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.
Some people might argue, but former rapper and musician Vanilla Ice was a visionary.
Truth be told, he probably wasn�t talking about business analytics when he eloquently penned those famous lyrics in �Ice, Ice Baby.� But, he could have been.
We live in a collaborative world today�whether we like it or not. The realm of �social� is slowly morphing personal and professional, ultimately making life more efficient and transparent.
And some people and organizations are still rejecting this notion altogether.
Which is why at a company of approximately 400,000, with team members spread across the world, collaboration is a way of life, and a necessity in the IBM survival kit.
It bridges the gap of the world of social with the world of business. It allows us to now connect people and insights to gain alignment inside of the organization, as well as hold people accountable.
Decision making is no longer a game of telephone where important elements of that decision are lossed as it is passed on�one person at a time. When the decision is finally executed, does anyone even know if it was right, if the right people were involved, who made the decision, or why?
That�s where the power of business analytics and collaboration come together.
Organizations can lose tremendous productivity as they search for invaluable information hidden in various meeting notes, manual processes, emails and people�s notebooks.
Collaborative business intelligence(BI) streamlines and improves decision-making by providing capabilities for forming communities, capturing annotations and opinions, and sharing insights with others around the information itself.
It also allows organizations to communicate and coordinate tasks to engage the right people at the right time.
In fact, industry analyst Dave Menninger from Ventana Research commented that �innovative organizations recognize the processes involved in BI are as important as the technology and take steps to provide collaborative support to their BI activities.�
With built-in collaboration and social networking, collaborative BI harnesses the collective intelligence of the organization to connect people and insights and gain alignment.
What was once a dysfunctional buffet style decision making process is now a formal dining experience, with collaborative BI as the lazy susan passing reports and dashboards around the table for feedback and discussion.
Everyone now has input into the process, can easily connect with and understand context with others who are relevant to the decisions being made,and can now learn from history with a centralized corporate memory.
But realistically, before we can all sit down and enjoy this collaborative feast, it must be an accepted practice in the organization.
Culture is at the heart of this. It has to want to happen. Collaboration cannot be forced.
And, once you have embraced it�well, there�s no turning back.
Before too long, you have access to the people and expertise you need to discuss and refine ideas, data and information for the best results.
Had Vanilla Ice lived in today�s world of social networks and business analytics, he might have been able to lengthen his career, better market himself, sell more records, write better songs, connect with fans and shave less eyebrows.
Ok, maybe not.
But, he would have lived true to his mantra of collaborating with his producers and writers and listening to the general collective before making any decisions.
(I apologize if you now have Vanilla Ice stuck in your head for the rest of the day, but at least you�ll be thinking about how you can establish collaborative BI processes across the organization.)
Learn more about IBM Cognos Collaboration by:
� Registering for the January 17 IBM TechTalk: �Enabling Better Decision Making Through Highly Collaborative BI� (Begins at 12:00 pm ET)
� Watching the demo to see how to use built-in collaboration and social networking tools to connect people and insights
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
If you're one of the millions filling out a bracket this year (all for fun of course), I'm sure you've been asked or have asked that question.
Yes, it's time when the NCAA men's basketball tournament distracts us from our jobs as we maniacally scan the internet and listen to so-called experts hoping to get that edge and finally master the ancient art of bracketology. Sadly,Paul the Octopuspassed away recently so that �secret weapon� is no longer viable.
Sure, accurately predicting which teams are in the Final Four is important, but what separates the masters from the novices is predicting the winners/upsets in the early rounds. You can play it safe and pick the higher seeds to win, but that's a silly strategy. Besides, all four top seeds have only advanced to the Final Four once in 30 years. (Sorry President Obama.)
Rely on the data. On Monday, Nate Silver's FiveThirtyEight ran anarticle entitled, "How We Made Our NCAA Picks," which took an analytical approach to predicting the winners.
Like IBM, he sees the value in analyzing historical data to make informed � and better � decisions.
And let's be honest, everyone is looking for that competitive edge � whether its bragging rights for the brackets, or outmaneuvering the competition in business. The answers are as simple as mining mountains of data to find Key Performance Predictors (KPPs) � those 15-20 data variables that are the most relevant.���
KPPs then help guide any organization to build an amazing level of intimacy and knowledge, allowing them to determine how a specific customer is likely to behave at a precise moment in time.�
In the NCAA tournament, Nate analyzed the results for all tournament games since 2003 (a total of 512 games) and evaluated which factors best predicted success. As Nate pointed out, "The goal is to have a system that makes good statistical sense and also makes decent basketball sense, as opposed to identifying a bunch of spurious correlations."
Not all data is created equal.In fact, sometimes the correlations you think exist, turn out to be counter-intuitive. That's where KPPs come into play. And, it's why predictive analytics makes good business sense. For instance, one of our insurance customers learned that clients who remove pets from the house prior to a fire are often convicted of claims fraud. And, phases of the moon are a predictive indicator of when crime is likely to occur.�
In the NCAA setting, Nate discovered that teams playing games within 50 miles of their campus have a 24-2 record; and, conversely, teams traveling at least 1,000 miles are 121-174.
Does this change the way you think about your bracket?
That's why IBM is "betting" big on predictive analytics.IBM is hoping businesses will realize that picking "winning" customers based on mascots, team colors or flipping a coin is also a silly strategy.
Today, it's better to rely on the data to be told how to take action than making a haphazard decision that could seemingly be based on unnecessary bias (like picking an alma mater such as Boston University over Kansas). Sorry Terriers!
What if you could determine when a part might fail in a car?� Or the right time and conditions to perform surgery?�Or when a crime will occur in a specific part of town?
Or, what if a call center agent at a communications service provider could quickly and easily determine which inbound customer calls are the best candidates for an up-sell, cross-sell or retention offer, and then deploy personalized, real-time recommendations that have the greatest likelihood of acceptance by the customer?
Thousands of these types of daily decisions can now be automated and optimized for significant � and measurable � benefit.�No longer are the same bad decisions made over and over again.�
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.
Guest post from Becky Smith, Product Marketing, IBM Business Analytics
In a way, doesn't everyone in business analytics have a little bit of Don Quixote in them?
We're all on a bit of quest to find answers hidden in our never-ending, growing piles of data. And often, we find ourselves tilting at windmills and fighting futile battles with IT, with spreadsheets or with silo'd applications that only do one specific task.
Close your eyes for a minute and let yourself dream about a world where there is a single vendor that delivers a family of products including reporting, analysis, modeling, planning and collaboration.
What if that family of products not only has a common set of capabilities but is also integrated and talks to other members of the family so they all live happily together? And, what if a mid-sized business could get the same self-service experience, what-if scenario modeling and the ability to discover and assemble their data across both business intelligence and performance management as a large enterprise?
This is a notion we call "right sized analytics." An enterprise solution might be too big and expensive for your organization, while an individual point solution might be too small and limiting and unable to provide the needed integration with a mid-sized or larger enterprise solution.
Right sized analytics means having an entire family of products built on a common foundation that enables individuals, workgroups and enterprises to access and analyze corporate information and easily share insights across the organization. Basically, it doesn't matter where you begin your analytic journey, as long as you have the comfort knowing that the solution will grow with you.
For example, an organization might have an issue with customer retention. Through the analysis of support calls, an individual user might uncover that there is a 15 percent increase in dissatisfied customers due a product defect. This information can then be shared back into the analytics solution so the manufacturing and support organizations can improve product quality, and plan to offer customers special promotions and increase customer service for the entire customer base, respectively.
Does this scenario only exist in your subconscious?
That one man, scorned and covered with scars,
Still strove, with his last ounce of courage,
To reach ... the unreachable star ...
In feedback from our customers, they've talked about the battles scars they've received in their quest for a common set of systems working seamlessly together. Stay tuned for part 2 of this post and hear why that business analytics star is no longer unreachable and how a complete family of products can:
� Address the needs for your organization � regardless of size
� Empower individual users, workgroups and enterprises
� Allow the organization to start anywhere and go everywhere
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!"
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.
We've all turned into spooked ostriches with our heads stuck in the ground.
As Matthew Broderick eloquently re-stated in a Super Bowl commercial reprising his famous Ferris Bueller role, "Life moves pretty fast. If you don't stop and look around once in awhile you could miss it."
In the world of social media, it seems everyone is buried in their mobile devices these days reporting on the minutiaof their lives. In fact, it was reported that tweeting records were set during Super Bowl XLVI with 13.7 million total tweets sent during the game and 12,233 tweets per second by the end of the game.
I wonder if anyone really watched the game?
Unknowingly, Twitter has turned us into play-by-play announcers, bad stand-up comedians, "Negative Nancy's,� and critics. We share everything. Is it really necessary to tell everyone what you had for breakfast, what you liked most (or for Boston fans, least) about Tom Brady's performance, the coffee shop you just checked into on Foursquare, your opinion of that Matthew Broderick commercial, or what movies and actors you predict will be Academy Award winners?
If Twitter is a never-ending barrage of babble and nonsense, does it really matter?
You�re damn right it does.
Consumers have become a force de nature in the Twitterverse. Their opinions are unfiltered and unadulterated, yet unfortunately, still quite underrated when it comes to using the data to enhance customer experience. As MTV�s �Real World� once promoted, �It�s time to stop being polite, and start getting real.�
Twitter is raw, real and in your face. Businesses have it easy these days. No longer do they have to go through the formal process of focus groups and lengthy analysis. Want to know what someone is thinking, log onto Twitter.
No dodging the mighty consumer these days.They have become increasingly influential, especially as their opinions travel faster and to a wider group of consumers.
Accountability and honesty reign supreme. If consumers don�t like an organization�s strategic business decision (e.g. Susan G. Komen), new product (Netflix/Qwikster), or advertisement (Groupon/Tibet), there�s no dodging the verbal arrows.
The organizations, however, that decide to take action and analyze the millions and millions and millions of data points created in the socialsphere will own the competitive edge and be able to respond that much quicker. It�s just a matter of separating the noise from what really matters, the consumer�s thoughts, opinions, sentiment and behaviors.
Enter social analytics, the latest in noise-cancelling devices that deliver insights into what people are thinking, why they are thinking it, and most importantly, what organizations can do about it. By eliminating the minutia, social analytics helps businesses understand positive and negative sentiment,pinpoint top influencers, measure the volume of commentary and identify the geographic origin of comments across multiple channels.
Getting back to the Super Bowl�think about the value buried inside of those 13 million tweets � for advertisers, for psychologists, for the city of Indianapolis, for the NFL, etc.
And as the Twitter feed flies off the charts with major sporting events, one can only predict the same activity for the upcoming Academy Awards, especially with the commercials, the fashion faux pas, the glitz and glamour, the acceptance speeches and most importantly, the winners and losers.
Speaking of which, IBM, The Los Angeles Times and the University of Southern California Annenberg Innovation Lab have created the Oscars Senti-Meterto establish a model for measuring the volume and tone of worldwide Twitter sentiment to better understand moviegoers' opinions and customer trends.
So yeah, I guess Twitter matters. It might be noisy, but it�s chocked full of yummy goodness.
If businesses don�t check into Twitter and look around once in awhile, there�s a lot they could miss (and a lot of customers they could lose).
IBM recently conducted tests in its labs that revealedIBM Cognos BI v 10.1.1 to be at least on par and better by 14 to 46 percent when compared to Microsoft Windows 2008 Server.
IBM Cognos BI application performance between similarly configured IBM POWER6 and IBM POWER7 systems showed significant performance advantages for IBM POWER7 servers.
IBM conducted a variety of tests to match the different ways of using IBM Cognos Business Intelligence services and system resources. The test systems used similar server configurations and current processor generation. Download the free report here.
Other findings included:
�Performance improvements of as much as 41 percent for workloads such as running HTML and PDF-based reports and portal navigation
�Performance improvements of as much as 26 percent for workloads such as running large and highly formatted PDF reports, locally processed calculations, interactive analysis activities and complex queries mixed with lighter workloads
For example, an IBM customer had developed a Cognos Business Intelligence application to distribute PDF based reports by email; as implemented and before optimization this application was performing at a rate of 11 multi-page reports per minute.
After the customer applied recommended AIX tuning parameters, the application performance improved to 150 multi-page PDF reports per minute.
On average, most applications might see performance improve two or three fold by applying AIX level tuning.
To provide a comprehensive view of the potential performance impact of optimizations made in Cognos Business Intelligence v 10.1.1, IBM used a broad range of tests. See the graphic that lists the performance improvements for the 20 different tests used.
For more information:
�Downloadthe whitepaper, �Best Practices and Advantages of IBM Power Systems for Running IBM Cognos Business Intelligence,� to see the full performance results.
NOTE:Performance is based on measurements and projections using standard IBM benchmarks in a controlled environment. The actual throughput or performance that any user will experience will vary depending upon many factors.
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