Transporting analytics to the Internet of Things

By IDB Guest Blogger: Lee Ann Dietz, SAS

Why are so many companies across a diverse set of industries investing in and around the Internet of Things? Everywhere I go, every blog I read … I sound like my favorite band from the 80s: the Internet of Things is watching me.

In reality, it’s the reverse: I’m seeing the Internet of Things (Iot) everywhere: companies investing in sensors, networking and applications with the expectation that this investment will increase revenues, lower costs and improve profitability over the short and long term.

While the term the Internet of Things was coined in 1999 by Kevin Ashton at Procter & Gamble, the mainstream application of IoT is just getting started. As the trend has heightened, I’ve been evaluating the potential for IoT to support better decision making in travel and transportation.

My experience in the travel and transportation industry has always been about using analytics to support decision making. In fact, I started my career as a pricing strategy analyst at American Airlines. And now I’m fully converted. You can call me an evangelist for the IoT in transportation, especially because the potential to take data coming from the IoT, incorporate purposeful analytics and reach better decisions quickly, is significant.

This week, I am presenting as a guest of the Institute for Defense & Business at the 2015 NDTA-USTRANSCOM Fall Meeting on the convergence of IoT and analytics in the transportation industry.

I am speaking directly about why I have become so enamored with the ability of the IoT to deliver business value, especially in transportation.  It’s really quite simple: the IoT delivers value through the data that the things provide for decision-making.

This data is collected by sensors and devices on railcar components, semi-truck engines, or other elements within the transportation value chain. And it is now available on a real-time, or streaming, basis.  This provides the ability to learn from trends in that data quickly and act upon those trends within seconds.

Gone are the days where data is collected over the span of weeks or months, sent to an analyst for review, and – after another week or two of data crunching – the analyst presents a report or PowerPoint to her boss with recommendations for changes.

As Tom Davenport said:

To make the Internet of Things useful, we need an Analytics of Things.  This will mean new data management and integration approaches, and new ways to analyze streaming data continuously.

So, in order to take advantage of the streaming big data (and it is big data by every definition of the phrase) coming from the sensors, we must reconsider how we use analytics.  Remember, that I didn’t say that the analytics themselves must change.  In most cases, we can use the same analytics applied to streaming data as we used in a batch model.

What we need is a good understanding of where we apply the analytics: on the edge, at rest or in the middle. Let me explain:

  • Analytics on the edge means analysis at the specific device or sensor.
  • Analytics at rest means data pulled out of the stream and used for high-performance analytic model development
  • Analytics in the middle takes place on data as it’s streaming. Some analysts have called this middle ground “the fog,” and it’s relevant because it can be a combination of the streaming data itself enriched with sitting data such that we can detect more complex events sooner.

We have now arrived at a different place in the analytic continuum.  The optimal analytics experience is a multi-stage analytics experience.  It includes continuous queries on data in motion and at the edge, with incrementally updated results.  This new process moves analytics from centralized data warehouses to edge analytics, which are closer to the occurrence of the events.

What does multi-stage analytics of IoT data look like? It happens fast (seriously, we are speaking about microseconds or msecs at this point) and at very high volumes.  It requires specific business rules that give instructions on whether to save, discard, aggregate, transform or enrich the streaming data without overloading the entire system or network.  Multi-stage analytics includes pre-determined data mining, decision making, alerting, scoring, and profiling of the data to exploit the value of the streaming data.  And, it might also include managing the data differently – creating “out of order” handling to make the data source streams understandable to the analytics and the decision-makers.

We have all the building blocks in place to exploit the value of the Internet of Things and the Analytics of Things: sensors, or assets, creating data, the communications network connecting the data and the analytic and computing applications that make use of the data flowing to and from the things.

The Internet of Things can be transformative in transportation operations: in maintenance and engineering, you will have more information sooner, which means you can predict the maintenance needs of individual assets before failures occur and proactively service assets at an opportune time when your asset is near a repair facility.  This reduces costs across your operations.  In supply chain situations, you can monitor inventory levels on a near real-time basis, develop better forecasting models and optimize this inventory, when and where you need, lowering supply costs, increasing efficiency and enhancing revenue opportunities. On the customer side of transportation, you can enhance the customer’s experience by providing real-time forecasts of arrivals and notifying them sooner if delays occur.  And, happier customers are loyal customers.

The Internet of Things opens up tremendous opportunities for transportation companies, generating significant streaming data which can be relevant for decision-making.  However, it is critical to apply the appropriate analytics to streaming data in order to derive value from that data.

Multi-stage analytics is not rocket science; it’s simply the judicious application of the right analytics at the right time in the right place to the right data, which is what you need to exploit the value of the Internet of Things.  That is why I’ve become an Internet of Things and Analytics of Things evangelist.

 

This content is reposted with permission from SAS Voices, where the original post appeared.

Students Get a Taste of Government Analytics at IDB Hosted Panel

By IDB Guest Blogger: Gail Bamford, SAS

When Tom Davenport and D.J. Patil suggested in their article published in Harvard Business Review that the data scientist is the “sexiest profession of the 21st century,” these in-demand professionals became part of the discussion surrounding the big data ground swell.

On April 10, 2015 at the 3rd Annual Business Analytics Forum at Indiana University’s Kelley School of Business the Institute of Defense and Business (IDB) and SAS had an opportunity to address over two hundred students, faculty members and industry partners who have a vested interest in grooming these superstars of the future.

The Kelley School’s Institute for Business Analytics , co-chaired by Vijay Khatri and Frank Acito, was one of the first programs of its kind established to prepare students for careers in business analytics. The Institute hosts this conference to make their students savvy in real world analytics and bridge the gap between academia and the real world. They actively seek out and engage with industry partners who provide internships and other pathways to employment.

This year’s conference topics included the Internet of Things (IOT), supply chain analytics, and healthcare analytics. The IDB, SAS, Deloitte and IBM were invited to inject the importance of analytics to government.

sas infographic government analyticsGovernment Analytics Jobs

Government is a heavy user of analytics and acutely feels the pain of the analytics skills gap across all agencies. A 2014 GovLoop survey reported that 96 percent of those surveyed – 46 percent of whom self-identify as experts or analysts – believe their agency has a data skills gap

Last year’s audience gave the thumbs up to our “Data vs. Gut” panel discussion, which featured retired military officers giving their perspective on the evolution they’ve seen of analytics use in decision making in defense organizations. This year, due to unforeseen circumstances, our defense panelist was not able to make it. Van Noah, panel moderator, smoothly shifted gears and reworked the flow of the conversation to provide the students in the audience with a broad preview of how they can apply their data skills in government analytics jobs.

If you were not able to see the discussion in person, here is a summary of the content these experts were able to shed light on:

  • Since this panel discussion was held just five days before the income tax filing deadline of April 15th – and stories about scammers were prolific in the media — what better subject to open the conversation?
  • Indiana University Kelley School of Business 04.10.2015Van Noah from IDB was able to represent the government sector and weave in some thoughts on how the military could better use analytics to select candidates for intensive training programs. For example, pilot training is expensive, so using analytics to identify students who are likely to wash out early is much safer and more cost effective than letting all students progress to the next phase of training.
  • Satish Lalchand from Deloitte has a long history of working with analytics in government and provided examples of how the federal government is leveraging analytics to combat fraud and improper payments. He also shared his perspective on how agencies can get started once they see the business need for analytics and what it takes besides analytics to make better decisions.
  • Eric Zidenberg from SAS has been involved with public safety organizations for many years and talked about how analytics are currently being used on the southwest border to make better decisions on which cars should be sent secondary inspection for illicit materials.
  • Dion Rudnicki from IBM segued nicely into talking about how the Memphis Police Department was using analytics to better allocate resources to reduce crime. In a city previously identified as #1 for crime in the US, and where there are only 2,500 police officers to protect the population of 650,000, analytics allows government to place the right resources at the right places at the right time. The result: a 30% drop in crime.

Next Generation Data Scientists

Students preparing for careers in data science have a lot of options when they enter the workforce. The data analytics talent gap exists in every sector in our global economy. When students weigh their options, they should consider the jobs that support the public sector. Serving government will give them a chance to make a real difference. Government needs these talented, data scientists of the future!

Gail Bamford is an IDB guest blogger and has over 30 years of experience working in the public sector IT market. She has been with SAS since 2006 and is passionate about helping close the analytics talent gap. For more than a decade, SAS and the IDB have worked collaboratively to raise the level of awareness of the value analytics to defense leaders.

idb.org/veterans

Data, Gut, Watson, and Angelina Jolie

The fourth and last of the Data vs. Gut panel series took place at the 2014 North Carolina Federal Advanced Technologies Review (NCFATR) conference on June 5. Scott Dorney, Executive Director of the North Carolina Military Business Center invited the Institute for Defense and Business (IDB) and SAS to give a repeat performance of the original Data vs. Gut panel discussion last fall.

The discussion was moderated by IDB president, Mark Cramer, and featured Karen Terrell, VP of SAS Federal and former IDB Executive Fellow, Rear Admiral Erroll Brown (USCG ret.).June 5, 2014 NCFATR Panel "Data vs. Gut"

Data

In every panel discussion we’ve hosted on Data vs. Gut, the importance of data leads the discussion. Good data is critical to successful analytics. It’s not surprising that this comes up over and over since there are many examples of bad data leading to disastrous decisions with disastrous consequences. In doing analysis, be mindful of:

  • Data quality: How accurate is your data? Accurate data analysis is only as good as the data put in. A lot of data is still entered manually and is incomplete or incorrect.
  • Timeliness: How important is timely data in your analysis? Time-critical decisions depend on easy access to the most up-to-date data.
  • Data relevancy: What data do you need to solve your problem? You don’t have to analyze every bit of data you have access to. What data is relevant to your situation?

Brown had some observations and suggestions.

  • Problem: Clearly understand the problem you are trying to solve.
  • Data: Is the data right and is it the right data? Make sure you know your data. Make sure it is accurate and timely. And, make sure the data you are using in your analysis is relevant to the problem you are trying to solve.
  • Tools: Get the right analytic tools for the job. One size does not fit all. You have to have the right algorithms, or “algos” too.

Evolution of Data and Analytics

June 5, 2014 NCFATR Panel "Data vs. Gut"Terrell shared with the audience her observation of how the usage and value of data has evolved. In her 20+ years of serving the government market, she has seen customers move from basic reporting and descriptive statistics to advanced and predictive analytics. Government routinely uses analytics to detect and prevent fraud these days. Other areas trending up are predictive asset maintenance and identification of high risk procurements.

She also noted the “democratization” of analytics. If you know your data and know your business, you can unlock insights in your data with powerful visualization tools that make it possible to slice and dice your data from your desktop. It’s easy and you don’t have to be a statistician to understand the graphic output.

High performance analytics is allowing more data to be analyzed faster and with a greater degree of granularity. Terrell mentioned a large department store chain that is using analytics to do markdown optimization on 270 million SKUs in 850 stores. Optimization models determine which items to mark down and by how much. What used to take 30 hours to run, now takes less than 2. This saves the company millions of dollars every year in unnecessary markdowns.

Data vs. Gut

Terrell brought the data vs. gut discussion to a very human level when she mentioned Angelina Jolie, who saw both her mother and aunt die at early ages from cancer. In her NY Times Op Ed piece she talks about the reality and her decision. But, her radical decision was not made in a vacuum. She did her research and gathered the data. She carries the “faulty” BRCA1 gene and found out she had an 87% risk of developing breast cancer. With this knowledge, she was able to make the difficult, but data-driven decision to have a preventative double mastectomy.

June 5, 2014 NCFATR Panel "Data vs. Gut"Brown cautioned about machine-to-machine decision making. “Computers don’t make you smarter.” That’s where gut comes in. Humans must have a symbiotic relationship with machines. But, Brown, who spent several years at IBM after his retirement from the Coast Guard, couldn’t resist bringing Watson into the conversation. Watson learns like an individual and can tell you how it got to an answer or outcome. That’s more than he says he can get out of his seventeen-year old when he has confronted him with “How’d you get to that (stupid) decision?”

So if Watson processes data more like a human than a computer and learns as it goes, what will the data vs. gut discussion be in 10 years?


Gail Bamford is an IDB guest blogger and senior marketing professional with over 25 years in information technology. She has been with SAS, the leader in advanced analytics software, since 2006 and supports business units focused on delivering analytic solutions to Defense, National Security, Higher Education and K-12.

Additional Resources:

Download white paper based on Data vs. Gut panel #1 – “How Analytics Improves Decision Making at the Department of Defense: Finding new ways to add value and insights to big data.”

Read Blog on Data vs. Gut panel #2 – “SAS – IDB Defense Panel Serves Up Advice for Analytics Students at Kelley School Business Analytics Summit 2014”

Read Blog on Data vs. Gut panel #3 – “Shining Light on Data in Austere Environments”

SAS – IDB Defense Panel Serves Up Advice for Analytics Students at Kelley School Business Analytics Summit 2014

It shouldn’t be a surprise to anyone who touches the world of big data and analytics that there is a huge skills gap looming. Employers in sectors that rely heavily on analytics have their radar up and are actively competing for talent.  The smart ones have outreach programs and are working with universities to groom analytics talent.

Universities are also quite aware of this gap and are developing programs that cultivate these skills during both undergraduate and graduate programs.  One of the schools that is ahead of the curve is Indiana University (IU) and it’s Kelley School of Business.  Not only have they embraced analytics in the business curriculum, they actively partner with industry to expose students to real data and real-world analytics that give their students a leg up in the job market.

Last Friday, April 4th, IU conducted the very impressive 2nd Kelley Forum on Business Analytics

Kelley has always been an outstanding business school and it was no great shock when Dean Idie Kesner announced in her opening remarks that IU’s business program had just broken into the BloombergBusinessweek top-10 business school list for the first time!  The Kelley School’s outreach to partners and industry is commendable and I think it’s one of the factors that sets them apart.

It was Dr. Vijay Khatri, co-chair of the Institute for Analytics, in the Kelley School who reached out to SAS and the Institute for Defense and Business (IDB) to have a defense conversation at the Forum.  Dr. Khatri attended a joint SAS – IDB panel discussion on decision making in DoD last October and wanted to bring a military perspective to students and faculty.

 

Photo: April 4, 2014 - 2nd Kelley Forum on Business Analytics

April 4, 2014 – Lt Gen (Ret.) Loren Reno (R) speaking at 2nd Kelley Forum on Business Analytics along side Dr. Kyle Cattani, Dr. Alfonso Pedraza-Martinez, and LTC (Ret.) Eric Hansen (Photo Credit: Indiana University)

The Forum panel, “Should we Trust our Instincts – or the Data: The Role of Analytics in Decision Making” included Lt Gen (Ret.) Loren Reno, who retired as the senior Air Force Logistician, and LTC (Ret.) Eric Hansen, who actually is a real analytics guy  (Hansen managed a team of analysts for the Joint IED Defeat Organization, where they used analytics to understand the network that supported the making and positioning of road-side bombs.)  The panel was rounded out by two Kelley School professors of operations management: Dr. Kyle Cattani, whose interests lie in supply chain management, logistics, and business analytics; and Dr. Alfonso Pedraza-Martinez, who has worked in the area of humanitarian logistics. Mr. Van Noah, Program Director for several logistics and supply chain focused education programs at IDB, served as the panel moderator.

Photo: April 4, 2014 - 2nd Kelley Forum on Business Analytics

April 4, 2014 – Mr. Van Noah, Program Director at the Institute For Defense and Business, moderating the 2nd Kelley Forum on Business Analytics (Photo Credit: Indiana University)

To answer the question posed in the panel title, you need to trust both.

“Analytics can inform decisions and make them more dependable,” commented Reno.  But, you have to use your instincts and question results that seem counter-intuitive.

Perhaps the most potent advice to come out of the panel was the need to establish TRUST and COMMUNICATE effectively with leadership.  Analysts need to:

  • Make sure your analysis is accurate.  Thoroughly understand the data, the algorithm, and the assumptions that go into each model.
  • Simplify things down.  If there are tradeoffs or assumptions made, be able to explain them.
  • Be able to describe the outcome in business terms and articulate what the decision maker is supposed to do with the information you provide.
  • Understand the decision maker you are presenting to and how he/she likes to consume information.  Speak in their language.
  • Develop good listening skills too – so you can add real value to the conversation.

Dr. Khatri was smart to include government and military sessions at the Forum. The DoD needs bright, young analytics talent to be part of its future. Drastic budget cuts never seen before are forcing the military to radically change the way it accomplishes its mission.  Personnel with analytics skills will play an important role in what it will take to become the efficient and effective force of the future.


 

Gail Bamford is an IDB guest blogger and senior marketing professional with over 25 years in information technology. She has been with SAS, the leader in advanced analytics software, since 2006 and supports business units focused on delivering analytic solutions to Defense, National Security, Higher Education and K-12.

Learn more about IDB’s relationship with Indiana University here.
Learn more about IDB’s relationship with SAS here.

Additional Resources:

Military Personnel Analytics

IDB Executive Fellow Emeritus, former Assistant Commandant of the Marine Corps, and current President of Audio MPEG General Richard Neal, USMC (Ret.) was interviewed by SAS in their “Point of View” video series, which features subject matter experts in thought leadership interviews. For the eighth consecutive year, SAS the leader in business analytics software and services, sponsors the IDB Executive Fellows program.

The topic was “Military Personnel Analytics” and during General Neal’s interview he was able to discuss a variety of issues affecting the Marine Corps that included:

  • How can analytics be used to make RESET more manageable?
  • The Department of Defense is a data rich environment but information poor. How can the United States Marine Corps better utilize the data it already collects to make decisions?
  • One of the current Commandant’s priorities is to better educate and train our Marines to succeed in an increasingly complex environment. How are the IDB programs better equipping upcoming leaders?
  • What are the most important skills current Marines need to have to become successful leaders in the future?

You can view his interview by clicking here or on the image.

General Richard Neal, USMC (Ret.)

IDB Executive Fellow Emeritus General Richard Neal, USMC (Ret.) discusses Military Personnel Analytics

Comments and discussion of this interview are most welcome below!