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 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.
� "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).
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.
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.
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 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
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.