Category Archives: Data Science

Appearance on Data Radicals: Frameworks and the Art of Simplification

This is a quick post to highlight my recent appearance on the Data Radicals podcast (Apple, Spotify), hosted by Alation founder and CEO, Satyen Sangani. I’ve worked with Alation for a long time in varied capacities — e.g., as an angel investor, advisor, director, interim executive, skit writer, and probably a few other ways I can’t remember. This is a company I know well. They’re in a space I’m passionate about — and one that I might argue is a logical second generation of the semantic-layer-based BI market where I spent nearly ten years as CMO of Business Objects.

Satyen is a founder for whom I have a ton of respect, not only because of what he’s created, but because of the emphasis on culture and values reflected in how did it. Satyen also appreciates a good intellectual sparring match when making big decisions — something many founders pretend to enjoy, few actually do, and fewer still seek out.

This is an episode like no other I’ve done because of that history and because of the selection of topics that Satyen chose to cover as a result. This is not your standard Kellblog “do CAC on a cash basis,” “use pipeline expected value as a triangulation forecast,” or “align marketing with sales” podcast episode. Make no mistake, I love those too — but this is just noteably different content from most of my other appearances.

Here, we talk about:

  • The history and evolution of the database and tools market
  • The modern data stack
  • Intelligent operational applications vs. analytic applications
  • Why I feel that data can often end up an abstraction contest (and what to do about that)
  • Why I think in confusing makets that the best mapmaker wins
  • Who benefits from confusion in markets — and who doesn’t
  • Frameworks, simplification, and reductionism
  • Strategy and distilling the essence of a problem
  • Layering marketing messaging using ternary trees
  • The people who most influenced my thinking and career
  • The evolution of the data intelligence category and its roots in data governance and data catalogs
  • How tech markets are like boxing matches — you win a round and your prize is to earn the chance to fight in the next one
  • Data culture as an ultimate benefit and data intelligence as a software category

I hope you can listen to the episode, also available on Apple podcasts and Spotify. Thanks to Satyen for having me and I wish Alation continuing fair winds and following seas.

Joining the Profisee Board of Directors

We’re announcing today that I’m joining the board of directors of Profisee, a leader in master data management (MDM).  I’m doing so for several reasons, mostly reflecting my belief that successful technology companies are about three things:  the people, the space, and the product.

I like the people at both an investor and management level.  I’m old friends with a partner at ParkerGale, the private equity (PE) firm backing Profisee, and I quite like the people at ParkerGale, the culture they’ve created, their approach to working with companies, and of course the lead partner on Profisee, Kristina Heinze.

The management team, led by veteran CEO and SAP alumnus Len Finkle, is stocked with domain experts from larger companies including SAP, Oracle, Hyperion, and Informatica.  What’s more, Gartner VP and analyst Bill O’Kane recently joined the company.  Bill covered the space at Gartner for over 8 years and has personally led MDM initiatives at companies including MetLife, CA Technologies, Merrill Lynch, and Morgan Stanley.  It’s hard to read Bill’s decision to join the team as anything but a big endorsement of the company, its leadership, and its strategy.

These people are the experts.  And instead of working at a company where MDM is an element of an element of a suite that no one really cares about anymore, they are working at a focused market leader that worries about MDM — and only MDM – all day, every day.  Such focus is powerful.

I like the MDM space for several reasons:

  • It’s a little obscure. Many people can’t remember if MDM stands for metadata management or master data management (it’s the latter).  It’s under-penetrated; relatively few companies who can benefit from MDM use it.  Historically the market has been driven by “reluctant spend” to comply with regulatory requirements.  Megavendors don’t seem to care much about MDM anymore, with IBM losing market share and Oracle effectively exiting the market.  It’s the perfect place for a focused specialist to build a team of people who are passionate about the space and build a market-leading company.
  • It’s substantial. It’s a $1B market today growing at 5%.  You can build a nice company stealing share if you need to, but I think there’s an even bigger opportunity.
  • It’s teed up to grow. On the operational side, I think that single source of truth, digital transformation, and compliance initiatives will drive the market.  On the analytical side, if there’s one thing 20+ years in and around business intelligence (BI) has taught me, it’s GIGO (garbage in, garbage out).  If you think the GIGO rule was important in traditional BI, I’d argue it’s about ten times more important in an artificial intelligence and machine learning (AI/ML) world.  Garbage data in, garbage model and garbage predictions out.  Data quality is the Achilles’ heel of modern analytics.

I like Profisee’s product because:

  • It’s delivering well for today’s customers.
  • It has the breadth to cover a wide swath of MDM domains and use-cases.
  • It provides a scalable platform with a broad range of MDM-related functionality, as opposed to a patchwork solution set built through acquisition.
  • It’s easy to use and makes solving complex problems simple.
  • It’s designed for rapid implementation, so it’s less costly to implement and faster to get in production which is great for both committed MDM users and — particularly important in an under-penetrated market – those wanting to give MDM a try.

I look forward to working with Len, Kristina, and the team to help take Profisee to the next level, and beyond.

Now, before signing off, let me comment on how I see Profisee relative to my existing board seat at Alation.  Alation defined the catalog space, has an impressive list of enterprise customers, raised a $50M round earlier this year, and has generally been killing it.  If you don’t know the data space well you might see these companies as competitive; in reality, they are complementary and I think it’s synergistic for me to work with both.

  • Data catalogs help you locate data and understand the overall data set. For example, with a data catalog you can find all of the systems and data sets where you have customer data across operational applications (e.g., CRM, ERP, FP&A) and analytical systems (e.g., data warehouses, data lakes).
  • MDM helps you rationalize the data across your operational and analytical systems.  At its core, MDM solves the problem of IBM being entered in your company’s CRM system as “Intl Business Machines,” in your ERP system as “International Business Machines,” and in your planning system as “IBM Corp,” to give a simple example.  Among other approaches, MDM introduces the concept of a golden record which provides a single source of truth of how, in this example, the customer should be named.

In short, data catalogs help you find the right data and MDM ensures the data is clean when you find it.  You pretty obviously need both.

My Appearance on DisrupTV Episode 100

Last week I sat down with interviewers Doug Henschen, Vala Afshar, and a bit of Ray Wang (live from a 777 taxiing en route to Tokyo) to participate in Episode 100 of DisrupTV along with fellow guests DataStax CEO Billy Bosworth and big data / science recruiter Virginia Backaitis.

We covered a full gamut of topics, including:

  • The impact of artificial intelligence (AI) and machine learning (ML) on the enterprise performance management (EPM) market.
  • Why I joined Host Analytics some 5 years ago.
  • What it’s like competing with Oracle … for basically your entire career.
  • What it’s like selling enterprise software both upwind and downwind.
  • How I ended up on the board of Alation and what I like about data catalogs.
  • What I learned working at Salesforce (hint:  shoshin)
  • Other lessons from BusinessObjects, MarkLogic, and even Ingres.

DisrupTV Episode 100, Featuring Dave Kellogg, Billy Bosworth, Virginia Backaitis from Constellation Research on Vimeo.

 

Kellblog’s 2017 Predictions  

New Year’s means three things in my world:  (1) time to thank our customers and team at Host Analytics for another great year, (2) time to finish up all the 2017 planning items and approvals that we need to get done before the sales kickoff (including the one most important thing to do before kickoff), and time to make some predictions for the coming year.

Before looking at 2017, let’s see how I did with my 2016 predictions.

2016 Predictions Review

  1. 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.
  2. 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.
  3. 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.
  4. Cyber-cash makes a rise. CorrectBitcoin 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.
  5. Internet of Things goes into trough of disillusionment. Partial.  I think I may have been a little early on this one.  Seems like it’s still hovering at the peak of inflated expectations.
  6. Data science rises as profession. Correct/easy.  This continues inexorably.
  7. 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.
  8. 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.”
  9. 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.
  10. 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).
  11. 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.
  12. 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.

  1. The United States will see a level of divisiveness and social discord not seen since the 1960s. Social media echo chambers will reinforce divisions.  To combat this, I encourage everyone to sign up for two publications/blogs they agree with and two they don’t lest they never again hear both sides of an issue. (See map below, coutesy of Ninja Economics, for help in choosing.)  On an optimistic note, per UCSD professor Lane Kenworthy people aren’t getting more polarized, political parties are.

news

  1. 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.
  1. 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.
  1. 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.
  1. 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.)
  1. 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

  1. 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.
  1. 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.
  1. The IPO market comes back. After a year in which we saw only 13 VC-backed technology IPOs, I believe the window will open and 2017 will be a strong year for technology IPOs.  The usual big-name suspects include firms like Snap, Uber, AirBnB, and SpotifyCB Insights has identified 369 companies as strong 2017 IPO prospects.
  1. 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.

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