When the Apple Macintosh hit the market, analysts were not impressed. But Steve Jobs’ vision ended up transforming our lives. Apple is celebrating its 30th anniversary this year and has become a global household name. Jobs’ ability to direct his organization to develop easy to use products not only met users’ expectations, but also introduced new ways to use phones, tablets and computers, and set new standards for the industry.
While individual efforts by business units may produce new efficiencies and incremental revenue gains, only leaders can launch and guide coordinated enterprise-wide organizational and business transformation. Leaders use experience and resources to understand their organization’s value chain and their industries. They develop business insight, and use analytics to guide decisions and set strategies.
To start, organizations need to evaluate their current capabilities across four organizational pillars: people, processes, infrastructure and culture. Effective leaders develop strategies to leverage and redirect existing capabilities to achieve their vision.
As we celebrate President’s Day in the USA this week, we’re reminded of and inspired by leaders like Washington and Lincoln who shaped the history of the country. Genuine organizational transformation also requires committed and visionary leaders. The application of analytics to derive business insight helps these leaders fine-tune their vision, validate their assumptions, and develop sound business transformation strategies.
Over the last few years, the main qualifications for a general manager of a sports team have changed dramatically. Gut feel and experience have been replaced by analytical insight and predictive modeling. If Billy Beane started the trend with his Moneyball approach to team building, GMs like Daryl Morey (Houston Rockets) and Theo Epstein (Chicago Cubs) have taken analytics from cool to essential.
And it’s not just GMs that are embracing analytics:
I was recently part of team discussing enterprise architecture with a chief IT architect, and we were explaining how SAS can integrate into their existing infrastructure, add business value on top it and even fit into their future planned infrastructure. This conversation was one of the reasons I blogged about how analytics is the ultimate renewable resource.
Why is the architecture important? Without a system that has been engineered and designed to scale, the breadth and depth of your analytics doesn't matter. After all, if you can't deliver the information to decision makers in a timely manner, it doesn't matter how advanced your analytics are or how well you can solve business problems.
Bridging the Rift between Dev and Ops As a member of the Product Marketing team at SAS, I spend a good part of my time researching – analyst reports, industry journals, blogs, social channels – and listening to what our customers are saying. Early last spring I began noticing the term “DevOps” showing up with more frequency. My background is in programming. I understand Dev, and I understand the role of Ops, or IT Operations. The oil and water act of bringing together Dev + Ops to form DevOps sounds interesting, but there’s more to it than simply removing the space between the two words. So what is DevOps all about?
The term DevOps is new - the concept isn’t I began researching DevOps, and after just scraping the surface, I found myself redirected to topics such as The Deming Cycle, Just In Time, Total Quality Management and even Japanese terms like Kanban and Kaizen. For the most part, these topics explain processes for improving quality and efficiency of manufacturing environments - assembly lines, putting together cars.
Then the light bulb went off. Creating and delivering software applications is not all that different from assembling cars - a series of steps from beginning to end to create a product and deliver it to customers. So just like the assembly line for building a car, software goes through an assembly line process, of sorts, that includes Business, Development, and IT Operations. Since there are methodologies to increase efficiency and quality in end-to-end manufacturing of products, why isn’t there a methodology to drive end-to-end efficiency and quality of the creation and delivery of software? Read More »
From time-to-time marketers, journalist, and thought leaders find ways to describe things in a new way. It’s a time-honored tradition guaranteed to attract eyeballs and sell books. Lately there has been a lot of buzz about the Internet of X, a way of describing a uniquely identifiable collection of objects connected to the Internet. But how many objects are we talking about? It’s estimated that by 2020 there will be 50 billion objects connected to the Internet. Getting to this number requires more than just computers and smart phones. It includes all kind of IP addressable devices. Learn more about the exponential growth and availability of data, both structured and unstructured, in this short video.
Big Data… What it means to you
See why the buzz about big data continues to grow. Learn how SAS can help you make wiser business decisions by harnessing the power of big data. http://www.sas.com/big-data/
At the Analytics 2013 conference attendees tend to be more technical than those at a standard business confernece. Knowing this, I set out to get a better understanding of what statistical techniques they use to do their analyses. Of course that depends on the business question they are trying to solve. But I thought it would be interesting to see what techniques are favored or used for a particular problem.
Valentine’s Day is one of those make-or-break holidays for gift retailers. They are selling "nice to have" items, not necessities. Many use some type of analytics to segment customers for personalized messages. It's not as straightforward as it sounds, especially if the organization hasn't committed to an enterprise-wide approach to using data. If the organization isn't tracking household spend across different buying channels over time (and multiple holidays), it could be wasting money on catalogs or failing to send the right marketing messages to those primed to buy.
One gift retailer has used analytics to figure out that there are some customers that only buy at a certain holiday (like Valentine's Day). Inundating these customer with messages at other holidays is a waste of money, even a turnoff. But sending a reminder message that “Your Aunt June would love some chocolates this Valentine's Day” before the holiday, triggered a purchase. This effort wasn't possible when different parts of the company held close to their own bits of data and ran promotional offers more from gut feeling than analytical thinking. It took an enterprise-wide effort – and a culture shift directed by C-level executives – to empower those targeted "Aunt June" messages.
Getting to that enterprise-wide view isn't easy. One way to approach it is to use the Information Evolution Model, an approach originally put forth by SAS Senior Vice President Jim Davis in the book Information Revolution: Using the Information Evolution Model to Grow Your Business. This is an enterprise and strategic organization-maturity model for identifying, evaluating and improving information use – one worth the time to learn.
Last month I shared the idea of fostering better communication and understanding between the SAS users and the IT organization by visiting each other at your places of work. A variation on that idea is for both parties to share a visit to a safe, neutral, inviting location where there is a lot to see and learn and where there’s a chance to network with thousands of others who use SAS or who support the use of SAS. I have the perfect place in mind: SAS Global Forum 2014!
This year, SAS Global Forum is being held from March 23-26 in Washington, DC at the Gaylord National Resort and Convention Center. The theme of this year’s conference is Potential of One, Power of All – which nicely sums up the benefits gained when the SAS shop and IT work together rather than operate independently.
When the Seattle Seahawks won the Super Bowl, they didn’t do it because of just one factor – a great quarterback or an amazing running back. It was widely considered a team effort, helmed by a visionary coach who worked on developing a strong culture, cultivating the right players and encouraging some activities not typically associated with football like team yoga and meditation sessions.
What can businesses learn from the Seahawks’ example? It’s not about getting one thing right. This is especially true when it comes to analytics. So often companies think that if they just buy the right solution, they will magically increase revenue or profit. Lately, the publicity surrounding the need for a data guru has led some firms to mistakenly believe it is all about getting one of those.
The reality is that making data driven decisions, especially in the era of Big Data, is about getting four things right: People, processes, technology and culture. And while no one person can get the team into the end zone, having executive sponsorship (i.e. a strong coach like Pete Carroll) is critical to assembling the group that can score touchdowns.
Acronyms are funny things. Need an example? Try decoding this sentence: How is event stream processing (ESP), applied to electrical submersible pumps (ESP) in the oil and gas industry, like extrasensory perception (ESP)?
Even if you had extrasensory perception you would still need some clarification if that sentence contained acronyms only. Another humorous example is the importance of MDM for MDM in the utilities industry - or how master data management (MDM) is important to meter data management (MDM).
Now back to the real intent of this blog, which is to explain how embedding real-time analytics into sensor equipment for oil and gas producers can improve the operating efficiency of electrical submersible pumps in such a way that magic or extrasensory perception might be suspected.
Submersible pumps are very expensive to run, and any breakdowns can be costly from both time and money perspectives, as well as a safety perspective. Simple, if-then business rules applied in-stream can improve operations. However, when you add predictive and prescriptive analytics, you stop reacting to events and start proactively avoiding problems before they begin.