IBM officially invites you to InterConnect 2012 from October 9-11 in Singapore, where business and IT leaders partner to turn opportunities into outcomes.
In the video below, IBM Vice Presidents Scott Hebner and John Dunderdale explain how, on today's increasingly smarter planet--where everything is becoming more
interconnected, instrumented, and intelligent--a new type of business leadership is emerging as technology dramatically impacts the success of businesses.
To win in this new era of interconnected industries, businesses and consumers, business and IT leaders have to connect with the people who can help turn
opportunities into outcomes. They need to:
Unleash innovation with a new approach to the economics of IT.
Manage to the velocity of today's rapidly changing markets and businesses.
Better leverage the volume, variety, and velocity of information to fundamentally reinvent relationships with your individual clients, employees and business partners.
InterConnect 2012 is the only conference where you can connect with an international network of peers in exchange sessions based on your areas of interest. It also helps you
connect to the issues and �hot topics� such as Cloud Computing, Mobility, Big Data, and Security that are changing the shape of your world and business and IT today and
In this era of interconnected industries, businesses and consumers, a new kind of leadership is required to turn opportunity into business outcomes. Smarter businesses are capitalizing on information as an indispensable resource and using technology as the catalyst for unleashing innovation. They are expanding the digital world of the back-office into the front-office.
Given this new reality, Business and IT leaders are collaborating to better align business and technology investments in order to respond to three business imperatives:
� Re-invent relationships and uncover new markets
� Manage the velocity of business change
� Implement the new economics of IT to fund new innovations
At InterConnect 2012, collaborate with business decision-making peers, and see how they�re working with technology leaders to fulfill the vision of their senior leadership. Meet face-to-face with technical decision-makers and industry experts, and define new ways to achieve your organization�s strategic goals.
Hot Topic Sessions
At InterConnect 2012, participate in a rich selection of Hot Topic Sessions hosted by senior IBM thought leaders. Learn directly from IBM clients and business partners from a variety of industries, around the globe, as they showcase successful strategies that leverage the breadth and depth of IBM software and systems. Hot topics include:
� Changing the Economics of IT with IBM PureSystems
� Defending Against Cyber-threats with Security Intelligence and Behavioral Analytics
� Rethink IT. Reinvent Business with Cloud Computing
� Transforming Critical Business Processes
� Unlocking Opportunities with Big Data Analytics
� Gaining Competitive Advantage through Software Innovation
� Creating Exceptional Experiences by Combining Social and Commerce Best Practices
� Speeding Innovation and Extending Reach with Mobile Enterprise
� Transforming IT for Insight and Efficiency with Smarter Storage
� Enabling Growth with Enterprise Systems
Dig deeper into the initiatives that are shaping successful businesses by participating in subsequent facilitated Exchange Sessions, where you�ll learn to apply the lessons learned to your organization. End each day of InterConnect 2012 with a clear plan of how to share the expertise with your teams and begin charting the path to future value.
The InterConnect Solution Center will be open throughout the event, situated at the heart of InterConnect within the Compass Ballroom at Resorts World Sentosa. With topic-focused zones and interactive demonstrations, the Solution Center will bring IBM Business Partners and Subject Matter Experts together with customers and select IBM executives to form a single, unified Software and Systems showcase.
Network with peers in a comfortable, informal setting on the evening of Tuesday, October 9th at the Solution Center Welcome Reception, and at a special event at Universal Studios on the evening of Wednesday, October 10th.
At a glance, the conference agenda is as follows:
IBM InterConnect 2012 Agenda Day 1: Tuesday 9th October
08:00 � 9:30 Pre-Event: Business Partner Forum AND Pre-Event: Press & Analyst Forum
09:30 � 10:00 Networking Break/Solution Center
10:00 � 10:15 Opening General Session Host & Special Guest Speaker
10:15 � 10:35 A source of Global Innovation, the art of the possible in Growth Markets (Jim Bramante)
10:35 � 11:05 Turning opportunities into Outcomes (Steve Mills)
11:05 � 11:45 Unleashing Innovation: The New Economics of IT ( Rod Adkins)
11:45 � 12:00 Customer Guest speaker
12:00 � 13:30 Lunch/Solution Center
13:30 � 14:10 Managing the Velocity of Change (Leblanc)
14:10 � 14:50 Re-inventing Relationships and Uncovering New Markets ( Mike Rhodin)
14:50 � 15:05 Customer Guest Speaker
15:05 � 15:40 Special Guest Speaker
15:40 � 16:00 Networking Break/Solution Center
16:00 � 18:00 Connections: PEER � EXEC � SOLUTIONS EXPO � EXCHANGE SESSIONS by Business Imperative
6:00 � 7:30 Solutions Expo Reception
IBM InterConnect 2012 Agenda Day 2: Wednesday 10th October
08:30 � 09:30 General Session - Clients and BP Best Practices
09:30 � 09:45 Break
09:45 � 11:15 Hot Topic Sessions
11:15 � 11:30 Break
11:30 � 1:00 Hot Topic Sessions
13:00 � 14:00 Lunch Break/Solution Center
14:00 � 15:30 Hot Topic Sessions
15:30 � 16:00 Networking Break/Solution Center
16:00� 17:30 Connections: PEER � EXEC � SOLUTIONS EXPO � EXCHANGE SESSIONS by Hot Topic
17:30 � 19:00 SOLUTION CENTER Reception
19:30 � 21:30 Gala Event at Universal Studios Singapore!
IBM InterConnect 2012 Agenda Day 3: Thursday 11th October
08:30 � 09:30 General Session - Clients and BP Best Practices
09:30 � 09:45 Break
09:45 � 11:15 Hot Topic Sessions
11:15 � 11:30 Break
11:30 � 1:00 Hot Topic Sessions
13:00 � 14:00 Lunch Break/Solution Center
14:00 � 15:15 Connections: PEER � EXEC � SOLUTIONS EXPO � EXCHANGE SESSIONS by Hot Topic
15:15 � 15:30 Break
15:30 � 16:30 Closing General Session
There is a German proverb that says, �The eyes believe themselves. The ears believe other people.�
Londoners are taking this proverb very serious. The London Eye (Ferris wheel), which is one of the most visited attractions of London and the spot where the New Year�s fireworks are sparked off each year, is transforming into a sentiment monitor.
With every Olympics, we get to see the splendor of each host nation. In 2008, China confirmed that it had an ability to stop rain, and now London is turning its busiest tourist attraction into a social media �mood ring,� a partnership between EDF Energy and a group of graduates from the Massachusetts Institute of Technology (MIT).
They developed an intuitive algorithm that linguistically analyses tweets related to the Games. Tweets will be scanned for Olympic-relevant terms such as "Olympics," "Torch Relay," "London 2012," and EDF's own hashtag, "#energy2012."
This initiative rides on the social media wave that the Olympics might be star bursting. This is really cool, but having lived in London for almost a decade, and being involved in social media, I see large loop holes. Just a bit of a pessimistic, "init."
The Olympics represent an incredibly diverse environment � from races to languages to communities. So what are the details behind the analytics on the �Eye�? Is this algorithm going to track sentiment in languages other than English? Moreover, what are the details behind neutral or ambivalent sentiment?
Let�s look at a beautiful example of some noise: On Twitter, Mr. Hancox said that for Londoners, "It's as if someone else is throwing a party in our house, with a huge entry fee, and we're all locked in the basement."
How will the eye in the sky pick up that statement and how will fellow Londoners and the rest of the United Kingdom react?
I would have thought that Boris Johnson, London�s mayor, would have done this in a better way.
Well it comes down to basics, we all learn from our mistakes. So rather than just monitoring the buzz and view how we faired, we need to analyze this social data and take corrective measures to ensure that negative sentiment is minimal, keeping in in mind we can�t please everyone!
Take for example, RTL Nederland, a Dutch entertainment company that produces the �X Factor.� It analyzed the sentiment of more than 71,000 online conversations about the show to understand audience needs and preferences and, based on the online feedback, altered the show for the final episodes to increase viewer satisfaction.
In today�s business environment, no business can survive without analyzing all available data, including social media. A mega event like the Olympics is no different. Social analytics is therefore an understatement.
In the case of the London Eye and the Olympic organizers, I hope their ears are open and are really listening to the feedback so they can take appropriate actions to improve these games, or 2016 in Rio de Janeiro.
For now, let's watch the sentiment on the wheel and then decide.
Guest post from Burke Powers, Managing Predictive Analytics Consultant, IBM Business Analytics
Today, every company of appreciable size has some social media presence. Most companies I speak with are either just monitoring social media or are engaged in �spray-and-pray� tactics that are only loosely tied to corporate goals.
To realize the value in social media it is important to integrate social media into broader customer analytics programs and business decision making.
Too often, companies ask, �What are customers saying about us?�
An objective like this is too vague to direct an analysis and identify actions. What we really need to be able to ask is, �Product XYZ will launch in two weeks. We have done A, B, and C campaigns to create awareness and to position the product.
�What kind of buzz (as measured by D, E, and F KPI�s) has this created around each of our message points?
�Are there other topics that we did not anticipate?
�Can we setup real-time reporting of the topics so that we can monitor the customer reaction to the product once they begin using it?
�Can we monitor any emerging, unanticipated topics after the launch?�
The objective should focus on an area of the business where you are confident additional insight can lead to quick improvements. The best opportunity might be related to a product, the service level of a critical customer touch point, competitor actions, a specific brand attribute, or a customer behavior.
The sheer volume of social data requires some planning. There are a limited number of data aggregators (major aggregators include BoardReader, Gnip, & DataSift) and each comes with its own benefits and trade-offs.
To choose an aggregator that best fits your needs, decide how important data history is, the cost of hosting the data, and the importance of access to all social media data (full fire-hose) versus sampling.
Secondly, decide whether to integrate additional data sources. Using the same filtering and reporting for social media and survey verbatims makes them more comparable for analysis and reporting. Also, determining whether to include internal social network data from Yammer or Lotus Connections may be a factor.
3) Plan and Execute the Analytics
By its nature, social media data is going to be different from what most business analysts are used to analyzing. It is unsolicited and unstructured and tends to be rich in attitudinal and usage information. It is frequently strongly positive or strongly negative.
But, it provides tremendous value because it has rich customer narratives of every product feature and customer touch-point that no other data source can offer. It brings traditionally dry analysis to life for business decision makers.
Most existing social media analytics tools offer only a limited ability to search and trend terms as well as view some sort of sentiment. Some allow filtering by the source metadata as well. These are necessary elements of any serious analysis, but stop short of offering the tools needed to take the data to an actionable level.
To be truly useful across many parts of the business, the free-text data needs to be understood in context and translated into an accessible format for reporting and analysis. This capability is one of the strongest differentiators for IBM Cognos Consumer Insight.
4) Motivate Actions
Once the analysis is ready, it is time to deliver the information to the decision maker at the right time, in the appropriate context to make a decision, and in a persuasive manner.
Finally, be sure to include a rich narrative quote that illustrate the argument and provides an additional persuasive hook that augments the analysis and builds buy-in from the �gut� of business leaders.
For example, let�s say your company recently launched the �Wonder Widget.� You are preparing the first report on how the product has been received by customers. Include a positive customer quote to support the data and drive the point home.
Ideally, the quote says exactly what your analysis leads to, �I love your new �Wonder Widget,� it is already making a difference. Except for one thing, the XYZ dial has got to be moved closer to the display so that I don�t have to look away. Fix this and I can easily justify ordering more units.�
There are many social metrics that could be used, from numbers of followers or tweets generated, to the ratio of issues resolved, and to issues raised via social channels.
Additionally, you could track the results via click-throughs usingIBM Coremetricsor email campaign response using IBM Unica.
You also might choose to experiment through customer support channels and monitor perceptions via both social media and surveys.
Finally, the metrics and actions need to be tied back to financial metrics either as revenue-generating or cost-reducing. This may require knowing the cost of resolving an issue via a social channel versus contact center or perhaps the cost of a response via one promotional channel versus another.
Identify a New Objective and Repeat
Now that we�ve gone through the process from beginning to end, it can now be repeated again with a new objective. A disciplined approach using these best practices will generate rapid returns on virtually any social media analytics endeavor.
For more information:
Read the whitepaperon techniques for gaining valuable customer insight with social media analytics
Guest post from Anuj Marfatia, Senior Market Manager, IBM Predictive Analytics Solutions
Usually when traveling for work or vacation and right after takeoff, I undoubtedly begin to panic, much like the mother inHome Alone. I constantly worry that I left the garage door open, the iron on, my kids behind, food out for the dog, or most importantly, if I put my vintage1963 Issue #4 Avengers comic bookback in its protective cover (don�t judge).
Beyond this unnecessary distress, protecting my arm rest from the chatty passenger beside me, and browsing throughSkyMall, I sometimes read the passenger safety document. Have you ever looked through one of these? It�s unintentional comedy.
In the past few years, almost all airlines have included an �exercises� section in the pamphlet.
As an economy class passenger, I have to laugh at such pictorials � as most of these exercises (see image) are almost impossible given that my knees are already touching the seat in front of me. Now, if only I were a contortionist�
In all seriousness, do you know why these exercises are important?
Studies have shown that many emergencies and future health issues are correlated to inactivity while flying, and one in every 20,000 passengers has an in-flight emergency (source). One serious, yet preventable, issue is venous thromboembolism (VTE) that occurs when a blood clot in a leg vein (deep vein thrombosis or DVT) travels through the body to the lung.
Based on a BBC News report,some 75 percent of air-travel cases of VTE have been linked to lack of movement while in the air. I can sleep well knowing that economy passengers, like myself, are no more likely to develop clots than the more fortunate passengers in business or first class.
While I try to do some sit-ups, lunges, and pull-ups on the plane (kidding of course), it would be great if I knew how likely I was to get VTE or DVT or how much I would have to exercise to minimize my risk of attaining VTE or DVT.
Wouldn't it be cool while purchasing a ticket or at check-in, you would be informed of the health risks on a certain flight? That would be a red eye-opener.
While such a thought may seem like something from a science fiction movie or occur 100 years from now� think again. Predictive disease management actually exists today!
There is a lot of information about a patient that can be used (HIPPA-compliant, of course) to determine the likelihood of disease occurrence or treatment effectiveness.
Based on the study above and numerous other studies, drugs that doctors prescribe are still relatively ineffective.
Doctors do use their best knowledge and experiences, in most cases, but many times they are not utilizing ALL of the information that is available to them when making a decision about the patient. (This is also why some of theIBM Watsonapplications inhealthcareare so interesting to watch.)
This is wherepredictive analyticscomes into play. Predictive analytics software pulls in information from all the disparate data sources, such as from health information systems, Excel, and even from Facebook and Twitter (for those cases you told your friends that last night�s Ethiopian food left you �indeposed�).
The software enables healthcare organizations to transition to a new model and find more effective ways to treat patients and develop new treatment protocols. For example, a predictive outcome could be that Jane Doe has a 95 percent probability of positively reacting to a certain treatment, essentially increasing the quality of care and containing costs.
This is why I�m happy to hear researchers at Hospital Santa Barbara, a research and treatment center in Spain, analyzed patient records and other research data to establish a new, reliable diagnostic model for DVT enabling earlier diagnosis and treatment in high-risk patients. (Learnmorehow Santa Barbara Hospital used IBM SPSS predictive analytics.)
While Spain may have their own economic issues, I�d like to thank them for helping to begin the journey of a DVT-free flight � so I can fly the friendly skies without worrying about my health.
Last Sunday was Father�s Day. This is a paradoxical �holiday� in the U.S., as it is a day to honor fathers with gifts and food, but they are still required to work in the yard, fix stuff, yell at kids and run errands.
I received thoughtful, useful and handmade gifts from my three wonderful kids. They included a converter that lets me play my iPhone through my cassette tape deck in my car (needless to say I�m not driving a 2012 model); a homemade comic strip card about mutant aliens; and, a personalized gum wallet made of duct tape (see picture below).
The real challenge was what to get my father for Father�s Day. In fact, I face this conundrum every gift-giving occasion with my father.
As those of you with fathers can attest, the typical dad has everything he will ever need in his entire life by the age of 31, plus or minus two years. And, I mean everything � tools, gadgets, sweaters and golf paraphernalia.
This personal challenge is what prompted me to use the recently released IBM Analytical Decision Management to provide a recommended action related to my gift selection. My strategic objective was to have my father accept and enjoy my gift.
Because we have been talking a lot about Customer Analytics, Next Best Action and IBM Signature Solutions at this year�s IBM Business Analytics Analyst Summit (search #ibmbas12 on Twitter to follow the commentary), you can understand why I could easily configure my IBM Analytical Decision Management solution. (Hint: Replace �father� with �customer� and �gift� with �offer.�)
Following were the steps to my recommended decision:
�Using years of historical fatherly gift giving data (e.g., ties, golf shirts, jive coupons with the promise of a �car wash�), I restricted the analysis of my data so that the recommended action(s) would be based only on those gifts given in the summer months (e.g., nothing with long sleeves).
�I also opted to exclude �no action� from the recommended action list, which is often a viable decision for retention offers but not for gift giving to my father, especially if I hope to stay in the Peckman will. Just kidding. Sorta.
�I defined the list of the potential recommended outcomes linked to my objective: Give a product, a service; or a combination of the two. Then, I built new business rules and predictive models that were not included since the last time I used IBM Analytical Decision Management. For example, new rules:
If (golf_hndcp[current] > golf_hndcp[lastyear]) & (golf_complaints > 3) then add risk points;
If balance_giftcard > 0 then add risk points;
If (favorite_child[current_month] = me) then subtract risk points;
� and so on.
�Similarly, I created new predictive models:
Before deploying the gift giving decision management solution for use in the field by end users (like me, my wife, my children) I ran all the proper �what if� scenarios and used the new constraint-based optimization functionality in an attempt to maximize enjoyment and minimize effort to carry/use and subject to cost constraints. (To see the other new features in IBM Analytical Decision Management, read the data sheet.)
For example, a new Audi has a predictive acceptance of 100 percent (1.00) but falls outside cost limits for the gift; and, $5.00 tickets to Ballet in the Park (performed by an up-and-coming troupe of back-ups to the back-up dancers) fall within cost constraints, but have a predictive acceptance of less than 2 percent or 0.01667.
By completing all of these steps, �IBM Decision Management for Gift Giving� (the next Signature Solution?) is ready to generate a recommended action to my wife�s question, �What should we get your dad for Father�s Day?�
My recommended outcome >>> Gift certificate to the Olive Garden.
The next step is to put my updated application up into the cloud (read more about Analytical Decision Management SaaS) so my extended social network can run the SaaS version for batch gift recommendations.
And, in case you have any wild ideas, I have a patent pending on the personalized gum wallet made of duct tape.
There was anarticlein The New Yorker last week entitled, �Why Smart People are Stupid.�
Its premise stated, �When people face an uncertain situation, they don�t carefully evaluate the information or look up relevant statistics. Instead, their decisions depend on a long list of mental shortcuts, which often lead them to make foolish decisions. These shortcuts aren�t a faster way of doing the math; they�re a way of skipping the math altogether.�
Given all the work organizations do to collect and align data, there really is no reason why foolish decisions should be made any longer, especially when there�s a huge price tag associated with bad decisions.
And, when you think about how many decisions an organization makes on a daily basis (thousands, millions?), being foolish is no longer an option � especially calculating the cost between one foolish decision and a million foolish decisions.
And, most of these transactional or tactical decisions need to be made in an instant, such as a customer service agent deciding to give a customer a discount to combat churn; an insurance claims system determining whether a potentially fraudulent activity should be escalated for investigation; or, a logistics manager deciding if a truck is safe to put on the road for the next delivery.
To end this foolishness, IBM has introducedAnalytical Decision Managementto help organizations automate and optimize decision making in real time to ensure the best outcomes occur every time.
Essentially, Analytical Decision Management takes the complexity out of big data by quickly analyzing and embedding analytics directly into business systems (in a call center, on a website, on the manufacturing floor) to empower employees and systems on the front lines with the ideal action.
It also allowsbusiness users to run multiple �what if� simulations, compare the outcomes of different approachesand test the best business outcomes before the analytics are deployed into the operational system. Even analytics follow the old adage, �Measure twice, cut once.�
IBM Analytical Decision Management
According toIDC, the Decision Management software market is expected to exceed $10 billion by 2014. To meet this growing demand,IBM Analytical Decision Management is the first in a series ofIBM Smarter Analyticsinnovations that will change how organizations weave analytics into the fabric of their business, fueling all systems, decisions and actions to consistently deliver optimized outcomes, while adapting to changing conditions.
The newly released Analytical Decision Management combines and integratespredictive analytics, business rules, scoring, and now, optimization techniques, into an organization�s systems to:
�Maximize every customer interaction to grow revenues and increase loyalty
�Detect and prevent threats and fraud in real time to reduce risk
�Proactively manage resources by predicting equipment failure, staffing downtime and service disruptions to contain cost
For example, Santam Insurance is using Analytical Decision Management to transform its claims processing byenhancing fraud detection capabilities and enabling faster payouts for legitimate claims. In fact, in the first four months of use, Santam saved $2.4 million on fraudulent claims. (Readthe full case study.)
Santam can now automatically assess if there is any fraud risk associated with incoming claims and allow frontline claims representatives to distribute claims to the appropriate processing channel for immediate settlement or further investigation, which in turn, optimizes operational efficiency.
As all customers and claims are not created equally, Analytical Decision Managementadapts its recommended actions in real time to accommodate changing conditions as new data is collected and outcomes are recorded.
Analytical Decision Management is also equipped to automatically prepare, cleanse and transform data for the best possible analytics through the newEntity Analyticscapabilities.
There can be challenges when diverse enterprise-wide data is integrated � especially when this data contains natural variability (e.g., Bob versus Robert), unintentional errors (e.g., a transposed month and day in a date of birth), and at times professionally fabricated lies (e.g., a fake identity).
The Entity Analytics feature allows data scientists to overcome some of the toughest data preparation challenges and create the most complete view of an individual record. Users can generate higher quality analytic models and, as a result, organizations will enjoy better business outcomes whether the goal is detecting and preempting risk or better responding to a customer�s needs.
Guest post from Kurt Peckman, Program Director, IBM Predictive Analytics
Last Friday I took a different train into my office here in Chicago.
This particular station has a diner located right next door and within steps of where I would be catching my train. They only serve breakfast and lunch and it immediately hits me that I�ve stumbled upon a diner with an optimized location and manufacturing schedule.
Speaking of which, I had optimized my wait time for my train. No gross surplus of minutes to waste on the platform; no deficit of time causing a heart attack-inducing sprint from my car to the train. I immediately headed to the diner.
The waitress, who I�ve never met before today, immediately greeted me with, �Hi, honey� $1 egg sandwich today?�
I didn�t fall for the �honey� play. I�m old enough to know that any good waitress worth her salt will refer to me as: honey, sugar, handsome, and the like in an attempt to up-sell me from coffee to coffee plus. And given my experience in up-selling myself (discussed in my last blog) I was naturally on guard.
However, I was very, very intrigued by the price of the $1 dollar egg sandwich.
I said, �No, thanks,� which was tough to do. I love egg sandwiches and one dollar is a heck of a deal for a diner-based product. (Notice the use of the word �deal� and not �price,� which implies �value� to me.) I am trying really hard not to eat so many egg sandwiches so I declined. But, the critical fact in this story is that I paid $1.75 for a cup of coffee.
Secretly, what I really wanted to do was take the entire day off of work to interview �Flo� the waitress (my customer service rep), the chef, and other patrons about the implications of the $1 egg sandwich. I especially wanted to interview the owner (who I think was sitting in the corner reading a paper) as to how the execution of the egg sandwich is tied to his overall business strategy.
How was that price determined? Is it an optimized price? Can a diner really make a profit on a $1 egg sandwich? If so, does it include the cost of all goods: materials, labor, overhead (e.g., utilities, wear and tear on the grill, depreciation on the spatula, etc.)?
Or was the pricing objective pull marketing for the diner? The deal didn�t prompt me to go into the diner, and I�m not even sure there was a sign out front stating the terms of the deal. But, there was signage inside that I realized only after she pitched the deal. Now my mind was spinning.
Is Friday the best day for the egg sandwich promotion? Is this an optimized campaign � right offer, price, channel, day and time? I didn�t even get a chance to ask if every Friday is a $1 egg sandwich day. If so, I might be inclined to invite my colleague Bob (who regularly commutes to/from this station) about the end-of-week-deal at this diner.
Given my love of egg sandwiches, I might even be tempted to take to social media to sing the praises of this diner.
Other questions scrambled my mind: do they pre-make the $1 egg sandwiches? They must. There is no way the diner can meet the short-term, burst demands dictated by the average time one waits for a train.
And what is the optimized inventory of egg sandwiches that minimizes spoilage and maximizes freshness, demand, labor�? The $1 egg sandwich production quickly becomes an n-dimensional optimization problem.
And by �optimization� I mean the mathematical definition: maximizing (or minimizing) some outcome or value within a set of predetermined constraints. A classic example is an investment portfolio: we are all trying to maximize the value of our portfolio subject to the constraints of contributions, time, risk, market direction, etc. But I digress� back to the eggs.
Maybe the $1 egg sandwich starts at $2 earlier in the day and, by the time I arrived, the decision was made to drop price due to surplus inventory. Wouldn�t it be something to find out that a mom & pop diner was using sophisticated optimization algorithms to price egg sandwiches that maximize profit and minimize spoilage?
At this point three things become apparent:
1. Tying strategy to execution is as critical to the mom & pop diner as it is to Global 100 companies;
2. The best decision management solutions must include an optimization component; and,
3. I have an unhealthy obsession with egg sandwiches.
Guest post from Kurt Peckman, Program Director, IBM Predictive Analytics
About a month ago I moved.
I closed after lunch on a Friday afternoon. The only reason that is relevant to this story is the timing: my cable provider called me the next day � Saturday morning around 9 a.m.
I knew it was my provider, thanks to caller ID. Granted I�m not that old, but not too long ago you had to actually answer the phone to know who it was. In fact, I now have a phone that will announce out loud who is calling me. Ah, technology.
Being a Wisenheimer, I answered the phone not with a �hello,� but with, �I bet you are calling about the sale of this house.�
Without missing a beat, the customer representative answered, �Yes I am, and I�d like to get you the best possible package for your new house.� Note the use of the word �best.�
Thus began my willingness to be retained.And, at the time I wondered to what extent predictive analytics were being used to �retain� me during the conversation.
Because �best� was enough to get my attention, I let him ask me the location of my new house. He was quick to pull it up and confirm the deal he had in mind could actually be pitched.
�Yep, looking at your location, I can get you set up with the following package at [about half of what I was paying before!].�
Here is the critical fact in this story: the �package� he pitched included internet connectivity speeds at 2X-3X what I had before the move AND a television package that was two upgrades above what I was leaving. All for half the price I was paying before the move. Too good to be true?
Efficient retention. Impressive.
As someone who has held sales positions, works in predictive analytics, and has a technical background, I could really appreciate the efficiency of this win-win transaction. My provider retained me as a customer on a Saturday morning with a single 10-minute phone call AND my new house will have quadruple the package of the previous house for half the price.
Hold on. It gets more impressive from the telco�s standpoint.
Then I had a revelation. After only two weeks in the new house enjoying my new services (key word �my,� read �personalized�), I figured out that if I paid more than I am paying now � but not much more than I used to pay in the old house � then I could have the top-of-the-line package: super-duper connectivity, high definition, DVR, and on and on.
That is to say, I just up-sold myself as a result of a 10-minute phone call on Saturday morning four weeks prior!
Needless to say, my telco provider must be leveraging elements of a robust Decision Management solution. In particular, I�m sure they used my high predictive score for up-sell, coupled with the business rules that governed the initial offer, such as�
�IF (provider_jump = false) and,
�IF (previous_package = XYZ ) and,
�IF (number_complaints < 2) and�
�to produce an outcome that demonstrates the importance of predictive analytics and rules to guide optimized and automated decisions.
Said another way, my telco provider not only retained me, but got more monthly subscription revenue out of me in a very efficient manner.
And this is just one personal example from telco. Think of how predictive analytics and rules can (and are!) being used in tandem to optimize and automate recommendations in retail (e.g., customer analytics), manufacturing (e.g., preventative maintenance), insurance (e.g., claims processing), and beyond.
Speaking of optimization, stay tuned for Part III of my Decision Management series.
And, if you missed my �Ode to Rules� in Part I, you can read it here.
Guest post from Kurt Peckman, Program Director, IBM Predictive Analytics
Rules are meant to be broken.
No one likes restrictions, to be controlled or be told what to do. In reality, however, rules are broken so better, stronger, and more appropriate rules can be created.
In other words, an established rule is often a starting point (or some critical point) of a rule�s �evolution.� Good rules evolve so better actions and decisions can be made.
For instance, some rules are about governance. Traffic rules govern some of the largest, most complex systems in the world. Motorists are surprisingly (mostly) cooperative thanks to these �rules of the road.�
And, what�s even more interesting is the apparent global rules of the road (that apply everywhere) in contrast to parts of the world have �local� rules � due to geography, culture and necessity. For example, making a right-hand turn in the United States is very different then Australia�s �hook turn.�
Rules are also about policy. For example, never go in your mom�s purse, never call someone after 9:00 p.m. or before 9:00 a.m. (Yes, I�m showing my age. I realize that nowadays we text each other 24/7).
Speaking of texting, ALL CAPS � as a rule � means you are screaming at someone. Oh, and never, ever text an image that will come back to haunt you later. Don�t be a Weiner. When you are on the golf course, there�s a rule that you shouldn�t talk about business before the 3rd or 4th hole � and try to finish up by the 15th or 16th.
I once had a psychology major tell me the vast majority of interpersonal behavior can be explained by two rules: birds of a feather flock together and opposites attract.
Think about the rules that apply to reviewing and selecting candidates for a job opening from hundreds of applicants, quickly building a large world-wide team for a last-minute project, or even during a round of speed dating.
These show the fine line between governance and policy and demonstrate how �rules� become important in guiding decisions. Specifically, they become a necessary component of a Decision Management solution �especially when the volume of decisions increases and the time to make decisions dramatically decreases.
Decision Management allows users to automatically deliver high-volume, optimized decisions at the point of impact, such as in a call center, on a website, in a store, etc.
Overall, rules help link day-to-day execution to organizational objectives. Consider sports. Every rule book for every sport has a catch-all rule that enables an official to make a �judgment call.�
In basketball a referee has discretion when determining if someone is being malicious on a foul. There are criteria (e.g., a set of rules) to determine if a foul is flagrant � was the player really going for the ball, did the foul seem to have the right balance of aggression and sportsmanship, was the foul committed during a breakaway.
Finally, let�s discuss gaming. In casinos, each game has its own set of rules. I like this as an example because there are global rules about gambling (the house always has the edge) and local rules (in the US you have to be 21 years old to gamble). The local rule in my house is that I always win.
Consider all the systems I mentioned � traffic, sports, gaming � and consider the complexity of these systems, then think about how a good set of evolving rules helps establish structure, policy and governance.
But, rules can be inflexible and limiting to good decision making. Decision Management solutions must have rules, but they also can�t rely entirely on these rules. After all, a good process might be bad if it speeds up bad decisions or outcomes.
Rules must be balanced with business analytics for optimal decisions. I�ll cover that in part 2 of this discussion.
By the way, what is your favorite rule that you like to break?
�Analyzing people�s behavior instead of what they claim their behavior is tells a completely different story about that individual,� said Baker. �Most of the time people will say something that is designed to make you feel better and make them feel better about themselves.�
Online people have an array of personas that might fluctuate depending on time of the day, their physical location and mood.
Baker is speaking at the upcomingIBM Finance Forum (May 9) and IBM Performance (May 10) events in Calgary, Alberta, Canada, and cautions that organizations need to take into account that a lot of what is said online is for public consumption, to impress friends and brand themselves.
�It might be important to know that someone is proud of liking some type of music and that may have an impact, but that person might only listen to it when with friends,� said Baker.
Leveraging social media analytics, while important to understand customer behavior and sentiment, is only one piece of the puzzle. Integrating social media data with demographic, purchase history and other unstructured data (e.g., call center notes, emails) will provide a holistic view of the customer and a better recommendation of how an organization might interact with them.
Baker suggests this now allows organizations to focus on what is happening today or five minutes ago.
�The combination of big dataand real-time analytics allows organizations to make the most of the current moment with a customer who is at the cash register or on the phone, and optimize that interaction. One of the most important things organizations have is their relationship with the customer and the customer data,� said Baker.
All this data is turning organizations into scientific laboratories. Analytically mature organizations are now testing their hypotheses endlessly and allowing the data models to keep churning out the best possible scenario � such as ROI to a marketing campaign.
This could be why �data scientist� is the popular analytics job description de jour.
And, while data analysis is a means to an end, it�s an end that continuously changes so the analysis is a never-ending process (which is good for the analytics industry).
�The amount of data is going to keep rising exponentially, and soon we're going to be surrounded by sensors and the rise of artificial intelligence and more machines likeIBM Watsonthat will bring written and spoken knowledge into business opportunities,� said Baker.
A good example of this, according to Baker, is in the healthcare industry. Aging societies are going to push for more electronic sensors so those people can stay in their homes and their activity can be monitored. Then, any changes in patterns can be detected and doctors can intervene sooner than later, turning the nature of medicine from reactive care to preventative care. Ultimately, societies will save money and not bankrupt themselves as the population grays.
�Watson is an incredible tool to be used by us and not replace us,� said Baker. �We used to have to know and hold a lot of facts in our heads and dig through it to come up with answers. And now machines, like Watson, are taking over more and more of the work. I�m very interested in how we're going to reorganize our own minds in order to work well with these machines.�
That is why, Baker argues, that using analytics can reveal truths that aren�t seen and get us to think more deeply about what is really happening.
Tell me if this sounds familiar: You're pondering whether to do something potentially risky -- perhaps quit a job, switch to a completely different career path or even start a business. You have many motives to do so, yet the road ahead seems very unclear, and you're uncomfortable with that. And someone else says, �Oh, go for it. Everything in life is risky. You could get hit by a bus any day... but that doesn't stop you from leaving the house.�
Well, that�s true, of course, but as an argument it has a really basic problem: it's number-free.
Not all risks, in other words, are the same. The risk of getting hit by a bus is different from, and much smaller than, the risk of starting a business, watching as it slowly fails and getting into deep debt.
Making such a decision reasonably competently means finding a way to clarify, quantify and prioritize the kinds of risks you're facing in a given strategy -- and weighing them against the benefit you're trying to create.
This, in essence, is a problem confronted every day by businesses making complex decisions. They'd like to create improvements or pursue new goals in a given area. But in a perfect world, they'd also like to avoid getting hit by a bus.
By no coincidence, this is also a major focus of IBM's considerable interest in advanced business analytics -- recently described by Mike Rhodin, Vice President of IBM Solutions Group, as �the silver thread woven throughout our portfolio.� Risk assessment and mitigation are central to business strategies -- almost all strategies, in almost all industries. And advanced analytics can deliver some of the best available insight to accomplish that.
Get a moment of clarity -- actually, get lots of them
Toward getting a little more clarity about this area, I talked to John Kelly, Worldwide Market Segment Manager for IBM's Business Analytics group about IBM's perspective in this area... and how that perspective is going to be explored at the forthcoming Vision 2012 conference to be held from May 14-17 at the JW Marriott Grande Lakes in Orlando.
Like me, Kelly sees analytics as a powerful visualization tool -- a way to understand different possible futures, and steer your organization into a future that offers more benefit and lower risk.
�Customers are looking to improve decision making and business performance through increased insight and business intelligence,� he said. �That's exactly why IBM has recently labeled analytics as one of our four major strategic directions -- we know how much potential this area really has. And we'd like our clients to realize as much of that potential as possible.�
Risk assessment and mitigation, of course, have a long history in some areas (like finance) and are less well understood and established in other areas (like technology startups), but the root appeal remains the same in every case. If you want to get the best possible outcome, you need to establish the most likely, and most potentially devastating, pitfalls.
Analytics tools can work almost like a car's high-beams, helping you navigate and get where you're trying to go more safely. That's a goal that almost any business leader, in any industry, at any organization of any size, can understand and appreciate.
Regulatory compliance stands out as a growing challenge
And beyond that general value proposition, IBM is making considerable strides in applying analytics effectively in areas that are of particular concern to its clients. One such area: regulatory compliance and policy management.
In the wake of major scandals dating back more than a decade, these regulations have increasingly been created with the stated goal of minimizing various forms of unacceptable risk to the public, to business employees and customers as well as to stockholders. And that, of course, is a laudable goal.
But complying with those regulations can be a headache even for the best-intentioned organizations that are really committed to compliance and dedicating tremendous resources to the job. Even when compliance seems to have been achieved, it hasn't always been. New regulations appear every year; it's not the easiest thing in the world to know which apply in a given case, and under what conditions, and what the best organizational response should be.
IBM, it seems, can help. �Our solutions deliver analysis and reporting, to provide visibility into the state of risk in the enterprise including evidence of compliance or remediation status, trending and point-in-time analysis and ad hoc querying,� said Kelly.
Consider what that means in practical terms. Not only can you understand much more clearly, quickly and easily the extent to which your organization is in compliance, but you can also demonstrate that compliance on demand, in whatever level of detail is required. In the event of an audit, such a demonstration will be essential -- and avoiding potentially hefty penalties and fees will be much simpler. What organization wouldn't be interested in solutions like that?
One solution family drawn from IBM's analytics portfolio is particularly strong in the area of compliance and risk: IBM OpenPages. This suite of tools focuses specifically on governance, risk and compliance, not just identifying and monitoring risk, but also putting in place a programmatic way to communicate and manage risk exposure across the enterprise to reduce unexpected losses, penalty and fines (not to mention reputational damage), while at the same time improving decision making.
Its compliance capabilities, for instance, are directly on point. Organizations routinely create (and enforce) policies to drive compliance... but not always in as governed and coherent a fashion as they might. (Banking industry, I'm looking at you when I say that.)
OpenPages Policy and Compliance Management automates the lifecycle of compliance policies from cradle to grave, reducing redundancy and optimizing the policies you keep in a way that spans resources, business groups, projects and workflow. Organizations that have a formal implementation of risk mitigation, but would like to tune or enhance it to better align with their current and future needs (not to mention future regulations), will find this solution particularly compelling.
A better outcome can result from risk-aware decision making
Risk management is increasingly becoming a strategic, executive-sponsored solution that many organizations view as providing a competitive advantage where risk and performance are aligned and where governance, risk and compliance is part of �annual strategic planning�.
An integrated governance, risk and compliance program also has a wealth of information that can be leveraged for risk-aware decisions. Through business intelligence and reporting, information from an integrated program is being utilized beyond the risk and compliance office and being leveraged by business managers to make risk-informed decisions about resource and investment allocations in product planning.
Optimize your risk management strategies in many dimensions
Other OpenPages solutions -- which inter-operate with each other, via a shared foundation of data -- are available to deliver similar capabilities in related fields like:
Operational risk management. This offering can identify, manage, monitor and analyze operational risks of all types, all from a single point of command to spur a particularly agile response. From better, more accurate insight comes a faster and more comprehensive remediation.
Financial controls management: Regulations like Sarbanes-Oxley in the United States are mirrored by similar regulations in other countries around the world -- and for global organizations, each crossed border represents a new set of financial regulations with which to comply. This solution focuses on reporting, offering a centralized architecture for analysis, documentation and data management.
IT governance. IT has become central to almost everything organizations do today. As a result, risk assessment for IT assets, services and data is needed to ensure that IT delivers the intended value -- ideally, on time and under budget -- even in the case of complex projects that take years to complete.
Internal audit management. For large organizations that proactively conduct audits of their own, this solution is a natural fit. Using it, they can automate many of the basic processes involved, as well as connect the results logically to other risk assessment initiatives they have in place.
Anyone interested in getting more information on these and related topics should definitely consider attending the previously mentioned Vision 2012 conference.
This is the premier global conference for finance and risk professionals, and the most high-profile stage for IBM to discuss everything it has to offer in this rapidly evolving, increasingly hot area.
When I asked Kelly to sum up in a nutshell what IBM will be discussing at Vision 2012, he said this:
�IBM Risk Analytics enables the Smarter Analytics approach -- turning risk information into insight, and insight into better business outcomes.�
About the author Guest blogger Wes Simonds worked in IT for seven years before becoming a technology writer on topics including virtualization, cloud computing and service management. He lives in sunny Austin, Texas and believes Mexican food should always be served with queso.
As the old adage goes, �Fool me once, shame on you; fool me twice, shame on me.�
If you�ve never heard this expression before, it means that the first time a negative action occurs, the accountability is on the one that did it. When it happens a second, third, or fourth time, the accountability is on the individual (or group) that allows it to keep happening.
For instance, in Tennessee in 2009Amanda Sue Kelley, 19, was arrested seven times on charges that ranged from drug possession to domestic assault and theft. Her last offense, police say, she wrenched open the door of a parked car, pointed a gun at a woman changing her 13-month-old daughter�s diaper in the back seat, and demanded cash. (Source: The Tennessean)
This is such the case of the existing criminal rehabilitation system. Those being fooled are the taxpayers that are allowing their money to be spent on an inefficient and ineffective system. A bold statement, maybe, but let me explain�
Repeat criminal offenders cost the system more money than one time criminals, and it wasreportedthat more than 40 percent of offenders in the U.S. return to state prison within three years of their release. In the UK, onestudyof 14 prisons, most of which hold short-term inmates, found reconviction rates of more than 70 percent, and according to anothersource, these criminals are committing up to 2,000 murders, rapes and other serious offences every year just months after completing a punishment for a previous crime.
The term for this is recidivism � which refers to measuring the rate that criminals violate their parole or are arrested for new crimes. In fact, theU.S. Justice Department estimatesthat a 10 percent decrease in recidivism can generate a collective savings of $635 million. And more importantly, one less repeat criminal on the street is, at minimum, one less crime committed in our neighborhoods.
What if there was a way to anticipate which individuals were likely to become repeat offenders after they commit their first crime? Or, what if high risk individuals can receive the appropriate attention or be placed in the rehabilitation program that is best suited to change their unlawful path?
Government agencies worldwide that are responsible for public safety are already usingBusiness Analyticssoftware to analyze data on criminals to provide insight into complex relationships and patterns, such as past offense history, home life environment, and gang affiliations among others, to better understand and predict which inmates have a higher likelihood to reoffend.
For example, theU.K. Ministry of Justiceneeded a way to analyze vast amounts of crime and offender data to understand which proactive measures would be likely to prevent recidivism. The ministry turned toIBM SPSS predictive analyticsto analyze millions of prisoner files. The analysis is helping them develop treatment targets for prisoners throughout their sentence to reduce the probability they will commit crimes upon their release.
The Ministry of Justice now has more accurate crime prediction rates with violent crime recidivism prediction improving from 68 to 74 percent, and general offenses recidivism prediction improving from 76 to 80 percent.
Yes, it is a complex situation. Yes, politics comes into play. And no, this is nothing like the �precogs� in the fictional Tom Cruise movie, Minority Report.
However, predictive analytics is proving to be highly effective by helping organizations like the Ministry of Justice take measures to ensure that inmates receive the best services tailored to meet their rehabilitative needs, while being proactive to stop future crime and better protect citizens.
Another old adage says, �One definition of insanity is doing the same thing and expecting different results.� Let�s try a smarter solution to achieve better results.
We tend to look at government tax collection agencies as bad guys.Sure, they �take� our hard-earned money, but that money often goes to fund vital public service programs.
Here in the United States, those that plan to file and pay their personal income tax returns by Tuesday, April 17 are among the 86 percent of people that are typically compliant.
The real �bad guys� are actually those 14 percent who continually add to the growing �tax gap,� the difference between the annual amount of taxes owed and the amount voluntarily paid on time, which is now averaging more than $350 billion annually.
It�s the focus on these individuals where the work of tax collectors will kick into high gear with the use ofBusiness Analytics. This software makes it easier for tax collection agencies worldwide to pursue and maximize returns on habitually delinquent accounts, and minimize the financial burden on compliant taxpayers.
Tax agencies primarily use audits to ensure compliance with tax laws and maintain associated revenue streams. By using Business Analytics, tax agencies can nowquickly risk-assess accounts and accurately identify those with a high probability of delinquency. It also helps determine the most profitable collection strategies and saves funds by focusing limited audit resources on the most productive cases.
With a combination of business rules and analytics from IBM, New York has transformed its approach from �pay and chase� to �next best case� that results in the highest rate of return.
Government agencies are becoming more efficient by reducing the number of audits performed on compliant accounts, and more effective by supporting federal, state and city programs with recovered tax dollars.
Business Analytics allows tax organizations to analyze and identify patterns in multiple data sources, such as taxpayer profiles, previous filings, call center notes and audit history; and share these findings visually across geographic and jurisdictional boundaries.
Prioritizing delinquent accounts based on debt size and likelihood to pay allows agencies to anticipate taxpayer behaviors and events to identify next best action to take with each account as well asoptimize staff resources.
For instance, sometimes a phone call or a letter is the best strategy for certain accounts, while audits on accounts that yielded high returns can be focused on new returns with similar attributes.
As more and more tax revenue is lost due to fraud, tax evasion and various forms of tax cheating, it�s time for governments to turn a little investment in analytics technology into a tax return everyone can appreciate.
For more information:
�Registerand attend the IBM Business Analytics Government Forum (May 23, Washington DC)
There�s a hidden side to everything. That�s the premise of the New York Times best-seller, blog and recent movie, Freakonomics.
But it�s also the premise behind analytics. Organizations often find insights in their data, but they have to be willing � and able � to ask good questions.
Stephen Dubner was in Chicago recently speaking at the IBM Performance and IBM Finance Forum events. My colleague at IBM, Crysta Anderson, spoke with him about the truths hidden in data and how companies are applying analytics to their advantage � or not. See some highlights below.
Dubner finds it �encouraging� that companies are starting to really use their data, but he cautions against data for the sake of data. The rise in data has led to meaningless statistics, like the hemline index or Groundhog Day index. Such indices often reflect correlation rather than causation. As Dubner explained, if someone landed here from Mars and noticed that people used umbrellas whenever it rained, they may conclude that putting away umbrellas would stop the rain.
More data has also prompted more attempts at visualizing data, as seen by the plethora of infographics. While some people � including Dubner � prefer their data in tables, others want to see graphs or images to represent the information. But infographics have also made it easy to visualize those meaningless statistics, sometimes leading to bad decisions.
So how do we decide what data matters? And how can we best use the data at our disposal?