Category Archives: Analytics

In-Memory Analytics: The Other Kind – A Key Success Factor for Your Career

I’m not going to talk about columnar databases, compression, horizontal partitioning, SAP Hana, or real-time vs. pre-aggregated summarization in this post on in-memory analytics.  I’m going to talk about the other kind of in-memory analytics.  The kind that can make or break your career.

What do you mean, the other kind of in-memory analytics?  Quite simply, the kind you keep in your head (i.e., in human memory).  Or, better put, the kind you should be expected to keep in your head and be able to recite on demand in any business meeting.

I remember when I worked at Salesforce, I covered for my boss a few times at the executive staff meeting when he was traveling or such.  He told me:  “Marc expects everyone to know the numbers, so before you go in there, make sure you know them.”  And I did.  On the few times I attended in his place, I made a cheat sheet and studied it for an hour to ensure that I knew every possible number that could reasonably be asked.  I’d sit in the meeting, saying little, and listening to discussion not directly related to our area.  Then, boom, out of left field, Marc asked:  “what is the Service Cloud pipeline coverage ratio for this quarter in Europe?”

“3.4,” I replied succinctly.  If I hadn’t have known the number I’m sure it would been an exercise in plucking the wings off a butterfly.  But I did, so the conversation quickly shifted to another topic, and I lived to fight another day.

Frankly, I was happy to work in an organization where executives were expected to know — in their heads, in an instant — the values of the key metrics that drive their business.  I weak organizations you constantly hear “can I get back to you on that” or “I’m going to need to look that one up.”

If you want to run a business, or a piece of one,  and you want to be a credible leader — especially in a metrics-driven organization — you need to have “in-memory” the key metrics that your higher-ups and peers would expect you to know.

This is as true of CEO pitching a venture capitalist and being asked about CAC ratios and churn rates as it is of a marketing VP being asked about keywords, costs, and conversions in an online advertising program.  Or a sales manager being asked about their forecast.

In fact, as I’ve told my sales directors a time or two:  “I should be able to wake you up at 3:00 AM and ask your forecast, upside, and pipeline and you should be able to answer, right then, instantly.”

That’s an in-memory metric.  No “let me check on that.”  No “I’ll get back to you.”  No “I don’t know, let me ask my ops guy,” which always makes me think: who runs the department, you or the ops guy — and if you need to ask the ops guy all the numbers maybe he/she should be running the department and not you?

I have bolded the word “expect” four times above because this issue is indeed about expectations and expectations are not a precise science.  So, how can you figure out the expectations for which analytics you should hold in-memory?

  • Look at your department’s strategic goals and determine which metrics best measure progress on them.
  • Ask peers inside the company what key metrics they keep in-memory and design your set by analogy.
  • Ask peers who perform the same job at different companies what key metrics they track.
  • When in doubt, ask the boss or the higher-ups what metrics they expect you to know.

Finally, I should note that I’m not a big believer in the whole “cheat sheet” approach I described above.  Because that was a special situation (covering for the boss), I think the cheat sheet was smart, but the real way to burn these metrics into your memory is to track them every week at your staff meeting, watching how they change week by week and constantly comparing them to prior periods and to a plan/model if you have one.

The point here is not “fake it until you make it” by running your business in a non-metrics-focused way and memorizing figures before a big meeting, but instead to burn the metrics review into your own weekly team meeting and then, naturally, over time you will know these metrics so instinctively that someone can wake you up at 3:00 AM and you can recite them.

That’s the other kind of in-memory analytics.  And, much as I love technology, the more important kind for your career.

SAP Cloud for Analytics: Tilting at Windmills

Back in the early 2000s when I was running marketing at Business Objects, Gartner’s then-lead BI analyst, Howard Dresner (known as the father of BI and the person who named the category) started pushing a notion called enterprise performance management (EPM).  Back then, EPM meant the unification of BI and planning/budgeting.

The argument in favor of EPM made sense and was actually kind of cool:  with BI you could ask any question, but BI never knew the correct answer.  What did that mean?

It meant that BI tools were primarily tied to operational systems and could tell you the value of sales/salesrep for any quarter in any region.  The problem was that BI didn’t know what the answer was supposed to be.  BI knew the cost of everything and the value of nothing.

The solution was tie to BI to financial systems, which were full of targets and thus could allow us not just to know the value of any given metric, but what the value of that metric should be.

It sounded great and I bought in.  More importantly, so did the category:

Then what happened?  In my opinion, pretty much nothing.  Sure Hyperion reps could increase deal sizes by trying to drop Brio licenses across the whole financial department, as opposed to just FP&A.  Cognos could cross-sell Adaytum, with the help of an overlay sales force.

But did integration happen?  No.  BI and financial planning/budgeting  consolidated, but they never converged.  This is interesting because it’s rare.  For example, by contrast, CRM really happened.  SFA vendors didn’t just acquire customer service vendors and marketing vendors — the three applications came together to create one category.

That didn’t happen with EPM.  You could always ask someone who worked at Hyperion my favorite question, “which side did you work on?” and you always heard either, “oh, the BI side,” or “oh, the finance side.”  You never, ever got asked to clarify the question.

Over time, EPM came to mean financial planning, budgeting, and consolidation (along with associated reporting/analytics) — and not the unification of BI and financial planning.

What did this prove?   You can put the two categories under one roof via consolidation, but the actual markets are oil-and-water and don’t mix together well.  Why?  Two reasons:

  • BI is a platform sale, EPM is an applications sale
  • BI is sold to IT, EPM is sold to the finance department

So other than selling to a completely different buyer with a completely different value proposition, they make excellent candidates for integration!  Put concretely, if you can’t talk about inter-company eliminations, AVB reports, AOPs, topside journal entries, long-range models, FX rate handling, and legal entities, then you can’t even start to sell EPM.  I marketed BI for 9 years and we talked a totally different language:  aggregate awareness, multi-pass SQL, slow-changing dimensions, and star schemas.  The two languages are not totally unrelated.  They are nevertheless different.

Despite this history, many vendors still seem hell bent on mixing EPM water with BI oil.  One cloud EPM vendor positioned themselves for years as a leader in “BI and CPM” somehow thinking the rock-bottom acquisition of a cheap scorecarding tool made them a player in the $15B BI market.

To be clear, I view EPM and BI as cousins.  Yes, in EPM we make scorecards, dashboards, and reports.  Yes, in EPM we do multi-dimensional modeling and analysis.  No doubt.  But we do it for finance departments, we tie our planning/budgeting systems to the general ledger and we are focused on both financial outcomes and financial reports.  Yes, we also care about integrating models across the organization — sales, marketing, services, and operations.  But we are not trying to sell generic infrastructure for making reports and visualizations across the enterprise.

Put simply, in EPM we use BI technologies to build financial applications that tie together the enterprise on behalf of the finance department.

Surprisingly, SAP didn’t get the consolidation-not-convergence memo.  This is somewhat amazing given that SAP is a strong player in both BI and EPM, but somehow hasn’t seemed to notice not only that the two markets never converged but also that there is a very good reason for that.  They are still tilting at windmills fighting to integrate two categories not destined for integration with a vintage-2002 message.

Here’s the press release:

SAP Redefines Analytics in the Cloud

WALLDORF — SAP SE (NYSE: SAP) today unveils the SAP Cloud for Analytics solution, a planned software as a service (SaaS) offering that aims to bring all analytics capabilities into one solution for an unparalleled user experience (UX).

Built natively on SAP HANA Cloud Platform, this high-performing, real-time solution plans to be embedded with existing SAP solutions and intends to connect to cloud and on-premise data to deliver planning, predictive and business intelligence (BI) capabilities in one analytics experience. The intent is for organizations to use this one solution to enable their employees to track performance, analyze trends, predict and collaborate to make informed decisions and improve business outcomes.

Note, that in addition to my strategic concerns, I have a few tactical ones as well:

  • This is a futures announcement without a date.  The service “planned.”  The “planned benefits” are stated.  The only thing I can’t find in the plan is an availability date.
  • Pricing hasn’t been announced either.  So other than knowing what it costs and when it will be available, it was an informative announcement.
  • While SAP is claiming that it’s previously announced SAP Cloud for Planning is included in the new offering, I have heard rumors on the street that SAP Cloud for Planning is actually being discontinued and customers will be moved to the new offering.  At this point, I’m not sure which is the case.

In the end, I’m not trying to beat on SAP in general.  I don’t love the Hana branding strategy, that’s true, but Hana itself (i.e., columnar, in-memory database) is a good idea.  I have no problems with SAP BI’s products — heck, my fingerprints still remain lightly on a few of them.  In EPM, we compete with SAP, so my agenda there is obvious.

But the thing I object to, the tilting at windmills, is that they are still banging the unify EPM and BI drum.  SAP’s new analytics may eventually end up a reasonable or good BI solution.  But if they’re betting serious chips on unifying BI and EPM it’s misguided.

Kellblog Ten Predictions for 2015

As we move into the third week of January, I figured it was “now or never” in terms of getting a set of predictions out for 2015.  Before jumping into that, let’s take a quick review of how I did with my 2014 predictions and do some self-grading.

  1. 2014 to be a good year in Silicon Valley.  Correct.
  2. Cloud computing will continue to explode.  Correct.
  3. Big data hype will peak. Gartner seems to agree, placing it in August midway past peak on the way to trough of disillusionment. Correct.
  4. The market will be unable to supply enough data science talent. Mashable is now calling data scientist 2015’s hottest professionPer McKinsey, this is a problem that’s going to continue for the next several years. Correct.
  5. Privacy will remain center stage.  Correct.
  6. Mobile will continue to drive both consumer and (select) enterprise. I got the spirit correct on this one, but I think the core problem is probably better thought of as multi-device access to cloud data than mobile per se.  That is, it’s not about using Evernote on my phone, but instead about uniform access to my cloud-based notes from all my mobile (and non-mobile) devices. Basically, correct.
  7. Social becomes a feature, not an app. Correct again.  The struggles of companies like Jive only validate that (enterprise) social should be a feature of virtually all apps, and not a category unto itself.
  8. SAP’s HANA strategy actually works. Well SAP didn’t seem to agree with this one, when Hasso Plattner wrote a post blasting customers for not understanding its business benefits.  But my angle was more – the merits of the strategy aside – when a company the size of SAP shows total commitment to a strategy it’s going to get results.  And it has.  And SAP continues to drive it.  Mostly correct.
  9. Good Data goes public. While this didn’t happen, I continue to believe that Good Data has a smart strategy and a solid product.  They raised $25M in September.  Maybe this year they will make me an honest man.
  10. Adaptive Planning (now, Adaptive Insights) gets acquired by NetSuite. This didn’t happen, either.  The prediction was based on the fairly well known play of OEM-ing something before acquiring it.  Time may well prove me right on this one, but a swing-and-a-miss for 2014.

Our “bonus” prediction last year was that my company, Host Analytics, would have a great year and indeed we did.  We grew new subscriptions well in excess of 100%, making us, I believe, the fastest growing company in the category.  We launched a new sales planning solution as part of our vision to unite financial and operational planning.  We hired scores of great new people to join us on our mission to create a great EPM company, one that transforms how enterprises manage their financial performance.  And we raised $25M in venture capital to boot.

So, all in all, for the 2014 predictions, let’s call it 8.5 out of 11.

Here are my predictions for 2015.

  1. The good times continue to roll in Silicon Valley. If you feel “bubble,” remember that unlike in the dot-com days that most companies experiencing great success today have real, often recurring, revenue and real customers.   From a cycle perspective, to the extent there is a bubble coming, I’d say we’re in 1999 not 2001.
  1. The IPO as a down-round trend continues. One of the odder things about this time period is that I’m repeatedly hearing that successful IPO companies are pricing at down-rounds relative to their last private financings.  This doesn’t spell danger in general – because the public market valuations are both healthy and supportable – it just suggests a highly competitive later-stage private financing market is overbidding prices.  I suspect that will calm down in 2015 but down-round IPOs will continue in 2015.
  1. The curse of the megaround will strike many companies and CEOs. As part of the prior bullet companies are now often able to raise unprecedented amounts of capital at high valuations.  While those companies today may celebrate their $100M, $150M or $200M+ financing rounds, tomorrow they will wake up with a hangover that looks like:  huge pressure to invest that money for growth, even in dubious growth opportunities; anxious board members who need a 3x return in three years atop already stratospheric valuations; companies missing plan when the dubious growth opportunities don’t deliver; and CEOs who get replaced for missing plans that were unrealistic in the first place.  Before you take a megaround, be careful what you wish for — you sometimes get it.
  1. Cloud disruption continues. Megavendors will continue to wrestle cloud disruption and their cloud strategies.    They will continue to talk about success and high growth in the 10% or less of their business that is cloud, while asking investors to ignore the lack of health in the 90% that is non-cloud.  As part of a general Innovator’s Dilemma problem, they will be forced to explain and defend cloud strategies that will hopefully help them long term but depress results in the short term (as SAP had to do last week.)
  1. Privacy becomes a huge issue. People who were once too busy to care when Facebook changed their security setting are now asking who can access what and how.  The Internet of Things will only exacerbate this focus as more data than ever will be available.  In the past, you could see my pictures and status updates.  Now you can know where I am, when, how many hours I sleep at night, when I exercised, what temperature I set my thermostat to, and when I’m home.  The more data that becomes available, and the more readily you can be de-anonymized, the more you will start monitoring your privacy settings and previously unread site terms and conditions.
  1. Next-generation apps continue to explode. Apps like Slack and Zenefits will continue to redefine enterprise software.  While Slack is a technology, design, and integration play in the collaboration space, Zenefits is more of a business-model disruption play (i.e., give us the rather large commissions you rather invisibly paid your health insurance broker and we’ll give you free, high-quality HR software).  Either way, consumerization, design, and the search for new business models / revenue opportunities will continue.
  1. IBM software rebounds. IBM used to be a stronger player in software than it is today (e.g., recall that they invented the relational database). Watson aside, things have been pretty quiet on the IBM software front. Cloud-wise, while they claim to have a $7B business, it’s pretty invisible to me, and it does seem that Amazon has beaten them in low-level categories like IaaS.  While I’m not sure what happened – I don’t track them that closely – they do seem to have just faded away.  Once thing’s for sure – it can’t continue.  While there are contradicting stories in recent press, IBM does appear to be in the midst of a large re-organization, and I’m going to bet that, as a result, they come to market with a stronger software and cloud story.
  1. Angel investing slows. Much has been written about the financing chokepoint where tens of thousands of angels are funding companies that then need to get in line to get funded by the approximately 100 or so VCs who do A rounds.  The first-order result is that many companies think “wow this is easy” on raising a angel round only to die 12-18 month later when they fail to raise VC.  The second-order result, which I think will start kicking in this year, is that angel money will be harder to come by as the system corrects back to a balanced state.
  1. The data scientist shortage continues. With more “big data” and a huge supply of analytic tools and computing power, the limiting factor on analysis-driven business is neither data nor technology.  It’s our ability to find people who can correctly leverage it.  Tell every college kid you know to take lots of stats, analytics, and computing classes.  Or better yet, to go get a degree in data science.
  1. The unification of planning becomes the top meme in enterprise performance management (EPM). EPM has a long history of helping finance departments prepare annual operating budgets and financial reports, but increasingly—in recent years – planning has quietly decentralized to the various departments and divisions within the enterprise.  For example, sales ops increasingly builds the sales plan, marketing ops the marketing plan, and services ops the consulting and professional services plan.   (This is why I sometimes call this trend the “rise of the ops person” as they are increasingly acting as stealth FP&A.)  What’s needed is to unite all these plans and put them on a common planning framework so the CFO and CEO can do what-if analysis and scenario planning holistically across the organization.

My Keynote Speech from Host Analytics World 2013

I am happy to say that we had a simply tremendous Host Analytics World 2013 conference last week in Las Vegas, Nevada with over 300 people in attendance.

First, let me thank people for making the event such a success:

  • Our marketing team for executing it
  • Our customers for attending and presenting
  • Our partners for attending, presenting, exhibiting, and sponsoring
  • Our employees for attending, hosting, staffing, and presenting

Second, let me introduce one of the cooler, more unusual things we did at this year’s conference.  We had a graphic recorder from @the_ink_factory create, by hand, these visualizations for the keynote sessions of the conference.   while low-tech, they are very cool and remind me of mind maps, which I think are a great non-linear way to take notes.

BK53h8ZCEAEkfmz (1)

Finally, I wanted to share a link to my one-hour keynote presentation at the conference.  This presentation covers

  • A self introduction, including discussion of the reasons I joined Host Analytics.
  • An examination of the large market opportunity that stands before Host Analytics.
  • Live interviews with two Host Analytics customers:  Highlights for Children and Groupon.  (Since the camera didn’t capture their background videos, here is the first and the second.)
  • An EPM Maturity Model for helping people understand where they are in EPM adoption and benefits

My favorite part is at 40:30 where our VP of Services, Ron Baden, likens my joining and focus on customer success with Herb Brooks coaching the USA’s Miracle hockey team.