tl;dr: in the world of 2017 where these practical BI issues still reign supreme, a practical Michael Saylor has shown up to preach on MicroStrategy’s capabilities. Both the stock market and MicroStrategy competitors should take notice.
On April 19th, MicroStrategy World 2017 had its executive keynote session in DC. I’ve attended MicroStrategy (NASDAQ:MSTR) World in the past as an industry analyst and was interested in seeing how the keynote would come across from afar as an Amalgam Insights (AI) Principal Investigator.
Price is the ultimate test of value. Amalgam cannot emphasis this enough. No matter how valuable you think your product or service is, the ultimate business test of that value is whether someone is willing to buy it at the listed price.
One of my favorite topics in enterprise software is pricing. Despite the work done in value-based pricing over the past 50 years, the vast majority of pricing exercises still start with either a very basic cost-plus or percentage-based ROI model. This assumption has a key issue: it assumes that your product is a commodity. To explain why and to explain how to take a more value-based approach, consider what a price is.
When I attended Hub17 in San Francisco, representing Amalgam Insights (AI), I was looking forward to seeing how Anaplan’s go-to-market approach had changed, kept an eye out for key announcements, and looked for clues from the executive team on where Anaplan was heading next. In the process, AI also got some unexpected highlights and guidance on the future of the company.
Anaplan caught AI’s attention a number of years ago when it officially launched the Hyperblock, originally built by Michael Gould, to provide a combination of cube, cell-based, and columnar database architectures. This approach provided a foundational technology that was well-suited to massive and enterprise-scaled models. Once this technology was combined with a go-to-market productization that allowed business users to access the planning and modeling aspects of Anaplan in 2013, Anaplan became a strong solution in the enterprise planning market.
But between the hype, the party, the music, the free-flowing drinks, and the bright lights, Domo also has an excited customer base that was hungry for product announcements and gave strong feedback to new Domo features.
And there were some significant announcements, such as:
Domo’s planned “Mr. Roboto,” to use predictive analytics and machine language to support both an Alert Center for anomaly detection as well as a data science capability that currently looks like a predictive analytics and algorithm toolkit to support business performance challenges.
Domo Business-in-a-Box, a set of pre-built dashboards created to support major business departments, functions, and use cases across the entire organization. AI believes these dashboards will provide a shortcut for enterprises to quickly translate enterprise data into relevant and contextualized departmental insights.
Domo Everywhere, which serves as Domo’s foray into embedded BI with White Label, Embed, and Publish options. AI believes that this capability is important in providing ubiquitous analytics and to allow end users to take advantage of business insights without having to always go back to any specific platform or software solution.
As well as feature improvements such as increased chart options, time-series and period based views, data slicing, and the industry pundits’ favorite: Domo Data Lineage, which got a fair amount of attention in its ability to track data sources, actions, quality, and timeliness. Although Domo is portraying Data Lineage as a feature enhancement for Domo Analyzer, AI believes that Domo will be pleasantly surprised at the enterprise need and interest for Data Lineage, as data governance and data trust have been increasingly trendy concerns for enterprise analytics.
In speaking with Domo executives, salespeople, and customers, AI also started to see a consistent playbook emerge around Domo that demonstrated how, beyond the hype, the platform started to work as a business insight platform compared to other cloud BI or traditional BI products. Behind the hype, here is what actually seems to be happening for Domo at a high level to gain enterprise adoption.
1) Domo speaks to an executive or key business manager who is stuck with some manual process that requires excessive spreadsheet or Microsoft Access usage. These use cases tend to be focused on marketing, sales, operations, or finance use cases that align with current trends in enterprise performance management
2) Domo is initially implemented through self-service capabilities by line of business decision makers who are able to integrate data with little to no IT support. Once Domo conducts deeper due diligence on the enterprise-wide need for analytics, an analytics or IT management takes the lead within the organization to connect Domo with data from the rest of the company.
3) Domo product deployment and implementation is generally accepted by customers to be simpler than traditional performance management systems such as Hyperion or Cognos as well as simpler than other traditional BI systems.
4) Once Domo is in place, the executive stakeholder and IT manager work together in bringing all relevant departmental data into Domo by hunting down the spreadsheets and local dark data that have traditionally driven the manual process.
5) After this initial implementation and win, Domo gets additional attention internally based on the ease of creating report, the efficacy that these departments see in supporting analytic insights, and the usage rates associated with Domo
This roadmap may not sound like rocket science, but the devil has always been in the details. By connecting the dots between executives, IT, implementation roadblocks, data ingestion, and employee utilization rates, Domo has quickly grown to a $120 million+ annual run rate over the past several years.
AI Observations on the State of Domo
AI notes that Domo has some very specific strengths as a business-oriented insight solution. Its DNA makes it very focused on user interaction, collaboration, and graphic design which results in a front-end product that can be extremely engaging compared to other perceived competitors in the cloud BI space such as Birst, GoodData, and Looker as well as data discovery competitors such as
Qlik and Tableau. One of the most clever things Domo has done is to create “Cards” to display specific data, where each card shows how often the data is being accessed and provides guidance on whether end users are using the data that they should be aware of. Domo’s App Design Studio also can publish with Adobe Illustrator, which provides massive graphic advantages over a variety of other analytic app studios. (And was highlighted on the keynote stage in showing an application built by GE Digital’s Kim Schuhman.)
However, Domo has also invested mightily in its own back end technologies as well, including a high performance massively parallel processing columnar database, data warehousing, and 450+ native integrations. AI wonders if Domo needs to continue investing in all of these areas on an ongoing basis or whether it would be more fruitful for Domo to create high-value named partnerships, such as Tableau has created with Informatica or GoodData has created with HP Vertica, to solve some of the back-end and integration challenges. At the end of the day, AI is impressed with Domo’s focus on data collection, process improvement, and user engagement areas where they are truly excellent.
That aside, Domo has built a full-fledged business intelligence platform with a strong focus on supporting usability and adoption. With a loyal customer base, a user experience that seems popular both with end users and with report builders, and an aggressive product roadmap to accelerate time-to-value and integrate machine learning into the platform, AI believes that Domo is well positioned to continue competing in the business intelligence and analytics markets by combining analytic consumption, business process alignment, data aggregation and data integration.
Amalgam Insights (AI) recently attended IBM Interconnect under the Social Influencer program with the goal of understanding how IBM is planning to position itself in context of technology market changes, investor demands to increase revenue, and the challenges of embracing innovation as one of the largest enterprises on the planet.
In observing IBM over the past few years, AI investigators have noted in the past that IBM faces the challenge of needing to create billion-dollar businesses just to maintain existing revenue. It is not enough for IBM to create a single startup such as Pivotal or Airwatch that ends up becoming a market leader in analytic application development or enterprise mobility. To drive 80 billion+ dollars in annual revenue, IBM needs to grow enough businesses to maintain pace while simultaneously divesting cash cows and declining margin businesses that are not strategic to future growth. Over the past couple of years, this has meant selling off assets such as Salary.com and semiconductor chip manufacturing (and possibly its mainframe division) while investing deeply into systems and capabilities that will drive upcoming business capabilities.
Yesterday, at the Boston Cloud Services Meetup at the Cambridge IBM Innovation Center, Amalgam Insights (AI) attended a Cloudyn-based event on “Overcoming the Challenges of Multi-Cloud Financial Management.” This presentation was headed by Account Executive Marcus Benson and focused on the challenges that Fortune 500 companies and managed service providers have in managing both complex single-vendor and multi-vendor cloud infrastructure environments.
Cloudyn is a cloud business and financial management solution founded in 2011 and set up as both a multi-tenant and multi-cloud solution running on AWS, Microsoft Azure and Google Cloud. Cloudyn supports a single pane of glass view for consolidated management and a real-time and continuous support of cost optimization for multiple vendors including Amazon Web Services, Microsoft Azure, Google Cloud, OpenStack, and Docker. Cloudyn has raised over $20 million in venture capital and seed funding and currently targets large enterprises, managed service providers, and companies with over 1 million dollars in annual cloud spend. Continue reading AI Vendor Profile: Cloudyn, Cloud Cost Management
Accounting has often been called the language of business and it is invaluable in managing the day-to-day financial costs, inputs, outputs, and outcomes associated with business activity. However, as companies start to understand the impact that non-financial drivers ranging from manufacturing outputs to headcount to service transactions to asset utilization rates affect the health of the business, executives have had to broaden the scope of considerations needed to track the health of the company.
As they have done so, businesses have had to shift even their financial departments to focus not just on dollars and cents, but to production units, employees, transactions, uptime, turnover, and loyalty. In doing so, the language of business has started to shift from accounting to a new paradigm of data.
On Friday, March 31st, Cloudera filed its S-1 with intention to IPO. The timing looks good considering the recent successful IPOs of Alteryx, Mulesoft, and Snap. But how does Cloudera actually match up with other tech companies in terms of being successful in the short and medium term?
Cloudera’s S-1 filing starts by describing the near-term growth potential of the Internet of Things and IDC’s estimate of 30 billion internet-connected mobile devices in 2020. Every analyst and consulting firm has some idea of whether this is going to be 20 billion, 30 billion, or 40 billion, but the most important aspects of this growth are that: Continue reading With Cloudera’s S-1, Hadoop and Big Data Finally Come of Age