The MIKE2.0 community is excited to announce the launch of our new podcast series “The Open MIKE.”  The Open MIKE Podcast is a video podcast show, hosted by Jim Harris, which discusses aspects of the MIKE2.0 framework, and features content contributed to MIKE 2.0 Wiki Articles, Blog Posts, and Discussion Forums.

View our first episode below:

And feel free to check out our community overview video for more information on how to get involved with MIKE2.0:

As always, contributions to the community are welcome and appreciated!

I was recently watching Bloomberg West, my favorite tech show. Emily Chang was interviewing Bill McDermott, co-chief executive officer of SAP AG. McDermott spoke about the company’s stellar second-quarter results and growth strategy.

You can watch the interview below or click here:

During the interview, McDermott commented on SAP clients’ widespread adoption of preconfigured apps–i.e., rapid deployment (RD). I want to touch in this post upon some of the data management issues involved in these types of projects.

By way of background, at a high level RD projects involve a vendor or system integrator effectively plunking down a preconfigured application like BI or CRM. The deployment typically takes a fraction of the time typically involved in these often laborious projects but, importantly, clients lose the ability to customize these applications. Also note that SAP is hardly the only vendor to conceive of this concept. I’ve heard about RD for the better part of a decade from more than a few firms.

Benefits Must be Balanced with Costs

Many CIOs chomp at the bit at the very thought of being able to “bang out” new applications and functionality. This is especially true at cash-strapped organizations. To many senior executives, the tradeoffs of RD projects are more than justified.

I’m not here to argue that point, but understand a few things about RD deployments. First, RD hardly gets around the data quality issues facing legacy systems or Waterfall and Agile projects. Specifically, GIGO still applies. Don’t make the mistake of assuming that a live system or application contains accurate or complete data just because it is live.

Second, many organizations’ data is in such disrepair that a good chunk of it can’t be loaded. Period. ABCDE is not a valid zip code. $0 is not a real salary unless you’re a CEO getting millions in stock options. You get my drift. Data unable to be loaded because it conflicts with application rules will be rejected.

Third, RD projects often involve benchmarks and industry KPIs. That is, a retail organization can compare its employee turnover or sales-per-square-foot to industry averages. That’s all fine and dandy, but remember that RD eschews customizations. The way that organization XYZ calculates turnover or same-store sales may differ slightly or significantly from that of other companies, effectively rendering comparisons moot. In turn, this can quash the user adoption of the very tool that XYZ crammed in.

Simon Says: Take Vendor Promises with a Grain of Salt

I’m not against RD as a concept. Organizations that manage their data well will, all else being equal, get more out of applications than organizations lacking such discipline. Just remember that there’s no magic wand, no secret sauce.

Perhaps embarking on a data cleansing project before commencing an RD project is the way to go. Better yet, see if you can try a cloud-based version of the application (even on a limited basis) to fully appreciate life with the new application before writing a big check.

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Everyone is talking about cloud computing, but most of the debate misses the point.  Cloud computing isn’t really about computers at all, it is about business services which are delivered in new ways.  Much of the time it is about combining offers in the market.

As with many new things, there is confusion over the terminology with Infrastructure as a Service (IaaS), Software as a Service (SaaS) and Business as a Service (BaaS) all overlapping with more general concepts of cloud.  While the more effective use of computing resources is a feature of cloud computing, it is not the most valuable.

As business gets more complex, it is harder and harder for organisations to maintain every capability internally.  Even the deployment of packaged software requires both IT and business project staff to have a good understanding of the business problem.  This is the reason why cloud solutions for specialised problems like staff expense management have become so popular so quickly.  Another reason why these have formed the first generation of deployed solutions is that they are easy to segment from other operations of the organisation.

Cloud delivered computing services, often provided on a pay for service basis also offers an alternative to the buy or lease options traditionally evaluated when acquiring computing resources. Use of on-demand, pay for use services also offers a way of temporarily delivering Information technology in support of merger, acquisition and separation activities.

Where consumers have had access to cloud-based services that simplify their lives by synchronising data, integrating their online purchases or bringing together their files they have grabbed them enthusiastically.  Presented with a simple way to manage their telecommunications or banking services they typically embrace it quickly.

In the near future, the cloud will make a range of payment solutions possible, possibly rendering mobile payments (through Near Field Communications or NFC) redundant before they’ve even hit the market.  What will make these new services interesting is that they will be independent of merchant or customer technology and rather combine location services with other aspects of the shopping experience.

One of the most common concerns that organisations raise about cloud computing is the potential for data to become fragmented, ungoverned or worse, exposed to foreign parties.  All of these concerns are valid, however cloud also offers the opportunity to provide a much tighter framework to protect and govern that very same information.  By assigning and contracting responsibilities, a proper governance structure can actually create greater accountability and remove many risks from within an institution.

The only certainty is uncertainty.  The competitive market for customers means that everyone is looking for an edge, something to bundle or use to add value.  Be it through loyalty points or one-off rewards.  With cloud, the third party becomes a service which participates fully in the client experience, but leaves the client relationship intact.

Without cloud, a retailer, financial institution or utility looking to white label a specialised product has to re-host the content on its own website and either hand-over the client to the third party or develop a full application on their own servers.  With cloud, organisations can offer third-party products delivered through the cloud as a seamless part of their own service.  Not just the website but also the retail, digital and call centre channels.

It is likely that banks will partner with retailers to provide an integrated online experience.  Why would a customer want to go all the way through to an online store if all they want to do is repeat a purchase they made in a previous month.  Their credit card statement on their internet banking portal is probably the first place they’d like to go to repeat the purchase.  Done properly, this will be a true cloud service with a seamless set of rich shopping applications embedded.

One of the best examples of this would be female cosmetics which are often purchased without change over many years.  Partnering with retailers or even wholesalers, banks can offer a re-order application within their banking environment without ever needed to develop retailing expertise within the IT or business teams.

While change is a challenge to incumbents, cloud computing provides an exciting opportunity to create an agile organisation and to launch products in response to new entrants almost as quickly as they can.

Whether or not you’re a fan of big government, if you read this blog then you’re probably at least open to the idea of Big Data. And, when it comes to Big Data, it’s hard to envision any organization with more data at its disposal than the US federal government.

Lamentably and for a variety of reasons well beyond the scope of any individual post, the US government is (putting it very politely) still muddling through Big Data. In fact, it is doing a mere fraction of what it could with so much potentially valuable data. The report, titled “The Big Data Gap” (registration required):

…found that as agencies look to leverage big data, the technology and applications needed to successfully leverage big data are still emerging. Sixty percent of civilian agencies and 42 percent of Department of Defense/intelligence agencies say they are just now learning about big data and how it can work for their agency. Federal IT professionals say improving overall agency efficiency is the top advantage of big data (59 percent) followed by improving speed and accuracy of decisions (51 percent) and the ability to forecast (30 percent).

With so many other pressing priorities, perhaps it’s understandable that the US federal government isn’t exactly leading the way when it comes to IT and cutting-edge data management. (My hunch is that the US isn’t alone here.) Yet, at some point doesn’t embracing Big Data have to become a priority? At what point do the excuses start to evaporate?

Technologist and pundit Tim O’Reilly has spoken and written extensively about the need for government to become a platform. (His eponymous publishing company recently released a large tome, Open Government, expanding on that very notion.)

I can only imagine the innovation that will invariably come from government employees, external developers, and everyday citizens once government embraces Big Data. Minimizing the number of superfluous, redundant, and antiquated data sources will spur a raft of applications and services. Perhaps this will finally allow the public sector to do more with less. Maybe the potential of so much technological advancement is just waiting to be unleashed.

Simon Says

Big Data could happen in one of the following two ways: bottom-up or top-down. I personally think that a senior mandate requiring the use of Big Data is unlikely and not even necessary. Rather, once one employee, once agency, or one department does something innovative and flat-out cool with Big Data, others will want to emulate that success. It’s my hope that then the dominoes will start to fall. Ultimately, employees, agencies, and departments not using Big Data will be the exception, not the rule.

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Facebook has fallen on hard financial times lately. That 100-billion-dollar valuation now seems wildly optimistic, put mildly. To be sure, Facebook is hardly the only company to slide after its IPO–and it won’t be the last. But is there something more dangerous going on at the world’s largest social network–something that may threaten its very existence?

The Data Problem?

As James Ball writes in in The Guardian about the larger problem facing Mark Zuckerberg’s company:
While Google’s revenues are growing – not a bad feat in the current economy – the huge amounts of extra data it’s accumulating aren’t improving its actual ads: the money the company gets for each advert is actually falling. If more data doesn’t make these companies more cash, the rationale falls away. Google’s adverts make it a huge amount of money, and will continue to do so, but there’s no evidence that more user data is making those adverts more effective at generating profit than they already were.
A large chunk of Facebook’s business model is based on the “more data is better than less data” assumption. In theory, this will bring in advertisers and, ultimately, profits. (Parenthetically, this is precisely why Facebook scares the hell out of Google.)

But The Guardian piece begs the following important and fundamental questions about the value of data:

• Does data eventually reach a point of diminishing returns?
• Is more data always better than less data?

We’ll probably find out over the next few quarters or years if Facebook is able to monetize what is perhaps the largest trove of data in the history of the world. What’s more, if Facebook can’t do it, will any organization be able to make sense–and, more important, money–from vast amounts of user-generated information?

Your Organization is Not Facebook

Now, don’t for one minute dismiss the need for (and value of) Big Data, sentiment analysis, semantic technologies, and other modern data management techniques. Facebook’s struggles hardly prove that there is no legitimate value to be gleaned from such things. Let’s say, for the sake of argument, that Facebook can’t justify such a lofty valuation (now or in the future). This in no way means that its data is worthless. It just might not be worth as much as some think. In other words, the argument here centers around how much that data is worth–not whether the data is worth anything.

Simon Says

It’s my firm belief that the vast majority of organizations need to both manage their existing data better. I can think of few that wouldn’t benefit from increasing the types and amount of data they manage. If there is such a thing as diminishing returns to the value of data, Facebook is much, much closer to realizing it than your organization is. Even if Facebook’s stock plummets to zero (and Larry Page bought drinks for everyone in Silicon Valley), it behooves organizations to embrace the “more is better” data theory. Structured, unstructured, and semi-structured data are extremely valuable assets, not liabilities.

Much like any company, the big question for Facebook is not what kind of data it has. Rather, it’s “What can it do with that information?”

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The old publishing model can be summed up in three words: print then sell. It worked for centuries but, over the last several years, has started to crumble.

A recent Wall Street Journal piece entitled “Your E-Book Is Reading You” sheds light on the seismic shift taking place right now in the publishing industry–especially ebooks. From it:

Publishing has lagged far behind the rest of the entertainment industry when it comes to measuring consumers’ tastes and habits. TV producers relentlessly test new shows through focus groups; movie studios run films through a battery of tests and retool them based on viewers’ reactions. But in publishing, reader satisfaction has largely been gauged by sales data and reviews—metrics that offer a postmortem measure of success but can’t shape or predict a hit. That’s beginning to change as publishers and booksellers start to embrace big data, and more tech companies turn their sights on publishing.

Sound familiar? At least from this observer’s perspective, the publishing industry is hardly alone in being late to the Big Data dance. Now that ebooks have entered the zeitgeist, publishers are finally starting to realize that they can benefit from the data gleaned from readers. In reality, this is no revelation at all: Customer data matters! Same old, same old, right?

Sell Then Print

Actually, Big Data (along with other things) is enabling a fundamental shift in commerce. As the semantic web inches closer, organizations will have increased ability to test the new products and enhancements to existing products before launching them. Throw in funding platforms like like Kickstarter, IndieGoGo, and a bevy of others effectively allow for people to make a widget after they have sold a certain number of them. And then there’s A/B testing, a topic that I discussed on this site recently. Collectively, we will be able to make better decisions.

Does this take the guesswork entirely out of product design, marketing, and R&D? Of course not. But we’re increasingly seeing more data-oriented and and analytical approaches applied to traditionally “warmer and fuzzier” areas of business.

Think that an enhancement will be popular? Test it. Will a new product be embraced in a particular country? Test it. In terms of publishing, the new model is evolving to sell then print–a complete juxtaposition of the old way of doing things. It doesn’t hurt that technology has kept up. In this case, print on demand makes all of this possible.

Simon Says: Let’s Look at the Data

Of course, not everything can tested in a practical way. for instance, drug companies spend roughly $800 million (USD) to take a drug to market. In this case, testing isn’t financially feasible.

Still, many other formerly qualitative questions today lend themselves to quantitative analysis. Does this type of employee tend to do well in this type of environment? Sure, we can guess. But the better answer is “Let’s look at the data.”

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When most people think of data, images of complex Microsoft workbooks and spreadsheets come to mind. Tables with rows and columns of structured data like dates, stock prices, sales, home sales, and invoices.

Historically, many analysts and execs alike have had to think about data in this rather pedestrian way. To some extent, BI projects started in the mid to late 1990s changed that, although many organizations never “got around” to them. Excel was the killer app for this type of thing: simple, relatively powerful, and good enough.

These days, however, data visualization tools like Tableau and others allow users at all levels within an organization to think of data in a fundamentally different way. To paraphrase from the classic Peter Frampton album, data is starting to come alive.

Stories Over Spreadsheets

Are we talking about the death of the spreadsheet? Of course not. I just don’t see that happening anytime soon. However, no longer is Excel with attendant charts and pivot tables the sole means by which to present data, particularly to decision makers.

In the words of Kris Hammond, CTO of Narrative Science, a joint research project at Northwestern University Schools of Engineering and Journalism, ”For some people, a spreadsheet is a great device. For most people, not so much so. The story. The paragraph. The report. The prediction. The advisory. Those are much more powerful objects in our world, and they’re what we’re used to.”

No argument here, but simple Excel charts can’t possibly do justice to certain types of data. Look at the following figure:

One could make the argument that this is the equivalent of data art.

Simon Says

Get out of the “data is boring” mind-set. It doesn’t have to be. SaaS-based and open-source tools allow even cash-strapped organizations to make data interactive, informative, and dare I say exciting. Forget new colors, fonts, or superficial treatments. More than ever, it’s easy to make your data tell a story, to learn new things from visualized data that would otherwise be lost in plain-Jane columns and rows.

Without question, data can be turned into information and, ultimately, knowledge. Old school employees and execs need to realize that decisions for the most part today should be made based upon solid data, but the presentation of that data need not be boring.

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Test Everything. That’s the recent title of a recent Wired magazine article on the merits and usage of A/B testing. The piece is nothing less than fascinating and I encourage you to check it out. In this article, I’d like to chime in with my own thoughts on the subject.

Benefits

Perhaps first and foremost, A/B testing allows you to generate your own data. That is, organizations can be proactive with regard to data management. This is in stark contrast to the practices of far too many companies that rely almost extensively on much more reactive data management. A/B testing does not eliminate the need for judgment. There’s still some art to go with that science. But, without question, A/B testing allows data management professionals to increase the mix of science in that recipe. From the aforementioned Wiredpiece:

“It [A/B testing] is your favorite copyeditor,” says IGN co-founder Peer Schneider. “You can’t have an argument with an A/B testing tool like Optimizely, when it shows that more people are reading your content because of the change. There’s no arguing back. Whereas when your copyeditor says it, he’s wrong, right?” This comment stings retroactively, as forty-eight hours later I would cost his company umpteen clicks with my misguided “improvement.”

Think about the power of data. In my experience, data naysayers often discount data because, in large part, they’re “not numbers’ people.” While some people will always find reasons to discredit that which they don’t understand, A/B testing can provide some pretty strong ammunition against skeptics. After all, what happens when website layout A, book cover A, or product description page A shows twice the level of engagement as its alternatives? Even a skeptic will have to admit defeat.

Limitations

Of course, A/B testing is hardly a panacea. The basic laws of statistics still apply, not the least of which is the notion of statistical significance. A site that gets 50,000 unique hits per day can reasonably chop its audience into two and, in the end, feel confident that any results are genuine. Without getting all “statsy”, there’s not much of a chance of either Type I or Type II errors with such sample sizes. Now, if a site gets 50 unique visitors per day, the chance of seeing a “false positive” or failing to see a legitimate cause-effect relationship are considerably higher. Beyond statistics, though, there’s a stylistic or design issue with A/B testing. Consider the famous quote by Steve Jobs:

“It’s really hard to design products by focus groups. A lot of times, people don’t know what they want until you show it to them.” – BusinessWeek, May 25 1998

Do you really want to crowdsource everything? There’s something to be said for the vision of an individual, small team, or small company. Giving everyone a vote may well drive a product to mediocrity. That is, in an attempt to please everyone, you’ll please no one.

Simon Says

Whether A/B testing is right for your organization hinges upon a bevy of factors. Timing, culture, and sample sizes are just a few things to consider. If you go down this road, though, don’t stop just because you don’t like what the data are telling you.

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