I gave the keynote address to George Washington University’s DATA Conference on December 2. This is what I told the students. Please reply with your thoughts and ideas to extend the conversation on how to make the world a better place through data.
Think about how you can use data and data science to make the world a better place. We are now in a unique time in history because we now have huge amounts of data being collected by all the digitized systems in the world (almost 1 ZB or 1 times 10 to the 21st power Bytes) and the Data Science techniques are becoming more powerful and easier to use. These two factors will give you the ability to do more to improve the lives of your fellow students, their professions and society at large than has ever been possible before.
Data Science innovation will be central to solving humanity's grand challenges by capitalizing on this unprecedented quantity of data now being generated on human behavior and attitudes, human health, commerce, communications, migration and more. You can help to accelerate and advance the development and democratization of Data and Data Science solutions that can address specific global challenges related to poverty, hunger, health, education, the environment, and others.
To help stimulate your imagination, I will present several examples from our work at IBM. The key is to combine your growing expertise in Data Science, with your passions. At IBM, we are encouraging students to combine Data Science studies with other disciplines, such as natural science, social sciences, healthcare, etc. - - the problem domains where the Data Science can be put to work.
For the first example of “Doing Good”, I’d like to tell you about IBM Fellow Chieko Asakawa. She became blind at the age of 14, and as a result has devoted her professional life to building solutions to allow her and other blind people to access the world and regain their independence. Chieko has developed an object recognition solution so she can “see” ordinary objects in her home and at stores, and allow her to pick out wine or know the directions on a package – all using machine learning. She has also developed an indoor navigation system that helps her to easily get from place to place at work. Both use smartphones as the user interface. See these links for more details on Chieko’s inventions: Image rec: https://www.youtube.com/watch?v=RNp4OpToAdQ (many interesting solutions, Chieko’s is featured at minute 17); Nihonbashi Tokyo NavCon: https://www.youtube.com/watch?v=mlGcutE2t2A ; TED talk: http://www.ted.com/talks/chieko_asakawa_how_new_technology_helps_blind_people_explore_the_world ).
The second example is from IBM’s Cognitive Build Competition. Two IBM employees, Karibi and Jenn proposed and prototyped a solution to help children with Autism. The solution, dubbed Pino after Karibi’s newphew, uses Watson Conversation service to help children with autism communicate more independently by providing real-time verbal prompts. It can also be used with other conditions that affect communication ability, such as stroke and Alzheimer's disease. I met Jenn a few weeks ago. She told me, “At a birthday party a couple of years ago, I saw how upset my son was when he didn't receive a cupcake because he couldn't say "yes" when offered one. He needs a therapist or caregiver to prompt him to answer basic questions. He has a communication device that can help him speak, but it requires him to know he needs to respond… When Cognitive Build started, I thought it would be great if my son's communication device could be cognitive so it could help him to be more independent when I'm not around.” Learn more at this link: https://medium.com/cognitivebusiness/addressing-autism-project-pino-3741ce13d39
The third example is about the opioid epidemic, which has become one of the worst health crises in US history. In 2015, more than 90 Americans died every day from opioid overdoses, a number comparable to deaths in car accidents and projected to have risen further in 2016 and 2017. The Centers for Disease Control and Prevention (CDC) estimate the total economic burden of prescription opioid abuse to be $78.5 billion a year, including healthcare costs, lost productivity, and criminal justice involvement.
For many addicts, the problem often begins with legitimate healthcare treatment in which opioid painkillers are first prescribed, such as for surgeries or chronic back pain. During treatment, some patients become addicted and go on to suffer the well-documented consequences of addiction, while others do not, even if they become long-term users. To combat the epidemic, it is vital to understand the exact circumstances under which medically sanctioned treatments can devolve into addiction. That’s where data science comes in to play.
This summer, we took the first steps in tackling this question in a project within our Science for Social Good program. The team, led by Bhanu Vinzamuri, focused on analyzing the relationship between factors surrounding an initial opioid prescription and a subsequent diagnosis of addiction. We found that those people that received initial prescriptions for more than 7 days has a significant correlation to Long-Term usage, as does use of Synthetic Opioid prescriptions. We also confirmed that days of supply matters much more for addiction than quantity (e.g. in milligrams of morphine equivalent) prescribed per day. Other factors that were positively correlated with long term use and which should be used by doctors when prescribing opioids were age, certain regions of the country, rural location, healthcare utilization and depression, osteoarthritis, or diabetes. See more projects at http://www.research.ibm.com/science-for-social-good/#projects
Because of the power that Data Science and data is bringing to Humans, we need to be sure it is a force for good and not for evil. IBM and XPRIZE Foundation believes Artificial Intelligence (and the data science algorithms it uses) will be central to solving humanity's grand challenges. Solutions to pressing problems related to health and wellbeing, education, energy, environment, and other domains important to humanity can potentially be found by capitalizing on the unprecedented quantities of data and recent progress in emerging AI technologies. That’s why IBM is putting up $5 million for the Watson AI XPRIZE. See https://ai.xprize.org/ for more details.
But even if you are not up for competing for the AI XPRIZE, there is lots that you can do. Find a societal problem that you are passionate about. It all starts with a problem or need, like Chieko’s blindness, or Jenn’s child with autism, or the opioid crisis. Then come up with an idea or approach. There is a lot of data now available. Our Data Science Experience is out there on the web for you to play with. It is designed to allow data scientists, business analysts, stakeholders, and programmers work together on a data project. It’s easy to use. Go out and try it. There are tutorials to guide you. It is at https://datascience.ibm.com/ . Don’t just study the problem and write a school paper, create a solution that helps people. Your university’s office of entrepreneurship can help you to build a business case for your solution. Finally, consider pitching your idea to one of the many Pitchfests that are around. One I’m familiar with that exposes your ideas to corporate sponsors such as IBM is NCET2. They are at https://ncet2.org/. Go ahead and make the world a better place!
My work this year has taken me from Big Data and Analytics towards Cognitive Computing and what IBM is now dubbing Cognitive Businesses (or Cognitive Government in our case). Cognitive businesses leverage cognitive computing technology (think Watson) to enhance, scale, and accelerate the expertise of their personnel. Below is the summary of the first part of a symposium I co-chaired last week. I'm happy to answer any questions you may have.
The AAAI Fall Symposia on November 12-14 included tracks on 1) AI for Human-Robot Interaction, Cognitive Assistance, Deceptive and Counter-Deceptive Machines, Embedded ML, Self Confidence in Autonomous Systems, and Sequential Decision Making for Intelligent Agents. This post will provide my general impressions of the Cognitive Assistance symposium.
Jerome Pesenti, IBM VP of Watson Core Development, provided the 1st day keynote. He started with the great quote from Fred Jelinek (Cornell/IBM/JHU) that “Every time I fire a linguist, the performance of the speech recognizer goes up.” He then talked about how deep learning is allowing reco systems that approach or surpass human performance. This led to a lively discussion with the audience on the universality of learning algorithms and whether the machines were learning in the same manner that humans learn something (no). Jerome finished with some applications of Watson including the Oncology Advisor, citizen support (e.g, tax questions), and security (finding relationships between data).
The rest of the morning was filled with examples of cognitive assistance for legal tasks such as filing a protective order (Karl Branting) and human-computer co-creativity in the classroom(Ashok Goel), and a tool to help SMEs define their vocabulary to find the most relevant content on the web (Elham Khabiri).
During lunch, much of the symposium had lunch together and a lively discussion ensued on cognitive assistance. One topic that I found interesting was on ultimate chess where human-machine teams compete. While these teams in the past have beaten computer-only teams, Murray Campbell noted that the advancements in chess playing computers are decreasing the value-add of humans to the team.
The afternoon session of Day 1 started with 2 interesting talks on cognitive assistance for helping those with cognitive disabilities. Madelaine Sayko described Cog-Aid which would include a cognitive assessment, recommender system (based on the assessment) and an intelligent task status manager for starters. Then Daniel Sontag described the Kognit technology program which includes tracking dementia patient’s behavior using eye tracking and mixed reality displays to assist the patient perform activities in daily living. Kevin Burns presented a sense-making approach that could be used by an intelligence analyst to help understand and define the Prior and Posterior probability calculations as new evidence is added. This could eventually be embodied into a cognitive assistant. Next came a presentation on capturing cybersecurity operational patterns to facilitate knowledge chaining by Keith Willett.
The final session of the day was a panel discussion of workforce issues associated with cognitive assistants led by Murray Campbell. Erin Burke of Fordham University Law School talked about how legal education must transition and that she is working at the intersection of law, big data, and cognitive computing. Jim Spohrer, Director of IBM’s University Programs, provided some predictions including that by 2035 everyone will be a manager and will have at least one Cognitive Assistant working for them. A lively discussion ensued with the audience about our forthcoming relationship with Cogs including whether we could trust them, unintended consequences, whether we can build common sense into a Cog, and whether our brains will atrophy as we depend on Cogs.
I’ll cover Day 2 in the next blog post.
In the 1980’s, John Naisbitt wrote, “We have for the first
time an economy based on a key resource [information] that is not only
renewable, but self-generating. Running
out of it is not a problem, but drowning in it is.[i]” Little did Naisbitt know how much information
we’d be creating 30 years later. By some
estimates we are generating over 1 zettabyte (1x1021) per year[ii]. How do you avoid drowning in all that data,
and gain insights? That is the realm of
Big Data Solutions.
Center recently ran a
seminar on Big Data. We started off
talking about the ‘big data conundrum.’
The volume of data is growing so rapidly, that the fraction of data that
an enterprise can analyze is decreasing.
Because of this gap, we’re getting ‘dumber’ about our organization and
job over time. This is driving the need
for improved analytics and platform technology that can help us to process this
large volume of data.
What do customers want to do with big data? Popular requests we’ve heard include: I/T log
analytics, RFID tracking and analytics, fraud detection and modeling, risk
modeling, 360o view of a
person/place/thing, call center record analysis, and fusion of multiple
unstructured objects (e.g., pictures, audio).
Since we now collect so much data, the possibilities are only limited by
your imagination –and our ability to extract insights from the data.
In order to process these large volumes of data, special
systems and applications are being deployed.
Many of these are based on the Apache Hadoop middleware which supports a
distributed file system and processing environment for scalability,
flexibility, and fault tolerance. IBM’s
big data platform includes offerings based on Apache’s Hadoop with enhancements
to improve workload optimization, security, and cluster hardening. The IBM offering (BigInsights) also comes
packaged with advanced analytical capabilities for data visualization, text
analysis, and support machine learning analytics. One interesting item was the announcement
that the enhancements would be packaged to allow them to work with other Hadoop
distributions, such as the Cloudera™ hadoop.
Another offering discussed in the seminar was the Stream computing
offering designed to efficiently process “data in motion,” such as stock ticker
streams and social media feeds.
One of the biggest challenges given the huge volume of
information is finding the right information.
Governments, Utilities, and financial companies have this problem in
particularly because of the huge volumes they deal with. A recent IBM acquisition, Vivisimo, has
developed a next-generation search engine to provide search across multiple big
data and traditional platforms. Vivisimo
provides a scalable search application framework that can perform a federated
search across many different data sources including the web, social media,
content stores, and more traditional structured database systems. One feature that may be particularly
appealing to government agencies and corporate environments is its ability to
map individual access permissions of each data item, authenticate users against
each target system and limit access to information a user would be entitled to
view if they were directly logged into the target system.
They offer a clever search tool that provides easy
navigation and discovery, using both structured metadata (faceted search) and
keywords that the program dynamically discovers based on analysis of
unstructured content. Vivisimo provides an agile development layer, to allow
users to quickly create applications and dashboards to discover, navigate and
The seminar also featured a customer case study of using big
data for cybersecurity mission operations. IP traffic is growing at 29% CAGR, and with it,
the cyber-threats they are facing. Unfortunately, the customer’s headcount
isn’t growing, so more automated ways are need to detect and respond to threats. For this application, timeliness is key –
dealing with threats in real-time. To
identify potential threats, they want to be able to compare current threat and
traffic data to norms from the recent past, and similar periods in the
past. Their solution utilizes the
Netezza data warehouse appliance for near real-term data and IBM BigInsights
for long term storage. The solution eliminates
as many mundane “data retrieval” tasks as possible for the analyst, and provided
the analysts with those datasets that had a high probability of being
“interesting.” In this way, the solution helps the analyst deal with the
extreme data volumes, and yet remains flexible to the changing threat
Do you have an opportunity to use massive amounts of data to
accomplish a business/mission objective that can’t be done when we were limited
to small volumes of data? Do you have an
innovative solution? We’d like to hear
your stories about big data.
For more on the Big Data seminar, see our ASC website under past events.
[i] Naisbitt, John,
Megatrends: Ten New Directions Transforming Our Lives, NY Warner Communications
Company, 1982, pages 23-24
[ii] IDC Digital Universe
At the end of the Superbowl, people created 12,233 tweets per second. And it turns out that was less than half the
number of tweets created in Japan
on December 9th, when 25,088 tweets per second were recorded about
the Castle in the Sky anime movie.
Which, according to the Chinese, is nothing compared to the 32,312
messages per second sent on their twitter-like Sina Weibo system during the
beginning of the Chinese new year.
Within the government space, we’re no strangers to our own Big Data. Whether you’re in the DOD or NASA, the IRS or
SSA, you’ve got your own Big Data to deal with.
Last week, Forrester Research released a report that should help those in
government understand the Big Data Market.
It is called “ The Forrester Wave™: Enterprise Hadoop Solutions, Q1 2012,
(February 2, 2012)” report. IBM Technologies evaluated were IBM InfoSphere
BigInsights (IBM’s Hadoop-based offering), and IBM Netezza Analytics. In this
evaluation, IBM was placed in the Leaders category of the Wave and achieved the
highest possible score in both the Strategy and Market Presence segments. In
the third segment, Current Offering, IBM received the second highest score. You
the complete report here.
The report by analyst James
Kobielus states, “IBM has the deepest Hadoop platform and application portfolio.”
The IBM Analytics Solution
Center in Washington, DC
also focused on how to handle Big Data at its January 19th
seminar. The seminar covered various
aspects of Big Data including data-in-motion processing software, Hadoop
software, SONAS (scale out network attached storage), and the Netezza data
1. Big Data in Motion
back to the Tweeting, if you’re a government agency and you need to get
actionable insights into 10s of thousands of tweets per second which might be
about an unfolding crisis, how would you do it?
InfoSphere Streams is unlike anything else in the market in its ability
to ingest, analyze and act on data “in motion” – that is, data is processed and
analyzed at microsecond latencies.
2. Hadoop Big Data
is an open source codebase supported by the Apache software foundation. It is designed to process large volumes of
unstructured data. For example, if a government agency wanted to analyze months
of tweets or documents in non-real time, the Hadoop distributed file system
would be a good choice. The enterprise
class IBM Hadoop-based offering, BigInsights, is designed with system
management, security, and performance features that go beyond what is available
in the open source. It provides the
ability to analyze and extract information from a wide variety of data sources,
and promotes data exploration and discovery.
Attached Storage, or NAS, has become a very popular way to provide storage
within an organization. However NAS has
a number of limitations when dealing with
Big Data including the number of objects (files) it can support, support
for very large files, the i/o bandwidth
it can deliver to applications, and fragmented data management across multiple
systems. The IBM SONAS system is
designed to overcome these limitations and look like a very large virtual
system to the applications.
4. Data Warehouse Appliance
data warehouses when used for large volumes of structured data can be costly to
operate and maintain, and can be very slow when used for sophisticated
analysis. The Netezza appliance is a
dedicated device requiring no tuning or storage administration and with special
hardware chips to accelerate the performance of advanced analytics.
Want to learn more?
- More details on the topics can
be found at the ASC Website under
- On the educational front, we
provide free online training through BigDataUniversity.com. To
date, more than 13,000 students have registered for courses on Hadoop,
cloud computing and more.
We are working with a broad range of clients to help them define
their big data strategies. We look forward to working with you on your Big Data
The Forrester Wave™: Enterprise Hadoop Solutions, Q1 2012,
Forrester Research, Inc., February 2, 2012. The Forrester Wave is copyrighted
by Forrester Research, Inc. Forrester and Forrester Wave are trademarks of
Forrester Research, Inc. The Forrester Wave is a graphical representation of
Forrester's call on a market and is plotted using a detailed spreadsheet with
exposed scores, weightings, and comments. Forrester does not endorse any
vendor, product, or service depicted in the Forrester Wave. Information is
based on best available resources. Opinions reflect judgment at the time and
are subject to change.
In these tough fiscal times, all agencies are going to be
focusing on doing more with less. How
does one get more done with less budget and staff? Consider turning to Analytics.
The consulting firm Nucleus Research has been looking at the
Return on Investment (ROI)
for various types of IT projects.
According to David O’Connell, Principal Analyst at Nucleus Research, “projects
involving analytics have some of the highest ROIs of any projects studied.”
Nucleus Research recently studied an analytics project IBM performed at DC
Water, the local water authority for Washington,
DC. In 2008, IBM began a first of a kind project
using advanced analytics to create a smarter water system that analyzes data on
valves, storm drains, service vehicles, truck routes and more to optimize its
infrastructure. With some pipes and other assets that date to the Civil War,
maintaining high levels of service while replacing older infrastructure is an
The project has resulted in the following benefits from a combination of IBM
Asset Management and Analytics technology and services:
Field Services trucks can be automatically
routed to optimize work management. This results in more work orders being
completed each week, as well as up to 20 percent reduction of fuel costs
related to fewer truck rolls and reduced "windshield" time.
Revenue loss from defective or
degrading water meters allowed recapture of $3.8 M because the analytics behind
the advanced metering infrastructure delivers more timely identification and
replacement of those meters. Revenue was
also recaptured because DC Water can now identify and bill locations where
there is unmetered water usage.
DC Water has been able to identify
assets most critically in need of repair using predictive analytics, so aging
infrastructure replacement programs can be more accurately scheduled,
preventing costly incidents that reduce service quality, such as outages and
water main breaks. This reduces both
maintenance labor costs and call center
costs associated with emergency incidents.
Nucleus Research reported in its case
study that the DC Water project resulted in $19.677 M of benefits over 3
years with a cost of $883 K, giving an ROI of 629%.
In 2010, Nucleus Research studied a number of other public
sector analytics projects. The results
from these projects are shown in the chart below. On average, the analytics projects have
resulted in an ROI of almost 600%! This
means that over 3 years, the projects have returned benefits 6 times the
original cost of the projects. The
payback period has been less than a year in all cases. This is important to government agencies because
it means you can see cost savings in the same fiscal year that you invest in an
According to David O’Connell, Principal Analyst at Nucleus
Research, “When government entities adopt
analytics, returns are high for two reasons.
First, waste such as leaky water mains, defective meters, or benefits
overpayments can be identified and eliminated.
Second, by making information more readily available, employees spend
less time looking around for information and more time getting their jobs done.” O’Connell went on to say, “Another improvement is better use of
workers’ time. The more an organization
knows about the public it serves, their needs, and the means of delivering
service, the smarter managers’ decisions are when they hand out workers’
Has your agency implemented any analytics projects? What’s been your experience?
Don't feel comfortable sharing
publicly? I'd be happy to hear your thoughts directly as well (firstname.lastname@example.org).
(net savings year 1 + net savings year 2 + net savings year 3)/3 * 100
Watson is the only computer on the planet that can answer a Jeopardy!
question in less than three seconds - fast enough to be competitive with the
world’s best human players.
of you that missed the match click here
to see a video clip from the match.)
But can a Watson-like computer help the government?
Watson was optimized to tackle a specific challenge:
competing against the world’s best Jeopardy! contestants. It does this by sifting through large amounts of unstructured information to find potential answers and assigning a confidence measure to each potential answer. When it has high confidence in an answer, it will buzz in and offer the answer. Beyond Jeopardy!,
IBM is working to deploy this technology
to businesses and governments dealing with the information overload
problem. At work, few of us are like
Ken Jennings, able to instantly answer almost every question thrown at us - -
with an 80-90% success rate. There is
simply too much information and more information is coming in all the
time. Whether we’re in finance, HR, IT,
or another area, our success at work depends upon dealing with huge volumes of
information, sifting through it to find
the “good information”, and then using the information to make decisions to do our
job. Technology like that used in Watson can provide for our consideration potential answers as well as the "evidence" it used to come up with potential answers.
In discussions recently with some of our military colleagues,
they came up with numerous ideas for deploying Watson-like technology. They cited the problem of “request overload” - - dealing with all the
requests for Predator and similar UAV missions.
How could they deploy their limited resources to best effect? Another person mentioned the problem of
sifting through all the intelligence information – most of it in the form of
unstructured information formats such as video and text – to find the relevant
information to a mission they were planning.
Another discussed the problem of monitoring their “situational
awareness” and how hard it was to keep track of all the data coming in. “Could Watson help monitor our security
posture and alert us to potential threats?” asked another.
Are you dealing with massive amounts of information? How could a Watson-like system assist you at
work? Do you want to recruit Watson to
work for your agency? We want to hear
your thoughts either in this blog or directly.
Write to me at email@example.com.
We’re hosting 2 free Watson Overview Briefings
on July 26 and 27. More
information at our website: www.ibm.com/ascdc
Frank Stein, Director, Analytics Solution Center
Sam Palmisano, Chairman and
CEO of IBM got together with Michael Dell, Chairman and CEO of Dell to release
an op-ed piece last week that the government can save $1 Trillion through the
use of IT.
for the statement.
Jeffrey Zients, Chief Performance Officer,
penned a blog
shortly thereafter titled, “Seeing Eye to Eye with the Tech CEO Council.”
Many of the
examples cited in the Palmisano/Dell statement relate to the use of analytics:
- Consolidating the government’s myriad supply
chains is likely to save $500 billion.
- Applying advanced analytics to reduce fraud
and error in federal grants, food stamps, Medicare payments, tax refunds
and other programs could save $200 billion.
- Using predictive
technology, New York
State is validating
tax refund requests and saving $889
million by catching phony refunds.
- Identifying suspicious Medicare activity using
analytics has shown North Carolina
how to save $25 million in just three months.
In addition to
helping to uncover fraud, waste, and abuse, I’d like to suggest 3 other ways analytics
can help the government to save money.
- Streamlining Processes: Analytics can help streamline and
optimize programs, reducing the costs of implementation while improving
service to citizens. For example,
IBM worked with Social Security to streamline their processing of
disability claims so that the majority of claims can be expedited with
little risk of allowing through unacceptable claims.
- Managing Performance: Performance management solutions can
help the management and staff of agencies to know their up-to-date
performance, and quickly spot and trouble-shoot performance issues before
they become major problems. Performance management can also help identify
successful approaches that can be replicated throughout and across
- Better decision-making: Analytics can help
agencies decide which programs to fund or the most effective
approach to take for a particular program.
By using modeling, simulation, and other data-driven approaches,
agency staff can make decisions that both save the tax payers’ money and
deliver the best results. For
example, by modeling and optimizing the US Postal Service transportation
network, USPS is able to increase utilization of assets and save hundreds
of millions of dollars.
I’d like to hear
your ideas for how agencies can save money through employing analytics. Write to me at firstname.lastname@example.org.
See our website for
further information on using analytics in government: www.ibm.com/ASCdc
Director, Analytics Solution Center
Apparently, pretty good, according to Nucleus Research. They recently completed 2 ROI Case Studies of 2 government analytics projects. Both showed impressive results:
- Alameda Country Social Service Agency's Social Services Integrated Reporting System (SSIRS) had an ROI of 631% and a payback of 2 months
- Memphis Police Department's Blue CRUSH (Criminal Reduction Utilizing Statistical History) had an ROI of 863% and a payback of 2.7 months
The ROI calculations may even be conservative as Nucleus Research appears to assume that the agency and department will pay taxes on the annual benefits from the solutions.
The SSIRS system helped Alameda County reduce overpayments to non-compliant citizens, improve their win rates when claimants appealed discontinuation of benefits, and improved caseworker productivity. The system is essentially a Business Intelligence solution giving the caseworkers access to information about their clients, with dashboard and drill down capabilities. It also provides the caseworkers and managers with immediate information on "how am I doing?". Providing caseworkers with information on their clients' work participation rate and other performance metrics was key to improving the performance of the social service agency. The solution combined Cognos Business Intelligence, Infosphere Identity Insight, and an Infosphere warehouse to hold all the data. Identity Insight helps the caseworkers track the relationships between the various clients (e.g., parent/child) that may impact services offered. Here is a video where Don Edwards, Assistant Agency Director, talks about the solution: YouTube Video
The Blue CRUSH solution helped the Memphis Police Department (MPD) to identify crime "hot spots" and then target these areas for increased attention. As a result, MPD has reduced violent crime without additional staffing. The solution uses IBM SPSS Predictive Analytics software to analyze crime data pertaining to type of criminal offense, time of day, day of week, location, and the weather. The solution was developed with the assistance of the University of Memphis Department of Criminology and Criminal Justice.
Memphis Police Department received a National award from Nucleus
Research for this solution. They were one of only
ten companies and governmental agencies to receive the Nucleus
Research ROI award. Out of 350 technology projects that were
submitted, the Memphis Police Department was one of only two
governmental agencies to receive an award. The other governmental
agency was the US State Department.
If you'd like more information on these two case studies please contact me at email@example.com.
More information about Analytics, including our Fall Analytics Seminar Series,
can be found at www.ibm.com/ASCdc
Director, Analytics Solution Center
on the weather and climate occurring around the world in
Because weather fascinates many
of us and is experienced by all of us, the report provides good examples of how
data can be analyzed, reported, and visualized.
The first observation I'd make is that the report focuses on
unusual or anomalous events. It tries to
put them in historical context. For
example, I learned that the U.S.
had the wettest October since records were collected 115 years ago. And Toronto
had a snow-free November for the first time in recorded history. In all data analysis - weather data,
financial data, or performance data – it is important to pull out the
significant events from the rest of the data, or the “noise” as we say. NOAA does this by comparing the past year’s
data with their historical data to find out where the year stood in comparison
to all the other years. Similar analysis
can be done by other agencies whether the metric is road-miles constructed or the percent of students receiving student aid that graduated. What is key is comparing the results in light of the historical data and trying to gain insights on what the trend is and what it means.
Because 2009 was the end of the decade, they have also
compiled some data at the decade level rather than at the year level. While the 2009 average global temperature was
the fifth warmest year on record, the 2000-2009 decade was the warmest on
record for the globe. And the decade
before, 1990 – 1999, was the warmest on record at that time. The use of multi-year averages is a good
example of smoothing that can be done to help ferret out significant
information and remove the year-to-year fluctuation in large collections of
time series data. The graph showing the
decade data makes the trend very obvious (Source NOAA report, chapter 2).
Many of the charts in the report show the yearly results as
a delta from the long term average, e.g. last year’s average surface
temperature was .5ºC above the 1961-1990 average using NASA/GISS data. By graphing
the time series data against the long term average, the anomalies standout. Other
charts show the actual values and it is possible to discern trends in the data.
For example, the lower tropospheric temperatures are increasing by
approximately .15ºC per decade. One can
use this information to predict the climate for future decades, which could
have value for policy purposes.
The report also highlights the very strong monthly and seasonal variability in the U.S. surface temperatures in 2009 that would be obscured if one just looked at yearly averages. Another analytical technique - Modeling - can be used to help analyze why the "why" behind the data. Why did 2009 show such strong variability? The report indicates that in 2009 the global climate switched from the La Nina conditions that dominated 2008 to El Nino sea surface temperature (SST) conditions in the tropical Pacific ocean. Was this the cause? NOAA global climate models were subjected to the Pacific SST observed data and the results are show below. While not all of the variability appears to be explained by the model, the warm first quarter over the Great Plains and cold summer seems mostly consistent with the impact of La Nina during the winter and El Nino during the summer.
The State of the Climate report shows good examples of many
data analysis techniques including historical analysis, near-real time
reporting, reanalysis of past data using newer, improved techniques, averaging
of multiple datasets to improve reliability, and drill down capabilities from
decades, to years, to seasons, to months,
and from global to regional to country and state. They also use
interesting visualization techniques.
Those interested in data analysis, as well as weather,
should download this report from the NOAA Website. (Arndt, D.S.,M.O. Baringer, and M.R. Johnson, Eds., 2010: State of the Climate in 2009, Bull. Amer. Meteor. Soc.). Note: While NOAA does use IBM Technology in its Research, the report does not state which technology is used in the reported climate studies and I don't intend to imply any relationship between this report and IBM.)
Those interested in further information on Analytics
including our fall schedule of events, please visit the Analytics Solution
Center website at www.ibm.com/ASCdc.
If your agency uses analytics in interesting and novel ways,
I'd like to hear from you. Please write to me at ASCdc@us.ibm.com.
Frank Stein, Director
The news last week was all about the weak job market.
Fed Chairman Ben Bernanke characterized the
job market as showing “continuing weakness.”
Well, guess what?
The job market
for those with Analytics skills is very hot.
Monster has over 1000 job listings for Business Analytics jobs.
Here at IBM, we have over 100 openings for Business
Analytics and Optimization jobs. Some of
these are associated with our Public Sector Practice, consulting to Federal,
State, and Local Governments or developing data-intensive, analytics solutions
to help them perform their mission.
Why are there so many jobs in this field? Businesses and governments today must figure
out how to do more with less.
Organizations can analyze data coming from their business processes to
develop new approaches to streamlining or even optimizing their business. In the past, many decisions involved in
running an organization were based on “gut instinct.” Today, it is not longer defensible to make
decisions in this way when it is possible to make “fact-based” decisions using
hard data. Data stored in a Business
Intelligence system can be used by every level of an organization to help staff
understand their business better, detect problems, and develop solutions that
will allow them to accomplish their mission better, cheaper and faster. Sophisticated analysis can be done on the
data to predict what will happen if the current trends continue, determine how
to achieve the best outcome, and study the impact of external uncertainties
such as the economy or the weather.
According to the Bureau of Labor Statistics in their
2010-2011 Occupational Outlook Handbook,
the employment of operations
research analysts is expected to grow 22 percent over the 2008-18 period. While not all analytics jobs require an
operations research degree, this gives a good indication of the long term
trend. We know that technology is continuing
to improve both in terms of raw compute power and in the design of efficient
algorithms to analyze and optimize solutions.
This increasing capability will drive the demand to add “smarts” to many
more systems and processes, and will drive the need for analysts who can apply
the technology. So analytics isn’t just
a good short term career choice, but a good target for long-term career
To do these jobs, though, requires in-depth skills and
knowledge. Skills in operations research
(OR) techniques, data mining, optimization, decision theory, and data analysis
are needed, along with some background in IT systems. The ideal candidate will also have some
domain knowledge about government or business functional areas since it is very
hard to apply the mathematical techniques in abstraction.
How to Find Analytics Jobs
Most Analytics jobs aren’t listed under “analytics” and many
won’t even come up under that keyword.
Use search terms like ‘business intelligence,” “performance management,”
“optimization,” and “operations
research.” If you have experience with
actual analytics software such as Cognos, SPSS, Intelligent Miner, or ILOG,
both Monster and IBM’s website return hits on those keywords.
Want to learn more about jobs at IBM in Business Analytics?
Go to www.ibm.com/employment
and click on the “Search for Jobs at IBM” link
You may also write me at ASCdc@us.ibm.com
Analytics Solutions Center of Washingtion, D.C. Director