If you’re having trouble viewing this video, watch it on Vimeo via this link:Data Storage for Midsize Businesses
The following links are to the infographic featured in this video, as well as links to other related resources:
- The Top Trends In Storage (the Interactive Infographic from IBM that was featured in this video)
- IBM Storwize Family for Big Data and Cloud: http://youtu.be/M0hIfLKUk1U (YouTube Video)
- IBM Storwize V7000 Storage Efficiency: http://youtu.be/S2p4IQxIiec (YouTube Video)
- What’s New in IBM Storwize V3700: http://youtu.be/IdxnH9YWF9s (YouTube Video)
- IBM Storwize Family Ease of Use GUI: http://youtu.be/SBfDyrzTg-E (YouTube Video)
- IBM Data Storage Solutions for Midsize Businesses: ibm.com/midmarket/us/en/storage.html
This video was sponsored by the IBM for Midsize Business program, which provides midsize businesses with the tools, expertise and solutions they need to become engines of a smarter planet. I’ve been compensated to contribute to this program, but the opinions expressed in this video are my own and don’t necessarily represent IBM’s positions, strategies, or opinions.
Business intelligence is one of those phrases that everyone agrees is something all organizations, regardless of their size, should be doing. After all, no organization would admit to doing business stupidity. Nor, I presume, would any vendor admit to selling it.
But not everyone seems to agree on what the phrase means. Personally, I have always defined business intelligence as the data analytics performed in support of making informed business decisions (i.e., for me, business intelligence = decision support).
Oftentimes, this analytics is performed on data integrated, cleansed, and consolidated into a repository (e.g., a data warehouse). Other times, it’s performed on a single data set (e.g., a customer information file). Either way, business decision makers interact with the analytical results via static reports, data visualizations, dynamic dashboards, and ad hoc querying and reporting tools.
But robust business intelligence and analytics solutions used to be perceived as something only implemented by big businesses, as evinced in the big price tags usually associated with them. However, free and open source software, cloud computing, mobile, social, and a variety of as-a-service technologies drove the consumerization of IT, driving down the costs of solutions, enabling small and midsize businesses to afford them. Additionally, the open data movement lead to a wealth of free public data sets that can be incorporated into business intelligence and analytics solutions (examples can be found at kdnuggets.com/datasets).
Lyndsay Wise, author of the insightful book Using Open Source Platforms for Business Intelligence (to listen to a podcast about the book, click here: OSBI on OCDQ Radio), recently blogged about business intelligence for small and midsize businesses.
Wise advised that “recent market changes have shifted the market in favor of small and midsize businesses. Before this, most were limited by requirements for large infrastructures, high-cost licensing, and limited solution availability. With this newly added flexibility and access to lower price points, business intelligence and analytics solutions are no longer out of reach.”
This post was written as part of the IBM for Midsize Business program, which provides midsize businesses with the tools, expertise and solutions they need to become engines of a smarter planet. I’ve been compensated to contribute to this program, but the opinions expressed in this post are my own and don’t necessarily represent IBM’s positions, strategies, or opinions.
Since just before Christmas I posted An Enterprise Carol, I decided just before New Year’s to post An Enterprise Resolution.
In her article The Irrational Allure of the Next Big Thing, Karla Starr examined why people value potential over achievement in books, sports, and politics. However, her findings apply equally well to technology and the enterprise’s relationship with IT.
“Subjectivity and hype,” Starr explained, “make people particularly prone to falling for Next Best Thing-ism.”
“Our collective willingness to jump on the bandwagon,” Starr continued, “seems at odds with one of psychology’s most robust findings: We are averse to uncertainty. But as it turns out, our reaction to incomplete information depends on our interpretation of the scant data we do have. Uncertainty is a sort of amplifier, intensifying our response whether it’s positive or negative. As long as we react positively to the little information shown, we’re actually attracted to uncertainty. It’s curiosity rather than knowledge that leads to increased cognitive engagement. If the only information at hand is positive, your mind is going to fill in the gaps with other positive details. A whiff of positive information is all we need to set our minds aflutter.”
In his book Thinking, Fast and Slow, Daniel Kahneman explained “when people are favorably disposed toward a technology, they rate it as offering large benefits and imposing little risk; when they dislike a technology, they can think only of its disadvantages, and few advantages come to mind. People who receive a message extolling the benefits of a technology also change their beliefs about its risks. Good technologies have few costs in the imaginary world we inhabit, bad technologies have no benefits, and all decisions are easy. In the real world of course, we often face painful tradeoffs between benefits and costs.”
In his book What Technology Wants, Kevin Kelly explained that technology has a social dimension beyond the mere functionality it provides. “We adopt new technologies largely because of what they do for us, but also in part because of what they mean to us. Often we refuse to adopt technology for the same reason: because of how the avoidance reinforces or shapes our identity.”
So, in 2013, as the big data hype cycle comes down from the peak of inflated expectations, as the painful tradeoffs between the benefits and costs of cloud computing are faced, and as IT consumerization continues to reshape the identity of the IT function, let’s make an enterprise resolution to deal with these realities before we go off chasing the next best thing. Happy New Year!
“It is widely assumed that big data, which imbues a sense of grandiosity, is only for those large enterprises with enormous amounts of data and the dedicated IT staff to tackle it,” opens the recent article Big data: Why it matters to the midmarket.
Much of the noise generated these days about the big business potential of big data certainly seems to contain very little signal directed at small and midsize businesses. Although it’s true that big businesses generate more data, faster, and in more varieties, a considerable amount of big data is externally generated, much of which is freely available for use by businesses of all sizes.
The easiest example is the poster child for leveraging big data — Google Search. But there’s also a growing number of open data sources (e.g., weather data) and social data sources (e.g., Twitter), and, since more of the world is becoming directly digitized, more businesses are now using more data no matter how big they are. Additionally, as Phil Simon wrote about in The New Small, the free and open source software, as-a-service, cloud, mobile, and social technology trends driving the consumerization of IT are enabling small and midsize businesses to, among other things, use more data and be more competitive with big businesses.
“Each minute of every day, information is produced about the activities of your business, your customers, and your industry,” explained Sarita Harbour in her recent blog post Harnessing Big Data: Giving Midsize Business a Competitive Edge. “Hidden within this enormous amount of data are trends, patterns, and indicators that, if extracted and identified, can yield important information to make your business more efficient and more competitive, and ultimately, it can make you more money.”
However, the biggest driver of the misperception about big data is its over-identification with data volume. Which is why earlier this year in his blog post It’s time for a new definition of big data, Robert Hillard used several examples to explain that big data refers more to big complexity than big volume. While acknowledging that complex datasets tend to grow rapidly, thus making big data voluminous, his wonderfully pithy conclusion was that “big data can be very small and not all large datasets are big.”
Therefore, by extension we could say that the businesses using big data can be small, or mid-sized, and not all the businesses using big data are big. But, of course, that’s not quite pithy enough. So let’s simply say that big data is not just for big businesses.
This post was written as part of the IBM for Midsize Business program, which provides midsize businesses with the tools, expertise and solutions they need to become engines of a smarter planet.
Since ‘tis the season for reflecting on the past year and predicting the year ahead, while pondering this post my mind wandered to the reflections and predictions provided by the ghosts of A Christmas Carol by Charles Dickens. So, I decided to let the spirit of Jacob Marley revisit my previous Enterprise CIO Forum posts to bring you the Ghosts of Enterprise Past, Present, and Future.
The Ghost of Enterprise Past
Legacy applications have a way of haunting the enterprise long after they should have been sunset. The reason that most of them do not go gentle into that good night, but instead rage against the dying of their light, is some users continue using some of the functionality they provide, as well as the data trapped in those applications, to support the enterprise’s daily business activities.
This freaky feature fracture (i.e., technology supporting business needs being splintered across new and legacy applications) leaves many IT departments overburdened with maintaining a lot of technology and data that’s not being used all that much.
The Ghost of Enterprise Past warns us that IT can’t enable the enterprise’s future if it’s stuck still supporting its past.
The Ghost of Enterprise Present
While IT was busy battling the Ghost of Enterprise Past, a familiar, but fainter, specter suddenly became empowered by the diffusion of the consumerization of IT. The rapid ascent of the cloud and mobility, spirited by service-oriented solutions that were more focused on the user experience, promised to quickly deliver only the functionality required right now to support the speed and agility requirements driving the enterprise’s business needs in the present moment.
Gifted by this New Prometheus, Shadow IT emerged from the shadows as the Ghost of Enterprise Present, with business-driven and decentralized IT solutions becoming more commonplace, as well as begrudgingly accepted by IT leaders.
All of which creates quite the IT Conundrum, forming yet another front in the war against Business-IT collaboration. Although, in the short-term, the consumerization of IT usually better services the technology needs of the enterprise, in the long-term, if it’s not integrated into a cohesive strategy, it creates a complex web of IT that entangles the enterprise much more than it enables it.
And with the enterprise becoming much more of a conceptual, rather than a physical, entity due to the cloud and mobile devices enabling us to take the enterprise with us wherever we go, the evolution of enterprise security is now facing far more daunting challenges than the external security threats we focused on in the past. This more open business environment is here to stay, and it requires a modern data security model, despite the fact that such a model could become the weakest link in enterprise security.
The Ghost of Enterprise Present asks many questions, but none more frightening than: Can the enterprise really be secured?
The Ghost of Enterprise Future
Of course, the T in IT wasn’t the only apparition previously invisible outside of the IT department to recently break through the veil in a big way. The I in IT had its own coming-out party this year also since, as many predicted, 2012 was the year of Big Data.
Although neither the I nor the T is magic, instead of sugar plums, Data Psychics and Magic Elephants appear to be dancing in everyone’s heads this holiday season. In other words, the predictive power of big data and the technological wizardry of Hadoop (as well as other NoSQL techniques) seem to be on the wish list of every enterprise for the foreseeable future.
However, despite its unquestionable potential, as its hype starts to settle down, the sobering realities of big data analytics will begin to sink in. Data’s value comes from data’s usefulness. If all we do is hoard data, then we’ll become so lost in the details that we’ll be unable to connect enough of the dots to discover meaningful patterns and convert big data into useful information that enables the enterprise to take action, make better decisions, or otherwise support its business activities.
Big data will force us to revisit information overload as we are occasionally confronted with the limitations of historical analysis, and blindsided by how our biases and preconceptions could silence the signal and amplify the noise, which will also force us to realize that data quality still matters in big data and that bigger data needs better data management.
As the Ghost of Enterprise Future, big data may haunt us with more questions than the many answers it will no doubt provide.
I realize that this post lacks the happy ending of A Christmas Carol. To paraphrase Dickens, I endeavored in this ghostly little post to raise the ghosts of a few ideas, not to put my readers out of humor with themselves, with each other, or with the season, but simply to give them thoughts to consider about how to keep the Enterprise well in the new year. Happy Holidays Everyone!
While checking out the new Knowledge Vaults on the Enterprise CIO Forum, I came across the Genefa Murphy blog post How IT Debt is Crippling the Enterprise, which included three recommendations for alleviating some of that crippling IT debt.
The first recommendation was application retirement. As I have previously blogged, applications become retirement-resistant because applications and data have historically been so tightly coupled, making most of what are referred to as data silos actually application silos. Therefore, in order to help de-cripple IT debt, organizations need to de-couple applications and data, not only by allowing more data to float up into the cloud, but also, as Murphy noted, instituting better procedures for data archival, which helps more easily identify applications for retirement that have become merely containers for unused data.
The second recommendation was cutting the IT backlog. “One of the main reasons for IT debt,” Murphy explained, “is the fact that the enterprise is always trying to keep up with the latest and greatest trends, technologies and changes.” I have previously blogged about this as The Diderot Effect of New Technology. By better identifying how up-to-date the IT backlog is, and how well — if at all — it still reflects current business needs, an organization can skip needless upgrades and enhancement requests, and not only eliminate some of the IT debt, but also better prioritize efforts so that IT functions as a business enabler.
The third recommendation was performing more architectural reviews, which, Murphy explained, “is less about getting rid of old debt and more about making sure new debt does not accumulate. Since IT teams don’t often have the time to do this (as they are concerned with getting a working solution to the customer ASAP), it is a good idea to have this as a parallel effort led by a technology or architectural review group outside of the project teams but still closely linked.”
Although it’s impossible to completely balance the IT budget, and IT debt doesn’t cause an overall budget deficit, reducing costs associated with business-enabling technology does increase the potential for a surplus of financial success for the enterprise.
On a previous post about the consumerization of IT, Paul Calento commented: “Clearly, it’s time to move IT out of a discrete, defined department and out into the field, even more than already. Likewise, solutions used to power an organization need to do the same thing. Problem is, though, that it’s easy to say that embedding IT makes sense (it does), but there’s little experience with managing it (like reporting and measurement). Services integration is a goal, but cross-department, cross-business-unit integration remains a thorn in the side of many attempts.”
Embedding IT does make sense, and not only is it easier said than done, let alone done well, but part of the problem within many organizations is that IT became partially self-embedded within some business units while the IT department was resisting the consumerization of IT because they treated it like a fad and not an innovation. And now those business units are resisting the efforts of the redefined IT department because they fear losing the IT capabilities that consumerization has already given them.
This growing IT challenge brings to mind the Diffusion of Innovations theory developed by Everett Rogers for describing the five stages for the rate at which innovations (e.g., new ideas or technology trends) spread within cultures, such as organizations, starting with the Innovators and Early Adopters, progressing through the Early and Late Majority, and trailed by the Laggards.
A related concept called Crossing the Chasm was developed by Geoffrey Moore to describe the critical phenomenon occurring when enough of the Early Adopters have embraced the innovation so that the beginning of the Early Majority becomes an almost certainty even though mainstream adoption of the innovation is still far from guaranteed.
From my perspective, traditional IT departments are just now crossing the chasm of the diffusion of the consumerization of IT, and are conflicting with the business units that crossed the chasm long ago with their direct adoption of cloud computing, SaaS, and mobility solutions not provided by the IT department. This divergence caused by the IT department and some business units being on different sides of the chasm has damaged, and potentially irreparably, some aspects of the IT-Business partnership.
The longer the duration of this divergence, the more difficult it will be for an IT department, that has finally crossed the chasm, to redefine their role and remain relevant partners with those business units that, perhaps for the first time in the organization’s history, were ahead of the information technology adoption curve. Additionally, even the communication and collaboration across business units is negatively affected by different business units crossing the IT consumerization chasm at different times, which often, as Paul Calento noted, complicates the organization’s attempts to integrate cross-business-unit IT services.