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.
Change is hard in business. A few things routinely get messed up:
Pulling the trigger. Think: “wait, are we still discussing this change or did we just decide to do it.” I can’t tell you the number of times I’ve heard that quote in meetings. I think continuous partial attention is part of the problem. Sometimes, it’s just straight-up confusion as the enthusiasm for a new idea ebbs and flows in a group conversation. It can be hard to tell if we’ve decided to change or if everyone’s just excited about the idea.
Next-level engagement. Think: “wait, I know we all like this idea on the exec staff, but this decision affects a lot of people at the next level. I need some time to bounce this off my leadership team and get their input before we go ready/fire/aim on this.”
Communications. Think: “wait, this change is a big deal and I know we just spent every minute of the three-hour meeting deciding to do it, but we need to find another hour to discuss key messaging (5W+2H) for both the internal and external audience.”
Anticipatory execution. Think: “While we had not yet finally approved the proposal for the new logo, it was doing very well in feedback and I just loathed the idea of making 5000 bags with the old logo on them, so I used the new one even though it wasn’t approved yet.”
When you screw up change a lot of bad things happen.
Employees get confused about the company’s strategy. “First they said, we were doing X, and then the execs did an about-face. I don’t understand.”
The external market, including your customers, get confused about what you are doing. This is even worse.
You can end up with 5,000 bags that have neither your old logo nor your new logo on them.
You can make your management team look like the Keystone Cops in one of many ways through screwing up sequencing: like dropping off boxes before the big move is announced, or employees finding out they’ve been laid off because their keycards stop working.
In order to avoid confusion about change and the mistakes that come with it, I’ve adopted a principle I call the “sailboat tack principle” which I use whenever we are contemplating major change. (We can define major as any change that if poorly executed will make the management team look like clowns to employees, customers, or other stakeholders.)
If you’ve ever gone sailing you may have noticed there is a strict protocol involved in a tack. When the skipper wants to execute a tack, he or she runs the following protocol.
Skipper: “Ready about”
Each crew member: “Ready”
Last crew member: “Ready”
Skipper: “Helm’s a lee.”
That is, the skipper does not actually begin the maneuver until every involved crew member has indicated they are ready. This prevents partial execution, people getting hit in the head with booms, and people getting knocked off the boat. It also implicitly makes clear when we are discussing a possible course change (e.g., “I think we should set course that direction”) from when we are actually doing it (e.g., “Ready about”).
For those with CS degrees, the sailboat tack principle is a two-phase commit protocol, used commonly in distributed transaction processing systems.
I like the sailboat tack protocol because the extra discipline causes a few things to happen automatically.
People know implicitly when we’re just talking about course changes. (Because no one is saying “OK, so do we want tack here?”)
People know explicitly when we are actually making the decision whether to execute change.
The result of that extra warning — “hey, we are about to do this” triggers numerous very healthy “wait a minute” reactions. Wait a minute: I need to ask my team, I need to make a communications plan, I need to examine the compensation impact, I need to think about what order we roll this out in, etc.
I’m Dave Kellogg, advisor, director, consultant, angel investor, and blogger focused on enterprise software startups.
I bring a unique 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 for $300M), Aster Data (big data, acquired by Teradata for $325M), and Nuxeo (content services, acquired by Hyland / Thoma Bravo).
I periodically speak to strategy and entrepreneurship classes at the Haas School of Business (UC Berkeley) and Hautes Études Commerciales de Paris (HEC).