A little over two years ago, I began what was once the unthinkable: I became a Mac guy again. After more than a decade of exclusive PC use, I became fed up with Microsoft’s products and terms of service. I made the jump.
However, buying a Mac and completely weaning myself from my PC are not one and the same. In fact, as expected, it has been a transition more than a clean break. That is, I didn’t follow Jerry Seinfeld’s Band-Aid advice.
A few legacy apps like Microsoft Access and my admittedly long-in-the-tooth accounting system forced me to straddle the fence for a few years. However, as Windows XP nears its decommission date, I am going into 2013 with the intent of being Microsoft-free.
To do this, I needed to purchase a new accounting program. By way of background, for the last ten years a “mature” accounting system called MYOB. It wasn’t the sexiest application, but it got the job done.
As I exported the data from MYOB to Quickbooks (for the Mac), I noticed that my data management habits weren’t exactly perfect over the past decade. (Nothing major, but a few things annoyed me in my quest for data perfection.) In a few cases, I had duplicate vendor records. Some of my customer master information was incomplete.
What to do? I spent some time in Excel doing some “winter data cleaning.” I considered the following questions:
What better time to cleanse this data than now? (I’ve said many times that new system implementations represent opportune times to clean things up.)
Why not purge records vendors and customers with which I have had no contact in the last five years? (For instance, I no longer pay the same electric and cable companies that I did while living in New York and New Jersey.)
Why not start life with Quickbooks as cleanly as possible?
I had no one else to blame. “Simon, Inc.” is a very small shop and I do all of my own bookkeeping. Still, the way that I do my books has slightly changed over the last decade.
Simon Says: Be Your Own Chief Data Officer
I’ve written before on this site about the role of the chief data officer (CDO). It was high time that I took my own advice. While this small business example might lack the nuance of a large organization, I’d argue that the same principle applies. It’s my data and I alone take responsibility for it. Why not make it as clean as possible before migrating to a new system?
In fact, I’m going to make this an annual occurrence. Cleanse what I need, purge what I don’t, and review it all.
Executive turnover has always fascinated me, especially as of late. HP’s CEO Leo Apotheker had a very short run and Yahoo! has been a veritable merry-go-round over the last five years. Beyond the CEO level, though, many executive tenures resemble those of Spinal Tap drummers. For instance, CMOs have notoriously short lifespans. While the average tenure of a CMO has increased from 23.6 to 43 months since 2004, it’s still not really a long-term position. And I wonder if Big Data can change that.
In a recent article for Chief Marketer, Wilson Raj the global customer intelligence director of SAS, writes about the potential impact of Big Data and CMOs. From the piece:
CMOs today are better poised than ever not only to retain their roles, but to deliver broad, sustainable business impact. CMOs who capitalize on big data will reap big rewards, both personally and professionally. Bottom line: Businesses that exploit big data outperform their competition.
Necessary vs. Sufficient Conditions
The potential of Big Data is massive. To realize it to an optimal level, however, organizations need to effectively integrate transactional and analytical data and systems. Lamentably, many organizations are nowhere close to being able to do this. That is, for every Quantcast, Amazon, Target, and Wal-Mart, I suspect that dozens or even hundreds of organizations continue to struggle with what should be fairly standard blocking and tackling. Data silos continue to plague many if not most mature organizations.
Utilizing Big Data in any meaningful way involves a great deal more than merely understanding its importance. Big Data requires deploying new solutions like NoSQL databases, Hadoop, Cassandra, and others. Only then will CMOs be able to determine the true ROI of their marketing efforts. That is, accessing and analyzing enterprise and external (read: social) information guarantees nothing. A CMO will not necessarily be able to move the needle just because s/he has superior data. (Microsoft may have all of the data in the world, but so what? Bing hasn’t made too many inroads in the search business and Surface isn’t displacing the iPad anytime soon.)
Think of access to information as a necessary but insufficient condition to ensure success. As I look five and ten years out, I see fewer and fewer CMOs being able to survive on hunches and standard campaigns. The world is just moving too fast and what worked six months ago may very well not work today.
Some believe that Big Data represents the future of marketing. I for one believe that Big Data and related analytics can equip organizations with extremely valuable and previously unavailable information. And, with that information, they will make better decisions. Finally marketers will be able to see what’s really actually going on with their campaigns. Perhaps problems like the one mentioned at the beginning of this post can finally be solved.
While more expensive than their alternatives, Apple products are worth a premium in the eyes of many consumers. Credit their ease of use and popularity and elegant design. And it is this very popularity that should ensure the continued development and support of new and existing apps. Translation: Apple’s ecosystem is stronger than ever, something hardly lost on technology decision makers in large organizations.
Apple’s penetration of the enterprise stems from many factors. Exhibit A: Its ecosystem. The strength of Apple’s ecosystem means that enterprise apps will continue to be developed for its products–and probably at an increasing rate. Force.com and Jive software are but two examples.
Apple’s ecosystem includes–and, in fact, may center upon–the rapid deployment of apps. While apps don’t really work for complex ERP and CRM apps (yet), the AppStore model better is clearly a superior one. Launching apps requires far less IT involvement and cost relative to traditional deployments. While initially proven in the consumer space, companies like Genentech are adapting it to the enteprise world.
And the model just makes sense, especially among talented, in-demand employees–many of whom who have left jobs because they were forced to use deficient technologies.
Finally, while not a major factor, Steve Jobs’ death shed light on his genius. Today, it’s just plain hip to be associated with Apple.
As brilliantly as Apple has executed, that alone doesn’t explain the whole story. No, we have to look outward. Apple can credit a number of other external factors for its increasing enterprise penetration, including:
End user and IT frustration with existing applications, infrastructure.
Too many chiefs. Many IT departments are fed up with attempting to navigate complex EULAs, OEM agreements, and support issues among a cadre of vendors such as Microsoft and PC manufacturers like Lenovo.
Disappointment with ROI on past IT projects.
A new breed of CIOs and IT heads. These folks are less conservative and more open to new ways of doing things.
Of course, with respect to the tablet, until recently the iPad until recently faced no legitimate alternative. While that has changed with the success of Amazon’s Kindle Fire, the iPad is clearly a superior—if more expensive—device.
In the next part of the series, I’ll take a look at the future.
In “Can You Use Big Data? The Litmus Test“, Venkatesh Rao writes about the impact of Big Data on corporate strategy and structure. Rao quotes Alfred Chandler’s famous line, “structure follows strategy.” He goes on to claim that, “when the expressivity of a technology domain lags the creativity of the strategic thinking, strategy gets structurally constrained by technology.”
It’s an interesting article and, while reading it, I couldn’t help but think of some thought-provoking questions around implementing new technologies. That is, today’s post isn’t about Big Dataper se. It’s about the different things to consider when deploying any new information management (IM) application.
Let’s first look at the converse of Rao’s claim? Specifically, doesn’t the opposite tend to happen (read: technology is constrained by strategy)? How many organizations do not embrace powerful technologies like Big Data because they don’t fit within their overal strategies?
For instance, Microsoft could have very easily embraced cloud computing much earlier than it did. Why did it drag its feet and allow other companies to beat it to the punch? Did it not have the financial means? Of course not. I would argue that this was all about strategy. Microsoft for years had monopolized the market the desktop and many on-premise applications like Windows and Office.
To that end, cloud computing represented a threat to Microsoft’s multi-billion dollar revenue stream. Along with open source software and the rise of mobility, in 2007 one could start to imagine a world in which Microsoft would be less relevant than it was in 2005. The move towards cloud computing would have happend with or without Microsoft’s blessing, and no doubt many within the company thought it wise to maximize revenues while it could. (This isn’t inherently good or bad. It just supports the notion that strategy constrains technology as well.)
The Control Factor
Next up, what about control? What role does control play in structure and strategy? How many organizations and their employees have historically resisted implementing new technologies because key players simply refused to relinquish control over key data, processes, and outcomes? In my experience, quite a few.
I think here about my days working on large ERP projects. In the mid-2000s, employee and vendor self-service became more feasible but many organizations hemmed and hawed. They chose not to deploy these time-saving and ultimately more efficient applications because internal resistance proved far too great to overcome. In the end, quite a few directors and middle managers did not want to cede control of “their” business processes and ownership of “their” data because it would make them less, well, essential.
Simon Says: It’s Never Just about One Thing
Strategy, culture, structure, and myriad other factors all play significant roles in any organization’s decision to deploy–or not to deploy–any given technology. In an ideal organization, all of these essential components support each other. That is, a solid strategy is buttressed by a healthy and change-tolerant structure and organizational culture. One without the other two is unlikely to result in the effective implementation of any technology, whether its mature or cutting-edge.