ColdLight

For Immediate Release: We are pleased to announce the ColdLight CEO, Ryan Caplan will be a featured guest speaker at this year’s 4th Annual Eye on Pharma Forecasting Excellence 2010 Conference in Boston, Ma.

The topic of this year’s presentation is “Ready fire aim fire!: The future of adaptive marketing for Pharma”.  Ryan will be presenting a joint case study in the future of promotional mix optimization with one of its clients, Shire Pharmaceuticals. Co-presenter Hans Nagl is the director of Promotion Response Insights for Shire, and is one of the world’s foremost innovators in the use of data to optimize promotion response for pharmaceutical brands.

This year’s conference focuses on key topics challenging the Pharmaceutical Industry, including:

• • Emerging Markets, emerge victorious – execute the winning strategy when assessing market potential and accessibility of new territories
  • A change of tactics… as small patient segments become new targets, what does this mean for your forecasting process?
  • Develop an S&OP strategy which goes beyond the basics and translates into a meaningful strategy across the business
  • Get in-sync with R&D: Bridge the gap between clinical development forecasts and evaluate early stage products effectively

“We’re honored to be included in this industry leading event,” said Caplan, “it is a real who’s who in Pharmaceutical Forecasting innovation. I’m very much looking forward to sharing the podium with Hans Nagl. He is not only a great forecasting guru, but is known as an innovator in the space.”

For details on the conference, please visit the conference website.

Why has turning data into competitive action so hard for so long? Look at these top 5 questions below and you’ll see why:

1. Why do traditional analytics solutions require years of training and expertise?

In the old days, it was assumed that experts with advanced training in statistics and computer science would be the only people that would ever need to build predictive models. After all, they were used for scientific research purposed primarily. But that has all changed now that many businesses are realizing the power of using data to make smarter decisions. Businesses that might benefit from advanced data analytics find themselves facing the daunting task of hiring people with years of expertise and training to build and maintain predictive models.

2. Why does it take so long to get results?

Traditional data analysis is time consuming because it is labor intensive. Even with the advanced tools available to scientists, it takes a long time for experts to build and perfect predictive models. The process is trial and error to a large extent, and there are only so many trained experts to go around.

3. Why are the results so hard to understand the results and to put those results into action.

Traditionally, the results of advanced data analysis were meant for scientists, not business people. So, the results are often hidden within complex language and equations that require an expert to interpret. After all, the analytic tools were designed for scientific research. Does the average business person really understand the difference between ordinary or weighted least squares… do they really need to?

4. Why is it so expensive to get results with traditional solutions?

Expert People + Hardware + Software Licenses = Expensive

5. Why can’t it be easier and more cost effective for my business to get predictive actionable insights from my data?

It can. It is. The answer is Neuron™. The world’s first automated analytic engine from ColdLight. Neuron  is Software as a Service, designed to automate the entire analysis and predictive insights process for you.

ColdLight’s Neuron is powered by next-generation technology that automatically discovers patterns in massive sets of data and offers action plans to achieve your critical goals. Neuron automates data analysis for you, making it easier and faster for you to turn large amounts of data into competitive action

• Neuron requires absolutely NO expertise in statistics or data mining
• Neuron performs the work of thousands of statisticians in minutes.
• Neuron is the ONLY product on the market that delivers automated,. discrete action plans that show you how best to achieve specific business goals (“To achieve your goal follow this plan…”)
• Neuron is Software as a Service, no hardware to install or maintain
• Neuron is fast, easy, cost effective and designed for your business.
• Neuron integrates easily into 3rd party products and services

At first thought, the answer seems easy.  You have 100 billion neurons; Neuron has far fewer eponymous processing units.  You are the product of millions of years of evolution; Neuron is an advanced system, but is the culmination of merely a quarter century of development.  You have common sense; Neuron doesn’t know how to boil water.  That being stated, the situation is not so simple.

To see why, we need to examine what human beings are good at and how they are different from artificial neural networks.  Although we have a large, long-term memory store at our disposal (it has been estimated that we can remember up to 50,000 distinct faces), our short term memory is rather limited – it varies between a few and up to ten distinct items depending upon how they are organized internally.   The implications of this for human intelligence are profound.  We tend to develop models and theories from a few well-chosen characteristic examples and we are able to examine only the most salient aspects of these examples.  This works extremely well when the number of variables at hand is relatively small and there is a great deal of regularity in the data. It has allowed us to develop everything from MRI machines to the theory of relativity.

But consider the situation when thousands or millions of records exist and each of these consists of many hundreds of independent variables.  For example, a company may have a record for each financial transaction each with dozens of variables describing the customer’s actions. This may include demographic and past behavior information.  Moreover, this data may be noisy or even contradictory, either because the information was recorded incorrectly or simply because of quirks in purchasing behavior.  It is well beyond the ken of human processing to take all of this data and develop a coherent model.  Of course, it may be possible to develop intuitions regarding why certain people buy certain products, but intuition will break down in all but the most obvious cases.

This is where Neuron comes in.  Neuron has no trouble examining hundreds of factors, extracting out the most important of these, and determining the correlation between the input features and the desired output.  In an important sense, it is Neuron’s lack of common sense – or preexisting bias – that allows it determine non-obvious relationships in the data.  This combines with the ability to entertain thousand of possibilities at once to give it insight that can surpass native human capabilities.

In summary, we can state the following.  When working in a relatively confined domain in which the determining factors are well-understood and cleanly relate to the desired outputs, humans will often outperform computational systems because of the flexibility of their model-building behavior.  But in the increasingly common case in which vast amounts of data must be pored over to extract meaningful relationships, Neuron can often outshine even the most qualified expert in the field in question.

Russel Conwell, the famous orator, minister and founder of Temple University, gave a famous lecture on finding your own “personal acre of diamonds.” As the story goes (and I may get some small details wrong here), there was a wealthy farmer who had all he needed in life to be secure and happy. Wealth, health and family. During a chance meeting with a stranger, the stranger told the farmer that diamonds were the most precious and most valuable stone on earth and one could not truly be wealthy without them. The farmer became obsessed with the pursuit of diamonds for once he had diamonds, he would have true wealth. He set about looking for diamonds. Diamonds were already discovered in abundance on the African continent and the farmer sold his farm to head out to distant shores in search of the rare stones. He wandered all over the continent, as the years slipped by, constantly searching for diamonds and wealth that he never found. Eventually he went completely broke and threw himself into a river and drowned somewhere off the coast of Spain.

Meanwhile, the new owner of his farm picked up an unusual looking rock about the size of a country egg and put it on his mantle as a sort of curiosity. A visitor stopped by and in viewing the rock became so excited he could barely contain himself. He told the new owner of the farm that the funny looking rock on his mantle was about the biggest diamond that had ever been found. The new owner of the farm said, “Heck, the whole farm is covered with them” – and sure enough it was.

The farm became known as the Kimberly Diamond Mine. It is the richest diamond mine the world has ever known. The original farmer was literally standing on “Acres of Diamonds” but instead of looking in his own property, he sold that very farm to seek diamonds on a distant continent.

Think about this story as it relates to your business. You could consider data in the your business to be like the farm in the story, laden with diamonds simply waiting to be extracted. How many businesses do you suppose there are that spend inordinate amounts of energy, time and resources searching for key answers from outside experts, outside data vendors, and external market information. Surely, someone out “there” has the answers. But what if the answers, like the diamonds, are right there in your very own backyard. You only need the tools and the desire to mine them. The idea of “data mining” in the traditional sense is not what I’m referring to. Simply producing reports of different views of data hoping to find some diamonds is not the answer. If instead you develop and organize specific critical business questions and apply the right type of analytics against your data, you may find the diamonds you seek.

Every day I hear clients say, we need to acquire better data, we need more data, we need to buy a new set of data from the vendor, and in some cases… I need a new data vendor. I often ask, “What are you doing with the data you bought last year, or last week?” After a shrug, the reply is typically, “Well that data is old, I need new stuff, because that is what my competition is buying…new stuff.” While I’m not diminishing the value of current information, is getting more and more data really the answer? Is new data and fresh data the only thing that matters? Not if it is going to sit on the shelf like the last 50TB of data you accumulated.

If used properly, discovering patterns in your historic data can yield tremendous insights. Consider this fictitious example: every year between May 1 and June 1 on the East Coast for the past ten years new car buyers were more likely to buy white 4 door sedans than any other vehicle. If you simply focused on this month’s numbers you’d miss that pattern and the opportunity to overstock white 4 door sedans in late April on the East Coast.

Is there an equivalent in your business? Are there undiscovered opportunities sitting in your data right now? How many of these diamonds are you searching for on a distant continent instead of in your current data store? The answer may be to start looking in your own backyard. It is very likely that you have a lot of data and have yet to harvest the diamonds. The key to beating your competition may be using the very resources that are sitting quietly in a spreadsheet or database just waiting to be harvested.

ColdLight CEO Ryan Caplan introduces bEye Network’s Ron Powell to the world’s first pay-for-use, cloud-based, Prescriptive Intelligence engine service. ColdLight’s revolutionary product Neuron offers a simple service-based interface to embed smart analytics into other products enabling better decisions based on relevant data. Listen to the podcast.

One question I’m often asked is, “Who is ColdLight’s biggest competitor?” People love to hear about who you compete with so they can properly put you in a mental bucket of how alike you are to everything else in the world. In a sense, they’re asking how you conform to the current paradigm and differentiate yourself enough to be interesting. To me, conformity is simply not part of the plan.

I typically respond that our competition is the status quo. By status quo,  I am referring to the traditional approach of hiring statisticians to sit in front of advanced software tools, building and studying complex analytic models trying to discover important insights in data. In my opinion, this process has not kept up with the onslaught of data and the demand for instant turnaround that businesses require.  There simply is not enough time to build and refine models following the approach of the status quo.

Consider the business era of the 1950s pre-desktop computing. The age of the typing pool. The age of manual correction tape. The age of paper. Some of you are probably not even old enough to know what some of these things are. They are, more of less, extinct; they are a part of the retired fabric of a business world long since revolutionized by technology. They are extinct because breakthroughs in technology put the power of desktop publishing into the hands of every business person at an affordable price. The investment in teams of manual typists was replaced with more powerful and more cost effective desktop publishing software fully equipped with spellcheckers, grammar checkers and advanced layout tools. Technology challenged and usurped the status quo.

One could mourn the loss of the typing pool. One might argue that, for the good of our economy, it is important to create or at least maintain jobs. However, those people in the typing pool didn’t disappear. They found new ways to make the business more effective and more efficient. The shift from manual, rote, mundane activities to creative activities enables business people to search for new and innovative ways to beat their competition.

Let me bring you back to the world of data analysis. Do I think human data crunching automatons (aka data analysts) are headed for extinction? No, absolutely not… yet.  Do I think that the role of the data analyst will change? It already has changed significantly over the past ten years and will continue to evolve rapidly. The tools available today enable analysts to build sophisticated models in record time, but that time savings is only significant when compared to the status quo: a human-intensive process. With a new paradigm of machine intelligence working on the intensive data crunching challenges, the role of the data analyst can be transformed into a master of creativity seeking out important questions to ask of machine intelligence-driven analysis in order to identify the best strategic course of action.

The data analyst will not disappear any time soon. The role - like all roles in business - will continue to adapt and evolve. Interaction with machine intelligence will become increasingly more integral to the process of data analysis. The machine intelligence will do the investigation and present back appropriate insights to the creative business person.

The company of the day is ColdLight, based in the US. ColdLight has the goal of delivering Prescriptive Intelligence into the hand of the 80% of business users. You will find below an interview with Ryan Caplan, President and CEO of ColdLight. All the best to them and congrats for being the startup of the day!   Website: www.coldlightsolutions.com.

Visit the Microsoft Start-Up of the day website

Interview with Ryan Caplan, President and CEO of ColdLight

Basic Introductory Questions:

How do you feel about being the most promising ‘company of the day’ per Microsoft?

It really is an honor and an achievement to be considered “Company of the Day” feature by Microsoft. When we first began designing our Neuron user experience, it was critical that it be easy to use and offer a design that was unique and beautiful. It was clear to us that Microsoft WPF was the right direction for us. We take it as a significant validation of our business model, our technical approach and our achievement to date as a company that Microsoft has considered us.

How would you describe your company’s product and mission?

Our goal is to deliver Prescriptive Intelligence into the hand of the 80% of business users for whom traditional predictive analytics technology is simply too complex or expensive. Our SaaS model makes it simple and inexpensive to get answers to your most complex business questions in seconds, with no hardware to install, no software to maintain.

Where did the idea for this company come from? What was the genesis of the idea?

ColdLight’s founders spent many years integrating and designing business intelligence and predictive analytics tools. The result was always the same. Business users loved the visualizations and reports, but were left scratching their heads asking “These charts are beautiful, but how can’t this tool tell me how to achieve my business goal?” Unfortunately the answer was “No.” So we founded ColdLight to solve this major gap in the market. We wanted the solution to be simple enough to learn in minutes, easy enough to deploy in hours and one that required no ongoing support or maintenance. Our SaaS-based Neuron platform provides the business person with powerful advanced machine intelligence to help them get ahead and stay ahead of their competition. It is delivered in a beautiful user experience. It is incredibly simple to use. And customers love it.

Impact of externals:

How did you fund the company? VC, Angel, Bootstrap? What is the chronology of funding?

To date the Company has been funded by individual Angel Investors and Ben Franklin Technology Partners of Southeastern PA.

Are you currently seeking funding? If so, what kind and how much?

We are presently seeking $1M – $1.5M in growth capital to expand sales and the depth of our product suite.

What about the BizSpark Program? What do you think? Are you going to join? Why?

We are very excited about the BizSpark program, not only because it helps early stage companies maximize their capital that might otherwise be spent on expensive software licenses, but because it is a forum in which to interact with experts in Microsoft technologies. Given that we are using the latest and greatest in WPF for our presentation tier, it is critical that we have access to the experts to support our development goals.

Product and Market questions:

Describe your offering. What do you sell and how do you sell it?

Our flagship product Neuron is defining a new breed of intelligent software applications capable of scouring vast volumes of data, making strategic recommendations and actually learning from success and failure. Imagine fusing millions of financial transactions, account balances, CRM data, individual portfolio performance and cross-referencing the results with financial news from major sources around the world, major stock indices and demographic data. Neuron identifies your most at-risk customer profiles and offers the perfect Prescription to ensure that you retain those customers. Neuron then monitors the results of the actions you take based on those recommendations to refine its thinking in the future. The result: your business retains your most profitable customers. ColdLight’s patent-pending technology extends beyond the financial services space into other key target markets. Neuron can proactively identify at-risk patients for health providers, identify optimal customer acquisition and retention strategies, and understand consumer purchasing habits to promote customer loyalty and more. We sell Neuron as a SaaS-based monthly subscription and will soon be offering a pay-for-use model.

Do you have any Software IP? More specifically, is there something unique you are doing in the marketplace?

Our Prescriptive Intelligence engine is proprietary to ColdLight. The ability to fuse volumes of data and automatically identify the best courses of strategic action is unique and proprietary. We use a very sophisticated series of algorithms and techniques to achieve the results. Also, our user experience has patents pending due to the simple way in which are rendering very complex multi-dimensional data.

What would you describe as your “primary” market? Are there any secondary markets you service?

Our primary focus is on Customer Retention with a secondary market in Operational optimizations. Most of our early customers are focused, especially in this economic environment, in getting ahead of the customer attrition curve. They want to minimize customer attrition and are using Neuron to help them achieve that. In addition, we see a lot of traction in operational improvements such as reducing medication administration errors in hospitals, optimizing climate controls in commercial buildings and even setting optimal price points for consumer goods.

Do you have a growth plan or strategy? Any plans for Internationalization?

We are planning to grow organically and through strategic partnerships.

Developer/People Questions:

How many employees do you currently have? How many software developers/engineers?

We presently have 8 employees, 5 of which are developers.

Are you hiring? If so, what kinds of positions?

We are growing pretty quickly so we are hiring accordingly. Right now we seek the most talented individuals who have a passion for building beautiful products in Java and WPF. In addition, we are looking for great Sales professionals that understand consultative selling.

What technology platforms are you building on? Why? (No taboos)

Our presentation tier is built in WPF and we are porting to Silverlight in the coming months. Our Neuron core services and adaptive intelligence engine are built in Java. We like the flexibility that WPF affords us on the front end, allowing an incredibly dynamic user experience while the scalability and ease of implementing our core engine services in Java provide us with scalability, OS independence on the backend. We are deploying Neuron in the Amazon cloud which opens up a whole host of opportunities for us when it comes to SaaS deployment. Essentially we can achieve incredible performance, groundbreaking technology and keep our prices very competitive for even small businesses. In many ways, we feel we have the perfect blend of the right technology for the right solution.

Color Questions:

Do you have a role model or someone you have looked up to? If someone in particular, whom?

I grew up watching Cal Ripken. I always admired not just his raw skill and his ability, but his passion and work ethic. If there is one thing I learned from him it is that talent is not enough. It takes perseverance, passion, and the ability to withstand uncertainty and setbacks. That is true in baseball, its true in business, and it is true in life.

What were some of your previous endeavors before starting this company?

Prior to joining ColdLight I ran an international development team for Sanchez Computer Associates, the creators of one of the first real-time banking engines, and I was COO of Electronic Ink , a software design and development consultancy that focused on the user experience.

Do you have any advice for young software entrepreneurs?

Find a great idea that you are passionate about and that the market needs desperately. Without either of those, you are going to have a hard time. If the passion is not there, it is hard to get to market. If the market is not there, it is impossible to make money. Surround yourself with smart people and be willing to take risks. And lastly, try to make more good decisions than bad ones.

Where do you see opportunities today and in the future regarding the Software/Internet arenas?

I think the huge future of software is in cloud computing. The idea of deploying commercial software in the cloud is only beginning to show its power. I think that all applications will migrate here and open up possibilities that we cannot even imagine. I think it is finally time for true Software as a Service to come to life.

Right now, the last thing we need to think about is another challenge to business success. But, lets look beyond the current economic environment for a minute and think about what population growth means to the future of business decision making. We’ve all heard about the “brain drain” overseas. Where highly skilled people leave to pursue higher paying jobs in other countries. But that may only be the beginning. What if we are just not able to scale to support the number of highly trained human beings required to support a growing population?

Doctors will need to care for more patients, more quickly. Suppliers will need to get more products into the hands of more buyers more efficiently. Business people will need to harness more and more information to make impactful decisions more quickly.

We are addressing some of these challenges already through the automation of simple services today. Supermarkets are putting in automated checkout to minimize wait times and cost of checkout personnel. Banks #1 performing employees are ATM machines, allowing the banks to service more customers more quickly. The list goes on.

As our population grows, the demand on these highly skilled, highly trained resources that make strategic business decisions will become further challenged. Population growth requires us to have more and more automated intelligence to support our decisions and actions. Intelligent technology-based products can  help us keep pace with the increased demand for smarter faster decisions in a world where resources are becoming exponentially more scarce as population growth soars.

I was recently reading a posting entitled “The Age of Mass Intelligence“. The article prompted some interesting points regarding the mass availability of information to the growing population of the world and the proliferation of intelligence around the globe.

Sure, there are still many people without internet access, cable TV and other mass media channels. But, clearly we live in an age where people across socio-economic ladder have more equality in their access to information than ever before in history. Our information is more unfiltered than ever before, there is more information generated by average people, and the information is available instantly.

Information that once took many months to circulate, was expensive to disseminate, and was reserved only for the elite levels of society, now has become a constant stream of information for all of us average folks.

There is no argument regarding the information available now. But I disagree with the title including the word Intelligence. Information and intelligence are not the same. I would argue that this is a proliferation of information, not intelligence. Sure, people have access to more information than ever before. But does that benefit society? I think that the proliferation only benefits society if it is a means for gaining intelligence. With intelligence, we can collectively make better choices, make smarter decisions.

It is having the capacity to understand the information that really leads to intelligence. I have a lot of books on my shelf at home that I’ve never read. In fact it is a big problem in my life. I love buying books, but can’t seem to find the time to read them. Does simply having all of that information at my disposal make me more intelligent?

I don’t know. I haven’t read that book yet.

What will business owe to the decision maker?  How does the enterprise empower the decision maker inside the organization?

Companies are judged in so-called “Best Places to Work” lists based upon the things they give to employees like great health benefits, great work environments, flex time, daycare and whether there is a cappuccino maker in the break room. An interesting article this past week from CIO magazine points out the fact that 40% of executives trust their gut over their companies’ significant investment in Business Intelligence due to the fact that good data is not available or there is an inability to share information across the organization.  This brings to the forefront the question; what does an organization owe the employee to help them make the best decisions possible for the business? Throughout this article Accenture and Aberdeen Research go on to confirm that employees lack the most basic of tools to make everyday decisions that contribute to the bottom line of the business.  It seems assumed that the “job will get done” with simply good people who know their job.  If those people in decision making capacities lack even the most basic tools how can they be expected to make the right decisions?

“The 61 percent of respondents that said ‘no good data was available on which to make decisions’ is striking, given the terabytes of internal and customer-related data available at most organizations today.  It’s also, of course, indicative if the sad state of data management inside organizations. “

A decision is only as good as the data behind it.  Today, more than ever before, each and every decision needs to be based in fact.  The only way to accomplish this is for business to deliver information that can be acted upon at the time of decision.  Business Intelligence as it exists today, the single version of the truth, does not support effective decision making.  The “gut” represents the subjective and qualitative factors that business users have to gain trust that the enterprise is capturing along with the quantitative ones in creating the single version of the truth.   Most BI implementations are based upon the notion that volume of data into a single repository provides that single version of the truth.  Most still leave things out and thus degrade the truth…or at least omit some of it.

Businesses know this needs to change; needs to evolve.  Business users have to gain transparency into the data in order to make decisions that achieve the goals of the business.  Companies have a responsibility to give users the trust in the most basic of tools to do their job: data.  Maybe the next “Best Places to Work” list that comes out will focus on trust rather than a cappuccino maker in the break room.

Its no secret that the key to acquiring new customers and retaining your most profitable customers takes more than intuition and gut decisions these days. It takes strategic action based on data, the right data. In a down economy, using data to make better business decisions is more important than ever. But having a lot data is not enough. Having collected the RIGHT data is truly the key.

The instinctive approach to using analytics to make business decisions may be to simply start capturing new data, and lots of it. Many companies have deployed world class CRM and ERP systems to collect volumes of data, spending years collecting lots of data and then begin to explore what types of questions they can ask of the data. That’s backwards from a logical perspective. And this can be a costly error. To be in the drivers seat of analytic decisions, you need to start from the business perspective and make sure that you are collecting the right data to answer your critical business decisions.

Starting by articulating your business goals and directing your data collection strategy around those goals will save you a lot of time, money and aggravation. Many businesses spend inordinate amounts of money acquiring data only to find out that when they try to solve their real business challenges with that data, their data is incomplete or inaccurate. Wasted time collecting the wrong data = wasted money.

Ask yourself: “To answer this business question, what data would I need to support the answer”. For example, if you are most interested in learning how to retain your most profitable customers, you will want to collect as much data about customers activities, their profitability, the length of time they are customers, why they leave and why they stay”. Sure, the list could get much longer, and should. But the point is, collect as much data as you can to answer the questions.

You should revisit your data collection / business goal alignment every quarter (at a minimum) to ensure that you are keeping pace in your data collection strategy. It is all too easy to lose sight that data collection is not a static exercise, but needs to be as fluid as your dynamic business decisions. When you want to make data informed decisions, you want to be sure that the data is already there ready to be work for you.

One of the greatest challenges to the pervasive implementation of predictive analytics is the persistence that we as humans know, that we don’t know until after the event when evidence can support our assumptions. And even then the facts can be disputed. This is paralyzing business.

A pertinent article recently featured in Wired magazine touts the survival rates for certain cancers as nearly 90% when detected early, far greater than the 10-20% when the cancer is discovered in a more advanced stage. It discusses the combination of statistical approaches to biomarker discovery with traditional and emerging imaging techniques to uncover microscopic clues that a cancer may be present well before the caustic treatments of radiation therapy, chemotherapy and surgery are required. This immediately strikes a chord and very quickly re-paths my thinking about stopping cancer which affects nearly all of us directly or indirectly. “More than a third of all Americans—some 120 million people—will be diagnosed with cancer sometime in their lives.”

Early detection isn’t about knowing. It’s about predicting, with a certain level of probability, and understanding that even the best oncologist can’t see patterns in cancerous tumors that may be detected with advanced blood tests. Discovering patterns in business data which may predict a valuable opportunity is no different; our business acumen and gut can’t help us see trends hidden deep inside our corporate data.

The article sums up some of the psychological challenges we face when gaining comfort with the notion of early detection — of cancer or business opportunity.

“At first, [early detection] makes things more complicated. It introduces more doubt and complexity into an already complicated equation. But in return, early detection promises that this doubt can be quantified, that these new variables can be broken down into metrics, analyzed, and factored into our health decisions. Early detection proposes that the result of this calculation — complicated and ambiguous as it is — will yield better results for individuals and for their families. In exchange for a modicum of doubt, it offers a maximum opportunity for hope.”

If early detection has the ability to reshape the research to eradicate one of the greatest killers of our time with the ultimate bearing on life and death, understanding the factors which help us maximize or minimize our business outcomes seems like a necessity. We may not know the answer ahead of time, but these clues will help end the analysis paralysis so often experienced in corporate meetings and provide an edge to the businesses that employ advanced analytics.

If you watched the opening ceremonies in Beijing, you probably would agree that it was an absolute marvel of human coordination. Putting any political differences aside, the ability to transform 15,000 human beings into a highly coordinated digitial and visual display was jaw dropping. What’s this have to do with Business Intelligence… I actually think that the coordination of the Chinese performers is the exact type of highly sophisticated orchestration that businesses should be seeing from their competitive data.

Coordinating millions of data points should be as seamless and as simple to the viewer (or user) as the experience of watching the outcome of 15,000 people in mass synchronization, with perfect execution. I’m not suggesting the process behind the scenes needs to be simple per se, but to the end benefactor, the user, it must be. After all, who cares about the preperation, what business users need is the action.

But instead, BI products require the user to be writer, producer and director of the entire event, rather than the person who ultimately is supposed to benefit from the experience of interacting with the technology.

I think the industry has had the process upside down and backwards. Maybe we can learn something from the spectacle of the opening ceremonies to try to find better ways to transform data into breakthrough innovation rather than trying to mold business users into mathematicians.

Recently, it seems that every article published about the future of Business Intelligence expounds the power and benefit of dashboards vs traditional reporting. It seems that the methods of building complex queries and data cubes is beyond the reach of the average business user and the only solution is to “dumb down” the output and interaction methods. The answer – so far – appears to be dashboards.

Dashboards conceal the queries and technology enabling a business manager to quickly understand the current state of the organization. The user can drill down into details and calculations that were fabricated and assembled by a more technical user. However, if the user has a question about the data beyond what’s present in the dashboard, BI promptly falls back into the world of a technical person constructing a picture of the truth at the request of a business user.

There may be another way. Rather than seeing dashboards as an end, they must be re-established as a means. The very nature of a dashboard is retrospective or real-time at best; a car does not predict the speed of your vehicle five miles down the road, but rather informs the driver of his/her current velocity. Dashboards capture the decisions that were already made and fail to provide any recommendations to the user as to what decisions to make in the future. Dashboards are “easy to use” simply because they provide little functionality, not because they have clearly articulated a specific business intelligence need.

Dashboards are a stepping stone to the future of business intelligence. A necessary step, but a costly one. Companies who see dashboards as the end game will spend exorbitant amounts of money creating or implementing a “solution” only to find out in the months and years to come that dashboards provide the illusion of intelligence. Information delivered more quickly and in a graphical format is not intelligence. Dashboards still require the human user to interpret, build consensus and make decisions.

What if a truly intelligent business system could provide recommendations around decisions? We’re not talking about notifications, but actually recommending specific actions around a business condition. Instead of generating a dashboard of sales trends, a smart system could provide the recommendation for how to act on the trend. If this is the case, viewing the dashboard is only necessary to understand why the recommendation was made. The user no longer needs to interpret the data, but simply build consensus around the recommendation (do we act or not?) and take action. The idea of digging through dashboard after dashboard seeking the truth will seem arduous and unfulfilling.

Dashboards will continue to be an integral aspect of BI software as a means of quickly conveying complex information in a simple format. Why a user will need to view a dashboard is an evolving question.

Ryan Caplan, CEO of ColdLight, was featured on the June edition of bEye Network, a Business Intelligence thought leadership organization. In this interview, Ryan explains how ColdLight’s technology detects and responds to the ongoing, changes in business conditions by watching internal and external data sources, uncovering conditions that require focus, and enabling swift and intelligent action. Listen to the podcast.

Shadan Malik wrote a nice piece in DM Review called, “Dashboards: The New Face of Reporting”. He talks at length about the “at-a-glance” ability of a dashboard to present information quickly to today’s business decision makers. Dashboards really are great. But, still, unless you can actively interact with them – pull levers and change assumptions in real time, they are still “reports”. Reports look at what happened in the past – even the recent past.

We will get more insight when we reach into our data and spot the patterns and conditions that have direct meaning on the health of our enterprise. When the dashboard tracks the ebb and flow of data (like vital signs) then we move from the “report” into the “condition-spotting” world of real time. Metaphorically speaking, I want to know that I am heading for rain. I already know if it has rained.

When we empower that dashboard with artificial intelligence driven tools (like Neuron) we begin to get interactivity between the enterprise and the decision maker we will someday be able to steer the enterprise around obstacles. After all, that dashboard metaphor comes from cars, and neither cars nor companies stay still.

A recent article by Kate Maddox from Marketing Metrix cites IDC to that say lead generation is the top marketing priority for tech marketers this year – everyone is looking for the best customers when the economy dips. “With aversion to risk by C-level executives, the slight down turn and concern for growth in the next year, …. more companies are shifting to lead generation programs.” That means buying lists and hoping to get a 2.5% return. Whatever it is that makes the 2.5% respond is a key to finding more responders. The “key” is complex and may take into account many invisible attributes like “time of day” and “weather conditions”. Companies need cost effective ways to find more of these great responders in their lead generation efforts. Deep internal data combined and connected to external data is part of that answer. We want business analytics that identify the visible and invisible attributes of the “better customer” to be repeatable.