The great reckoning begins. Correct/nailed. As predicted, since most of the bubble was tied up in private companies owned by private funds, the unwind would happen in slow motion. But it’s happening.
Silicon Valley cools off a bit. Partial. While IPOs were down, you couldn’t see the cooling in anecdotal data, like my favorite metric, traffic on highway101.
Porter’s five forces analysis makes a comeback. Partial. So-called “momentum investing” did cool off, implying more rational situation analysis, but you didn’t hear people talking about Porter per se.
Cyber-cash makes a rise. Correct. Bitcoin more doubled on the year (and Ethereum was up 8x) which perversely reinforced my view that these crypto-currencies are too volatile — people want the anonymity of cash without a highly variable exchange rate. The underlying technology for Bitcoin, blockchain, took off big time.
SAP realizes they are a complex enterprise application company. Incorrect. They’re still “running simple” and talking too much about enabling technology. The stock was up 9% on the year in line with revenues up around 8% thus far.
Oracle’s cloud strategy gets revealed – “we’ll sell you any deployment model you want as long as your annual bill goes up.” Partial. I should have said “we’ll sell you any deployment model you want as long as we can call it cloud to Wall St.”
Accounting irregularities discovered at one or more unicorns. Correct/nailed. During these bubbles the pattern always repeats itself – some people always start breaking the rules in order to stand out, get famous, or get rich. Fortune just ran an amazing story that talks about the “fake it till you make it” culture of some diseased startups.
Startup workers get disappointed on exits. Partial. I’m not aware of any lawsuits here but workers at many high flyers have been disappointed and there is a new awareness that the “unicorn party” may be a good thing for founders and VCs, but maybe not such a good thing for rank-and-file employees (and executive management).
The first cloud EPM S-1 gets filed. Incorrect. Not yet, at least. While it’s always possible someone did the private filing process with the SEC, I’m guessing that didn’t happen either.
2016 will be a great year for Host Analytics. Correct. We had a strong finish to the year and emerged stronger than we started with over 600 great customers, great partners, and a great team.
Now, let’s move on to my predictions for 2017 which – as a sign of the times – will include more macro and political content than usual.
Social media companies finally step up and do something about fake news. While per a former Facebook designer, “it turns out that bullshit is highly engaging,” these sites will need to do something to filter, rate, or classify fake news (let alone stopping to recommend it). Otherwise they will both lose credibility and readership – as well as fail to act in a responsible way commensurate with their information dissemination power.
Gut feel makes a comeback. After a decade of Google-inspired heavily data-driven and A/B-tested management, the new US administration will increasingly be less data-driven and more gut-feel-driven in making decisions. Riding against both common sense and the big data / analytics / data science trends, people will be increasingly skeptical of purely data-driven decisions and anti-data people will publicize data-driven failures to popularize their arguments. This “war on data” will build during the year, fueled by Trump, and some of it will spill over into business. Morale in the Intelligence Community will plummet.
Under a volatile leader, who seems to exhibit all nine of the symptoms of narcissistic personality disorder, we can expect sharp reactions and knee-jerk decisions that rattle markets, drive a high rate of staff turnover in the Executive branch, and fuel an ongoing war with the media. Whether you like his policies or not, Trump will bring a high level of volatility the country, to business, and to the markets.
With the new administration’s promises of $1T in infrastructure spending, you can expect interest rates to raise and inflation to accelerate. Providing such a stimulus to already strong economy might well overheat it. One smart move could be buying a house to lock in historic low interest rates for the next 30 years. (See my FAQ for disclaimers, including that I am not a financial advisor.)
Huge emphasis on security and privacy. Election-related hacking, including the spearfishing attack on John Podesta’s email, will serve as a major wake-up call to both government and the private sector to get their security act together. Leaks will fuel major concerns about privacy. Two-factor authentication using verification codes (e.g., Google Authenticator) will continue to take off as will encrypted communications. Fear of leaks will also change how people use email and other written electronic communications; more people will follow the sage advice in this quip:
Dance like no one’s watching; E-mail like it will be read in a deposition
In 2015, if you were flirting on Ashley Madison you were more likely talking to a fembot than a person. In 2016, the same could be said of troll bots. Bots are now capable of passing the Turing Test. In 2017, we will see more bots for both good uses (e.g., customer service) and bad (e.g., trolling social media). Left unchecked by the social media powerhouses, bots could damage social media usage.
Artificial intelligence hits the peak of inflated expectations. If you view Salesforce as the bellwether for hyped enterprise technology (e.g., cloud, social), then the next few years are going to be dominated by artificial intelligence. I’ve always believed that advanced analytics is not a standalone category, but instead fodder that vendors will build into smart applications. They key is typically not the technology, but the problem to which to apply it. As Infer founder Vik Singh said of Jim Gray, “he was really good at finding great problems,” the key is figuring out the best problems to solve with a given technology or modeling engine. Application by application we will see people searching for the best problems to solve using AI technology.
Megavendors mix up EPM and ERP or BI. Workday, which has had a confused history when it comes to planning, acquired struggling big data analytics vendor Platfora in July 2016, and seems to have combined analytics and EPM/planning into a single unit. This is a mistake for several reasons: (1) EPM and BI are sold to different buyers with different value propositions, (2) EPM is an applications sale, BI is a platform sale, and (3) Platfora’s technology stack, while appropriate for big data applications is not ideal for EPM/planning (ask Tidemark). Combining the two together puts planning at risk. Oracle combined their EPM and ERP go-to-market organizations and lost focus on EPM as a result. While they will argue that they now have more EPM feet on the street, those feet know much less about EPM, leaving them exposed to specialist vendors who maintain a focus on EPM. ERP is sold to the backward-looking part of finance; EPM is sold to the forward-looking part. EPM is about 1/10th the market size of ERP. ERP and EPM have different buyers and use different technologies. In combining them, expect EPM to lose out.
And, as usual, I must add the bonus prediction that 2017 proves to be a strong year for Host Analytics. We are entering the year with positive momentum, the category is strong, cloud adoption in finance continues to increase, and the megavendors generally lack sufficient focus on the category. We continue to be the most customer-focused vendor in EPM, our new Modeling product gained strong momentum in 2016, and our strategy has worked very well for both our company and the customers who have chosen to put their faith in us.
I thank our customers, our partners, and our team and wish everyone a great 2017.
This week Gartner research vice president John Van Decker and research director Chris Iervolino took the bold move of splitting the corporate performance management (CPM), also known as enterprise performance management (EPM), magic quadrant in two.
Instead of publishing a single magic quadrant (MQ) for all of CPM, they published two MQs, one for strategic CPM and one for financial CPM, which they define as follows:
Strategic Corporate Performance Management (SCPM) Solutions – this includes Corporate Planning and Modeling, Integrated Financial Planning, Strategy Management, Profitability Management, and Performance Reporting.
Financial Corporate Performance Management (FCPM) Solutions – this includes Financial Consolidation, Financial Reporting, Management Reporting/Costing/Forecasting, Reconciliations/Close Management, Intercompany Transactions, and Disclosure Management (including XBRL tagging)
It’s bold. It’s the first time to my recollection that an MQ has included product from different categories. Put differently, normally MQs are full of substitute products — e.g., 15 different types of butter. Here, we have butter next to olive oil on the same MQ.
It’s smart. Their uber point is that while CPM solutions are now pretty varied, that you can pretty easily classify them into more tactical/financial uses and more strategic uses. Highlighting this by splitting the MQs does customers a service because it reminds them to think both tactically and strategically. That’s important — and often needed in many finance departments who are struggling simply to keep up with the ongoing tactical workload.
It’s potentially confusing. You can find not just substitutes but complements on the same MQ. For example, Host Analytics and our partner Blackline are both on the FCPM MQ. That’s cool because we both serve core finance needs. It’s potentially confusing because we do one thing and they do another.
We are stoked. Among cloud pure-play EPM vendors, Host Analytics is the only supplier listed on both MQs. We believe this supports our contention that we have the broadest pure-play cloud EPM product line in the business. Only Host has both!
In a hype-filled world, I think Gartner does a great job of seeing through the hype-haze and focusing on customers and solutions. They do a better job than most at not being over-influenced by Halo Effects, and I suspect that’s because they spend a lot of time talking to real customers about solving real problems.
Thanks to the 700+ folks who attended my keynote address at last week’s Host Analytics World 2015 conference in San Francisco. We were thrilled with the event and thank everyone — customers, partners, and staff — who made it all possible.
Below is a 76-minute video of the keynote presentation I gave at the event. Enjoy! And please mark your calendars now for next year’s Host Analytics World — May 9 through May 12, 2016 — in San Francisco.
In this post, I’ll discuss why Modeling Cloud matters to customers, to the market, and to the company.
Why Modeling Cloud Matters for Customers
Ability to build non-financial models. Planning and budgeting tools are built for planning and budgeting. As such, you want them tied to the general ledger (GL) so, for example, you can easily get actual vs. plan for periodic reporting. But that requires a level of financial intelligence that can become cumbersome; in a typical planning system every line needs to tie to an account in the GL, be a debit/credit account type, be associated with a legal entity, and have an associated currency. That intelligence, which is so wonderful when making budgets, becomes baggage when you just want to build a model — for example, of bookings capacity given productivity and ramping assumptions, or new sales model given advertising spend, conversion, trial, and purchase rates. That’s why most models today are built in Excel and completely disconnected from the financial planning system.
Ability to integrate non-financial models. The problem with departmental Excel-based modeling is that everything ends up disconnected from the central financial planning. Consulting can tell you what happens to billings if you hire 5 more consultants in the East and sales can tell you what happens to bookings if you hire 6 more salesreps in the East, but you need to start mailing spreadsheets around if you want to see the financial outcomes (e.g., revenue, EPS) of such changes.
Enterprise-wide scenario analysis. The beauty of connecting departmental modeling to the corporate financial plan is that you can perform enterprise-wide sensitivity analysis. Say we’re thinking of making a big Eastern region push next year. When the models all tie to the financial plan, we can see the financial outcomes for the company associated with such a push, and what it means to setting expectations with board and Wall Street. This captures the real spirit of what is often called driver-based planning.
The bookings-to-revenue bridge. Models can help the finance team better forecast revenue because sales tends to be bookings-oriented whereas finance is revenue-oriented. Everyone knows that given a pipeline of 100 opportunities there can be scores of combinations where sales hits the bookings target, but each one produces different revenue depending on the composition of the orders. This is also, more subtly, true of sales expense because any given combination will consist of a given set of deals, for a given set of products, by a given set of saleseps, and each product may have different incentives on it, and each salesrep may be in a different stage of acceleration in their compensation plan. By modeling bookings and doing scenario analysis of various combinations of orders, finance can better predict revenue, expense, and ultimately EPS. In a world where a minuscule EPS miss can knock off 20% of a company’s valuation in a heartbeat, this is a critical capability.
Why Modeling Cloud Matters to the Market
Cloud penetration. EPM is under penetrated by the cloud, with cloud-penetration of less than 5% today. That means that 95% of all EPM systems sold in 2014 (between $3-4B worth) were on-premises. By comparison, sales force automation (SFA) is about 50% cloud-penetrated. While cloud-based planning and budgeting tools have existed for over 5 years, most cloud vendors are still working on completing their suites, with a handful introducing consolidation only in the past one to two years, and just two vendors offering a modeling engine in the cloud. While it’s not the only factor hindering cloud penetration, rounding out cloud EPM suites will definitely help accelerate moving EPM to the cloud.
Market penetration. Cloud aside, EPM is an under-penetrated market, overall. A recent survey by Grant Thornton, 40% of companies reported that they weren’t using any EPM system, relying only spreadsheets for FP&A work. This implies the $3-4B EPM market could nearly double simply by better penetrating target customers. And the best way to penetrate these companies is not by attacking Excel, but instead to bring an intelligent Excel strategy that makes it easy to import and build both budgets and models that are connected to the financial planning system.
Customer penetration. EPM is under-penetrated within EPM-consumer companies. Many EPM customers start with a dream of true enterprise-wide planning, but fallback to EPM deployment only within finance and rely on emailed spreadsheets for the “last mile.” That’s too bad because mailing spreadsheets is both insecure and error-prone. This situation develops often in on-premises EPM because the hassle of deploying the software across all potential users is simply too high and because the software itself is built for finance not end users. Cloud EPM — with cloud modeling — will help with improving customer penetration not only because it introduces new reporting and slicer/dicer options, but also because — in the case of our Modeling Cloud product — it introduces the new ability to build and manipulate sub-models which give end users the data they want — and only the data they want — without having to rely on IT for configuration.
Why Modeling Cloud Matters to Host Analytics
Unique position. With Modeling Cloud in the product line, Host Analytics now has the most comprehensive EPM suite in the cloud. If you look at our primary cloud competitors, one does low-end planning and budgeting, one does visualization and mobile, and the other does cloud modeling but has only both new and functionally thin applications for core finance.
The finance choice. Host Analytics has always been the finance department’s choice when it comes to core EPM (planning, budgeting, consolidation). That’s because experienced finance people understand the depth and breadth that we bring to the cloud and aren’t interested in buying either unproven solutions or solutions that they will outgrow.
The operations choice. With Modeling Cloud, Host Analytics is now also the operations choice. Be it sales ops, marketing ops, or services ops, Host Analytics allows ops departments to do the planning and modeling that they require — and to do so in a way that easily integrates with the core financial planning system. This gives them the best of both worlds — the ability to build any model they could build in Excel, using Excel formulas (and even using an Excel front-end if they so desire) and to do so in a way that automatically integrates with the core financial plan.
The best architecture. Only Host Analytics offers a true multi-dimensional (i.e., OLAP) backend and an architecture built atop cloud-native, dynamic, elastic, NoSQL technology where we deliver phenomenal multi-dimensional analysis and leverage modern/standard components for managing physical storage, sharding, and parallelism. This provides us with a huge advantage going forward both in terms of productivity and scaleability.
It’s been about 2.5 years since I joined Host Analytics and I’m quite proud of the work done by our entire R&D team in industrializing the core products, introducing a new layer of solutions, and now rolling out the industry’s most innovative cloud-based modeling engine.
The intent of each of these graphical devices is the same: to provide a simple picture that selects the top vendors in a category and positions them by (1) a rating on the quality of their strategy and (2) a rating on the quality of the execution of their strategy.
While the ratings are inherently subjective, each customers has his/her own unique requirements, and “your mileage may vary,” these matrices are useful tools in helping customers make IT supplier decisions.
To start with a brief word from our sponsor, I’m pleased to note that:
Host Analytics is the best-positioned cloud EPM vendor on Gartner’s magic quadrant for what they call CPM (corporate performance management.)
Host Analytics is the only cloud vendor in the leaders segment on Forrester’s wave for what they call FPM (financial performance management).
While the temptation is to immediately examine small positioning deltas of the charted vendors (as I just did above), I’d note that one of the best uses of these diagrams is to instead look at who’s not there. For example,
Anaplan is omitted from Gartner’s MQ, Forrester’s Wave, and Ovum’s DM. I believe this is because they come to market with a value proposition more around platform than app, and that most analysts and customers define EPM as an applications market. In plain English: there is a difference between saying “you can build an X using our stuff” and “we have built an X and can sell you one.”
Tidemark is present on Forrester’s wave, but omitted from both the Gartner MQ and the Ovum DM. I believe this is because of I what I’d characterize as “strategic schizophrenia” on Tidemark’s part with an initial message (back in the Proferi era) around EPM/GRC convergence, followed by an enterprise analytics message (e.g., infographics, visualization) with a strong dose of SoLoMo, which bowed to Sand Hill Road sexiness if not actual financial customer demand. Lost in the shuffle for many years was EPM (and along with it, much of their Workday partnership).
The only cloud vendors on the matrix are Host Analytics and Adaptive Insights (fka, Adaptive Planning).
Host Analytics is shown edging out Adaptive Insights on overall technology assessment.
Adaptive Insights is shown edging out Host Analytics on execution, which is quite ironic given that Adaptive recently ousted its CEO, something, shall we say, that typically doesn’t happen when execution is going well.
I’m Dave Kellogg, advisor, director, consultant, angel investor, and blogger focused on enterprise software startups. I am an executive-in-residence (EIR) at Balderton Capital and principal of my own eponymous consulting business.
I bring an uncommon perspective to startup challenges having 10 years’ experience at each of the CEO, CMO, and independent director levels across 10+ companies ranging in size from zero to over $1B in revenues.
From 2012 to 2018, I was CEO of cloud EPM vendor Host Analytics, where we quintupled ARR while halving customer acquisition costs in a competitive market, ultimately selling the company in a private equity transaction.
Previously, I was SVP/GM of the $500M Service Cloud business at Salesforce; CEO of NoSQL database provider MarkLogic, which we grew from zero to $80M over 6 years; and CMO at Business Objects for nearly a decade as we grew from $30M to over $1B in revenues. I started my career in technical and product marketing positions at Ingres and Versant.
I love disruption, startups, and Silicon Valley and have had the pleasure of working in varied capacities with companies including Bluecore, Cyral, FloQast, GainSight, MongoDB, Recorded Future, and Tableau.
I previously sat on the boards of Granular (agtech, acquired by DuPont), Aster Data (big data, acquired by Teradata), and Nuxeo (content services, acquired by Hyland), and Profisee (MDM, exited to Pamlico).
I periodically speak to strategy and entrepreneurship classes at the Haas School of Business (UC Berkeley) and Hautes Études Commerciales de Paris (HEC).