Rethinking Business Intelligence: Information or Decisions

Traditional business intelligence leaves executives with the same amount of work, but with even more information to sort through. The number of decisions, the unit of work, is not diminished.
Traditional Business Intelligence asks, “What information do you need to make better decisions?” The outcome is hopefully beautiful well designed reports and dashboard that support decisions.  The problem is that you still have to make decisions.
Decisions are work.  Having more information doesn’t reduce the amount of work required to make decisions. In fact, it makes decisions more work.  More information does not create less work.
The flaw is thinking that the business decisions are calculations. (more…)

8 isn't enough. Why you need a better sales system.

How many products can the average salesperson keep in their head while they are making reccomendations to customers? You will be shocked to find on average it is only 8.  What if 8 isn’t enough?  What if you have more than 8?  What if you have twice that?  Ten times that?
If you have more than 8 products, when a member of your sales team calls on a current client, they only recommend the products that they are most familiar with.  You have provided them an entire playbook, and they use only a tiny fraction.  Unless, those 8 products just happen to be the correct product for a customer, your sales staff just put a client into the wrong product.  They assumed that everyone was like them.
Think about that for a second.  The whole point of cross-sell is to keep your clients happy and purchasing from you. Matching the wrong product with the wrong person means reduced satisfaction, frustration, and loss of loyalty.  On the balance sheet, you are losing income, not maximizing profitability, and your salespeople are misusing their time.  How do you improve this? (more…)

When should you Update Predictive Models?

New clients often have questions about why and how frequently CAN needs to update their predictive models.  Predictive models need to be updated because everyday new data is being created.  For example, your customers are buying more, subscribing or unsubscribing.  The environment is constantly changing.  While predictive models can handle a lot of new new data, overtime environmental changes build up causing predictive models to lose their effectiveness.  After a month, quarter, or a year it is necessary to update predictive models with new data.
As these new patterns emerge its important to periodically take time to investigate your data, update your models, and challenge your assumptions about your business. But how often should you do this?

How Much Data Do I Need For Predictive Analytics?

Before beginning any predictive analytics project, its essential to investigate the breadth and depth of data available. However, at what point is it acceptable to say you have enough data to start?
The politically correct answer to this question is that it depends. Depends on what though?
Well for starters, certain types of data science and predictive analysis projects require more specific data requirements. In an extreme case, predicting survival rates of people or machines may require data spanning their entire lifespan. However, in most cases, data requirements are less stringent.
In most cases taking a snapshot of 3 to 5 years worth of data can yield a breadth of patterns surrounding consumer and business behavior. Why? (more…)

The Predictive Analytics Revolution – Are you sitting on the sidelines?

Predictive analytics (or Big Data) is here to stay. You may not understand it. You may not believe that it really works. But the reality is this: your competitors (and it may be just one or two of them) are using predictive analytics to chew up market space as you remain on the sidelines.
Don’t believe me? (more…)

Predictive Analytics in Retail

Data is no good if you can’t get it quickly enough and act quickly enough on it. Its all about getting the data fast and acting on it fast
– Andrew Pole
Predictive analytics solves many differnt problems in a many facets of business. The New York Times published an article in February about the retail chain, Target. Target knew that if they could predict buying patterns in a certain group of consumers, they could influence those consumers purchases. Target was smart, they knew exactly who they wanted to go after.
Andrew Pole, a statistician working for Target, created a pregnancy-prediction model that was able to track spending habits and predict when a woman is pregnant. Pole said that, “We knew that if we could identify them in their second trimester, there’s a good chance we could capture them for years. As soon as we get them buying diapers from us, they’re going to start buying everything else too. If you’re rushing through the store, looking for bottles, and you pass orange juice, you’ll grab a carton. Oh, and there’s that new DVD I want. Soon, you’ll be buying cereal and paper towels from us, and keep coming back.” (more…)

Formula 1 and Predictive Analytics

A couple weeks ago, I discussed the use of predictive analytics in the transportation industry, specifically the use of acoustic bearing monitors to predict bearing failures on Union Pacific freight trains. Today the conversation turns to predictive analytics in a very different type of transportation, Formula One (F1) racing. Download our Case Study on Mechanical Failure and Predictive Analytics.
F1 is to many the pinnacle of motor sports. F1 has the most technologically advanced cars, the most skilled drivers (some compensated $50+ million per year), the most exotic race locations, and yes, the most beautiful paddock girls. Formula One is a closely sanctioned “space race” creating and refining innovative and ground breaking technologies including traction control, anti-lock brakes, direct injection, synthetic oil, kinetic energy recovery systems (KERS), carbon fiber, and computational fluid dynamics (CFD). Many advancements made in technology by F1 teams have contributed to the efficiency and safety of everyday vehicles. (more…)

New Pew Survey on Big Data | Big Data is the new Oil | 'Minority Report' software hits the real world

Why data trumps experience in trial conversion: “Using predictive analytics to qualify trial users and focus on those that are most likely to convert can double conversion rates. In a 2012 study, the Aberdeen Group published a finding that companies using predictive analytics have a 73% sales lift versus companies that did not. … Publishers should use predictive analytics to develop trial scoring rules. These scoring rules can constantly prioritize trials in their likeliness to convert which increases close rates and sales productivity. These same predictive analytics are useful in design of trial parameters such as length and access limits.” (Business Insider)
Connect Big Data With Customer Behavior to Improve Social, Email, and Web ROI: “Since we have lots of data, we have lots of integration challenges. … Mastering that flow of data between the places that generate it (click-stream, communities, sentiment analysis, email and SMS messaging, and portals) and the systems that utilize it (marketing automation, messaging delivery, and social publishing) is creating complexity, as well as opportunity.” (more…)

Big Data In The Travel Industry and More

How To Make The Most Of ‘Big Data’ In The Travel Industry: “There is a ‘big data’ revolution underway in the travel and hospitality industry but travel companies need to be clear about the challenges. … Data analytics is an interesting prospect for the travel sector as so many data streams can be combined. … Business analytics pulls insights from vast databases commonly referred to as ‘big data’. To be successful and maximise the value of this, firms need to be very focused and disciplined.”
— As firms plan to take the plunge, here are some expert recommendations: “Focus on areas where an impact can be made … Understand how to engage with consumers more efficiently … Identify patterns that can lead to insights around consumer acquisition, retention and marketing.” (Hospitality Net)
“Business analytics should itself be adaptive and regularly refined by new data that users feed back into the system as that is the whole purpose of predictive modeling…” [Brenda] Dietrich explained that data analysis allows companies to extrapolate outcomes linearly and decide what appropriate action to take next. Those actions also generate new data, which should be fed back into the analytics model so it is continuously refined, improved, and accurate, she said. …This notion of constantly ‘learning from the data’ is a new and exciting development in the analytics space, because it means a company can see, as time progresses in reality, whether it is moving toward X or Y, and decide the next step it should take, she said.” (ZDNet)
“Why is data science relevant?” “Benjamin Franklin is alleged to have said in response to the questioning of the value of the first hot air balloons, ‘What is the value of a newborn baby?’ Actually, data science is probably a long way from the newborn baby stage, although it still has a long way to go before it achieves full maturity.” (Network Computing)
Dell Provides Schools and Universities with Predictive Analytics: Schools and universities are turning to Dell’s Education Data Management (EDM) solution, a decision support system, to help personalize student learning, increase retention and graduation rates while improving planning, management and reporting. The solution integrates student performance and operational data with predictive analytics to help educators monitor student progress and intervene when needed to improve success. It tracks each student’s data between schools and over multiple years to help parents and educators monitor student progress and respond to developmental needs or hone in on specific interests and aptitudes. (EON)
The Hadoop bone’s connected to the SQL bone: “Microsoft has been working with Hortonworks to build a distribution of Hadoop for Windows Azure, its cloud platform, and for Windows Server.  Right now the service is available as a cloud service in a by-invitation beta that just entered its third release.  … Why would Microsoft be so bullish on technology that is open source, Java-based and largely Linux-facing in pedigree? Most likely it’s because Microsoft runs Bing. By some counts, Bing and Yahoo Search (which is Bing-powered) together have about 30% search market share and Turner announced in his keynote that Bing is now leading Google in search relevance. (ZDNet)
Adding Second-Tier analysis To Harness Big Data: The real challenge with Big Data is in going from individual siloes of data analytics to a bigger picture that successfully and meaningfully puts those analytics into the full-enterprise context. It’s how we map these analytical islands to each other that ultimately provides the support we need for improved quality in our decisions. … It is important that business and operational metrics be aligned to improve decisions and help ensure business survivability.
— A useful second-tier analysis effectively describes key business functions or processes and business assets, and then correlates operational reports and metrics to them. For example, linking accounts receivable to technology assets to operational practices and metrics can help expose significant enterprise risk. (SC Magazine)

Union Pacific Railroad Predictive Analytics

For most, seeing a Union Pacific diesel-electric locomotive painted in the historic Armour Yellow, Signal Red, and Harbor Mist Gray does not bring predictive analytics to mind. However, the largest railroad network in the United States is showing the effectiveness of analyzing non-conventional data forms to increase operational efficiency.
Applications in industries including financial services, insurance, retail, and healthcare are what commonly come to mind when discussing predictive analytics. Thinking outside the box, Union Pacific is currently using bearing acoustic monitors to identify faulty or failing bearings on trains. The acoustic detectors have been used for years, but enough data has now been collected to show the correlation between failing bearings and certain frequency spectrums. This correlation has been applied to real time acoustic readings to help predict bearing failures and ultimately decrease derailments and costly train delays. Download our Case Study on Mechanical Failure and Predictive Analytics(more…)

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