analytics

11月 102016
 

We often talk about full customer data visibility and the need for a “golden record” that provides a 360-degree view of the customer to enhance our customer-facing processes. The rationale is that by accumulating all the data about a customer (or, for that matter, any entity of interest) from multiple sources, you […]

The post The “tarnished record” – Alternatives to gold for fraud analytics appeared first on The Data Roundtable.

11月 102016
 

In the US presidential election, each of the 50 states has a certain number of electoral votes, based on the population. Typically, most states cast all their electoral votes for the candidate who wins in their state (all or nothing). But states can split their electoral votes if they want […]

The post You need a custom map, for US presidential election results! appeared first on SAS Learning Post.

11月 072016
 

Most enterprises employ multiple analytical models in their business intelligence applications and decision-making processes. These analytical models include descriptive analytics that help the organization understand what has happened and what is happening now, predictive analytics that determine the probability of what will happen next, and prescriptive analytics that focus on […]

The post Why analytical models are better with better data appeared first on The Data Roundtable.

11月 052016
 

bhutanGalit Shmueli, National Tsing Hua University’s Distinguished Professor of Service Science, will be visiting the SAS campus this month for an interview for an Analytically Speaking webcast.

Her research interests span a number of interesting topics, most notably her acclaimed research, To Explain or Predict, as well as noteworthy research on statistical strategy, bio-surveillance, online auctions, count data models, quality control and more.

In the Analytically Speaking interview, we’ll focus on her most interesting Explain or Predict work as well as her research on Information Quality and Behavioral Big Data, which was the basis of her plenary talk at the Stu Hunter conference earlier this year. I'll also ask about her books and teaching.

Galit has authored and co-authored many books, two of which — just out this year — include some JMP. First is Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro, with co-authors, Peter C. Bruce, Nitin R. Patel, and Mia Stephens of JMP. This first edition release coincides with the third edition release of Data Mining for Business Analytics: Concepts, Techniques, and Applications with XLMiner, with the first two co-authors listed above. As Michael Rappa says so well in the foreword of the JMP Pro version of the book, “Learning analytics is ultimately about doing things to and with data to generate insights.  Mastering one's dexterity with powerful statistical tools is a necessary and critical step in the learning process.”

The second book is Information Quality: The Potential of Data and Analytics to Generate Knowledge, which Galit co-authored with Professor Ron S. Kenett, CEO and founder of KPA and research professor at the University of Turin in Italy (you may recognize Ron and KPA colleagues as guest bloggers on the JMP Blog on the topic of QbD). As David Hand notes in his foreword, the book explains that “the same data may be high quality for one purpose and low quality for another, and that the adequacy of an analysis depends on the data and the goal, as well as depending on other less obvious aspects, such as the accessibility, completeness, and confidentiality of the data.”

Both Ron and Galit will be plenary speakers at Discovery Summit Prague in March. You can download a chapter from their book, which discusses information quality support with JMP and features an add-in for Information Quality, both written by Ian Cox of JMP. You can see a short demo of JMP support for information quality during the Analytically Speaking webcast on Nov. 16.

Whether your analysis is seeking to explain some phenomena and/or to make useful predictions, you will want to hear Galit’s thoughtful perspective on the tensions between these two goals, as well as what Galit has to say on other topics up for discussion. Join us! If Nov. 16 doesn’t suit your schedule, you can always view the archived version when convenient.

tags: Analytically Speaking, Analytics, Books, Discovery Summit, Statistics

The post To explain or predict with Galit Shmueli appeared first on JMP Blog.

11月 042016
 

Elections in the US are a 'target rich environment' for data analysts. There are surveys and forecasts before the election, and the presentation of results during and after the voting. What's your favorite election-related graph of all time? For the current (2016) presidential election, my favorite graphs are on the […]

The post Your chance to vote ... for your favorite election graph! appeared first on SAS Learning Post.

11月 042016
 

The digital age has fundamentally changed how brands and organisations interact with consumers. This shift has been a crucial part of the Third Industrial Revolution and helped spark the era of consumers sharing their data with different organisations. But now organisations are heralding the Fourth Industrial Revolution, and data is […]

Analytics: The lifeblood of the Fourth Industrial Revolution was published on SAS Voices.

11月 032016
 

Business and production systems have become much more capable at collecting data. Equipment collects a variety of sensor and parametric data, and today all kinds of information on buying habits and consumer preferences is available. This level of detail cannot be analyzed and comprehended with static, conventional reporting. Instead, business analysts, engineers and scientists can unlock insights and make discoveries with leverage provided by interactive, visual analytical software.

Analytical software has surfaced a new world of analytics that is characterized by these important traits:

  1. Data are “self-provisioned.” Users are able to get the data they need without assistance and without delay.
  2. The analytics are visual and interactive. As a result …
  3. Users can now conduct advanced analytics without a PhD in statistics.
  4. Analysts conduct their work “in-the-moment.” Insights often surface questions that analysts explore “in-the-moment” creating an active dynamic that further spawns discovery.
  5. Analytical thinking is completely coupled to the business thinking.
  6. More than descriptive, analytics are inferential.

An 'aha' moment

Consider this insurance example. Here demographic information from many thousands of current and potential clients was collected and maintained in a database. The insurance company was able to download the data into a spreadsheet and summarize the data but did they get the best exploitable insights? Answering even the simplest questions took days to acquire, splice and arrange the data.

Today, with integrated, interactive and visual analytics insights are revealed in seconds. The big question when it comes to prospective clients is how many of them were converted to new business and what are the factors that drive the conversion? By knowing this, focus can be brought to business practices that lead to higher rates of success.

screen-shot-2016-10-17-at-7-23-24-pm
We started by loading the data. With only a few clicks, tens of thousands prospective client encounters, including demographic information such as income, education, age, martial status, etc., were loaded. You can see from the image above that overall about 12.5% (the blue area) of these prospects were converted into paying customers.

Now to the question at hand: What factors determine success in winning new business? One more click (on the Split button in the lower-left) and an “aha” moment ensued.

screen-shot-2016-10-17-at-7-24-01-pm

The chart above shows that a particular factor (which, due to confidentiality I can’t disclose so we’ll call it ... ), “factor Xn,” leads to an incredibly high conversion rate (about 90% as seen in the blue bar on the right) for a good number of prospects and that the remaining prospects had little chance of succeeding.

The analysts were stunned at seeing this. This insight had eluded them because the overall conversion rate was masking a major distinction, identified by factor Xn, among the prospects. Keep in mind that these analysts spend day-in and day-out poring over data, but this important insight and others that were to follow remained locked within.

This insight spawned a bunch of questions. First, it appears changing sales representative instructions were in order. Second, why was it that the conversion rate for other customers was so incredibly low? This led to questions about pricing, packaging and the like in combination with demographics that would be investigated with designed experiments.

Why it worked

Looking back at the six traits above, we can see that in this case:

  1. IT established systems that allowed users to get the data themselves: "self-provisioned data."
  2. Indeed the analytics were highly visual. Yes, all the statistical information is provided, but it is made accessible through graphics and interactivity.
  3. No PhD in statistics was necessary. The analysis above involves recursive partitioning with cross-validation. A mouthful to be sure, but that complexity (and statistical jargon) does not get in the way of a business analyst or engineer gaining the highest possible number and quality of exploitable insights. They can focus on their subject matter unfettered. In fact, my experience is that the tool almost becomes invisible as the focus is on the subject matter.
  4. Unlike the old days, when I started in this game, there was no need to submit a request that instructs programmers in IT to amend a report that will arrive several days later. The lapsed time between question-and-answer was gone, and so was the dependency.
  5. The old division of labor between analytics and business was gone. They must be welded together to be effective and efficient at finding exploitable business, engineering and scientific insights.
  6. Notice that the analysis is not simply descriptive, as it was in the old days. It is inferential because it leads analysts to predict future outcomes and ask further questions.

Not only were the analysts impressed with the insight, but they were also excited about how readily it was derived.

Build your own culture of analytics

What does it take to bring the new world of analytics into your organization and support a culture of analytics?

This is where IT comes in -- obviously, they have a major role to play. IT no longer needs to worry about conducting analytics. It’s best left to the analysts. Instead, IT are now enablers of analytics. They can do this by:

  1. Maintaining the hardware and software infrastructure that supports operational and analytical needs.
  2. Making data available in an analytically-friendly way so that data may be self-provisioned. We do lots of work in this area to ensure that analytical data demands do not affect operations. For example, in pharmaceutical, semiconductor, solar and other industries, unimpeded real-time data must be collected for traceability. Analytical demand on IT infrastructure cannot affect operational systems.
  3. Support the likes of our company, Predictum, in developing integrated analytical applications that further facilitate analysis, store and transfer knowledge and insights and gain other efficiencies and cost savings in areas of operations, research and compliance.
  4. Secure all systems.

Securing systems is a rapidly growing and increasingly demanding responsibility for IT -- so much so that we find that IT folks are usually very happy to be relieved of the burden of conducting analytics or involving themselves with analytics that analysts can better support themselves. Their enabling role is much more consistent with their other activities and responsibilities. For example, IT supports order/shipping/billing systems, but they do not order, ship or bill themselves -- so why should they conduct business, science or engineering analytics?

With the Internet of Things, new more capable equipment and the internet’s expanding reach, we can expect an exponential increase in the amount and quality of data well into the future. It’s best to prepare for the opportunities presented by building a culture of analytics now. That involves designing the right data architecture, providing JMP and enabling business analysts, scientists and engineers to advance their subject matter expertise with analytics.

Editor's Note: A version of this blog post first appeared in the Predictum blog. Thanks to Wayne Levin for sharing it here as well.

tags: Analytic Culture, Analytics, Discovery

The post Want scientists and engineers to make more discoveries? Here's how appeared first on JMP Blog.

11月 022016
 

Being an Eagle Scout, the data for good movement caught my attention. I wondered if I could apply my computer skills in a way that might help. How about showing people better ways to visualize HIV/AIDS data - that might help doctors better understand the data, and therefore better treat […]

The post Building a better HIV/AIDS map appeared first on SAS Learning Post.