Storytelling with the Sounds of Big Data

Trying to internally “sell” the merits of a big data program to your executive team?  Of course, you will need your handy Solution Architect by your side, and a hard hitting financial analysis vetted by the CFO’s office. But numbers and facts probably won’t make the sales pitch complete. You’ll need to appeal to the emotional side of the sale, and one method to make that connection is to incorporate the sounds of big data.

By Tess Watson. Creative Commons. Courtesy of Flickr.
By Tess Watson. Creative Commons. Courtesy of Flickr.

There’s an interesting book review on “The Sonic Boom” by Joel Beckerman in the Financial Times.  In his book, Beckerman makes the statement that “sound is really the emotional engine for any story”—meaning if you’re going to create a powerful narrative, there needs to be an element of sound included.

Beckerman cites examples where sound is intentionally amplified to portray the benefits of a product or service, or even associate a jingle with a brand promise. For example, the sizzling fajitas that a waiter brings to your table, the boot up sound on an Apple Mac, or AT&T’s closing four notes on their commercials.

Of course, an analytics program pitch to senior management requires your customary facts and figures.  For example, when pitching the merits of an analytics program you’ll need slides on use cases, a few diagrams of the technical architecture (on premise, cloud based or a combination thereof), prognostications of payback dates and return on investment calculations, and a plan to manage the program from an organizational perspective among other things.

But let’s not mistake the value of telling a good story to senior management that humanizes the impact of investing deeper in an analytics program.  And that “good story” can be delivered more successfully when “sound” is incorporated into the pitch.

So what are the sounds of big data?  I can think of a few that, when experienced, can add a powerful dimension to your pitch.  First, take your executives on a tour of your data center, or one you’re proposing to utilize so they can hear the hum of a noisy server room where air conditioning ducts pipe in near ice cold air, CPU fans whirl in perpetuity, and cable monkeys scurry back and forth stringing fiber optic lines between various machines.  Yes, your executive team will be able to see the servers and feel the biting cold of the data center air conditioning, but you also want them to hear the “sounds” (i.e. listen to this data center) of big data in action.

In another avenue to showcase the sound of big data, perhaps you can replay to your executive team the audio of a customer phone call where your call center agent struggles to accurately describe where a customer’s given product is in transit, or worse, tries to upsell them a product they already own.  I’m sure you can think of more “big data” sounds that can accurately depict either your daily investment in big data technologies…or lack thereof.

Too often, corporate business cases with a “big ask” for significant headcount, investment dollars and more, give too much credence to the left side of our brain that values logic, mathematics and facts.  In the process we end up ignoring the emotional connection where feelings and intuition interplay.

Remember to incorporate the sounds of big data into your overall analytics investment pitch because what we’re aiming for is a “yes”, “go”, “proceed”, or “what are you waiting for?” from the CFO, CEO or other line of business leader. Ultimately, in terms of our analytics pitch, these are the sounds of big data that really matter.

Three Steps to Becoming a Genius Forecaster

Both Ben Bernanke and Edward John Smith got it wrong. They made terrible forecasts that either wrecked the economy, or in the instance of John Edward Smith (his ship).  Forecasting is hard, and even those who sometimes get it right, often fail on a continuous basis. But fear not, there are three steps you can take to drastically improve your forecast accuracy, but you’ll have to be willing to put in the work, and possibly put your ego aside to get there.

Captains of the Titanic. By Jimmy. Courtesy of Flickr Creative Commons.
Captains of the Titanic. By Jimmy. Courtesy of Flickr Creative Commons.

Simply stated, a forecast is “a prediction…of some future activity, event, or occurrence.” There are many types of forecasts including business, economic, financial, meteorology, political and more. In fact, everyone is a forecaster to some degree especially when we start thinking about future trends and how they might affect our families, companies, communities and…even countries.

But good forecasting is difficult, and even the so-called “experts” and pundits get it wrong more times than they’re right.  With this in mind, here are three tips (surely there are more) to becoming a better forecaster.

First, understand that domain knowledge of a particular area doesn’t necessarily mean you’ll see the future better than anyone else. An article from the Financial Times chronicled Canadian psychologist Phillip Tetlock’s quest to improve forecasting techniques. Over 18 years, Tetlock accumulated over 27,500 expert forecasts on politics, geopolitics and economics.  His shocking conclusion? According to the FT article, Tetlock discovered that so-called experts were terrible forecasters! These were people in their sphere of influence, with strong opinions and knowledge about things they understood quite well. However, their forecasting track records—over time—were no better than chance.  So if you believe yourself to be an “expert”, it’s probably better to take a more humble approach.

Second, if you want better forecasts, run your expert opinions by others. Phillip Tetlock, Barbara Mellers and Don Moore run “The Good Judgment Project”.  It’s a collection of more than 20,000 volunteer participants who offer up opinions on economic and geo-political events. Through their research, Tetlock, Mellers and Moore learned that when expert forecasters were broken into teams, their discussions and sometimes heated arguments bore better results.  With the biblical adage, “iron sharpens iron”, when you bounce your expert forecasts off others, research from the Good Judgment Project shows that you’ll end up with more accurate depictions of future events.

Third, bring your data—in fact, bring all of them. Sometimes, when making expert forecasts we assume that only what we deem as a relevant data set is needed (maybe what’s in the corporate data warehouse) for the best decision making.

However, because the world is complex, and there are often many variables that contribute to an event or outcome, it’s better to bring all your data to the task. So this means, data that might be locked away in non-tabular “messy” formats such as call detail records, machine logs, or JSON data sets can and should be processed, refined and analyzed.  And don’t be afraid to look for data sets outside what you own in your internal data stores. There are plenty of data brokers that might have data you need to help unlock the puzzle of where to direct your corporate resources next.

Looking for more on the latest in forecasting? Tim Hartford’s FT article is a great place to start. I’m also a fan of Cullen Roche’s macro approach to understanding markets and financial flows. And no discussion on forecasting would be complete without referencing Nassim Taleb’s sometimes caustic critiques of the forecasting profession.

So if you want to be a genius forecaster, follow these three steps. First, drop any bit of hubris that comes with the forecasting profession and be open to other opinions. After all, as John Kenneth Galbraith once said, “There are two kinds of forecasters: those who don’t know, and those who don’t know they don’t know.”

Next, once your guard is down, you’ll be able to run your ideas by other experts and maybe come up with a better idea than your original one. Don’t be afraid to argue your point. But also be wise enough to be quiet and listen.  You can learn a lot by simply closing your mouth and opening your mind.

Finally, bring all the data you need to solve a problem, not just the clean data, or those that are easily sourced. Sometimes, there’s signal in the noise. But if you want better forecasts, you’re going to have to do the really hard work to find it.

  • CAPEX Deferred Eventually Makes the Company Sick

    Wall Street analysts keep waiting for companies to spend money upgrading their infrastructures, but they shouldn’t hold their collective breath.  Instead of investments in IT, machinery, buildings and more, CEOs and CFOs are content to predominantly spend cash on stock buy-backs and/or dividends.  Deferring CAPEX spend, or “sweating the assets” will work for a little while, but it’s not a strategy for long-term success.

    Creative Commons. Flickr. By Jeff Hitchcock.
    Creative Commons. Flickr. By Jeff Hitchcock.

    Poor Los Angeles. Decades of not spending enough money to upgrade infrastructures is really catching up with the city.

    According to a New York Times article, “Infrastructure Cracks as Los Angeles Defers Repairs”, there’s a real breakdown happening with the public works infrastructure in Los Angeles. Take for example, massive flooding when a 90 year old water main broke outside of UCLA, flooding the campus with 10-20 million gallons of water and leading to millions in damages.

    Deferring necessary upgrades and repairs is costing Los Angeles.  The New York Times article mentions; “With each day…another accident illustrates the cost of deferred maintenance on public works, while offering a frustrating reminder to this cash-strained municipality of the daunting task it faces in dealing with the estimated $8.1 billion it would take to do the necessary repairs.”

    In the same manner, since 2012 companies have clamped CAPEX spending for their own infrastructures, instead choosing to spend cash on stock buybacks, or plain just hoarding cash on the balance sheet.  Granted, some of these monies are locked up off-shore, and cannot be repatriated without significant tax hits, but for now, companies are choosing not to spend much on upgrading their own infrastructure.

    In terms of information technology, deferring upgrade expenditures has the following implications:

    • Big data are only getting bigger
    • Moore’s Law’s keeps marching on, but some companies are using IT equipment that may be long past its depreciation cycle.
    • Software advancements continue
    • SLAs demanded by business units are in jeopardy of not being met

    Perhaps the slowdown for IT CAPEX has something to do with the rise of cloud computing. After all, Amazon’s cloud business has turned into a $2 billion or more business. That said, survey after surveystill shows reluctance for companies to move everything to the cloud, so perhaps there’s more to the story.

    The constant deferral of CAPEX has the real potential to make your company sick. Investments in computers, machines, plants, equipment, buildings and more are the backbone of a company. When CAPEX is intentionally constrained in favor of parking cash for a rainy day or buying back stock (at already high prices), much needed upgrades are deferred.

    Worse, constant deferrals of capital upgrades are like a “hidden tax” in that by not spending cash on upgrading creaking systems and infrastructure, it’s highly likely something much worse can happen down the road (i.e. the millions extra Los Angeles has to spend just to clean up the messes resulting from infrastructure failures).

    Getting back to Los Angeles and their years of infrastructure spend deferral, Donald Shoup, a professor of urban planning at UCLA says; “It’s part of a pattern of failing to provide for the future.”

    The problem is quite clear. Investments in CAPEX can only be delayed so long. Eventually, failure to spend means missed growth opportunities, frustrated customers, irritated employees, and exposure to much more downside risk if things “blow up” from trying to get by just one more quarter with aging infrastructure. Eventually, the piper needs to be paid. And when he finally gets paid, he usually asks for double.

    Building Information Technology Liquidity

    Turbulent markets offer companies both challenges and opportunities. But with rigid and aging IT infrastructures, it’s hard for companies to turn on a dime and respond to fluctuations in supplies and consumer demand. A corporate culture built on agile principles helps, but companies really need to build information technology “liquidity” to meet global disturbances head on.

    Creative Commons. Courtesy of Flickr. By Ze'ev Barkan
    Creative Commons. Courtesy of Flickr. By Ze’ev Barkan

    Liquidity is a term often used in financial markets. When markets are deep and liquid it means they have assets that can be exchanged or sold in a moment’s notice with very little price fluctuation. In liquid markets, participants usually have the flexibility to sell or buy a position very rapidly, using cash or another accepted financial instrument.

    Companies with liquid assets—such as lots of cash—can take advantages of market opportunities like picking up ailing competitors cheaply, or buying out inventory that another competitor desperately needs. Liquidity then, allows companies to take advantage of unplanned scenarios, and in some cases—to stay afloat when other companies are failing!

    In the same way, IT organizations desperately need to embrace the concept of “liquidity”—not by having extra cash lying around, but creating agile and flexible infrastructures that can take advantage of unplanned demand. This is especially hard when an estimated 75% of the IT budget is already spent on maintaining legacy infrastructure.

    Even worse, IT capacity planning efforts are often based on simple linear regression models or other quick and dirty heuristics that don’t account for huge spikes in demand such as a major corporate merger or “one-hit wonder” product.

    Companies need to build a “liquid” information technology capability that can respond quickly to market and competitive agitations. Richard Villars, Vice President at IDC, says that in building liquidity, IT must; “enable variable workloads, handle the data explosion, and (be able to promptly) partner with the business (when unplanned opportunities arise)”.

    What are some examples of IT liquidity? One scenario could be extra compute and storage available on-premises and reserved for unplanned demand. These resources could be “hidden” from the business by throttling back CPU for example, and then “released” when needed.

    A second scenario might be having contracts signed and cloud resources at the ready on a moment’s notice to “burst into” extra processing when required. A third option could be using outside service contractors on a retainer model basis to provide a ready set of skills when your IT staff is crunched with too many extra projects.

    In the financial world, liquid assets can allow companies to react and capitalize on market opportunities.  Liquidity in IT means that companies have enough extra compute firepower, people resources and are agile enough with IT processes to respond to unplanned events and demand, in whatever shape, form or order they arrive.

    Building resistance to and combating market disruptions is an essential quality—in some cases to thrive and in others, to simply survive.

    Adapting to Winds of Change with Cloud

    Look around at global economic conditions. More than skirmishes—near flat out war—in Ukraine, Gaza, Iraq and Syria.  China pushing up GDP numbers by loading local provinces with more debt. European economies are on the mend, but not yet turning the corner. Fickle western consumers more pre-occupied with the latest smartphone app than the new product you’re selling. It’s in stressful economic conditions that you need to make sure your business has the ability to cycle capacity up or down when needed. You need cloud computing.

    According to an article in the Financial Times, during the World Cup, Ghana’s authorities had to “import 50 megawatts of energy from neighboring Ivory Coast” just to keep televisions on during Ghana’s National Soccer team’s games. Fortunately, Ivory Coast had enough spare electricity to sell to Ghana, because there might have been riots in the streets had Ghanaian authorities not figured out a way to meet the demands of thousands of televisions.

    Just like Ghanaian authorities, many businesses are unprepared for volatile capacity needs and capricious consumers who want what they want, and now.  That’s why enterprises that not only have a cloud computing strategy, but the ability to quickly deploy cloud resources on a whim, will ultimately fare better than those still trying to spell “c-l-o-u-d”.

    This means having an information architecture documented that includes cloud, signed agreements with providers, an understanding of applications and databases or file systems needed, security policies in place, applications written and ready to take advantage of cloud resources, data loading strategies (VPN or dedicated circuit?), processes to scale cloud resources up and down (and triggers when to do so), data governance for onsite and cloud systems, business continuity plans and more.

    There’s much work to do before you can take advantage of cloud resources, and just-in-time planning doesn’t cut it. With the flexibility, speed and power that cloud offers, there’s really no excuses to let opportunities to capture unplanned demand pass you by.

    Can you ramp up and down based on erratic business conditions? Can you weather economic fluctuations? Are you flexible enough to point resources towards unmet consumer demand?  Can you quickly adapt to global winds of change? Cloud computing infrastructures are ready. Are you?

    Are You Using Tricolons in Your Rhetoric?

    If you’re a presenter, or simply someone wanting to convey information in a memorable way, you have probably inadvertently or intentionally used the rule of three.  The rule of three is a teaching, writing or presenting device where a key concept is broken into three easy to remember pieces.  Does the rule of three apply to the fields of technology and business? Let’s dive a little deeper to find out.

    By Don McCullough. Creative Commons. Courtesy of Flickr.
    By Don McCullough. Creative Commons. Courtesy of Flickr.

    Financial Times columnist Sam Leith offers executives a few hints on how to make business presentations and documents more interesting. He says that by using a rhetorical device called a “tricolon”, anyone looking to influence or persuade can make their ideas easier to consume and comprehend.

    What’s a good example of a tricolon? How about Thomas Jefferson’s prose in the US Declaration of Independence where he writes; “We hold these truths to be self-evident, that all men are created equal, that they are endowed by their Creator with certain unalienable Rights, that among these are Life, Liberty and the pursuit of Happiness.” Notice the tricolon; “life, liberty and the pursuit of happiness” and how easy is it forget the first part of the sentence and remember the second. Why is this?

    Leith advances the concept that humans accept and retain information better when the Rule of Three is used.  “For reasons that remain neurologically obscure, the human mind adores things in groups of three: tricolons sound strong, memorable and coherent,” he says.

    Tricolons are found in all types of rhetoric from political speeches to children’s books. Take a look at this gem in Quentin Blake’s Angelica Sprocket’s Pockets:

          “There’s a pocket for mice,”

          “and a pocket for cheese”

          “and a pocket for hankies in case anyone feels that they’re going to sneeze”

    Here we have three pockets, but we mostly remember what is supposed to go in them, namely mice, cheese and hankies.

    We can use this rhetorical device in our business presentations and messaging for better conclusions.  For example, most readers of this column know that I have marketing duties for Teradata Cloud.

    While there are many compelling aspects of this particular solution,  I’ve boiled the ocean down to “fast, flexible and powerful”, where deployment in the cloud is faster than you’d expect, flexible enough to meet your needs for a little or a lot of analytic capability and powerful with the availability of three analytic engines. While it’s terribly tempting to create a longer checklist of all the benefits of this solution, I’ve intentionally limited myself to only three (and arguably even these require more refinement!).

    Want to make your next presentation more compelling? And added effect of the tri-colon is that it can provide a rhythm to our discourse.  Rhythmically, we can use tricolons to break up the monotony of an otherwise bland presentation (especially ones that technology executives are prone to deliver!).

    Going forward, let’s be sure to use more tricolons (i.e. Rule of Three) in our training materials, internal presentations, customer whitepapers, conference presentations and more. I’m pretty sure by doing so; we’ll end up much more interesting, memorable, and effective.

    Beware Big Data Technology Zealotry

    Undoubtedly you’ve heard it all before: “Hadoop is the next big thing, why waste your time with a relational database?” or “Hadoop is really only good for the following things” or “Our NoSQL database scales, other solutions don’t.” Invariably, there are hundreds of additional arguments proffered by big data vendors and technology zealots inhabiting organizations just like yours. However, there are few crisp binary choices in technology decision making, especially in today’s heterogeneous big data environments.

    Courtesy of Flickr. Creative Commons. By Eden, Janine, and Jim.
    Courtesy of Flickr. Creative Commons. By Eden, Janine, and Jim.

    Teradata CTO Stephen Brobst has a great story regarding a Stanford technology conference he attended. Apparently in one session there were “shouting matches” between relational database and Hadoop fanatics as to which technology better served customers going forward. Mr. Brobst wasn’t amused, concluding; “As an engineer, my view is that when you see this kind of religious zealotry on either side, both sides are wrong. A good engineer is happy to use good ideas wherever they come from.”

    Considering various technology choices for your particular organization is a multi-faceted decision making process. For example, suppose you are investigating a new application and/or database for a mission critical job. Let’s also suppose your existing solution is working “good enough”. However, the industry pundits, bloggers and analysts are hyping and luring you towards the next big thing in technology. At this point, alarm bells should be ringing. Let’s explore why.

    First, for companies that are not start-ups, the idea of ripping and replacing an existing and working solution should give every CIO and CTO pause. The use cases enabled by this new technology must significantly stand out.

    Second, unless your existing solution is fully depreciated (for on-premises, hardware based solutions), you’re going to have a tough time getting past your CFO. Regardless of your situation, you’ll need compelling calculations for TCO, IRR and ROI.

    Third, you will need to investigate whether your company has the skill sets to develop and operate this new environment, or whether they are readily available from outside vendors.

    Fourth, consider your risk tolerance or appetite for failure—as in, if this new IT project fails—will it be considered a “drop in the bucket” or could it take down the entire company?

    Finally, consider whether you’re succumbing to technology zealotry pitched by your favorite vendor or internal technologist. Oftentimes in technology decision making, the better choice is “and”, not “either”.

    For example, more companies are adopting a heterogeneous technology environment for unified information where multiple technologies and approaches work together in unison to meet various needs for reporting, dashboards, visualization, ad-hoc queries, operational applications, predictive analytics, and more. In essence, think more about synergies and inter-operability, not isolated technologies and processes.

    In counterpoint, some will argue that technology capabilities increasingly overlap, and with a heterogeneous approach companies might be paying for some features twice. It is true that lines are blurring regarding technology capabilities as some of today’s relational databases can accept and process JSON (previously the purview of NoSQL databases), queries and BI reports can run on Hadoop, and “discovery work” can complete on multiple platforms. However, considering the maturity and design of various competing big data solutions, it does not appear—for the immediate future—that one size will fit all.

    When it comes to selecting big data technologies, objectivity and flexibility are paramount. You’ll have to settle on technologies based on your unique business and use cases, risk tolerance, financial situation, analytic readiness and more.

    If your big data vendor or favorite company technologist is missing a toolbox or multi-faceted perspective and instead seems to employ a “to a hammer, everything looks like a nail” approach, you might want to look elsewhere for a competing point of view.

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