MicroStrategy is back. After a few years in which the business intelligence and analytics company made little noise, it’s back with new energy, new capabilities for ad hoc and departmental users and a new educational push around what it calls its “Map of the Intelligent Enterprise.”

MicroStrategy has flourished at the top of the BI market for more than two decades, specializing in enterprise-wide deployments at truly high scale – supporting thousands and, in some cases, tens of thousands or even hundreds of thousands of users. As smartphones and tablets emerged in the 2008-2011 timeframe, MicroStrategy was the first mover. To this day MicroStrategy remains at the forefront of delivering mobile BI capabilities with fluid, device-native interactivity.

The shift that MicroStrategy largely missed (along with many other established BI vendors), was the rise of agile, self-service capabilities aimed at mainstream, departmental and line-of-business users. That’s the void that Tableau, Qlik and, more recently, Microsoft PowerBI, have happily filled. Like other established vendors, MicroStrategy responded with ad hoc data discovery and data visualization capabilities of its own. Yet many MicroStrategy customers bowed to grassroots demand and embraced third-party self-service options for their ease of use.

What the self-service crowd learned, as use of their tools spread, is that high-scale deployments and establishing a single source of truth are not so simple. These vendors have been adding data-governance features, administrative controls and management capabilities to turn tools into what they now describe as platforms.

MicroStrategy’s strength, meanwhile, was long been its platform. At last week’s MicroStrategy World event in Las Vegas, company Founder, Chairman and CEO, Michael Saylor, vowed that the company is now ready to deliver “massive departmental productivity [that] syncs with the enterprise.”

Part of delivering on that promise rests on the product. MicroStrategy Dossier, introduced last fall, goes beyond dashboards to deliver collaborative analytical applications and workbooks. A revamped MicroStrategy Desktop interface introduced at the World event better supports data discovery. And a new Workstation interface was added to help power users quickly build and scale up enterprise content.

Beyond software, Saylor said that enterprise software companies have to provide “service and technique that ensures customer success.” Technique is about best practices, and that’s where MicroStrategy’s “Map of the Intelligent Enterprise” comes in. A printed, 16-inch by 23-inch fold-out copy of the map was provided to every MicroStrategy World attendee. Saylor referenced a digital version of the map throughout his keynote, making the point that the platform has to be deployed and used in the right way if it is to deliver a single version of the truth that empowers “pervasive analytics.”

Detailing the roles that should be in place, for example, Saylor said organization should have a lead analytics architect – just one person in charge — to create, publish and optimize the data layer so there aren’t multiple versions and interpretations of the truth. And there should be a lead application architect – again, a single point person – who sets standards for analytical applications so there aren’t overlapping and conflicting approaches that contribute to performance problems.

Large enterprises tend to have most of, if not all, the architectural and administrative roles detailed in MicroStrategy’s Map of the Intelligent Enterprise, Saylor told me. But where deployments have failed,  where performance is not as expected and where data inconsistencies have cropped up, Saylor said MicroStrategy tends to discover overlapping responsibilities and inconsistent standards, interpretations and approaches.

To address these breakdowns in best practices, MicroStrategy has stepped up its educational resources, including free options, for both customers and partners who can help customers get the most from their deployments. It’s also eyeing certifications and more community based training opportunities, but the first step is raising awareness of the resources already available.

MyPOV on MicroStrategy World

MicroStrategy World presented what I saw as an honest discussion of what’s required for customers to get to proverbial intelligent enterprise. Instead of pretending that products alone can deliver success, Saylor stressed that MicroStrategy’s improved ad-hoc data-discovery and departmental capabilities will be no better than disconnected Excel spreadsheets or Tableau dashboards if they don’t draw on a single source of truth and are not synchronized back to the enterprise environment as usage scales up.

I got the sense that most of the attendees of MicroStrategy World — more than 2,000 executives from enterprise customers ranging from AIG, Coca-Cola and Merck to Citi, Optum and UBS – were comfortable with what I would characterize as top-down messaging aimed at executives, analytics leaders, project managers and administrators of high-scale, enterprise-wide deployments.

As for the ease of use and agility of the new ad hoc and departmental tools, including Dossier, Desktop and Workstation, these were briefly demonstrated during Chief Technology Officer Tim Lang’s second-day keynote, but it was a fleeting glimpse. Yes, there were detailed track sections and hands-on workshops for those interested, but if MicroStrategy is truly serious about retaining (or regaining) the self-service ad hoc and departmental workloads, it could do a better job of demonstrating and even  celebrating its “massive departmental productivity” on the main stage. Show me, don’t tell me. Let the live and video-taped customer testimonials include more front-line analysts, Dossier builders and ad hoc explorers rather than just the top-dog analytics executives.

The passion at MicroStrategy World was most evident around what MicroStrategy has always done best — providing the platform for powerful analytical applications deployed at high scale. The company’s prowess with a variety of consumption options was also on display, now extending from multi-screen conference room displays at the high end down to smart phones and smart watches.

MicroStrategy is also pushing the envelope of voice interaction, with Saylor describing Amazon Alexa as the third most important platform to emerge after iOS and Android. Here’s where MicroStrategy and others could start to redefine self service. As I note in my recent report, “How Machine Learning and Artificial Intelligence will Change Business Intelligence and Analytics,” line-of-business users don’t necessarily want to have to open up a report or interpret a data visualization. And as the speed of decision making increases, there may be no time for such interaction. In that world, users will want to be able to simply ask questions and get precise answers and recommended actions.

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To Salesforce customer Bill Hoffman, Chief Analytics Officer at Minneapolis-based US Bank, the “A” in “AI” is about “augmented” intelligence because, as he said in a keynote at this week’s Dreamforce event in San Francisco, “there’s nothing artificial about it.”

US Bank has deployed Salesforce Einstein capabilities including Predictive Lead Scoring and Einstein Analytics (formerly known as Wave) for customer attrition analysis and retention efforts. It’s also using Einstein Discovery (formerly BeyondCore) to better understand customer behavior and cross-sell opportunities. The bank expects to roll out Einstein capabilities to more than 2,000 of its customer-facing financial advisers across the company in hopes of “personalizing service at scale” and “creating a differentiated customer experience,” Hoffman said.

Personalizing at scale is precisely the idea behind two “myEinstein” capabilities announced at Dreamforce. Also announced were two Einstein Analytics capabilities. All four capabilities are coming to the portfolio next year. Here’s what they promise to do for your business.

Einstein Prediction Builder: Plenty of Salesforce customers are using or considering machine-learning-based Einstein capabilities, most of which were detailed in my 19-page report published earlier this year. But at Dreamforce 2017 we heard the revealing stat that some 80% of the customer data in Salesforce is tied to custom (customer-defined) fields and objects. No surprise, then, that the number-one ask among Salesforce customers was for customizable, as well as pre-built, Einstein insights, predictions and recommendations.

Einstein Prediction Builder is a no-code capability designed to enable non-data-scientists to develop predictions using custom fields. Use cases are limitless, but popular use cases are likely to include cross-sell/up-sell, churn, CSAT and propensity-to-escalate analyses. Prediction Builder will be powered by the same machine-learning data pipeline that handles millions of Einstein predictions per day, but it will be opened up – starting with a February beta release and likely June general release – to custom fields and objects in Salesforce. Pricing has not been finalized.

Einstein Bots: Salesforce picked up strong natural language understanding and natural language translation capabilities through its 2016 acquisition of MetaMind. Einstein Bots, a second myEinstein feature, will couple these language capabilities with Salesforce data and the Salesforce workflow engine to power automated customer-service agents. The idea is to handle the bulk of the simple, frequent service cases, such as user password resets, while leaving the long tail of complex and infrequent service inquiries to human agents.

As with Prediction Builder, Einstein Bot development will be a no-code proposition. It will start with point-and-click selections and workflow setup and uploading of spreadsheets of sample customer-service interaction text to train the language model. Beta release is expected in February with generally availability to follow in June. Pricing will be set at general release, but I expect it to be based on the volume of cases handled over a specified time period, a common approach for bots. The Bots will start with text-based interaction, but voice-based interaction is likely to follow.

Einstein Data Insights: This new Einstein Analytics (formerly Wave) capability provides deeper insights into standard Salesforce reports from the Sales Cloud, Service Cloud, and, eventually, other clouds. Powered by the same engine behind Einstein Discovery, Einstein Data Insights will automatically surface important trends, outliers, changes over time and even data-quality problems within standard reports, displaying a combination of visualizations and textual explanations. Users will press a button embedded on a standard report and the visualizations and textual explanations will appear on the right side of the screen (see image below). This capability is also expected to see beta launch in February with general availability next June. The pricing model has yet to be determined.

Einstein data-explorer feature: This capability, which will be included with Einstein Analytics, will let you have “a conversation with your data,” says Salesforce, by typing in questions in plain English. Behind the scenes, keyword-driven interpretation will help you drill down on dashboards and visualizations to better understand not just what happened by why it happened. You could drill down on a total figure, for example, by typing “amount by product.” Or you could analyze performance by typing in “lost deals by product.” This feature is expected to be generally available in February.

MyPOV on Einstein’s Progress

As compelling as the coming Einstein capabilities are, the big question on the minds of many customers is “how much will it cost?” It seems we’re still in a chicken-and-egg phase in which both Salesforce and customers are trying to figure out how much Einstein capabilities are worth. Different sorts of predictions and recommendations have different values, depending on the cloud and the types of actions triggered. The size and nature of the customer adds another dimension of complexity, with large enterprises sometimes preferring all-you-can-eat enterprise deals. Salesforce, meanwhile, needs to establish clear revenue expectations to keep its investors and Wall Street happy. Innovation presents its challenges.

Picking up on trends in big data and open-source software pricing these days, one possible pricing approach would be to provide free access to Einstein development tools and a limited number of predictions or recommendations so businesses can get a sense of what they can do. Once the capability is deployed, Salesforce could apply volume-based per-prediction or per-recommendation charges would kick in. In this way, charges would be tied to the value delivered to the customer, although different customers would surely have different perceptions of value, so it might be hard to come up with a one-size-fits-all pricing scheme.

One thing that customers might have found confusing at Dreamforce was the distinction between Einstein and Einstein Analytics (formerly Wave Analytics). There were two separate keynotes at Dreamforce and there are two separate teams behind different sets of Einstein capabilities. But they are both part of one portfolio and a continuum of capabilities from descriptive and diagnostic analytics to predictive analytics and prescriptive recommendations and actions (as well as advanced language and vision capabilities and APIs for human-interactive applications). Before you can get to the predictive and prescriptive part you need to have good data and reporting in place.

US Bank is using capabilities across the Einstein continuum, and Bill Hoffman, when asked for advice during a keynote, said you have to start with data quality and you have to bring in key stakeholders and risk management and compliance partners from the beginning. In short, don’t expect to get the sizzle of “AI” without addressing the the meat-and-potatoes of data management and baseline reporting and analytics.

The unsung announcements that didn’t get as much attention at Dreamforce included a recent rewrite of the Einstein Analytics engine said to deliver a 30% reduction in data-ingestion and query times. Available data capacity was also more than doubled to 1 billion rows per customer. For easier data loading from external sources, Salesforce has added out-of-the-box data connectors for AWS Redshift, Google BigQuery and Microsoft Dynamics, and more than 20 additional pre-built connectors are to be added over the next six months. Finally, Smart Data Prep capabilities have been enhanced with data profiling, auto clustering, anomaly detection, filtering and transformation suggestions.

These upgrades aren’t the sexy stuff, but they are day-to-day productivity improvements that will help sell customers on handling analytics within Salesforce and advancing to Einstein predictions and recommendations.

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Oracle’s new Autonomous Database Cloud will be cheaper, faster and, with the addition of the Oracle Cyber Security System, safer than anything from Amazon Web Services (AWS). At least that’s the assertion Oracle Executive Chairman and CTO Larry Ellison wants everyone to remember from last week’s Oracle Open World 2017 (OOW17) event in San Francisco.

Whether Oracle’s claims are fair and accurate comparisons remains to be seen, as the first release of the Oracle Autonomous Database, through a Data Warehouse Cloud Service, won’t be available until December. Count on it being at least a few more months before independent reviewers can do independent tests against rival cloud services.

It should be about that same time – six months from now – that several other data-related announcements from OOW17 will actually be available. Some notable OOW17 announcements are generally available today, such as the Oracle Big Data Cloud, Oracle Event Hub and Stream Analytics Cloud services, and the Oracle Analytics Cloud Data Lake Edition. But whether it’s the next round of Adaptive Intelligent Apps, coming artificial intelligence (AI) and machine learning (ML) Platform as a Service capabilities, or a series of Oracle Analytics Cloud upgrades, many of the more interesting announcements from OOW17 will emerge over the next three to six months.

Here’s a rundown on 9 announcements to follow over the coming months.

Oracle Autonomous Database Cloud

Winning the cloud war is, of course, crucial for Oracle. Oracle has 480,000-plus customers, and it’s number-one product, hands down, is Oracle Database. If there’s a cloud equivalent to the domino theory, it’s that as cloud database selections go, so go the rest of a customer’s cloud choices. Thus, Ellison’s better-faster-cheaper performance and cost claims were on display all over OOW17. He also told attendees that with discounts, the database service will start at as little as $300 per month — though the 1-CPU to 1 terabyte-of-data specification seemed anemic, to say the least.

In CEO Mark Hurd’s keynote we heard that only about 14% of production workloads are now running in public clouds, but given growing cloud momentum he predicted that 80% of production workloads will be in the cloud by 2025. The lion’s share  of today’s database marketplace is Oracle’s to lose, so there’s huge pressure to prevent customers from even thinking about alternatives like Amazon RedShift or Amazon Aurora. The (according-to-Oracle) cost and performance claims flashed all over OOW17 were a “stick with Oracle” message to on-premises customers now considering the cloud.

Analysis: It’s important to recognize that the Oracle Autonomous Database Cloud is just that, a cloud-based service. Oracle Database 18c software, when it arrives, won’t have inherent Autonomous capabilities. To be Autonomous it has to be delivered as a service by Oracle. The same goes for the on-premises deployment option, which won’t be Autonomous unless it’s delivered “Cloud at Customer” style, with an Oracle Cloud Machine deployed in customer data centers but managed by Oracle.

Oracle has been talking for at least a couple of years about the efficiency it can offer through automation in the cloud, but the Autonomous Database Cloud is said to bring these advantages to a whole new level. Time will tell just how much extra oomph the ML-driven Autonomous tuning and optimization delivers compared to like-for-like Oracle 12c database services.

As for those tests results, the comparison of Oracle Database on Oracle Cloud vs. Oracle Database on Amazon is a shoe-in for the home team given Oracle’s ability to run the database on Exadata, which is not an option for Amazon and which which pushes down query processing to the storage layer, and thereby reduces the load,  before the query even gets to the database engine. Oracle DB on Exadata vs. Redshift on Amazon is more of an apples to apples comparison. Here’s where I’m eager to see independent test results.

Assuming that ML-driven database automation offers advantages – and I’m sure there will be many — the good news is that Oracle has a slew of other Autonomous database services in the pipeline, including Autonomous OLTP, expected next June, and Autonomous NoSQL and Graph database services likely to show up by OOW18.

Oracle Adaptive Intelligent Apps

Announced rather quietly at OOW16, Oracle Adaptive Intelligent Apps are a family of cloud-based, machine-learning powered apps that are integrated with Oracle cloud applications. The company spent the first half of 2016 putting the required machine learning data pipelines in place. Using a combination of customer-specific SaaS data and third-party enrichment data from the Oracle Data Cloud, Adaptive Intelligent Apps will deliver customer-tailored recommendations that will improve decisions, outcomes and business results.

Generally available today are Next Best Offers and Recommendations, a subset of Adaptive Intelligent experiences coming to the Customer Experience (CX) Cloud. Following the roadmap laid out last year, Oracle announced Adaptive Intelligent Apps for HR, ERP and Supply Chain Management at OOW17. The company said it expects this next wave of smart apps to be released within the next 12 months.

Analysis: Oracle Adaptive Intelligent Apps for CX are just getting out of the gate. Two customers on hand at OOW17, Team Sportia of Sweden and Moleskin of Italy, both said their deployments were just getting started. This is later than I anticipated in my 2016 report on Adaptive Intelligent Apps, but Oracle always conservatively said these apps would debut “within the next 12 months.” Oracle’s chief rival on this front is, of course, Salesforce Einstein, which saw lots of splashy announcements in 2017. I’m anxious to hear testimonials and deployment details from Salesforce Einstein customers at the upcoming Dreamforce event in early November.

I’ll be surprised to see new Oracle Adaptive Intelligent Apps outside of the CX arena  through the first quarter of 2018. I’ve learned to be cautious, so I’m guessing those HR, ERP and SCM smart apps are at least six months away and likely to debut in limited release.

Oracle Big Data and AI Advances

Oracle is in some cases keeping up and in some cases catching up with market leaders on the big data and artificial intelligence fronts. This summer Oracle announced the Oracle Big Data Cloud, which is a big data platform based on Hadoop and Spark and closely aligned with the ODPi standard also used by Hortonworks, Microsoft and IBM. Oracle’s previous offering, the Big Data Appliance based on Cloudera, is still available both on-premises or as a hosted service. But the future focus is clearly on Oracle Big Data Cloud, which separates storage and compute decisions and offers object storage as a low-cost alternative for high-scale data lakes.

To address streaming, real-time applications Oracle has added the Oracle Event Hub, which is based on open source Apache Kafka, for routing and processing. Oracle Stream Analytics is a rewrite of the company’s complex event processing technology that now runs on Apache Spark.

On artificial intelligence, Oracle President Thomas Kurian introduced a new AI & ML PaaS that will offer GPU compute capacity (both bare metal and VM) and a variety of open source AI frameworks, including Caffe, Keras and Tensorflow. Developers will be able to work with a variety of languages and notebooks.

Analysis: The Oracle Big Data Cloud is generally available immediately and puts Oracle more in step with the big data services available from AWS and Microsoft Azure. The Event Hub and Stream Analytics services are also both generally available today and fill a gap that Oracle had versus the AWS Kinesis portfolio and the Event Hubs and Stream Analytics on Microsoft Azure.

As for the AI & ML PaaS, it was announced at OOW17, but it’s not yet available (as per Oracle’s site). Based on the roadmaps I’ve seen, I’d expect the AI & ML Pass to be available within three to six months. AWS, Google Cloud Platform and Microsoft have all had GPU capacity available for some time. On model development and deployment, Microsoft last month introduced the beta preview of its next-generation Azure Machine Learning portfolio that promises end-to-end model lifecycle management. In short, my take is that Oracle is still catching up on AI cloud services and capabilities.

Oracle Analytics Cloud

Here’s another area where Oracle is moving quickly to stay in step with the market. Evolving beyond the Oracle BI Cloud Service and Oracle Visualization Cloud Service announced three years ago, the Oracle Analytics Cloud combines these two services and adds more to create a more comprehensive collection spanning data discovery, preparation, analysis and prediction. Standard and Enterprise Edition subscriptions were previously available. Oracle introduced a Data Lake Edition at OOW17 with subscriptions based on CPUs rather than users, thereby encouraging broad adoption.

Oracle announced a series of machine-learning and natural-language-processing-based enhancements to the Oracle Analytics Cloud at OOW17 and they’ll be available over the next three to six months. Automated Data Diagnostics is an “Explain” capability that will surface hidden drivers and guide users to data and analyses that they might not know to look at. Natural Language Insights will generate plain-text analyses of salient points on a chart, helping uses to focus on what matters. Improved “Ask” Natural Language Query capabilities will support synonyms and abbreviations and dynamically correct and reinterpret queries as you type. Oracle is also working on Enhanced Data Catalog capabilities including search and navigation across metadata and social tags as well as automated recommendations on related and relevant datasets to promote discovery.

Analysis: Oracle Analytics Cloud is on a path that’s similar to Microsoft PowerBI and Azure ML. Both vendors have created comprehensive portfolios and are seeking to leverage the strengths of their respective clouds and data platforms. The updates and enhancements announced at OOW17 mostly match state-of-the art capabilities that are already available in the market. For example, Oracle’s “Explain” feature is akin to Salesforce BeyondCore and a similar feature embedded in Microsoft PowerBI. Natural Language Insights is akin to Narrative Sciences and Automated Insights capabilities that Qlik and Tableau have both leveraged. State-of-the-art natural language query is available from multiple vendors, including Microsoft, IBM (Watson Analytics) and others.

My Overall Take on OOW17

As always, I came away from OOW17 impressed by the sheer breadth of applications and technologies available from the company. Oracle may not always be the first to introduce state-of-the-art capabilities, but it’s always the top competitor cited by rival database and data-platform vendors. The company’s sheer market presence in data platforms and applications puts it in a great position to also lead in analytics and coming smart applications. The key question, as with so many vendors these days, is how successful the company will be in transitioning existing customers to the cloud.

The surprise in the applications arena has been just how many all-new, greenfield customers Oracle has won with its SaaS applications. CEO Mark Hurd insists that Oracle’s core database business is outpacing the rest of the market, but that’s a licensing and subscription measure whereas I’m seeing a lot of open source growth that can’t be measured by the same math (even if the software is commercially supported).

Refreshingly, Oracle’s big data, data integration and analytics executives all seem to be hip to the open source movement. OOW17 saw a broad embrace of open source software, from Hadoop, Spark, Kafka and Cassandra (the latter by way of a partnership with Datastax) to Python, R and various open source deep learning frameworks. There will always be that Oracle bravado about its most successful commercial offerings, but I see a company that’s increasingly moving in step with a changing world of data platforms and technologies.

Big data scalability, hybrid cloud flexibility and smart “augmented” intelligence. These are the three plans that business intelligence and analytics vendor Qlik officially put on its roadmap at the May 15-18 Qonnections conference in Orlando, Florida.

Qlik also highlighted six important upgrades coming in the Qlik Sense June 2017 release – one of five annual updates now planned for the company’s flagship product (reflecting cloud-first pacing, though on-premises customers can choose whether and when to make the move). The June upgrade highlights include:

• Self-service data-prep capabilities
• New data visualizations and color-selection flexibility
• Qlik GeoAnalytics geospatial analyses added through the vendor’s January acquisition of Idevio
• An improved QlikSense Mobile app that supports offline analysis
• Support for advanced analytics capabilities based on R and Python
• Easier conversion of QlikView apps to Qlik Sense.

Most of these upgrades earned hearty applause from the more than 3,200 attendees at the Qonnections opening general session, but the sexiest and most visionary announcements were the ones on the roadmap. Here’s a rundown of what to expect, along with my take on what’s coming.

Building Toward BigData Analysis

Qlik’s key differentiator is its associative QIX data-analysis engine, which is at the heart of the company’s platform and is shared by its Qlik Sense and QlikView applications. QIX keeps the entire data set and rich detail visible even as you focus in on selected dimensions of data. If you select customers who are buying X product, for example, you’ll also see which customers are not buying that product. It’s an advantage over drill-down analysis where you filter out information as you explore

There have been limits, however, in how much data you can analyze within the 64-bit, in-memory QIX engine. Qlik has a workaround whereby you start with aggregated views of large data sets. Using an On-Demand App Generation capability you can then drill down to the detailed data in areas of interest. But the drawback of this approach is that you lose the powerful associative view of non-selected data.

The Associative Big Data Index approach announced at Qonnections will create index summaries of large data sets, drawn from sources such as Hadoop or high-scale distributed databases. A distributed version of the QIX engine will then enable users to explore the fine-grained detail within slices of the data without losing sight of the summary-level index of the entire data set.

MyPOV on Qlik big data capabilities: What I like about the Associative Big Data Index is that it will leave data in place, whether that’s in the cloud or in an on-premises big data source. It brings the query power to the data, eliminating time-consuming and costly data movement. The distributed architecture also promises performance. In a demo, Qlik demonstrated nearly instantaneous querying of a 4.5-terabyte data set. Granted, it was a controlled, prototype test, so we’ll have to wait and see about real-world performance.

Speaking of waiting, on big data, as on the hybrid cloud and augmented intelligence fronts, Qlik senior vice president and CTO Anthony Deighton set conservative expectations, telling customers they would see progress by next year’s Qonnections event. He didn’t rule out the possibility of an earlier release, but nor did he promise that any of the new capabilities would be generally available by next year’s event. As has been Qlik’s habit in recent years, it’s responding slowly to demands in emerging areas like big data and cloud.

Preparing for Hybrid Cloud

The business intelligence market has forced a binary, either-or, on-premises or cloud-based choice, said Deighton. He vowed that Qlik will change it to an and/or choice by fostering hybrid flexibility with the aid of microservices, APIs and containerized deployment. The approach will also require sophisticated, federated identity management, which the vendor has developed to support European GDPG data security and privacy compliance requirements set to go into effect next year.

In a prototype preview at Qonnections, Qlik demonstrated workloads being spawned and assigned automatically across Qlik nodes running on Amazon, in the Qlik Cloud and on-premises. The idea is to flexibly send workloads to the most appropriate resources. That could mean spawning public cloud instances on the fly when scale is required. Or it could mean keeping analyses on-premises when regulated data is involved. Qlik is working with big banks and hospitals, among other customers, to master microservices orchestration across on-premises, private-cloud and public-cloud instances. 

MyPOV On Qlik’s cloud plans: As noted above, Qlik made no promises as to when it will deliver on this flexible, cloud-friendly microservices vision, other than to say that we’ll hear more at Qonnections 2018. Qlik’s cloud offerings need these workload-management features, particularly where Qlik Sense Enterprise in the cloud is concerned. Customers want better performance as well as the granular services and APIs they’re used to from leading SaaS vendors. I believe it’s more important for Qlik to deliver quickly on this front than on any other, so let’s hope it’s something introduced before Qonnections 2018.

Augmenting Intelligence

There have been many announcements about “smart” capabilities this year. A few have of the capabilities have actually launched (like those detailed in my detailed reports on Salesforce Einstein and Oracle Adaptive Intelligent Apps), but most are works in progress. Some are conservatively described as automated predictive analytics or machine learning while others are billed as “artificial intelligence.”

Over the past year, Deighton and other Qlik executives have charged that competitive AI and cognitive offerings tend to remove humans from decision making. In keeping with this theme, the company announced that it’s working on “augmented intelligence” that will “combine the best” of what machines can do with human input and interaction. The approach will eschew automation in favor of machine-human interaction that will bring context to data and promote better-informed machine learning, said Deighton.

The general idea is for humans to interact with concise lists of computer-generated suggestions. This will happen through computer-augmented interfaces at various stages in the data-analysis lifecycle. When users bring data together, for example, data-analysis algorithms will be applied to suggest how the data might be correlated. In the analysis stage, algorithms will suggest the best analytical approaches. And once results are generated, data-visualizations algorithms will be applied to suggests best-fit visualizations. Humans will interact with the suggestions and make the final selections at every stage. Deighton promised something that will neither dump too many possibilities on users, at one extreme, nor create “trust gaps” by automating and remove human input from decisioning.

MyPOV on Qlik Augmented Intelligence: Based on conversations with Qlik executives, I’d say we’re in the early stages of Qlik’s augmented intelligence initiative. It all sounds good, but the details were sketchy. I heard a bit about analytic libraries and potential partnerships on the machine learning and neural net front. But executives weren’t ready to name partners or predict availability. In short, we may see the beginnings of Qlik’s augmented intelligence capabilities at Qonnections 2018, but Qlik execs were up front in describing the initiative as something that may take a few years to mature.

Qlik’s most direct competitors, including Tableau, Microsoft, SAP and IBM, are all working on smart data exploration, basic prediction and “smart” recommendation features of one stripe or another. IBM is actually on the second-generation of its cloud-based IBM Watson Analytics service. Yet we’re still in the very earliest phases of bringing advanced analytics, machine learning and artificial intelligence to the broad business intelligence market. I think 2017 may mark the end of the beginning. By 2018 and beyond, we’ll start to see vendor selections based on smart features rather than the maturing trend toward self-service capabilities.

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