It's been 25 years since the iconic 1980s movie Top Gun hit the big screen, but the message from the movie is still true today � �We have the need, the need for speed.�
If you�ve never seen the movie, it�s about Lt. Pete "Maverick" Mitchell, played by Tom Cruise, and his adventures to overcome shortcomings as a fighter pilot at the Top GunFighter Tactics Instructor program.
Maverick was dangerous, he took chances, relied on his gut, made poor decisions, and �his ego wrote checks his body couldn't cash.� (Sound like anyone in your organization?)
And most importantly, he didn't buy into the classic fighter pilot methodology, the OODA (Observe, Orient, Decide, Act) Loop, developed by U.S. Air Force Col. John Boyd, and taught at the real-life Top Gun.
Boyd's philosophy was simple: Those who could quickly process this loop and react to real-time events better and faster than their adversaries could then anticipate their adversaries� thought processes and decision-making to gain an upper hand.
It's actually the same strategy that is being applied today from commercial and government organizations with IBM SPSS Decision Management technology.
The Need for Speed
Decision Management � through the combination of predictive analytics, business rules and optimization � enables organizations to automatically deliver high-value, high-volume decisions at the point of customer impact. Watch a demo here.
Essentially, it gives organizations the ability toensure optimal outcomes by injecting predictive analytics directly into the business process, such as cross-sell or up-sell marketing campaigns, reducing customer churn or minimizing the impact of fraud.
Without the combination of analytics + rules + optimization to improve a business process, an organization can effortlessly increase the velocity of bad decisions. To paraphrase from the movie, �I�ll hit the brakes and the competition can fly right by.�
(Watch the short video below of James discussing Decision Management and his new book.)
For example, if a high-value customer dials into the call center of a retail bank to complain about a product or service, IBM SPSS Decision Management may predict, based on the customer's past behaviors and interactions, that this individual is likely to churn.
The information about the current complaint, combined with the customer's history, can then be used to create a customized retention offer on the spot to prevent churn.
The bank has easily removed any blind spots that had kept them from making the right decisions, every time, with its customers.
And in an indirect way, it has turned the call center into a profit center, empowering employees to become an extension of the sales team rather than just �complaint takers.�
Never Leave Your Wingman
By the end of the movie, Maverick had finally realized that by trusting not only himself, as well as the philosophy of the OODA Loop, he could be a successful fighter pilot.
In other words, Decision Management becomes any organization�s ultimate wingman, giving the confidence to make the right decisions, at the right time, with amazing speed and agility.
Do you have the need for speed?
James Taylor, CEO and principal analyst at Decision Management Solutions, talked with us at the IBM Information on Demand conference in Las Vegas about how Decision Management works, why it's so popular, how customers are using it and best practices to get started with this technology.
� "My business is changing on a weekly and sometimes daily basis, and in order to stay competitive I need quick access to the data without IT getting in my way."
Are these comments common inside of your organization?
It�s an interesting battle of wits: Business users needing that fast agility to get at information, and IT needing to ensure governance and control.
IT is often painted as the bad guys because they create roadblocks and are unable to deliver what the business wants � quickly and consistently.
Business is viewed like spoiled brats who have no patience or vision and ultimately rebel.
It�s a dysfunctional relationship that thrives only because these �factions� are more similar than they realize. And, they need each other. It should be very symbiotic�if only they realized.
They are both working towards the same goal: driving the business forward.
But, in order to feel like they are accomplishing their goals, they need freedom from each other. Some might say they need an �open relationship.�
IT doesn�t want to be strapped to a barrage of mundane requests. Business doesn�t want to be constrained to the complicated systems and processes IT has set up for them.
Ultimately, business wants to live in a world where they can easily access the information they need (from any source), manipulate the data without having to be a spreadsheet programmer, and share it with others.
IT wants to be able to leverage the analysis the business user has been working on and still maintain the governance and control to ensure consistent information and use of that information across the organization.
Depending on which side of the aisle you sit, there is an answer and an easy way both can be successful. In fact, both sides can have their cake and eat it too.
We invite you to view the first chapter of our Business vs. IT story in the video below.
In the simplest of definitions, analytics is all about maximizing probability.
In other words, how do you use the information around you to gain a better advantage?
For marketers, business analytics has become an easy way to measure and prove success, but also to support the decisions that drive campaigns, help anticipate customer actions and even guide the selection of messaging and content.
Yes, a scientific approach has become an absolute necessity for modern marketing.
Lest not scoff at the idea of cold, clinical data driving marketing decisions. Heck, it�s been proven that spending $1 on business analytics technology will yield almost $11 in return.
Using analytics to drive better customer experience unshackles the organization from ignorance and maximizes the probabilities for increased customer loyalty, better up/cross-sell and sales conversion.
These organizations focus their analytics capability to gain insight on cost reduction and not at consumer personalization.
Most marketing efforts focus on segmentation efficiency, such as increasing the conversion of a selected group of customers by reduction and removal of messages (for instance, avoiding delivery of identical catalogs to multiple household members), thus lowering the cost of communication.
These firms can increase customer retention by up to 9 percent, capture 2 percent more wallet share and convert an extra 3 percent of inbound contacts into a cross-sell event.
Stage Two � Sharing the Goods
To keep pace with the mobile generation, organizations within this second stage must have a clear customer analytics strategy that enables information sharing across any digital device and channel.
In fact, research shows that tri-channel buyers spent an average of two and a half times more than single-channel buyers.
The most sophisticated marketing organizations in this stage apply analytics for marketing event optimization, an approach that leverages analytics as a �horizontal control tower� to optimize the organization�s various direct marketing events over a given time period over multiple channels.
This better aligns marketing with customers� needs � and varying personas � related to those devices/channels.
Stage Three � From Reaction to Action
This stage focused on information responsiveness.
These organizations are leveraging �raw� data as it streams customers� social commentary and changing moods.
To avoid a veritable data deluge, these organizations focus on identifying the questions that � if answered � will impact their business the most.
This acts as a filter on data collection and helps an organization avoid the task of collecting all available information and then deciding what to do with it after the interminable wait to standardize and analyze it.
Companies able to perform real-time external data analysis combined with rules-based actions have experienced average conversion rates of 16.9 to 38.2 percent.
Stage Four � Next Best Action
This stage focuses on executing a strategy that enables information on demand.
This approach combines all the skills developed in earlier stages with in-depth segmentation approaches and leading-edge work in multichannel customer monitoring and real-time action recommendation (read: Decision Management).
Using predictive analytics (combined with business rules), organizations are able to engage with the customer throughout the buying cycle � from the point of needs identification through the exploration and discovery phase to the purchasing cycle.
Those companies able to apply real-time predictive analytics while executing a multichannel next-best action strategy had an average converted response rate of 24.1 to 64.3 percent.
� Understand the different stages and get a better handle of your organization�s analytics maturity by downloading the full "Customer Analytics Pays Off" white paper.
� Also, read the "Five Steps to Improving Business Performance through Customer Intimacy� white paper.
�Registerfor the �Customer Analytics Pays Off� webcast (Feb. 15 at 1:00 pm ET).
More than 4,000 IT professionals from 93 countries and 25 industries shared their opinions andprovided their views on future IT trends, including how they plan to use Business Analytics (see graphic on right).
The report provides IT and business professionals a roadmap of the four critical and interconnected technologies and skills that will be in greatest demand in the coming years: business analytics, mobile, cloud and social business.
The U.S. Bureau of Labor Statistics predicts that there will be a 24 percent increase in demand for professionals with management analysis skills over the next 8 years. Helping to fuel this increase is the rising use of business analytics by companies in their efforts to learn more about their customers, including buying habits and preferences, as well as protect against fraud and mitigate risk.
Analytics skills are no longer just a requirement for the IT professional; they�ve become a necessity for organizations to remain competitive.
In a recent blog post, IBM�s Erick Brethenoux discusses how this analytics skills gap is getting proven by the significant widening of the overall performance between those that have analytics skills and those that don�t. Watch a video of Erick discussing this �epidemic.�
These IT professionals who gain the necessary analytics skills can also be change agents inside of the organization.
To make sure that organizations have the necessary talent, universities such as DePaul, Yale and Northwestern are also developing programs to prepare business and IT professionals with the analytics skills to bridge this gap, including the sophisticated analytics inside of IBM Watson to help understand the meaning and context of human language.
Other key findings in the Tech Trends report include:
� 42% of respondents named Business Analytics as an �in demand� area for software development
� Analytics has the highest adoption tendency (90%) when compared with other technology areas
� Half of those who are not currently using analytics plan to do so within the next 24 months, to increase automation, streamline processes and do more with less in faster time
� Survey respondents selected education and healthcare as the areas that could benefit the most, with financial services, life sciences and government also ranking near the top
� There is a growing importance of open source platforms such as Apache Hadoop and Linux for Business Analytics software developers
Interview with Ari Kaplan, Manager of Statistical Analysis with the Chicago Cubs
Baseball has always been ripe for analytics.
Former Los Angeles Times sportswriter, Jim Murray once said that �baseball�s appeal is decimal points; no other sport relies as totally on continuity, statistics, orderliness of these. Baseball fans pay more attention to numbers than CPAs."
The game is measured from generation to generation, year to year, and game to game on statistics.
It�s how fans discuss the game; and more importantly today, it�s how Major League Baseball teams measure the performance of its players and operations to gain a competitive advantage.
The notion of analytics and baseball will be thrust further into the spotlight when the movie Moneyball (starring Brad Pitt as Oakland A�s General Manager Billy Beane) is released later this month.
I was honored to speak with Ari Kaplan, the head of statistical analysis for the Chicago Cubs and the first official hire by Tom Ricketts, the current owner of the team, about his role, the importance of analytics in baseball and how the use of analytics continues to evolve.
How did you get into analytics and decide to make a career out of it?
During a research fellowship while an undergrad at the California Institute of Technology, I demonstrated that the statistics generally used (Earned Run Average, Wins/Losses, Batting Average, Saves) were not the best way to explain how players performed. While this is accepted today, at the time saying something like this received lots of attention in the media and in the industry itself.
The owner of a Major League Baseball (MLB) team approached me to offer me a position. Once in baseball, I have been able to contribute in many areas � from technology and analytics to scouting, advance scouting, player development, contracts and arbitration, and business development. I decided to make a career out of it because this is my passion in life and I have been fortunate to have the opportunities along the years.
This is my second full-time season with the Chicago Cubs, and I have consulted with them over the past 15 seasons.
Can you describe what you do on a day-to-day basis?
Being in the Baseball Operations, I have had the opportunity to get involved in many areas. There is the long-term development of our analytics and baseball-related technology to position us to be consistent champions on and off the field.
On a day-to-day basis I help prepare information for the coaches for games, do special projects for the General Manager and other baseball management, and try to stay one step ahead looking for ways for us to improve. There is a rhythm to the baseball season � Spring Training, the MLB season, the Minor Leagues, the draft, signings, trade deadlines, organizational meetings, Winter Meetings. These events set the pulse of what we focus on month to month.
What advice would you give to individuals thinking about going into a career in analytics?
If it is truly your passion, get into the game any way you can, put in the hours, and learn as much as you can. Then hopefully you'll "stick" and get lucky enough to parlay that into a full-time position. Also becoming a writer for a website such as Baseball Prospectus, searching www.pbeo.com, and going to the Winter Meetings are good ways to get into the industry.
How do you measure your effectiveness as an analytics professional?
Our goals are to consistently make the playoffs, progress through the playoffs, and win the World Series. If we do those objectives, great; if not, we need to self-evaluate why not and adjust accordingly.
What is the most common misconception that the public has with the use of analytics within major league ball clubs?
There is a public misperception of a rift between "old school" and "new school" that is a bit sensationalized. Everyone has the common goal of being a winning organization, of effective teamwork, and of doing what it takes to get from good to great.
How has the use of analytics evolved in the past few years?
New technology such as Sportvision's PitchFX and HitFX has changed the use of analytics dramatically. We now have significantly more data on pitch types, velocities, locations, spin, break, and more that can be used for really meaningful and actionable advice. And soon, FieldFX will help better understand and quantify defense like never before.
Any interesting �aha� moments that you have uncovered that you can share from your analysis?
These are humans, not computers playing. And humans often have subtle and repeatable habits that can be taken advantage of. A good advance scout can find these, and also reviewing millions of pitches and game events can help in that effort. Finding a strength, weakness, or habit to help win even one additional game a year is worth all the effort.
What do you think of the new stats of evaluating players, such as WAR (Wins Above Replacement), UZR (Ultimate Zone Rating) or BABIP (Batting Average of Balls in Play)?
Using stats depends on what you are trying to do. Are you helping a coach relay actionable information to a player? Are you seeing how Minor Leaguers or amateur players might have an impact at the Majors? Are you forecasting and valuing a player�s contract relative to others? Each stat you list is a generalization that could be useful or not depending on the context of how it is used.
Is there a rivalry among analytics professionals in MLB?
There is a great sense of camaraderie in the analytics world � with tons of really useful free information in the public domain. New blogs and websites pop up that enable the overall analytics marketplace to vet out ideas and improve methodologies. Within ball clubs themselves there is often an advantage to keep methodologies closed and proprietary to maintain a competitive advantage. So there's a mix of both out there.
What feedback do you receive from ballplayers in regards to using analytics?
For 23 seasons, I have worked with managers, coaches, and players, including Hall of Famers, All-Stars, regulars, replacement-level players, and those that have never made it. Everyone's approach is different � some want to learn everything they can and have the ability to adjust. Some want to learn everything they can but can't physically adjust to that information. And some don't really care or focus on different approaches. There is no right answer. It all depends on the individual.
Like players or managers, do you take the wins and losses home with you?
Certainly, all of my essence is devoted to helping the Chicago Cubs succeed and rewarding generations of fans. I am passionate about the game, and passionate about winning, and take with great pride being a representative of the Cubs organization.
Register for the upcoming IBM Business Analytics Forum (Oct. 23-27 in Las Vegas) and see keynote speakers, Michael Lewis, author of the best-selling book, Moneyball, and Billy Beane, General Manager of the Oakland A�s.
Twas the night ofbusiness analytics, when all through the org
No one in IT was stirring, the business felt like a morgue. Cognos Mobiledashboards were delivered to the iPad with care,
In hopes that the CEO would soon review them there.
The business line managers were nestled all snug in their beds,
While visions ofDecision Managementdanced in their heads.
With business rules and predictive models working in sync,
Automated, optimized decisions happen in a blink.
While over in finance there wasn�t any stress,
WithFinancial Performance Managementit�s no longer a guess.
Away to the budgets everyone flies like a flash,
To create flexible, rolling forecasts to always know how much cash.
And as the year ends, it�s time to look back
To close, consolidate and report to keep everyone on track.
When, what to the CFO�s wondering eyes should appear,
But an easy way to complete thelast mile of regulatory reportingto stay in the clear.
To anticipate customer behaviors, it�s hotter than a flame,
The industry is shouting, and calling for business analytics by name!
"NowCognos! Now,SPSS! Now,AlgorithmicsandOpenPages! IBM is taking business analytics out of the Dark Ages!
Lose the excel spreadsheets and head to the top of the charts
Measure yourAQ, that�s where the journey starts!"
With all these pieces any organization should be so proud,
Confronting the obstacle of big data? Let�s take it to the cloud.
And to not forget about all the social media noise
Taking things a step further, and to make all business users merry
2012 is when analytics gets personal, like a sundae topped with a cherry.
Interact and explore, build models and share insight
All without the help of IT, oh yeah, that�s right!
So spring to the laptop or any mobile device,
Away the business will fly, decisions no longer made by a throw of the dice.
And hear all employees exclaim, analyzing with all their might,
"Business Analyticsto all, and to all a good-night!"
IBM today announced the newest version of IBM SPSS Statistics software,its integrated family of products that addresses the entire analytical process, from planning to data collection to analysis, reporting and deployment.
The new enhancements in IBM SPSS Statistics v21ensure that the most advanced analytics techniques are available to a broader group of business users, statisticians, analysts and researchers.Making it easier to access and manage big data, set up and perform analyses, and share results across the organization, IBM SPSS Statistics now includes:
�Simulation Modeling� Using Monte Carlo simulation techniques, users can now build better models and assess risk when inputs are uncertain.
�Advanced Techniques for Big Data�Quickly understand large and complex datasets using advanced statistical procedures to provide high accuracy and drive quality decision making.
�Improved Integration�Deploy analytics faster with seamlessaccess to common data types and external programming languages, including Java and IBM Cognos business intelligence.
Monte Carlo Simulation
The new simulation modeling feature is designed to account for uncertainty in data inputs, such as determining how weather conditions affect energy consumption, how costs of materials (e.g., steel prices) affect profitability of a construction project, or to better understand risks around investment planning.
By using Monte Carlo simulation, theunknown inputs and historical distributions are used to create confidence intervalsand visualizations(see graphic) to help make the best decision.
For example, energy and utilities organizations run simulations on potential weather temperatures, compared against historical weather temperatures, to then determine how much energy it would likely need to generate for an 85 degree day on August 31. This process can be repeated many times (typically thousands or tens of thousands of times), resulting in a distribution of outcomes so users can make the best decision.
Unlike other software packages, IBM SPSS Statistics doesn�tforce users to start from scratch, but allows them to leverage existing predictive models and existing data as the starting points for simulation.
IBM SPSS Statistics now makes working with big data easier, more scalable and ensures optimal performance when working with multiple predictors. By introducing a data file comparison tool, users now have the ability to compare datasets or data files to identify any discrepancies and ensure that the data values and records are compatible.
Users can now compare files for better quality control. For example, users can now find discrepancies between data sets that contain responses by the same respondents to a survey, but entered by two different people.
Also, IBM SPSS Statistics now allows operations like sorts and aggregations to be pushed back to the database, where they can be performed faster. Temporary files created by analytical procedures can be distributed across multiple disks, and large files can be compressed to save disk space when sorting, improving performance and speeding up analysis.
For example, users can run multiple analytical jobs at the same time while continuing to work on their desktops at other tasks. Users can also continue to run server jobs while disconnected from the server without sacrificing the quality of their analysis or output, then reconnect to access their completed jobs.
With IBM SPSS Statistics, users can now use a Java� plug-in to call IBM SPSS Statistics functionality from a Java application and have IBM SPSS Statistics output appear in the Java application.
Finally, IBM SPSS Statistics now provides the ability to easily import IBM Cognos business intelligence data for analysis. Users can now read custom data with or without filters, and import predefined reports from IBM Cognos directly into IBM SPSS Statistics.
You know that feeling you get when you surprisingly find money in a pocket of your clothes?
There�s nothing better. It's free money.
And according to Nucleus Research, a provider of investigative IT research and advisory services, that's exactly what business analytics is for organizations.
In a new report from Nucleus, they found that "Analytics pays back $10.66 for every dollar spent."
Let's put that another way. Let's say you spent $1,000; the return is $10,000. Spending $10,000? That's $100,000 in extra revenue. And so on... (I rounded down for easier math.)
This number was calculated from reviewing all of the Nucleus Research case studies that have been produced and examining the implementations of analytics applications, such as business intelligence (BI), performance management, and predictive analytics.
In fact, the report states that "with such high returns to be earned on the deployment of analytics, management teams should consider these technologies to be one of the most attractive investment opportunities available to the CFO."
In fact, it would bring a smile to any C-suite executive.
In speaking to David O�Connell, the author of the report, he says that it's a matter of black and white when it comes to those who have incorporated analytics into their business.
"We have found that if we lined up 3-4 firms in the same industry and vertical side by side, those using analytics to guide their decisions would win. Analytics provides such a competitive edge and improvement to the bottom line that we could almost start handing out pink slips to those firms not adopting."
The Cincinnati Zoo, an IBM business analytics customer that participated in a ROI case study (download here), was facing tough operating factors with admissions and donations going down.
"They needed to find ways in which they could change their business model that could make them more efficient and profitable," said O'Connell.
For example, the zoo used analytics to learn moreabout when visitors were most likely to buy ice cream and made smallchanges to the operating hours of the ice cream kiosks, leading to anincrease in food revenues by 20 percent.
For organizations in any industry, O'Connell believes that it only takes a few insights into data with lots of leverage that turns into serious ROI.
That's the power analytics bring to organizations � whether it's better understanding the cost for a customer segment, realizing if a product has high or low margin or determining thatphases of the moon were a big indicator when crime would occur.
It's very much like the butterfly effect where small, unrelated happenings can have major effects on results in another area.
As Nucleus proves, deploying analytics creates those few shifts that produce revenues or lower costs.
So why aren't more organizations taking advantage of this technology?
The report talks about skepticism to technologies like analytics, but O'Connell takes it further.
"There is a complete lack of understanding about how much can be learned from analytics,� said O�Connell. �Senior managers � the CXOs � don't realize how blind their decision makers are flying right now. Organizations are relying on faulty reporting, organizational folklore and gut feel."
To be successful, organizations need to communicate and understand where visibility pain points exist.
O�Connell believes that building a business case on cost reductions and revenue increases is the way to go.
�When you use analytics, you become aware of so much granular information. Organizations suddenly realize how much they didn�t know.�
Just like that $10.66 hidden inside your jeans pocket.
For more information:
� Watcha video of Cincinnati Zoo discussing how it increased revenues by half a million dollars in less than one year.
Food trucks are the latest trend in the United States.
They�ve become a culinary staple in most major cities across the country. In fact, there is a sweet temptress called Flirty Cupcakes that is often parked outside the IBM offices in Chicago.
They are an easy and affordable way to eat lunch or grab high-end sweets during the week�all from the back of a truck. Forget going to a restaurant; now the food comes to you. You just have to know where to find it.
And even more important for the food truck business�you have to know when and where to find your customers. And parking.
They�re always on the go managing inventory, identifying the best geographies, tracking weather patterns, following traffic reports, and analyzing their customers, especially on social networks. And, food truck owners need to be fast on their feet (or wheels) when making important business decisions.
And,it�s not just food truck owners, but everyone is constantly on the move these days. The days of downtime are pass�. Uninterrupted productivity is the new normal, on any device, especially tablets and iPads.
Mobility reigns supreme. (It was a hot topic at IOD11 this week too, especially with the announcement of IBM Cognos Mobile for the iPad.)
In the mobile world, business and pleasure really do mix. Today, users want everything on one device � from music, books, email, social networking, and now the ability to interact � securely � with the same materials (online and offline) they do while sitting in the office.
Consider these facts:
According to a recent report from wireless industry association CTIA, wirelessly-connected devices now outnumber the number of U.S. citizens.
Industry analyst Howard Dresner reported that 80 percent of organizations ranked Mobile BI as a top priority for executives.
In research from Gartner, Inc., 33 percent of all BI is being assimilated on mobile devices.
Simple, Secure & Reliable
In today�s digital world, the appetite for information never stops. Speaking of delicious consumption, let�s get back to the food truck.
Like any organization (especially retail food sales) having a Mobile BI strategy is paramount for its success.
It�s Tuesday morning and the food truck has just departed for its first stop in the city. Using IBM Cognos Mobile, the driver can easily:
Plan the route for the day and drill into the data to understand where the biggest demand will be for each location and at the appropriate time of day.
Equipped with �location-aware� intelligence, the truck can receive reports that are dynamically filtered with location-specific information on planned stops.
Manage inventory levels of supply based on the day�s route and past sales so they can be better prepared to remain longer or shorter at one location if sales are up or down.
After each stop, update the inventory and total sales on the spot to avoid any errors.
Constantly check for updates on weather and traffic for possible delays and the chance of no customers showing up if it�s raining.
Log other dimensions into the applications (such as weather and traffic) to help better identify trends and opportunities through forecasting, planning and analysis.
Update customers via social media of when and where they�ll be and for how long.
Capture customer feedback in real-time through surveys that customers can fill-out directly on the mobile device.
Just remember � the next time you get hungry and see a food truck parked in your city, think of the benefits that Mobile BI offers�and how it could enhance your organization�s ability to make better decisions.
For more information on IBM Cognos Mobile:
Readthe press release announcing IBM Cognos Mobile for the iPad.
Downloadthe new (and free) IBM Cognos Mobile App for the iPad in the iTunes store.
Watcha demo how users on anydevice can have the same rich and visual business intelligence experience they get at the office.
Guest post from Jing Shyr, Chief Statistician & Distinguished Engineer, IBM Business Analytics
It's the age-old question: why did the chicken cross the road?
With one chicken, the answer is easy to compute.
But, what if there were millions of chickens crossing the road? And each chicken had a mobile device and was tweeting out its opinions, desires, likes/dislikes, photos, and detailed descriptions of what it had for breakfast that morning. Oh, and what if that road was being monitored by millions of sensors?
With current statistical techniques, it's no longer easy to quickly understand why each chicken decided to cross that road and, more importantly, predict when they might cross again.
The business analytics and statistics industry faces tough data analysis challenges in the coming years, including lack of skills, easily consuming analytics, mobility and big data.
Having been around the analytics industry for many years, it is refreshing to see that businesses are taking statistics and data mining results and injecting them directly into the business (and directly into the business process itself). The Catch-22 is that while more and more organizations are realizing the benefits of analytics, finding those professionals with an understanding of how to not only capture and analyze the tsunami of data created daily still requires training and a unique skill set.
A recent McKinsey Global Institute report indicates that over the next seven years the need for highly skilled business intelligence workers in the U.S. alone will dramatically exceed the available workforce � by as much as 60 percent.
I often imagine a business analyst presenting results to an executive the same way I present to my students. When teaching a lesson on modeling, I often ask, "Do you see what I see?" Everyone stares with blank looks on their faces and says, "No! What do you see?"
Herein lies part of the problem. To help counteract the skills shortage, we have to make the software easier to use and force the software to be consumable versus strictly scientific. Communicating results is just as important as the results themselves. I strongly believe that statistical software needs to go through a revolution of its own and become as intuitive as a smartphone.
And speaking of smartphones...
Most statistical software produces an incredible amount of very large tables and charts, making it extremely difficult to comprehend in a mobile environment. I torture my eyes every time I try to read a report on my Blackberry.
Consumability means anywhere, anytime and through any device. It's time we hold statistical s
oftware to a higher standard.
Let me get back to the chickens for a moment.
The volume, velocity and variety of data today is seemingly overwhelming traditional statistical software. Not to be clich�, but Big Data is giving the statistics industry big problems.
Previously, if we wanted to analyze any data, we would follow the same logical flow: decide what we want to predict or classify and build a model by bringing in all the predictors (independent variables). The size of predictors are often well below 100.
Today, however, we are dealing with thousands of different variables making traditional statistical analysis a serious hurdle. The machine capacity is no longer capable and many algorithms have been outpaced by data capacity.
The challenge calls for a new process of data reduction before modeling and new computation algorithms are required to handle millions of records and fields quickly in a distributed environment without passing the data back and forth multiple times.
Most importantly, we don't need to be chicken when it comes to Big Data.
Creating new statistical techniques for Big Data will get us all to the other side of the road, and you'll never have to ask why.
It has almost become a daily occurrence when I arrive home from work and go through the mail to find yet another credit card offer from my bank, even though I already have a credit card with that very same bank.
Shouldn't they already know I have one of their credit cards? Do they even know me? Or, care to?
That�s the problem.
And according to Mark Smith, CEO and Chief Research Officer, atVentana Research, it�s imperative that banks and insurance providers anticipate and align their business around the customer.
�This is whybusiness analyticshas become a required must-have,� said Smith. �Business analytics is a significant set of technologies that is going through a huge transformation. It now plays a key role in everyday business processes, drives improvement and helps meet customer expectations and satisfaction.�
This is especially true for today�s customers who are savvier and price sensitive, and far less loyal. Customer experience now goes a long way.
Whether its banks or insurance providers (or any industry), business analytics is forcing disparate parts of those businesses to speak to each other, share data and create a personal experience for the customer.
However, according to research from Ventana, the problem is that only 1 in 5 banks and insurance companies are using business analytics at an innovative level of performance.
Smith said that these firms either have great people with skills, but struggle with technology, or have the technology but aren�t using it to drive improvements.
�Firms need a balanced approached across these elements in order to improve their analytics maturity,� said Smith.
What is holding these organizations back from taking the next step?
According to Ventana�s banking and insurance benchmarks, only 42 percent of banks and 37 percent of insurance providers are satisfied with their current analytic process.
Smith points to three main barriers that halt analytic success:
� Wasted time due to data related activities (preparation, fixing errors)
� Lack of intersection between the business and IT (read a recent blog post on this rivalry)
� Continued use of spreadsheets that increase risk factors and contribute to poor decisions
What can banks and insurance providers do to generate better value from their analytics?
According to Smith, 68 percent of companies surveyed said the most popular use of business analytics today is in customer service. What is most interesting, however, are the least popular uses which include customer profitability, voice of the customer, marketing campaigns, communications channel usage and social media (at a lowly 4 percent).
These are the areas where the use of customer analytics can have exponential effects, and according to Smith, become a true measure of innovative maturity.
This is when the analytic process gets deeply integrated into every business process and customer touch point to make every interaction personalized.
Guest post from Anuj Marfatia, Senior Market Manager, IBM Predictive Analytics Solutions
Not to frighten anyone, but there are only five weeks before the holidays. The pressure is on.
In the U.S., the holiday shopping chaos, advertisements, music and decorations now begins on Nov. 1, right after Halloween. I actually feel bad for Thanksgiving. Somehow the poor bird has lost its mojo, though I don�t have time to think about it.
With the holiday season in full swing and Black Friday looming, I�m already worried about missing out on this year�s most popular toys for my family.
Like everyone, I promise myself that I will shop earlier, but in the end, I am usually sifting through the shelves of Toys R Us or Target on Christmas Eve that are stocked with items that no one wants or are insanely overpriced.
So, I end up scrounging the floors, hoping that someone else had mistakenly dropped a toy that I could use. (How does it go? Someone�s garbage is another�s treasure?)
I have always been late to the popular Christmas toy party. Even as a child, I remember getting the Rubik�s cube not in the early 1980s when it was hot, but a mere 15 years later. I was determined not to be last when it made its comeback.
What always surprises me is that the popular items are usually talked about and expected to be popular a month or two before the holiday shopping season begins (I could bet today that the XBOX Kinect and My Pillow pets are going to be hot this year), and yet there is never any in stock � either on the shelf or online?
So what gives?
Aligning Marketing, Inventory and the Supply Chain
Assuming that the corporate strategy was not to provide fewer products, it has become apparent that organizations have a difficult time aligning inventory with demand � and the holiday season always puts a strain on operational processes.
Granted, it�s not an easy task for retailers to determine which product and how many of them need to be on which shelf of which retail location and then streamline the manufacturing and distribution processes to meet demand for that specific product.
Or is it?
Organizations traditionally have used the approach of viewing sales from previous months or years and extrapolating how many will be sold in the coming year. Then, manufacturing follows that schedule. At times, this process is more art than science.
And, these organizations aren�t receiving ample feedback from customers, nor are they listening to what their customers are discussing in the socialsphere. In addition, they don�t take into consideration:
� The complaints that recently came up on Facebook regarding a competitive product or an earlier version of its own product
� What to do if 10% of the warehouse team just quit?
� The operational processes affected knowing that raw material prices have increased by over 30% in just a few days
� How to account for decreased consumer income due to the economy?
The Gift that Keeps on Giving
Now, more than ever before, technology exists to analyze all the consumer and organizational data so decisions can be made in real-time to account for macro or micro changes.
Wouldn�t it be great to be able to predict price elasticity, how many products are needed to meet demand, where on the shelf it should go to maximize sales, and how much product can be manufactured with the raw materials and resources that are at hand?
Take, for example, a US-based consumer electronics retailer. The past few holiday seasons, some specific tablets were purchased almost immediately when placed on the shelf.
There was a lot of lag in the supply chain process and by the time additional products arrived to the store, the season was over, so they were leaving much money on the table and were overstocked during the New Year. In order to eliminate their excess stock, they were forced to provide additional discounts to try to open shelf space.
Last year, by deploying predictive analytics software they were able to better predict customer purchasing behavior and demand, and better anticipate failures in the manufacturing and supply chain processes to ensure that they had enough inventory during the holiday season.
Predictive analytics leverages all the consumer, distribution, inventory, and manufacturing data inside the organization, as well as all the social media conversations happening outside. It then runs that data through predictive models, so organizations have a probability or likelihood of what products need to be on the shelves (and always on the shelves) for the holiday shopping spree.
I�m not just being selfish when I say this, but, I know I can speak for many that if more organizations utilized predictive analytics to align supply and demand during the holiday season, there might actually be an XBOX Kinect under the tree this year.
Otherwise, it may be another year of extra Halloween candy stuffed in stockings. Sorry family!
For more information:
� Watch the Predictive Operational Analytics video
� Read the whitepaper on Predictive Operational Analytics
� Get insight into how an auto parts retailer used predictive analytics to align inventory and customer demand
In this edition of �Ask the Industry Analyst,� we sit down with Howard Dresner, Chief Research Officer of Dresner Advisory Services, and a well-known authority and author in the areas of Business Intelligence (BI) and Performance Management (PM).
Howard also recently was a guest on our monthly webcast, IBM Tech Talk, discussing best practices and trends in Mobile BI. The webcast is available on-demand here.
As we�ve seen, organizations today are looking for new user experiences that expand traditional BI solutions with planning, scenario modeling, real-time monitoring and predictive analytics. Using a limitless BI workspace supporting how people think and work � in the office, on the go and even offline � decision makers want to quickly search and assemble all perspectives of the business.
Below we chat with Howard about upcoming trends in BI and PM, the �operationalizing� of analytics, results from his 3rd Mobile BI Market study and what to expect in 2012, including cloud, collaboration and mobile.
You've been around the BI and PM industry for many years, what changes have been the most significant for customers?
At a macro level, BI has become very mainstream and the adoption of BI across large enterprises and SMBs is substantial. It has always been a high priority for users and management alike, however, now the technology is approachable for more average types of users. Vendors are now designing their products for end users in mind and not IT. This is especially true in the mobile world.
We are also starting to see customers using BI as part of their business applications (e.g. ERP or CRM) from the beginning. Customers are thinking of BI and analytics at the outset and the value this analysis provides, rather than processing the data and then waiting for someone to come up with an idea of how to analyze it.
Can you talk more about the mainstreaming of analytics?
Moving forward there are several things that are really important to customers. Topping the list is pushing BI and analytics further down into the operations of organizations to professional line management roles.
Traditionally BI has been a really valuable barometer providing a strategic perspective on the historical performance to date. As organizations continue to amass varying data sources � and more of it � they have to have an easy way to push this intelligence to tactical areas of the organization so quick decisions can be made as opportunities present themselves.
For example, it�s becoming increasingly important for retailers to correct something minor at a store level before it becomes a significant issue. I talked to one retailer who was alerted because a popular SKU was not selling well on a particular day as opposed to other days and compared with other stores. This information was passed to the store manager in real-time, and they learned that there was something physically in front of the display preventing customers to see the product.
Bottom-line: A minor course correction multiplied by thousands of stores can be extremely significant.
This is very similar to what we found in ourWisdom of Crowdsstudy in May 2011 along with advanced analytics (data mining), in-memory analytics, collaborative decision-making and mobile.
The world is changing, so fasten your seatbelt. Mobile (e.g. tablets) continues to have a huge impact on business and the way decisions are being made. For a number of individuals going forward this will be their primary device, especially younger employees who have never actually used a computer.
In a number of customer or patient-facing industries, mobile is just far more efficient and ideal. It also has a psychological aspect that makes the device far more approachable and creates a better conversation versus someone talking over a computer.
Tablets also holdcach�and a cool factor. And, executives are the number one consumers because of that factor, as well as the fact they provide tremendous value. Once the executives adopt them, then you will see a huge proliferation in the devices. This is very different from what we saw last year.
What were the major changes in the results of this survey over the last one you did?
From 2010 to 2011, executives jumped 12 percent as the primary targets, followed by middle management, who were the biggest jump for the primary focus of Mobile BI. It�s interesting to see executives get excited about Mobile BI as most of them own a tablet.
There was also a big increase in penetration and deployment plans. Globally last year, 73 percent said deployment would be under 10 percent, but now it�s 58 percent. Looking out even further, there are very ambitious and aggressive plans to deliver Mobile BI more broadly.
North America and smaller organizations appear to be leading the charge towards Mobile BI. North America because they tend to be early adopters and smaller organizations because they can most readily integrate and benefit from new technologies.
Finally, 65 percent said exclusive Mobile BI use wouldn�t be less than 10 percent over the course of the next two years. That sounds like a paradigm shift. It�s changing the way we work.
What is some advice that you can give to customers who currently have a Mobile BI solution or are thinking about deploying one?
If you are not doing mobile now, begin a proof of concept as soon as possible. This technology is not standing still. If you wait, you'll never do anything. There are huge direct benefits to the organization, which at the minimum are efficiency and effectiveness, especially for those in operational roles. Dragging your feet is not an option.
Secondly, you need to ask yourself, �What do I want to automate?� Anyone who is moving from their desk to somewhere � across campus, to a manufacturing shop floor, or the traditional road warrior � is mobile. So pick your targets. In fact, those people might already be out there and using their devices for business. Go find them and automate those people and their processes.
What are some of the key BI trends moving forward that will create opportunity for customers?
There are three key foundations of BI moving forward: Cloud, Collaboration and Mobile.
�Cloud� Today, smaller organizations seem to think that BI wasn�t made for them. That is untrue. They won�t have the technological staff or resources, but they will have an internet connection. BI can, and will, happen in the cloud for those who want ready-access to applications and data. And, more vendors continue to invest to make this technology a success.
�Collaboration� We have fewer expert resources and truth be told, email doesn�t really help us as much as we need it to. So, if I have a collaborative engine that is supporting my functional area, I can focus all the interactions in one place increasing the ability for structured and workflow collaboration. But, this will only be successful if the organization supports a collaborative culture.
�Mobile� Contrary to what IT and finance might want to believe, Mobile BI is going to shift the industry. The insights now follow you around and with more eyes on the data, organizations can better align their employees with the overall mission and increase the confidence in the decisions. It also creates a better culture of accountability and transparency. Eventually an organization will turn on its afterburners when the culture aligns with BI.
For more information:
�Listento the recent IBM Tech Talk with Howard Dresner
There's a series of AT&Ttelevision commercialsrunning in the U.S. that portray how quickly things move in today's digital age.
Twitter, Facebook and YouTube (among others) make it easy � and difficult � to keep up with the latest news, trends and funny baby or animal videos.
By the time you see these items on your desktop or mobile device you quickly realize you are behind the times. That was so :27 seconds ago. Or worse.
In the world of analytics this has never been more true.
27 seconds (or less) is all a retailer, telecommunications provider or insurance company has today to effectively interact with a customer and take the appropriate action � making an offer, fixing a problem, or identifying possible fraudulent activity.
Time is the essence�especially in the world of social media.
Reigning in social media chatter has become a necessity. It�s not just listening to what people are saying, but understanding what they are doing, what they�re thinking and how to better engage with them.
Henkel, a leading producer of laundry and home care, cosmetics and toiletries and adhesive technologies based in Germany, recently deployedIBM social analyticsto better understand what its customers were saying about its brands in the social sphere, and more importantly where, so it could refine its message and take better action.
One of the interesting discoveries for its cosmetics business was that customers that were talking about hair were doing it on a cooking social network. They figured that once at a site, people were likely to remain on that site and continue talking about various topics. Knowing this, Henkel was able to better optimize keywords and better market appropriately on this same site.
While Henkel is finding success, many organizations are still unable to tap this precious resource due to lack of understanding of analytics or lack of in-house analytics skills.
This is why more and more universities are creating programs specifically focused on analytics, includingNorthwestern University, who recently announced two new programs, a full-time Masters of Science in Analytics in the McCormick School of Engineering and Applied Science and a part-time Masters of Science in Predictive Analytics program in the School of Continuing Studies.
Students coming out of college today are byproducts of the digital age and intuitively understand social platforms. They are not only the largest consumers of digital information, but also the purveyors of the content, and are the ones that will parlay their social media prowess into a lucrative career that will turn this social data into business value.
Scott Kellert, a student at the McCormick School of Engineering at Northwestern, commented that organizations will soon realize they need his skills to turn vast quantities of data, especially social media data, into something meaningful that can be quickly applied to improve the business.
�What I love is that analytics can be applied to everything � from insurance fraud to marketing to student retention,� said Kellert. �The new program at Northwestern will take my skills to the next level. Future employers will have confidence that I will know exactly what to do when I encounter large data sets and how to get value from them.�
Value is the operative word�and quickly.
If 27 seconds is all organizations have, they better be precise � and be adaptive to data that changes every minute to catch trends as they are happening, such as in the entertainment (X Factor) or fashion (high-heeled shoes) industries.
Think about if organizations are actually still using a spreadsheet to analyze their data, let alone social media data.
Have you ever had that awkward conversation with a significant other where they tell you they just want to be friends?
Sometimes the news is hard to swallow. It forces you to ask yourself, �What could I have done better?�
This same tough conversation needs to happen with certain software applications too. People just stay in relationships with software for too long. That said, it�s time to have the �friend talk� and break up with spreadsheets.
You�ve never really loved them. It�s been a relationship of convenience � they just showed up one day on your laptop and the rest was history. Yes, they�re nice and have a good personality (as much as software can), but it�s time to cut the cord and just be friends.
Disclaimer:I am not attempting to disparage or declare war on spreadsheets. They serve a useful purpose and will always be a staple inside organizations, but they are not the analytic application you want to bring home and introduce to your parents.
Spreadsheets have been widely used for financial and cost accounting, data collection and analysis, and mathematics. But, when they are called upon to perform a task for which they are not designed or beyond the limit of their capabilities, spreadsheets can actually be a fatal attraction.
Mark Smith, CEO and Chief Research Officer at Ventana Research says that spreadsheets �can be one of the most expensive pieces of technology because of the risk and wrong decisions that are made due to their numerous errors.�
In fact, a number of studies have indicated that 90 percent or more of spreadsheets contained errors.
And Bruce McCullough, the software editor for the International Journal of Forecastingwrote that �Professional statisticians continue to write books with titles like �Statistics with Excel,� but they now warn students not to bet their jobs on Excel�s accuracy. They advise students to use a real statistical package when they need to do statistics.�
So, I guess you have to ask yourself, do you want a meaningful relationship with your data, such as being able to perform detailed analysis, find hidden patterns or make reliable forecasts, instead of a dangerous liaison that gives you little in return, besides frustration?
Speaking of meaningful relationships:
�Elie Tahari, a global fashion brand, found that its retail controllers were struggling with monthly budget reports because its 22 locations submitted spreadsheets separately. By turning this task over to a more robust business analytics solution, they were able to create a seamless reporting framework that provides granular, real-time information from the sales floor to its suppliers� inventory and production schedules. They reduced their reporting cycle from as many as two days to a few minutes, and saw a 30 percent reduction in supply chain and logistics costs. (Read the case study.)
�Checkers Drive-In Restaurants Inc., the largest chain of double drive-thru restaurants in the United States, relied on spreadsheets for financial planning processes that were taking up to three months each year. By breaking away from this burden, they are now able to get the same jobs completed in three weeks, do better forecasting and more quickly respond to changing economic conditions. (Read the case study.)
It�s been said that once you break up with someone, remaining friends is almost impossible. Things just get weird.
Not true with spreadsheets. They�ll still be hanging out on the laptop, going to the same meetings and most importantly, will play a prominent role in sharing the results of the analysis across the organization (if you so choose).
But they will be frustrated by their shortcomings and say, �I wish I could�ve done better.�
For more information:
� Registerfor the upcoming webinar: �The Risks of Using Spreadsheets for Statistical Analysis.� (February 15 at 12:00 pm ET)
� Readthe whitepaper: �The Risks of Using Spreadsheets�
� Attendour upcoming IBM Innovations in Business Analytics Virtual Launch (March 7, 2012) to see new solutions that will give you a more personal relationship with your data.
Guest post from Erick Brethenoux, Executive Program Director, Worldwide Predictive Analytics at IBM
In 1978, an emotional and dramatic award-winning documentary film was released titled �Scared Straight!� It profiled troubled teens who were taken into maximum security prisons to stand face-to-face with inmates who �explained� the harsh realities of life in prison.
The goal, according to the documentary, was �to keep at-risk teens from becoming tomorrow�s prisoners.�
Consider this an analytic scared straight moment.
We have a serious skills gap in the analytics field�and it�s getting worse.
A recent McKinsey Global Institutereportindicates that over the next seven years the need for highly skilled business analytics workers will exceed the available workforce by as much as 60 percent.
And by 2018, an additional 190,000 "deep analytical talent" workers plus 1.5 million more "data-savvy managers and analysts" will be needed to take full advantage of big data.
In conversations with customers and prospects, it has become apparent that there is a significant widening of their overall performance � both in terms of increased revenue and lower operational costs. It�s amazingly disproportional and not linear.
Organizations that are outperforming their peers, through the use of analytics, are making quicker progress, capitalizing on their growing experience while monopolizing an increasing amount of analytical talent.
This creates a wider gap, making talents even more difficult to secure for those companies jumping on the analytical bandwagon � therefore delaying their progress.
These are the harsh realities of analytical life. It�s time to make a change�and soon. Those organizations that don�t will be forced into a life of using Excel spreadsheets. Talk about doing hard time.
Spreadsheets, while fine for certain tasks, aren�t the answer to keep up with the ever-growing amount of data being created and the more and more complex decisions that have to be made.
Nor are they capable of handling the pressures of customer demand. The ability to respond to customers at the right place and right time, and with the right offer based on a customer�s current mood and sentiment, is a job for those that know how to navigatedecision management solutions.
It�s becoming an arms and skills race segmented among �The Haves,� �The Have Nots,� and the �Never Will.�
��The Haves�� These commercial and government organizations have taken the time to understand the value of analytics, hired the right people, received the proper training, identified the business issues, and deployed business analytics technology to create opportunities to drive the business.
They have been doing it for a number of years and will only keep advancing.
��The Have Nots�� These organizations are just beginning to implement analytics, or will soon. Unfortunately, they will also have to spend more money to catch up, in terms of services, training and domain knowledge. But, at least they are getting into the game.
The pool of analytics talent, however, is rather shallow at this point�either already hired by the �Haves,� or on the verge of retirement, so analytics projects will be limited or outsourced to technology vendors.
��Never Will�� These organizations will soon be destitute and on the street struggling to find anyone with basic statistics knowledge.
By the time these organizations even think about an analytics solution, even IBM might not have the resources and services to help them. The market will be so desolate that what even looks like a mirage won�t have any water. And what few professionals are available won�t come cheap.
But, all is not lost. There is hope as many of you will learn at theIBM Information on Demand(IOD11 and BAForum) conference next week in Las Vegas (Oct. 23-27).
There are many programs already in place to help ensure that organizations will reach their full analytic potential:
�Over the past five years,IBM has initiated academic programswith leading universities around the world, includingYale,DePaul,Ottawaand others, to provide analytics technology and training resources so students can be prepared for 21st century jobs in analytics.
�IBM is continually innovating to make analytics technology easier to use, such as the aforementioned decision management. Business professionals can now build a predictive model in just three clicks.
�Even business intelligence solutions are made-to-order nowadays�for everyone (from the individual to the mid-market to the enterprise�and anywhere (with the newCognos Mobile for the iPad). It�s true analytic freedom.
�And, IBM�sGlobal Business Servicescontinues to expand its pool of talent as it will have almost 9,000 card-carrying analytics experts by the end of the year.
Become an Analytics Champion
Engage today. Don�t wait. IBM has been helping organizations deploy analytics and get the most from their data for almost half a century. We would love a chance to discuss, and then we can advise on the right training and education.
And if you need a quick start to find out how mature your organization is with analytics�take ourAQ quizthat will guide you through the steps needed to continue on the analytics journey.
If you�re not aware of Moneyball, it�s a behind-the-scenes story of the Oakland Athletics and how they changed the game and leveled the playing field through the innovative use of analytics that allowed the team with the lowest budget to consistently compete against the big market, deep pocket teams.
Moneyballtook the veil off a much-treasured secret and demonstrated that new ideas could produce positive results in the traditional world of Major League Baseball.
The book was also recently made into a major motion picture � in theaters now � starring Brad Pitt as Billy Beane. It is receiving rave reviews.
The Information On Demand social media team had the opportunity to speak with Michael Lewis about his book, the movie and the parallels between baseball and business.
Who Is Going To Read A Book About Analytics?
In the late 1990s, Lewis was living in Berkeley, Calif., and started to pay attention to the local baseball team, the Oakland Athletics.
He had some awareness of the payroll discrepancies in baseball and thought it was strange how many games the Oakland A�s were winning given how little money they had in relation to the competition.
�The answer was so shocking to me that this team, in response to its financial disadvantage, was rethinking the game of baseball that I launched into Moneyball,� said Lewis. �This was a weird book for me. I had never written a word about sports and if you asked me what �Sabermetrics� was, I�d have guessed it would have had to do with fencing. I didn�t have any idea this world existed and didn�t realize how rich the environment was until I got into it as a writer.�
Beane, however, wasn�t worried about his secrets getting out. He was more concerned about what his mother might think of the way he spoke, mainly his profanity.
When Lewis asked him if he was going to be upset for giving away his secret formula, Beane laughed and said, �Do you really think people in baseball are going to read your book?�
Leveling the Playing Field
Today, every team in baseball has turned its sights to the once dark art of analytics and the playing field has been leveled. Now big budget teams like the Boston Red Sox are using this strategy to draft players and identify free agents.
�When the book came out, the markets were poised to become a lot more efficient,� said Lewis. �And, when the Red Sox decided they were going to apply this new way of thinking to players and baseball strategies�that was the beginning of the end for the A�s advantage. Now it�s normal. The war is over.
�If you�re a team that isn�t trying to be on the cutting edge of using data to better value players and strategies, you�re at risk of being exploited in the marketplace and everyone understands that.�
Don�t Let Statistics Become Fetishized
If baseball can take analytics to the field, why don�t more organizations use the technology in their game plans? The benefits are endless.
Lewis believes that any organization � from sports to business to government � �needs to be looking for new ways to mine their data, and new ways to think about their data.�
But he also warns that baseball provides a great best practice for any business thinking about deploying an analytic solution.
�The funny thing about this story,� said Lewis, �is that it�s true the Oakland A�s set about trying to create new data and generate new information that wasn�t on the baseball field.
�But, a lot of the inefficiency in the game came from the misuse of the data that existed. The data was there, but people were just not thinking about it properly. So you could easily calculate a player�s on base percentage, but baseball was not appreciating the value of the statistic.
�And, to me the story is not just the importance of the data, it�s a story of being careful how you use it once you have it. Because the minute you start to measure something and have a statistic, it has a tendency to become fetishized, like a player�s batting average.
�Unfortunately, it wasn�t a key offensive statistic and it led players to be misunderstood.�
It�s like a marketing department only doing simple segmentation to identify customers for a direct mail offer. This analysis provides a somewhat superficial view of the customer and leads to one-to-some direct marketing (and lot of junk mail).
Basically, it perpetuates accepted wisdom that all customers (and baseball players for that matter) are created equal.
Change is Good; Don�t Be Scared to be an Innovator
For baseball teams (and businesses and government agencies), now comes the hard part � continually innovating to find new statistics that have hidden meaning.
Lewis says the low-hanging fruit has been plucked because it was relatively easy to assign statistical credit and blame to what happens on the baseball field.
However, if athletes weren�t so expensive nowadays no one would care about the ramifications of clean data and in-depth analysis.
Nor would the business world � except in today�s environment, where acquiring a new customer is that much more expensive than proactively keeping one.
�The business decisions become extremely important,� said Lewis. �It�s worth investing in complicated ways of evaluating them [players and customers] because if you find a slight edge it means saving millions of dollars.�
That is why those in the C-suite (and in many instances IT organizations) need to become more accepting to analytical techniques and not be afraid of what the data often reveals, or how it might change business processes.
In Beane�s case, he had to change if his ballclub was going to be competitive and survive. He challenged baseball�s traditionalists and angered the gods of conventional wisdom.
Sometimes the world isn�t flat.
Sometimes it�s white, round and has 208 stitches.
For more information:
Listento an audio interview of Michael Lewis discussing the movie and his upcoming session at the conference.
Reada recent IBM interview with the head of statistical analysis for the Chicago Cubs.
Registernow for IOD11 and BAForum; and start building your schedule.
Guest post from David Pugh, Program Director, Product Management, IBM Business Analytics
I really love speaking to customers who are pushing our software to its limits. Those customers who are at the bleeding edge � the innovators and early adopters � regularly have great input to the creation of the products of the future.
Why? Because they are using the products in anger � pushing the software up to and beyond its intended use in order to drive competitive advantage and increased revenue.
The story starts five years ago when I went on a �world tour� to spend time with customers with some of my product management colleagues.
In particular, we were interested in customers who were actively deploying and using the results of predictive analytics in their day to day business operations, such as:
�Auto insurancecompanies using predictive modeling to determine on the fly � while the customer is on the phone describing the accident � whether the claim was possibly fraudulent
�Retail banksproviding personalized marketing offers to their online banking customers, trying to sell them additional products
�Mobile telecommunicationsproviders scoring millions upon millions of customers every night looking for any signs that they were going to defect to a competitor
As I said, the customers we visited were using our products in anger, like a coach demanding the most from his/her players.
For instance, the customers were using Decision Management in conjunction with other applications (e.g. CRM, call center, websites and campaign management); had stringent performance requirements; and, all had inventedtheir own methodology for managing / updating the predictive models that were being deployed into these front-end, operational environments.
It became apparent that the processes used to create, deploy and manage predictive models were eerily similar.
In fact, thanks to our customers, we were able to develop a list of best practices to easily create predictive models and inject the results of the analysis directly into business processes to improve outcomes.
The Seven Steps to Analytics Deployment
1)Acquire the Data.Customers use a mix of data including transactional, demographic, call center notes, social media, and attitudinal data from customer surveys as input to the modeling process.
2)Identify the AudienceDetermine the population for whom the outcome of the decision is valid. For example, with regards to insurance fraud, the customer may want to exclude any insurance claims that are caused by natural disasters (and process them a different way).
3)Define the Desired OutcomesThis is the heart of �The Seven Steps� where the customer determines the range of �Decisions� that could be delivered into their operational environment.
For insurance fraud, the desired outcomes that would be ideally delivered as a �Decision� to the call center agent processing the claim could be:
�Fast track the claim � low risk of fraud and low cost
�Push through standard processing � low / medium risk of fraud
�Refer the claim to the special investigations unit � possible fraud.
4)Enlist Business Rules and Predictive Analytics to Determine the Ideal Outcome.If Step 3 is the heart, Steps 4 and 5 are the brains. Staying with the insurance fraud example, there may be a number of policy (business) rules that need to be applied to the decision, such as �All claims made within two weeks of setting up the policy must be investigated for fraud.�
Customers were also building predictive models that determined � based on historical examples of fraud � the liklihood that this particular claimant was behaving fraudulently.
5)Optimize the Outcome.What if the business rules output says �Refer the claim to the Special Investigation Unit� and the predictive model says �Push through standard processing�? The user may decide whether rules override models or vice-versa.
For marketing applications it is here that a user could optimize which of the five valid marketing offers would be made based on factors such as liklihood to respond, revenue and cost.
6)Deploy, Deploy, Deploy!Take the intelligence defined in steps 1-5 and deliver it to the appropriate business process. Once the IT configuration has been completed this is typically a one-click process.
7)Report and Monitor.Watch the performance of the deployed application and ensure that it continues to perform well, as well as share the results across the organization in easy to understand reports and dashboards.
Business users typically need to update rules, predictive models or the way in which the result is optimized on an ongoing basis. Automated techniques such as �Champion-Challenger� modeling are used to ensure the best models are always deployed.
If you�re a customer using our products in anger, please get in touch. Your input will help us build the next generation ofIBM Business Analytics software.
For more information:
�Readthe whitepaper on how Decision Management creates a closed-loop system that continually incorporates valuable feedback into your decision-making processes.
�Watchthe video of industry analyst James Taylor discussing the importance of Decision Management.