with clustered, multitenant instances running on-premises and on one or more public clouds.

C3.ai’s combination of agility and scalability, along with flexibility and openness to third-party tools, seems to be gaining traction. At Transform 2020, company founder and CEO Tom Siebel (and also the founder and longtime CEO of Siebel Systems) reported that C3.ai is on track for a 143% increase in bookings to $447 million in fiscal year 2020, up from $184 million in FY 2019. That’s a respectable scale for an 11-year-old company with just under 500 employees. Yet, to realize the company’s ambitions, Siebel acknowledged that C3.ai will need the help of larger partners.

To that end, Siebel announced at Transform C3.ai’s latest partnership, a strategic alliance with IBM Services to become the company’s first preferred global systems integrator. The two companies said the deal will “fast-track the delivery of enterprise-scale industry and domain specific AI applications.”

The IBM deal follows on the heels of C3.ai’s 2019 joint venture partnership with Baker Hughes to jointly deliver C3.ai’s AI platform and applications, along with combined industry and technology expertise, to the oil and gas industry. And in 2018, C3.ai announced a strategic partnership with Microsoft to use Azure as its preferred public cloud platform (although it also announced separate partnerships that year with Amazon Web Services and Google Cloud Platform).

What most impressed me at C3.ai’s event was the number and depth of customer presentations detailing extensive deployments and ambitious deployment plans:

• Shell executive Daniel Jeavons, general manager of data science, updated attendees on a now-two-year-old C3.ai deployment that started with predictive maintenance IoT applications that now predict and prevent unplanned failures of literally thousands of valves and compressors. These now-monitored assets are in use in onshore and offshore oil and gas production facilities in more than 20 locations around the globe. Avoidance of outages at just one of these locations is, thus far, credited with saving $2 million. Next up, Shell is using the same data foundation and reusable components to develop a power-optimization application for electronic submersible pumps.
• 3M executive Jennifer Austin, manufacturing & supply chain analytics implementation leader, detailed progress on data-science-driven inventory optimization, pricing analytics and supply chain risk-management applications developed and delivered over the last two years. Austin acknowledged that 3M is still working on the challenges of change management user adoption, in certain areas. But the inventory optimization application alone is expected to generate as much as $200 million in annual savings while improving service levels.
• Bank of America executive Brice Rosenzweig, co-head of the bank’s Data and Innovation Group, detailed progress on building out a more business-user-accessible data platform and early (not-yet-in-production) progress on cash management and lending optimization applications built on the C3.ai platform. A customer since last fall, Bank of America is now considering at least a dozen additional use cases that would leverage the C3.ai platform, Rosenzweig said.

My Take on C3.ai’s Progress

C3.ai detailed its plans to push further into the banking and financial services sector, addressing use cases including anti-money laundering, customer churn management, cash management and securities lending and more. But executives stressed that C3.ai will emphasize “accelerants” of development and delivery rather than building out every possible application. It’s a sound approach, as financial services resist one-size-fits-all solutions for what might be differentiating capabilities. At the same time these accelerants, including reusable data objects and analytic objects, will all be built on the C3.ai platform. Thus, customers will have the advantages of rapid development and deployment, but their customized applications won’t break when C3.ai updates its platform.

A key strength for C3.ai – and one that every customer I talked to seems to be exploiting – is the openness to swap in and use preferred tools for X or Y while relying on the platform and type system architecture for abstracting complexity and running data-driven applications at scale. Bank of America, for example, use DataRobot for data science exploration while Shell uses a combination of Alteryx, Databricks and TIBCO Spotfire for rapid proof-of-concept work before bringing models and applications into production at scale on the C3.ai platform. C3.ai’s platform and ambitions are big and broad, so I see the company’s pragmatic openness to best-of-breed options and cloud-native services as sensible, realistic and in tune with the openness and flexibility that customers are after.

C3.ai executives repeatedly emphasized that its roadmap is very customer driven. In 2019 the focus was on adding customer-requested connectors and integrations, low-code development options and user-interface improvements. A Spring 2020 upgrade will deliver improvements to the data-exploration and hyper-parameter optimization features of C3.ai’s Ex Machina data science studio. The company’s Summer 2020 release will focus on the developer experience, with improvements supporting serverless computing, self-healing capabilities and sub-second deployment speeds.

With its deals with IBM, Microsoft and Baker Hughes, C3.ai is clearly scaling up its sales and delivery capacity, yet it remains a midsized company that obviously takes pride in staying close to its customers. “There’s no customer where we have a traditional arm’s-length [vendor-to-customer] relationship,” Siebel concluded in his keynote address. “All of our customers are on speed dial.”

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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 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 offerings 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.

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.)

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’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.

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The tenth annual Workday Innovation Summit, held April 22-24 in Sausalito, CA, brought more than the sweeping San Francisco skyline into focus. Workday shared its vision for providing machine learning (ML) and analytics across its platform while also broadening planning capabilities accelerated by last year’s acquisition of Adaptive Insights.

Workday is known first and foremost for its human capital management (HCM) and financial management ERP applications, but at the Innovation Summit, Workday gave equal attention to the analyze and plan aspects of what it described as its execute/ analyze/ plan platform. Workday’s advanced analytics capabilities are built on Prism, which is a data-access layer and analytics engine derived largely from the company’s 2016 acquisition of big data vendor Platfora. Next came Workday’s largest acquisition ever, last summer’s $1.55 billion acquisition of Adaptive Insights just days before that company was set to launch an initial public offering.

Here’s what’s in store on the machine learning, analytics and planning fronts along with my take on Workday’s vision.

ML, Graph and Analytics Unified

Workday is developing ML and graph processing capabilities that can be harnessed across the company’s applications. Echoing the spin I hear from most vendors, Workday execs vowed that the goal is to augment, rather than replace, human decision makers.

Don’t expect novel or esoteric ML-based applications out of left field: Workday said it will focus on concrete use cases and challenges that are already apparent to Workday customers. The first application-specific use of ML and graph analysis is the newly available Skills Cloud, which powers a Skills Insight feature that will discern team, employee and candidate skills and offer related recommendations on hiring, training, team building and project planning.

Workday’s ML capabilities are being built on open-source technology wherever possible, said Sayan Chakraborty, senior VP of tools and technology, including ensembles of open source and, in some cases, Workday-developed algorithms. These platform-level ML capabilities will also power augmented analytics and planning capabilities, sensibly leveraging one investment and unifying the ML/augmented analytics approach across the platform.

On the analytics front, Workday unveiled a “Discovery Board universal analytic canvas” that will provide a unified reporting architecture for all Workday users (including Adaptive Insights users — but read more on that below). The analytics team said it will take advantage not only of the shared ML, graph and pattern-detection capabilities available from the platform, it’s also harnessing natural-language generation (NLG) capabilities from Workday’s 2018 acquisition of stories.bi. That vendor’s Storyteller Engine generates textual descriptions and explanations of the patterns and insights gleaned from analysis of Workday transactions coupled with external data inputs.

Think of the analytic, reporting and Storyteller capabilities as another unified platform upon which Workday plans to introduce multiple augmented analytics applications, starting with a Workday People Analytics app, which has been released to design partners. The ML, graph and NLG-powered insights and recommendations generated by this app will focus on hiring, workforce composition, diversity and inclusion, talent and performance, and retention and attrition. While the Discovery Board canvas will provide self-service discovery and reporting capabilities, Workday is counting on the appeal of the augmented analytic applications and the need to blend external data to spark customer investments in Workday Prism Analytics data management and analytic capabilities that are not included with core Workday apps.

MyPOV on Workday’s ML and Analytics Plans: Machine learning and AI capabilities are still in their infancy, but enterprise software vendors are now expected to have a strategy and a roadmap at a minimum. Workday rival Oracle has been developing its Adaptive Intelligent Apps for at least three years, but most of the applications and capabilities delivered to date have been focused in the sales and marketing arena, where its rival Salesforce has been aggressively developing and delivering its Einstein and Einstein Analytics ML/AI capabilities. SAP is ramping up investment and augmented analytics capabilities on the SAP Analytics Cloud (SAC), which includes planning, but its sweep of capabilities remains somewhat bifurcated between on-premises apps and SAC.

I see Workday’s plan as cogent and sensible, and it’s just now delivering its initial applications. There’s also a clear roadmap to extend the augmented capabilities across HCM, finance, analytics and planning over the next 18 months and beyond. While Workday has not been the pioneer on the ML/AI front, I see it as a fast follower with a unified and focused plan.

Adaptive Insights, Integrated

The first thing to know about Adaptive Insights is that it’s keeping its name, although it’s now “Adaptive Insights, a Workday company.” The stand-alone identity recognizes the fact that Adaptive Insights was a thriving, cloud-based planning company with more than 4,200 customers.

Workday had its own, fledging planning application, introduced in 2016, but it acquired Adaptive Insights because it knew it needed to step up its capabilities. In fact, during a question and answer session at the Innovation Summit, Workday co-founder and CEO Aneel Bhusri confessed that if he had it to do over again, he would have introduced planning capabilities ahead of the Workday Financial Management application introduced in 2008.

Bhursi’s comment speaks volumes about the importance of planning and analysis capabilities. Driven by the need for agility amid disruptive business conditions, the leading planning systems have moved beyond finance and are powering HR, sales, and operational planning with finance. Planning and analysis gives companies a clearer picture of where they stand and whether they’re on target to meet their financial and operational goals. When there are exceptions, planning systems help companies quickly replan and reallocate resources to get results back on track. Most importantly, planning solutions do all this much more quickly, broadly and accurately than companies can manage with scores if not hundreds of disconnected spreadsheets.

Adaptive Insights has more of a small- and midsize-business (SMB) base than does enterprise-focused Workday, but the company was already moving upmarket with investments in scalability and enterprise-focused features introduced last year. In the wake of the acquisition, both companies decided to replace Workday’s workforce planning application with Adaptive Insights’ to gain its modeling capabilities. More than 100 of the roughly 300 customers using the original Workday Planning application are already switching over to Adaptive Insights.

MyPOV on Workday’s Adaptive Insights plans: As described above, Adaptive Insights will benefit from the investments Workday is making in unified ML, graph and analytic capabilities, and the integrations will show up first in workforce planning, where data obtained from the Skills Cloud will drive skills-gap planning and talent restructuring with financial modeling.

It remains to be seen how effectively Adaptive Insights can balance the needs of SMB and enterprise customers over the long term, but it was encouraging to me to see the entire executive team still in place, including CEO Tom Bogan, CMO Connie DeWitt, and chief product officer Bhaskar Himatsingka. Continuity counts with customers, and Adaptive Insights will also (presumably) be leaning on Workday’s team (as well as its unified platform capabilities) to bolster its sales, support and delivery capabilities for demanding enterprise-level customers.

“You have to empower whoever gets to the customer first.” This is the key message that Rob Tarkoff, executive VP and general manager of Oracle CX Cloud, shared at Oracle Modern Customer Experience (CX), March 19-21, in Las Vegas.

Tarkoff’s point was that customers rarely follow prescribed, linear journeys, so everybody must be armed with customer insight and context whether they’re in marketing, sales, commerce or service.

To gain contextual insight you need data, and that’s why Oracle announced Oracle CX Unity last October at Oracle Open World. Billed as a “customer intelligence platform,” CX Unity is designed to provide “a comprehensive view into customer interactions across channels and applications.”

CX Unity is Oracle’s entry into the emerging customer data platform (CDP) market, a category first defined in 2013 and that gave rise to The Customer Data Platform Institute (CDPI) in 2016. Search through CDPI’s directory and you won’t find any big tech vendors — just pioneers and startups mostly emerging from the marketing technology space. That’s about to change, with both Oracle and, on March 25, Salesforce, announcing their intention to offer CDPs. And it’s a safe bet that Adobe won’t be far behind, last year having announced a joint initiative with Microsoft and SAP to work together on an open customer data standard.

As defined by CDPI, a CDP is “packaged software that creates a persistent, unified customer database that is accessible to other systems.” The platform benefits marketers because it “allows a faster, more efficient solution than general purpose technologies that try to solve many problems at once.” The need for CDPs emerged as marketers started pursuing mass personalization at scale, and it’s only getting more intense as customers expect you know them and their most recent dealings with your company in real time.

Attendees heard lots of promises about the advantages that CX Unity will bring. Oracle has announced availability for CX Unity by late summer. It’s only natural for Oracle to deliver a CDP, but unlike marketing-only vendors, it has a duty to reconcile this offering with the company’s MDM and data lake offerings (as I discuss below).

As for the “next-generation” sales experience demonstrated at Modern CX, the description made it clear that it was an early vision statement — so not something I would expect to see in 2019. The demo combined automated voice-to-text call transcription/logging and contextual, AI-based recommendation capabilities. I’ve seen similar capabilities both from Oracle partners, like the OppSource Sales Engagement app, which is integrated with Oracle Sales Cloud, and from Oracle rivals (in pilot release).

The third big highlight from Modern CX for me was DataFox, which was acquired last October and mentioned by nearly every executive who took the stage at the event. Think of Oracle DataFox as an AI accelerator that will bolster the smart-recommendation capabilities of Oracle’s CX and ERP clouds, mostly through Oracle Adaptive Intelligent Apps.

On the sales front, DataFox studies the companies you sell to successfully in a business-to-business context using machine learning and then finds lookalikes. It also prioritizes leads and next steps and it recommends talking points to help salespeople accelerate their selling. DataFox look-alike capabilities will be used in the ERP context to avoid supplier risk and to spot best-fit alternative suppliers.

MyPOV on Oracle Modern CX Announcements

DataFox was clearly a good acquisition for Oracle, and I particularly like the “human-in-the-loop” (Mechanical-Turk-like) training and exception-handling capabilities. The next-gen sales demo struck me as “we’re working on it” trail balloon, but if it makes it to market in 2019 Oracle will, indeed, be among the fast followers on AI-augmented sales.

At the center of Modern CX was CX Unity, which executives described as “three years in the making.” I’m hoping for conversations with early beta customers by Oracle Open World in September.

I have to admit, it has to be a bit more challenging for a vendor like Oracle to jump into the CDP market. Unlike marketing-only vendors, I would think Oracle will be held to a higher standard to reconcile its CDP with its master data management (MDM), data warehouse and data lake offerings. I asked one Oracle exec about how CDPs and customer MDM will coexist, and he described them as complimentary, which they should be. But he went on to largely dismiss MDM as only relevant in a legal-liability context, resolving identities for ERP transaction purposes. In my view that underplays the value of MDM and you’ll want your steward-resolved customer MDM identities synchronized with your CDP.

I also wasn’t entirely comfortable with how easily Oracle’s CX executives picked on data lakes as a poor alternative to what CX Unity will offer. It’s all well and good for marketing types to try to put their arms around “customer” data, but as CDPs start to grow, I think larger enterprises and others with incumbent data warehouses and data lakes will start to see overlaps and redundancies.

What is and what isn’t customer-related behavioral data, and who decides which data to store and where? I think customers will expect more guidance (and, perhaps, integrations) from companies like Oracle than they would from marketing-focused startups to help them rationalize investments in data warehouses, data lakes and the emerging customer data platform.

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No matter how “easy” or “intuitive” self-service analytics might be, the route to consumption typically sends users off to a separate, analytic interface, be it a report or a dashboard with lots of options for exploration and deeper analysis. But when users have only seconds to make decisions, they don’t have time (and won’t bother) to leave their business application to consult available analytics.

Delivering insights quickly and within everyday applications is the idea behind HyperIntelligence, a new capability of MicroStrategy 2019, that latest release of that vendor’s venerable analytics platform. Available since December, HyperIntelligence got its splashy unveiling at MicroStrategy World 2019 (#Analytics19), held Feb. 4-6 in Phoenix, AZ, where it was one of three highlighted themes, along with Federated Analytics and Transformational Mobility. Here’s a closer look at all three, along with my take on MicroStrategy 2019.

Photo: MicroStrategy

HyperIntelligence Surfaces Insight In Context

HyperIntelligence is a new embedded analytics approach that automatically hyperlinks customer names, product names, employee names or any other selected word type (think analytical dimension), that shows up on any web-based interface. When a user hovers over a link using their cursor, a customizable “HyperCard” appears. Cards can deliver enough insight to drive decisions on their own, but users can also click and drill down on the data to explore and analyze.

Hyperlinks and cards can be color-coded by type, so employees and related cards might show up in red while customer names and related cards could show up in blue and products in green. Each user has control over which cards they use and when they use them, so their interfaces won’t light up like Christmas trees as HyperCards and linking options proliferate across an organization.

The possibilities for embedding analytics – whether descriptive, diagnostic or predictive — are limitless, ranging from Salesforce, SAP, Workday, Office 365 or any other web-based interface to home-grown portals and applications. MicroStrategy CEO Michael Saylor even made a Microsoft Power BI dashboard “smarter” during a keynote demo, adding a link and surfacing a card with data from MicroStrategy that wasn’t available on the dashboard.

Photo: Doug Henschen

Cutting-edge flourishes include “HyperVision” augmented-reality overlays that let you surface cards within live images. When a mobile device camera is trained on, for example, a retail shelf, HyperCards can be superimposed with insights on product inventory levels, expected shipments or profitability by product. HyperVoice adds an audible card option that can be personalized and triggered based on user identity, proximity and location.

Federated Analytics Feed an Open Ecosystem

Federation is MicroStrategy’s platform-powered approach to feeding authoritative, well-governed data to an open ecosystem of analytical tools. The list includes not just the vendor’s own Dossiers and MicroStrategy Desktop, but also Microsoft Power BI, Tableau and Qlik. The platform can also deliver analytics embedded into third-party apps through new REST APIs added in MicroStrategy 2019. The latest release also beefs up R- and Python-based data science work through integration of the open source CRAN and PyPI packages. The resulting predictions can be fed back into HyperCards, Dossiers, reports, custom applications and third-party systems.

MicroStrategy makes data accessible through its semantic modeling layer, which has been rechristened as the Enterprise Semantic Graph in MicroStrategy 2019. The Enterprise Semantic Graph is at the heart of new natural language query capabilities and emerging machine-learning and pattern-recognition-based recommendations. For now these smart/augmented analytic capabilities include recommending visualization approaches, data sets, or existing assets based on data selections and user- and role-based behavior patterns. MicroStrategy is also working on spotting correlations, patterns, exceptions and influencers within data sets, and it expects to deliver this functionality by year-end 2019.

Transformational Mobility

MicroStrategy helped pioneer mobile business intelligence starting a decade ago as smart phones and tablets emerged, and it has continued to refine these capabilities. The list of upgrades and tweaks in MicroStrategy 2019 is long. Mobile Dossiers on iOS and Android devices have gained new table-of-contents and filtering features along with new bookmarking, commenting and notification capabilities. The latter make it easier to return to favored analyses, share comments with collaborators and flag updates of interest. New Library features for mobile devices make it easier to search for and find certified, recommended and recently updated Dossiers, apps and other assets.

MicroStrategy’s latest SDKs for iOS and Android devices let you weave analytic content into custom mobile apps using XCode or JavaScript. Improved geospatial services bring better interaction with maps to phones and tablets. A responsive design editor helps developers optimize web and desktop views for mobile consumption.

My Take on MicroStratety 2019

HyperIntelligence was the biggest and newest news in MicroStrategy 2019 and it’s another example of this vendor coming up with something unique in the market. The vendor also announced that it has achieved parity in supporting cloud deployment on Microsoft Azure as well as Amazon Web Services, and important option giving customers flexibility.

I saw the Federated Analytics and Transformational Mobile announcements as refinements of existing MicroStrategy capabilities while the Enterprise Semantic Graph strikes me as something that has yet to reach its full potential as an enabler of smart, augmented analytics. I’m anticipating a richer story on this front at MicroStrategy World 2020.

Photo: MicroStrategy

As for the HyperIntelligence announcement, I think it’s promising and ready for broad adoption. Adoption will depend on the cost, which was not disclosed, and certain technical and security considerations. For now HyperIntelligence works with Google Chrome browsers with the aid of a plugin. Chrome accounts for more than half of all Web browsing, so that’s a good start, and MicroStrategy is working on supporting Safari and other browsers. At some firms, the plugins will get scrutiny from chief security officers. I talked to one bank customer who said plugins are frowned upon by security team, but he was excited enough by the promise of HyperIntelligence to explore approval.

MicroStrategy previewed HyperIntelligence more than a year ago, and there were a number of beta customers on hand in Phoenix. During a keynote panel, Jason Pelky, CIO at the Gilbane Building Company, a global construction-management firm, said his firm has develop HyperCards to give salespeople quick insight into prospects when they’re preparing for calls. Their first instinct is to do Google searches, he said, but HyperCards quickly reveal when prospects are actually past customers, surfacing relevant history and project insights.

French energy services giant Engie is using HyperIntelligence to deliver supplier and supply chain insights both inside and outside of its MicroStrategy-powered Procurement Information Center (PIC). The PIC system has more than 500 regular users of its eight core dashboards, according to Arnaud Droissart, Engie’s IS Procurement Manager, who presented on the topic. Droissart said the firm is using HyperIntelligence to surfaces stats, key metrics and other insights on suppliers, product categories and contacts to business users working within the company’s SAP Ariba system and various web-based ERP interfaces.

What’s particularly exciting about HyperIntelligence is that it promises to bring new levels of intelligence to existing interfaces, reports, dashboards, applications and systems without having to replace anything. It’s purely additive rather than being a rip-and-replace proposition. In short, it’s a highly customizable and broadly deployable technology that could redefine expectations for available embedded analytics.

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Salesforce has grown from a cloud CRM service into a massive platform with multiple sub-clouds and hundreds of discrete services. That’s one reason I found this year’s streamlined, less-is-more approach at Dreamforce 2018 to be a refreshing change.

When Salesforce CEO Marc Benioff said his (first ever?) sub-two-hour Dreamforce keynote would be “a celebration of you, the customer,” I was initially skeptical. The few big tech announcements — Customer 360, Einstein Voice, Quip Slides — seemed, well, incremental. Did they run out of big new tech challenges?

But as the individual cloud keynotes and more in-depth customer interviews commenced at Dreamforce 2018, it became clear to me that this event was about knitting together the myriad platform components and helping customers to combine and take advantage of the many capabilities. That’s probably why Customer 360 and the recent acquisition of API-integration vendor MuleSoft were the first topics discussed. It’s also why we’re seeing consolidation and blending of offerings, as in the example of Einstein and Einstein Analytics interfaces and packaging.

Customer 360 and MuleSoft

Salesforce is not one platform today; it’s really a collection of multiple platforms, including the core Salesforce/Lightning platform plus Commerce Cloud, Marketing Cloud, Heroku and more. Customer 360 is an effort to make it easier to reconcile customer identities across the total Salesforce ecosystem. It’s not a data warehouse or master data management system and it won’t be moving or copying data. Rather it will be a hub-and-spoke set of microservices designed for identity resolution and change data capture.

The promise is that Salesforce users will be able to resolve and maintain accurate customer identities – across Salesforce and connected external systems — even as customers change addresses, phone numbers, employers and married names. The bad news is that Customer 360 won’t be available until the second half of next year. The good news is that it will be an included, no-extra-cost part of the platform once it does arrive.

Dreamforce marked an introduction and coming out party of sorts for MuleSoft, the API integration vendor that Salesforce acquired four months ago. MuleSoft will remain an independent unit and brand that will continue to seek out and serve non-Salesforce customers, but the MuleSoft Anypoint Platform will also be the cornerstone of Salesforce Integration.

Salesforce and MuleSoft could not have asked for a better joint customer to join the Integration keynote than Jane Moran, CIO of Unilever. Given that the consumer-packaged-goods giant has more than 2,000 business apps, Moran quipped during the keynote that she’s more chief integration officer than chief information officer. MuleSoft’s Anypoint platform greatly speeds and simplifies integration, Moran said, because it creates a network of reusable API connections. The more integrations and components you create, the faster subsequent integration projects go as reusability drives a flywheel effect. Projects that used to take months or even years at Unilever can now be completed in weeks if not days, Moran said.

MyPOV on Customer 360 and MuleSoft: With its many acquisitions over the years, Salesforce has become a collection of platforms, and customers have increasingly struggled to resolve customer identities across Salesforce clouds (the Customer 360 role) and connect application functionality and data (the MuleSoft role). Thus, both of these moves were good choices if not overdue.

Unfortunately, Customer 360 is in pilot at this point. It’s said to be in testing with customer Crocs, but we’re told Customer 360 won’t be generally available until the second half of 2019.

Customer master data management and customer identity resolution technologies have been around for a long time, but what Salesforce is promising will be exposed right within CRM interfaces. This will give the front-line sales, service, marketing and other professionals who know customers and customer data best the ability to send update and correction requests directly to data stewards.

As for MuleSoft, Salesforce executives shared a clear roadmap on how they’re planning to better integrate MuleSoft with Salesforce while also maintaining the vendor’s stand-alone appeal to non-Salesforce customers. The key to making MuleSoft more accessible within Salesforce will be developing simplified interfaces for Salesforce admins who are not data-integration professionals. Salesforce user-interface developers are already working with MuleSoft to do just that.

Einstein Voice and Einstein Analytics Plus

Natural language interaction has become the hallmark of many an AI application, so the Einstein Voice announcement was no surprise. Salesforce shared some pretty slick demos at Dreamforce, but this technology is still in pilot and won’t be generally available until June 2019 at the earliest. To recap, there will be two offerings with the following capabilities:

Einstein Voice Assistant

• Log sales (or other) notes, update data and create tasks by speaking
• Get a personalized daily update through Amazon, Apple, Google or other smart speakers
• Call up voice-driven dashboards and data visualizations

Einstein Voice Bots

• Build and brand your own voice interaction bots
• Connect bots to your Salesforce data
• Support customers with help from Google Assistant and Alexa

On the Einstein Analytics front, Salesforce delivered a slew of previously promised capabilities, including additional connectors to popular third-party data sources, AI-based data-preparation recommendations, alert and notification capabilities, and out-of-the-box application templates with built in KPIs.

The key announcement was Einstein Analytics Plus, a new packaged bundling that brings together the Einstein Analytics Platform with Einstein Discovery in a combined offering at a discount (exact pricing is not yet public). Salesforce is also blending Einstein Analytics and Discovery capabilities within user-interfaces so users can go from descriptive and diagnostic analytics to predictions and prescriptive actions without toggling between UIs.

MyPOV on Einstein updates: Salesforce will initially use the speech-to-text capabilities native to Amazon, Apple and Google/Android phones and smart speakers at the front end of Einstein Voice interactions, but it’s also working on its own natural language processing capabilities. Identifying entities (people, companies, accounts, etc.) and deciphering the intent of questions and requests is the more challenging part handled by Einstein Voice. But when sensitive information such as account numbers might be involved, regulated institutions will demand a closed, Salesforce-only environment — including the speech-to-text – without exposure to public services.

On the blending of Einstein Analytics and Discovery, this is a welcome step and I really liked the vision of a holistic, “intelligent experience” (shown above) that Salesforce presented. Salesforce isn’t trying to sell Einstein Analytics to a handful of analysts who will build dashboards and reports for everybody else; it’s trying to deliver embedded analytics and smart capabilities to every CRM user.

The “Plus” discount for buying everything together will be welcome. Expense is always on customer minds and they have to show that they’re getting a return on their investment. With its emphasis on pre-built templates and educational opportunities during the Einstein Analytics keynote, Salesforce clearly understands that customers want fast time to value and evidence of adoption or subscriptions won’t be renewed or extended.

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Microsoft CEO Satya Nadella set the tone at the September 24-27 Ignite events in Orlando by sharing at least half a dozen stories of leading companies innovating and pioneering new business models with the aid of artificial intelligence (AI). It was a crisp, one-hour presentation long on vision and surprisingly short on promotion or even mentions of the significant technology announcements that followed.

Nadella warned the more than 30,000 attendees that the ability to innovate and drive new business models is as much or more about changing corporate cultures and business processes as it is about applying technology. And when the technology decisions are ready to be made, Nadella counselled executives to know which capabilities are commodities and which warrant custom development to drive differentiation. Nadella said he sees smart companies liberating data silos and moving the bulk of enterprise workloads and innovation initiatives to “the intelligent cloud” and the “intelligent edge.”

From the perspective of my data-to-decisions research, the standout tech announcements that followed Nadella’s keynote included the public preview release of Microsoft SQL Server 2019, a string of Auto ML and artificial intelligence (AI) enhancements, and new analytical capabilities for big and small data. Here’s a look at what’s available today and what’s coming.

SQL Server 2019 Evolves from Database to Unified Platform.

Introduced in public preview at Ignite, Microsoft SQL Server 2019 is an ambitious step forward from mere database to unified data platform. Those who wish to keep using SQL Server as a conventional database deployed in conventional ways will be able to do just that, but the release promises so much more. For starters, SQL Server 2019 is built to deploy and scale out using Kubernetes, so it’s ready to support whatever evolving, elastic hybrid deployment approach required.

The next innovation is big data cluster software (see image below) that puts both SQL Server and Apache Spark query and processing engines on top of HDFS (Hadoop Distributed File System) data nodes. The architecture also supports high-scale, columnar data marts said by Microsoft execs to beat Impala and other database-on-HDFS options on query performance.

Microsoft’s Polybase technology, previously used mostly to tap into HDFS, will be used by SQL Server 2019 to tap into external sources such as Oracle, Teradata, MongoDB, PostgreSQL and IoT sources without moving data. As shown below, the architecture brings together the worlds of SQL and big data on a single platform. A new Azure Data Studio user interface, also announced it Ignite, will support both SQL-based querying and a notebook-style experience for data scientists.

Data scientists and progressive data analysts and data power users have been blending structured, variably structured and unstructured data for years. In SQL Server 2019, all data, including highly structured data as well as raw log files, social streams, JSON data from mobile apps, web clickstreams and more can reside on one platform with elastic and scalable deployment capabilities and myriad data analysis possibilities. As noted, Microsoft SQL Server 2019 will still be deployable and usable as a conventional database. The heart of the database management system is all in the head node. But Microsoft expects visionary companies to also deploy the SQL Server 2019 big data cluster, included software that won’t be a separate SKU or extra-cost option.

MyPOV on Microsoft SQL Server 2019. This is a compelling unification of the SQL and big data worlds, but as a practical matter, many if not most enterprises have already re-platformed with some combination of Hadoop and Spark capacity. Many if not most also have a mix of cloud-based capacity including object storage. Finally, Microsoft’s rivals ranging from Oracle, Teradata and IBM to Cloudera, Hortonworks and MapR have also been working on blending, streamlining and cloud-enabling relational and big data capabilities.

I’m sure Microsoft SQL Server 2019 will keep current SQL Server customers on the upgrade path, but the Big Data Cluster capabilities are likely to get a mixed response. The unification will be very appealing In greenfield situations, but where there are legacy deployments, existing workloads and employee familiarity to consider, Microsoft will have to come up with very practical and cost-competitive migration options that avoid rip and replace. One Microsoft exec suggested existing HDFS instances could be brought under Microsoft management without having to move or recreate the clusters, but I suspect it won’t be that simple.

In short, I see SQL Server 2019 as driving greenfield, day-forward or gradual migrations to its unified platform, not immediate replacements. That’s fine. The platform won’t be generally available until next year and would-be customers will need time to plan their future data platform strategies.

ML and AI Roundup

AI figured in many of the customer innovation stories that Nadella shared, whether it was BMW’s Azure-powered Intelligent Personal Assistant, Buhler adding machine vision to  its grain-processing equipment to spot and extract foreign matter before it enters the food supply, or real estate services firm CBRE using Azure IoT to optimize energy usage and Azure Digital Twins to simulate and better manage office space. Microsoft made announcements on all five levels what it describes as its AI stack, shown below.

Cognitive Services: At the top are Microsoft’s Azure Cognitive Services, where the company announced the general availability of Speech services including speech-to-text, text-to speech and translation. It also delivered the ability to customize models by training against your own industry- or company-specific language and terms. MyPOV: These are the sort of services that Nadella alluded to as commoditized. Indeed, AWS, Microsoft and Google have all delivered vision, speech, language and translation services and the ability to do custom training, but I’d say Google has performance advantages on many of these core services.

Frameworks and Languages: Microsoft is beefing up support for Python and extending its work with Facebook, Nvidia and other partners on ONNX (the Open Neural Network Exchange) to encourage framework diversity and usability. MyPOV: It was interesting to see Google add support for Scikit-Learn and XGBoost at this year’s Google Next event, which was a win for framework diversity beyond TensorFlow. I like the objectives of ONNX and laud Microsoft for promoting openness, modeling efficiency and choice.

Productive Services: At this level the big news was the introduction of automated machine learning and hyper parameter tuning. These are must-have features if data science is to be made accessible to developers, power-user analysts and aspiring data scientists. Azure ML also gained a Python SDK, another acknowledgement of the rise of this language. MyPOV: Microsoft is in a close horse race with AWS and Google, both of which have introduced similar automation and hyper parameter tuning features.

Powerful Infrastructure: Microsoft offers GPU capacity, but it’s alone among its cloud rivals in heavily pushing FPGA (field programmable gate arrays) for accelerated inferencing. Here it added support for five more deep learning algorithms (beyond the few previously supported) for image classification and recognition. MyPOV: Microsoft is touting cost and power-consumption advantages over GPUs in the inferencing role, but the evidence I’ve seen is in whitepapers and lab benchmarks. I’m waiting to see more real-world adoption and production success stories that prove these claimed advantages.

Deployment: At the deployment level, Microsoft has a particularly strong strategy around IoT and Edge deployment, and it’s clearly gaining adoption. WalMart’s CIO, Clay Johnson, shared details at Ignite on retailer’s massive IoT deployment aimed at optimizing energy usage across its 10,000-plus stores worldwide. MyPOV: Azure Sphere was a brilliant move to get Azure security and services built right into the microcontrollers that power sensors and devices. Azure IoT Edge is a comprehensive environment that supports deployment of models and gathering of intelligence and predictions from the edge. There are plenty of real-world customer success stories.

In Other News…

Other interesting data-related announcements from Ignite included the coming integration of Azure Cognitive Services and Automated ML into Microsoft Power BI, which fits with my research and predictions early this year on “How ML and AI Will Change BI and Analytics.” The company also introduced Azure Data Explorer, a powerful ad hoc discovery and visual analysis cloud service for big data discovery. Data Explorer looks like a handy, scalable service offering fast, big-data querying through a SQL-like intuitive language that can handle structured and variably structured data including log files, clickstreams and JSON data.

Microsoft also made a splashy Open Data Initiative announcement with the CEOs of SAP and Adobe joining Nadella on stage. Some cynically read this as an effort to steal thunder from Salesforce Dreamforce event, which followed Ignite by just one day. I’ll take partners Microsoft, SAP and Adobe at their word that they’re trying to help customers by developing and promoting open data-sharing standards and data schemas that could be used by other software providers (including, say, Salesforce and Oracle). It’s very early days however, as executives acknowledged, so we’ll have to see if this bears fruit. We’ll know whether it’s real if we hear about it again next year or three years down the road.

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Sometimes one plus one really can turn into three. That’s the formula TIBCO is hoping will apply to multiple acquisitions in the data-management and data science spaces over the last 18 months including Alpine Data Labs and Statistica (both now part of the just-announced TIBCO Data Science Platform), and the CISCO Information Server, previously known as Composite Software and now called TIBCO Data Virtualization.

To set the stage, formerly public TIBCO was acquired by private equity firm Vista Equity Partners in 2014. It has annual revenues of about $783 million, according to Hoover’s estimate. Founded in 1997, the firm has long prided itself on punching above its weight among industry giants. It started out as a messaging and integration middleware vendor up against the likes of IBM and Oracle. The company has landed many large, notable customers over the years, including Adidas, Bank of Montreal, Coca-Cola, FedEx, Johnson & Johnson, Pfizer and Unilever.

TIBCO’s foray into analytics began with its acquisition of data discovery and visualization vendor Spotfire in 2007. The portfolio was extended in 2014 with the acquisition of Jaspersoft, a more reporting-centric, open-source business intelligence platform often used to embed analytical capabilities into commercial and custom software.

The analytics side of TIBCO is now billed as the “Augment Intelligence” portfolio, while the integration side is designed to “Interconnect Everything.” The two sides support each other, but I’ll focus here almost entirely on analytics. TIBCO NOW 2018, the vendor’s annual customer event, held September 4-6 in Las Vegas, gave some 1,600 attendees a great update and it gave me a better understanding of the company’s many acquisitions and strategy.

Analytics Update

Before we get into the vision and strategy, the key analytics-related announcements at TIBCO NOW included the following:

• Spotfire gets smarter. Set for general availability this fall, Spotfire X (as in the 10.0 release – skipping 8.X and 9.X) adds natural language querying (NLQ) and AI-driven recommendations. NLQ lets users explore data through typed, English-language questions rather than SQL code. The AI-driven recommendations feature employs an ensemble of machine learning techniques to scan data and spot interesting variables, exceptions and drivers. The recommendations are exposed as a list of notable data-exploration possibilities.
• Spotfire gets faster. Spotfire X gains a new Data Streams function from TIBCO’s StreamBase technology to support visual analytics on data streams. Spotfire Data Streams offers real-time intelligence for Internet of Things (IoT) applications and other (fast-growing) use cases for low-latency insight. Oil and gas, manufacturing, pharmaceutical and life sciences businesses are at the head of the list of potential customers. Spotfire Data Streams supports continuous monitoring as well as real-time analysis and comparison to historical data to support trend- and root-cause analysis.
• Data science options integrated. TIBCO Data Science is a start on the integration of various TIBCO analytics and machine learning capabilities, including last year’s Alpine Data and Statistica acquisitions as well as the TERR (R server) and Statistics Server offerings long associated with Spotfire. Bundling brings together everything under a single license while initial integrations enable Statistica analytics to be run from Spotfire Analyst. Statistica can also call out to the Alpine engine to run analytics in cluster.
• Jaspersoft gets cloud friendlier. TIBCO announced JasperReports IO to advance embedding of reports and visualizations in cloud-based apps. IO is a small-footprint rendering engine that wraps Java in RESTful endpoints for easier development. It’s initially available as a Dockerfile. Highly scalable microservices orchestrated by Kubernetes are a planned next step.

MyPOV On Key Announcements

Spotfire is a solid data discovery and visualization option that’s particularly popular with data-savvy analysts in manufacturing, oil and gas, life sciences, pharma and other demanding environments. TIBCO was ahead of the game in delivering integrated data-wrangling capabilities in 2015. Spotfire is not as intuitive for business users as is Tableau or Microsoft Power BI, but the addition of natural language query and AI-driven recommendations should help to make it more accessible.

The new streaming analysis capabilities in Spotfire X will give power users yet more sophisticated functionality for low-latency and IoT scenarios. Other upgrades in release X, including data visualization improvements and design-once, deploy-on-any-device responsive design, will please every Spotfire customer.

Every analytics vendor now seems to claim embedded analytics capabilities, but this is a market niche that Jaspersoft has focused on for a long time. Open source licensing gives developers a cost-effective, flexible foundation. With JasperReports IO, TIBCO is clearly intent on supporting state-of-the-art cloud architecture.

As for my take on the data science moves, let’s look at how the broader portfolio comes together.

The Data Intelligence Portfolio

To get a better understanding of TIBCO’s analytics strategy, I sat down with Brad Hopper, vice president, analytics product strategy, who drew out a simple pyramid to explain what he informally calls the data intelligence portfolio. It starts at the top of the pyramid with the data wrangling capabilities built into Spotfire. These features enable individual analysts and data scientists to combine, manipulate, clean up, standardize and otherwise munge data for visual analysis in Spotfire or in data science work.

Data analysts and data scientists don’t tend to methodically build out workflows with every data-manipulation step planned out in advance, as would an ETL professional. The focus is on letting these users wrangle the data, yet each step they take is captured and recorded in the background. Even if the steps weren’t applied in a linear, planned-out way, Spotfire’s Data Canvas retains a logically ordered, documented and trackable workflow of all the joins, transformations and cleansing and editing steps behind each new source that can be edited, perfected, reused and audited.

The next layer down in the Data Intelligence pyramid is the TIBCO Spotfire Data Catalog, which is built on technology from Attivio that supports machine-learning-based crawling and indexing of all data in the enterprise. This includes data lakes with unstructured information, which the catalog parses and enriches with metadata. Fuzzy matching helps to find relationships among data.

This middle layer is designed to aid search, discovery and data prototyping, so it’s aimed at teams using Spotfire, but Hopper also sees it coming into play for data scientists. Teams might develop marts for standardized dashboarding and visualization through Spotfire or reporting through Jaspersoft. The catalog and data wrangling tools will help data science teams spot and develop new data sets.

At the bottom of the Data Intelligence pyramid is TIBCO Data Virtualization (formerly Cisco Information Server/Composite Software). This is where IT can draw on the data workflows captured on the Spotfire Data Canvas and turn them into hardened data marts and sanctioned, production-grade data sources. The heavy lifting is done through virtualization rather than traditional ETL and data-movement. Also described, variously, as data fabric, logical data warehousing or virtual data layer, data virtualization approaches are gaining traction. TIBCO Data Virtualization competes directly with Donodo (which I recently wrote about in combination with Looker in this recent case study).

MyPOV on TIBCO’s Data Intelligence Strategy

Analytics and data science can only thrive where there is ready access to solid, well-managed data. Thus, I do like how the data catalog and data virtualization assets complement Spotfire and, potentially, Tibco Data Science. I’ve been following Composite Software for a long time, so it’s good to see stepped up investment in what’s now known as TIBCO Data Virtualization. I didn’t get to see enough at TIBCO NOW, so I’ve asked for deeper briefings on how DV works with modern cloud architectures, unstructured sources and object stores such as Amazon S3.

The breadth of TIBCO’s portfolio and the one-stop-shop/one-throat-to-choke opportunity it affords is likely to appeal to IT buyers. I’d make a point of getting analytics buyers in the loop, too. You might use DV to deliver IT-sanctioned data sources for data governance purposes, for example. One Spotfire customer lamented, during a DV session at TIBCO NOW, that users want direct, not virtualized, access to the tables and sources they already know. Adoption of TIBCO DV may require a bit of change management, even if it’s ultimately a boon to data access and governance.

As for TIBCO’s data science portfolio, I see the moves to bring together the Statistica, Alpine and Terr/Spotfire assets on one platform as appealing to existing customers, but I doubt the collection will win over greenfield converts. For one thing, they remain separate products with very basic points of integration at this point. For another, data scientists starting from scratch are embracing all things open source, including Jupyter and Zeppelin notebooks and direct coding of open algorithms. Alpine Data certainly tried to cater to this next-generation crowd, but it wasn’t winning in the marketplace.

For now, TIBCO’s access-to-everything licensing will appeal to existing Statistica and Alpine Data customers (as will the complement of catalog, data wrangling and DV capabilities, if they’re also Spotfire customers). Long term, however, I’d want to see deeper integration and refactoring that truly brings these assets together as a single product worthy of the “platform” billing, with connected workflows and consistent user interfaces.

In short, I like what TIBCO is doing with Spotfire, catalog capabilities and TIBCO DV. Only time will tell if the data science portfolio can become more appealing than the sum of its parts.

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Alteryx Offers Gift of Time for Analytical Innovation

Alteryx eliminates tedious and time-consuming data-prep work so users can spend more time on innovative, advanced analytical work.

This was the core appeal articulated at Alteryx Inspire, the company’s annual customer conference held June 4-7 in Anaheim, CA. Riffing on the event theme “Alter Everything,” CEO Dean Stoecker encouraged the more than 3,000 Inspire attendees to use Alteryx to eliminate error-prone, repetitive spreadsheet drudgery and drive transformations and new business opportunities through data science.

Alteryx laid out more than a dozen significant product enhancements at Inspire. It also set forth a roadmap toward machine-learning-based recommendation features and cloud-based deployment options coming to future releases. Here’s a closer look.

Alteryx Upgrades for 2018

Alteryx offers four products that it’s moving to synthesize into a tightly integrated platform. The core product that all customers use is Alteryx Designer, a desktop data-prep, blending and analytical tool that was introduced in 2006. Alteryx Server, added in 2010, provides a platform for connecting to data sources, collaborating around and automating Alteryx data workflows, and executing at big data scale. Today the vast majority of Alteryx revenue comes from an even split of Designer and Server licenses, but the company introduced two new products in 2017.

Using proceeds of its March 2017 IPO, Alteryx made two acquisitions in June 2017. Building on the acquisition of Semanta, a data cataloging and governance vendor, the company introduced Alteryx Connect, which offers data-discovery and collaboration capabilities. The assets of Brooklyn, NY-based startup Yhat were developed into Alteryx Promote, which supports analytical model deployment, monitoring and ongoing optimization.

The key announcements at Inspire were around more than a dozen upgrades across all four products. Some of these upgrades are already available, some are set for later this year and a few won’t be available until next year. Demos of several of these upgrades drew raves from attendees while offers seemed overdue. Highlights included:

A social data catalog and asset recommendations. Search enhancements now generally available make it easier for users to find the data they’re after. In Q3 Alteryx will beta release an upgrade whereby users will get recommendations on the most popular data sources blended with a particular data source when they drag and drop it into a workflow.

Workflow caching. This upgrade, set for beta release in Q3, makes it easier to develop reusable data-prep and data-processing workflows by saving interim steps during an iterative design session.

Data profiling. Now generally available, this feature provides statistical details on the number of records, range of values, average value and other details about data set. Quality stats, such as the percentage and number of missing or exception values, will be useful in driving cleanup recommendations.

Python SDK. Alteryx already offered extensive support for use of R-based models, but with so much analytical development moving to Python, Alteryx has added an SDK supporting analytic development in this ascendant language.

Interactive Data Grid. These enhancements to Alteryx Designer’s core interface, planned for Q3 and beyond, drew hearty applause from customers, as it will make it easier for users to search, sort and filter their data without leaving Alteryx.

MyPOV on Alteryx Upgrades: As noted, some of these upgrades, like key data-source connectors for Alteryx Connect, SAML support in Alteryx Connect and Alteryx Server, and the addition of data profiling and the Python SDK, seemed overdue. The workflow caching, Interactive Data Grid and a new Insight Tool for dashboards (which are all future enhancements) were real crowd pleasers. They look like they’ll deliver more problem-solving shortcuts around what would otherwise require tedious and repetitive manual work. Overall Alteryx seems to have stepped up its pace of development, so I’d expect less catch-up development and more innovative new feature development in future releases.

On the Horizon

Alteryx has plenty of competition both from dedicated data-prep vendors, such as Trifacta and Paxata, on the one hand, and advanced analytics vendors, such as RapidMiner, Knime and Domino Data, on the other (DataRobot and H2O are partners). Adding to the competitive pressure on Alteryx has been efforts by the likes of Tableau, Microsoft PowerBI, Qlik and other mainstream analytics vendors to add basic data-prep capabilities.

Roadmap themes going forward for Alteryx include:

• Knit together Connect, Designer, Server and Promote more closely to support seamless, end-to-end analytic workflows.
• Add machine-learning based capabilities across the platform to augment the skill of humans and support automation.
• Build out a container-based cloud deployment approach to support consistent, reusable use of the platform in hybrid and multi-cloud deployment scenarios.

Specific developments on the horizon include a Solution-Based Modeling Utility that will walk business analysts through the predictive modeling steps of data prep, analysis, model building and deployment through a wizard-driven interface. Also coming, in the ML vein, are data formatting, cleansing and join recommendations and natural language query capabilities.

MyPOV on Alteryx. I know from prior research (like this case study on Ford) that Alteryx is a great, time-saving and democratizing option for companies looking to make data-driven decision-making more of a self-service proposition. As was abundantly evident at Inspire, the company is growing quickly and has plenty of enthusiastic users of Alteryx Designer and Alteryx Server.

What remains to be seen is how successfully the company’s new products, Connect and Promote, can be knitted tightly into inseparable platform. Plenty of customers at Inspire told me they are considering adding these components, but few have done so as yet. A notable exception was Shell Oil Company, which offered a presentation on how it’s using Alteryx Promote as part of an effort to spread ML-based predictive analytical capabilities across the company.

What also remains to be seen is the extent to which native data-prep capabilities coming from the likes of Tableau will sap demand for more Alteryx licenses. Alteryx execs contended that these moves will only seed more demand for its platform. Whether entry-level prep happens in Alteryx or not, I see the company’s expanding analytical and model-deployment capabilities as the company’s bigger opportunity. Prep is something to get out of the way and make repeatable. Analytics is where companies are going to get more bang and business impact for the buck.

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“Under new management.” When you see these words in front of a local business, you take note, and it makes you wonder what has really changed?

Keynoting at the April 24-26 Qlik Qonnections event in Orlando, Qlik’s new CEO, Mike Capone, after just four months on the job, cut right to the chase and spelled out some important business changes made early in his tenure.

Explaining a reorganization that took place in January, Capone said the goal was to refocus Qlik sales and support efforts on large Enterprise customers while “getting out of the way” of channel partners catering to midsize and smaller commercial customers. The reorg brought a fresh round of layoffs (on top of the layoffs carried out in 2016 when private equity firm Thoma Bravo took Qlik private). Downsizing seldom looks good (except to Wall Street), but in a message to partners, Capone noted that Qlik is reinvesting savings from the reorg to boost partner support programs, including qualified lead generation.

In a message to customers, Capone promised accelerated development of advanced authoring and visualization capabilities in Qlik Sense, the vendor’s newer, webbier and more self-service oriented product. And despite an “inviolable” promise to maintain support for Qlik View, the company’s original product, Capone also announced new migration tools, a shared Hub interface for all Qlik apps and, most importantly, new licensing terms designed to make it easier to move from Qlik View to Qlik Sense. The licensing change will eliminate the need for a second license. Instead customers will pay a fractional increase in maintenance (somewhere less than 50% of current fees) to add support for Qlik Sense. The Hub and licensing announcements each got big applause from Qonnection’s 3,500-plus attendees.

Cloud, AI and Developer Announcements               

On product and product strategy, executives laid out Qlik’s evolved vision and capabilities on three fronts: cloud, smart capabilities (a.k.a., augmented intelligence) and developer support. Here’s the rundown:

Next steps to the cloud. Qlik’s cloud vision is to support hybrid- and multi-cloud deployment of Qlik applications and services with a microservices-based architecture that will eventually span and unite all deployment approaches. Qlik will take the next step to deliver on this promise in June by enabling Qlik subscription customers to move Qlik Sense applications into containerized, cloud-based instances than can run on Linux in public or private clouds. The Kubernetes, Docker and Linux support are new to Qlik, and it will enable customers to self-manage instances on Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform (GCP) or their own private clouds.

Containerization and Linux support will also benefit managed-cloud services, which are available from Qlik on the Qlik Cloud or from many partners on private or public clouds of the customer’s choice. Qlik’s two-year-old, software-as-a-service-based Qlik Sense Cloud was scarcely mentioned at Qonnections, but it remains an option for workgroup and departmental multi-tenant instances.

To better support the new range of deployment options, Qlik is introducing a Hub interface that give users one view — with one log-in, one user ID and one user entitlement – of all their Qlik applications, irrespective of where they’re running. What’s more, Qlik’s management console will enable administrators to set governance policies across all public cloud, private cloud and on-premises instances. Qlik demonstrated a role-playing scenario in which non-regulated (but still access- and security-sensitive) applications and data from a German subsidiary were exposed through a U.S.-based cloud instance. In contrast, privacy-sensitive data subject to compliance requirements in Germany was retained on premises in the demo, but it was made accessible with strict access controls.

“Smart” capabilities explained. Qlik announced an “augmented intelligence” initiative last year, stressing that it would support rather than replace human analysis. The company has been developing a Cognitive Engine that will power a number of features to Quick Sense. An Insight Advisor feature added in April recommends the most appropriate visualization options when you select a particular field for analysis. If you don’t actively choose one of these recommendations, the engine will automatically choose what’s seen as the best-fit option.

Coming in June, Qlik Sense will gain a hands-off approach whereby you can simply point the Insight Advisor at a data set (rather than a particular field) and it will automatically surface all statistically significant insights. After choosing one of these starting points, users can then select the particular field of interest, as noted above, and get additional recommendations to take the analysis even deeper.

Insight Advisor is designed to speed time to analysis for untrained users, but in the spirit of augmenting human intelligence you can interact with the tool and go against its recommendations. In this case, Insight Advisor will flash textual warnings explaining why a particular approach is not recommended. During a keynote demo, a Qlik exec selected a tree map view even though the Insight Advisor recommended a different visualization. Messages appeared noting that there were negative values in the selected data set and that tree maps can’t show negative values. Professionals who don’t want or need this sort of assistance can simply turn the Insight Advisor feature off.

In next steps expected sometime later this year, Qlik plans to enable its Cognitive Engine to start learning from the selections that users choose from among its recommendations. In addition, Qlik plans to team the Cognitive Engine with its associative QIX engine, which keeps entire data sets visible for analysis even as you focus in on selected dimensions of data. If you select customers who are buying X product, for example, QIX also shows you which customers are not buying that product. It’s an advantage over drill-down, SQL analysis where you filter out information as you explore, and Qlik says the combination will recommend powerful, serendipitous insights that other technologies would fail to see.

Targeting developers and embedded analytics. Perhaps the most surprising and, to me, forward-looking initiative at Qlik has been its effort to promote analytic customization, extension and development both within and outside of the confines of Qlik applications. As one Qlik executive put it, “analytics shouldn’t just be a destination [as in a report or a dashboard], it should be part of the journey.” The idea is to support embedding of analytics into other applications, including transactional applications, so you can turn insights into actions. An app could even be autonomous, quickly taking action based on insights without even requiring a human interface or interpretation.

To open up new possibilities, Qlik introduced Qlik Core, set for beta release this summer. Able to run on containers and Linux in the cloud, Qlik Core exposes the QIX engine through a microservices architecture and APIs. I talked to the CTO at Measur.io, a development partner and customer, who has been using and helping to guide the development of Qlik Core for more than a year. He said Qlik Core is powering the analytics behind the company’s Internet-of-Things (IoT) sensor applications and giving it all the cloud scalability and deployment flexibility the company needs.

Pricing for Qlik Core has yet to be set, but the licensing approach will combine free development with consumption-based charges for production workloads. Qlik has also open sourced libraries of capabilities, such as data loading with Halyard.JS, Qlik backend services with Enigma.JS, and charting with Picasso.JS, to facilitate cost-effective development and deployment. Qlik’s Qlik Branch developer community is promoting all of the above, and it’s said to growing quickly, with more than 27,000 registered members.

MyPOV on Qlik’s Progress

Overall I was impressed by Qlik’s new management team. Capone went straight to the customer hot buttons, including licensing and accelerated development. He’s also putting an emphasis on driving success through better customer outcomes, whether that’s through direct sales or channel sales. Customers that have better outcomes will naturally buy more software, so the company says it will do more to help customers succeed. Shelfware, meanwhile, is viewed as a failure and an outcome that should be avoided (and perhaps even dis-incented in sales incentive programs).

My consistent lament about Qlik, shared in my analyses from Qlik Qonnections 2016 and 2017, has been its slow pace to innovation. The criticism still applies on the cloud, AI and big data fronts, where Qlik is still delivering on capabilities initially announced 12 to 24 months ago. On cloud, for instance, the multi-cloud deployment option set for June release will initially be limited to consumption. Apps will have to be authored on-premises and then deployed for read-only access in the cloud. Cloud authoring, write-back capabilities and pull-through access to on-premises data sources are all on the roadmap. Similarly, the (late-to-the-game) Big Data Index that Qlik announced last year and demonstrated at Qonnections 2018 won’t see beta release until June or July. I don’t expect general availability before Q3, at the earliest.

I do like Qlik’s platform and QIX associative analysis strengths. I also like the push to enable developers to customize and extend Qlik apps and to create original apps that transcend conventional thinking about analytics. Where there’s confidence in an analysis, human interaction shouldn’t be required. That’s particularly true in emerging streaming and low-latency scenarios where there’s no time for human analysis.

My hope is that Qlik’s new management team continues to be responsive and that it will accelerate innovation. If I had to set the priorities I’d accelerate the practical stuff that customers care about today, such as hybrid-, multi-cloud and developer capabilities, while letting demand for AI capabilities build over the longer term.

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