please answer this after reading the article
What is the actual problem?
What are the known facts?
What decision is to be made?
How the problem ought to be solved?
What are the alternatives?
What are your recommendations?
New AI tools make BI smarter — and more useful
Data science democratized: What used to take data scientists
months to prepare may soon be put together in a few days by
data-astute business users.
By Maria Korolov, Contributing Writer, CIO | Apr 18, 2018 3:00
AM PT
Companies looking to make good on the promise of machine
learning for data analysis are turning to a somewhat unlikely old
friend. Business intelligence systems, largely the domain for
analyzing past performance, are being retrofitted with artificial
intelligence to bring predictive features to their reporting
capabilities.
The Symphony Post Acute Network is one such organization. The
health care company, which has 5,000 beds in 28 health care
facilities in Illinois, Indiana and Wisconsin, wanted to use
artificial intelligence and machine learning to improve care for up
to 80,000 patients a year recovering from procedures like knee
surgery, or receiving dialysis treatment. For example, buried deep
in a patient's medical core could be an indication that a patient
is particularly at risk for a dangerous fall and therefore requires
extra precautions.
Finding these indicators, which could be individual data
points or subtle patterns of data, is a perfect use case for
machine learning. But building the models isn't a simple job.
"I got bombarded with questions about predictions," says
Nathan Patrick Taylor, director of data science and analytics at
Symphony. "Even if I spend every waking moment building machine
learning models, there's no way I can do all that."
So the company hired two more data scientists. "And they're
not cheap," says Taylor. "But we weren't getting the return we
needed. It was very difficult and costly."
So two years ago, Symphony started looking at commercial
alternatives, vendors who already had the machine learning models
built and ready to go. Now the company takes the data already
collected in its data warehouse, sends it through the cloud-based
AI engines from its vendor, DataRobot, and the results are funneled
back into the company's Microsoft PowerBI dashboards every four
hours. "Right away, I got it, my CIO got it, and we were just blown
away," says Taylor. "It looked like magic."
Today, 240 doctors and nurses get the predictions and
recommendations right in their PowerBI dashboards, which they can
access through tablets and smartphones. So, for example, patients
at higher risk of falling are automatically flagged with a
staircase icon. Patients at high risk of re- admittance are flagged
with an ambulance icon.
Injecting AI into BI
Re-admission rates are a big deal for Symphony, Taylor says.
Hospitals and insurance companies look at readmission rates, and
each readmission ends up costing the company $13,500. "That's not
an insignificant amount of money," he says.
To find out whether the DataRobot predictions were useful or
not, Symphony originally rolled out the DataRobot feeds to just
some of its facilities, and ran a six-month study to see whether
there was a difference in the readmission rates. "If you can move
it 1 percent, you're doing really well," he says.
And the rates did improve, Taylor says — from 21 percent to
about 18.8 percent. "That's a significant improvement," he says.
"That won over our CEO."
Today, the company is starting to use the same approach to
look at contracts with insurance companies. "If we're not billing
properly for services, that's money we leave on the table," he
says.
The initial installation took about 20 hours, and involved
connecting the data feeds and setting up the learning models. Now,
if someone wants new types of predictions, a brand-new learning
model takes about six to eight hours to set up, he says, spread
across three working days.
In addition, existing models can be retrained at any time, he
says. For example, regulations might change, or medical staff could
start using new procedures. In addition, a model might drift over
time. Taylor retrains the models every three months, or whenever
there's a major policy shift. If there's a big change, the learning
model might need to be trained only on data that came in after the
new policies went into effect.
Managing the system no longer requires a highly trained data
scientist, he says, but it does require someone who has a basic
knowledge of statistics. At Symphony, the company also uses R code
to set up the models.
DataRobot also supports Python out of the box. Customers who
use other languages can also use any Rest API aware language to
call the DataRobot Rest API, including Java, C#, SAS, JavaScript,
and Visual Basic, according to Colin Priest, DataRobot's director
of product marketing.
AI's next step is self-service
"AI has been democratized," says Boris Evelson, vice president
and principal analyst at Forrester Research. "Until recently, it
required a data scientist to write code. Today, with these business
intelligence systems, I can point and click at a few data points,
choose the variable I want to predict — like a customer's
propensity to buy — and these predictive models are going to be
automatically generated."
Something that used to take a data science professional months
to put together can now be put together in a few days by someone
who can understand data and work with Excel, he says.
"Marketers are using this to predict and act on customer
behavior, business managers are using it to look at and predict
risk, supply-chain people are using it to look at and optimize
logistics," he says.
According to a recent Forrester survey of global decision
makers, improving data, analytics or insights platforms is one of
the top three use cases for artificial intelligence technologies.
And all the major BI vendors, including IBM, Oracle and Microsoft,
are hard at work in this area.
And if eight hours sounds like too long to set up a new
machine learning model, easier options are on their way. Soon,
users will be able to get the most common types of predictions
automatically, get recommendations, and have built-in image
recognition and natural language processing, as Gartner predicts
that natural-language generation and artificial intelligence will
be a standard feature of 90 percent of modern business intelligence
platforms within the next two years.
Equipping BI for textual and visual analysis
Natural language processing will enable users to ask plain
English questions, in the moment, when they need the information,
says Bruce Molloy, CEO at SpringBoard.ai. "I think it's a natural
evolution."
The narrower the domain, and the more relevant data a platform
has access to, the easier it will be for the vendor to add AI
capabilities, he says. Accounting platforms, or customer
relationship management systems like Salesforce, already have the
data they need, and there are certain questions that users are most
likely to ask. "With Salesforce, it will be very interesting to see
what they do," he says. "It's already constrained, and the work is
already partially done. They've already determined that these views
are important, and they can layer in the AI capabilities on top of
that."
The push to artificial intelligence is driven by increased
processing power, smarter algorithms, cloud computing and standard
interfaces. DataRobot, for example, takes advantage of both cloud
computing and standard Rest APIs, allowing it to support Trifacta,
Alteryx, and Domino Data Labs business intelligence systems, in
addition to PowerBI, Tableau, Qlik, Excel, R Shiny, and many other
dashboard tools.
AI-powered business intelligence dashboards can also process a
much wider variety of data than before. Symphony, for example,
doesn't just look at the hard numbers in the patient records, but
also at the patient progress notes made by doctors and
nurses.
There's a lot of information saved in unstructured formats,
information that could lead to useful insights or predictions, says
Josh Sutton, global head of data and artificial intelligence at
consulting firm Publicis.Sapient. And it's not just text.
"One of the largest sources of unstructured data that is a
source of business intelligence is visual imagery," Sutton says.
Marketing departments, for example, could benefit from analysis of
how their customers are interacting with products based on the
photos they share on social media.
Moving beyond descriptive analytics
But predictions and insights are just the first step of what
AI can add to business intelligence dashboards, says David
Schubmehl, research director for cognitive and artificial
intelligent systems at International Data. AI-powered dashboards
can also provide advice or suggest specific actions that users
should do next — or even offer carry out those actions for the
users.
"If widgets [sales] numbers are dropping, it could say what
this would mean for the future, and what you should do about it
now," he says.
That makes BI much more valuable.
"I think that's why so many people are adopting these kinds of
tools," he says. For example, Salesforce just made a big
announcement that its Einstein predictions had recently crossed
over a billion predictions a day in terms of prescriptive
intelligence helping people close new business, identifying new
leads, creating action-oriented capabilities. I think that's an
indicator that people want more than just descriptive business
analytics."
And we're still in the very early stages, he says. "Sometime
over the next two to three years, we'll probably hit full maturity.
People are just beginning to understand what the possibilities are
with artificial intelligence and machine learning."
In particular, AI still doesn't have common sense, says Rumman
Chowdhury, senior principal and global lead for responsible
artificial intelligence at Accenture.
"We're in the world of narrow AI," she says. Even if a
particular platform has an AI model built in and ready to go, the
user still has to understand the data that is being used, and its
relevance to the question at hand.
"You have to make sure it's appropriate for the output you
think you're getting," she says. "And I don't know if we will fully
replace human judgment in some of these areas. I don't know if we
can fully automate real decision making — or even if we
should."
General questions to consider for this Case
• What is the main theme in this article and how is the theme
related to this course?
• What are the principal implications for business
organizations?
• What are your predictions for the evolution/impact of any
technology listed in this article?
(Support your predictions with content from corresponding
chapters in the textbook, or from any reliable publications such as
Harvard Business Review, The Economist, Bloomberg- Businessweek,
CIO Magazine, PC Magazine, Wired, BBC News, Forbes, etc.)
read this then answer :
New AI tools make BI smarter — and more useful
Data science democratized: What used to take data scientists
months to prepare may soon be put together in a few days by
data-astute business users.
By Maria Korolov, Contributing Writer, CIO | Apr 18, 2018 3:00
AM PT
Companies looking to make good on the promise of machine
learning for data analysis are turning to a somewhat unlikely old
friend. Business intelligence systems, largely the domain for
analyzing past performance, are being retrofitted with artificial
intelligence to bring predictive features to their reporting
capabilities.
The Symphony Post Acute Network is one such organization. The
health care company, which has 5,000 beds in 28 health care
facilities in Illinois, Indiana and Wisconsin, wanted to use
artificial intelligence and machine learning to improve care for up
to 80,000 patients a year recovering from procedures like knee
surgery, or receiving dialysis treatment. For example, buried deep
in a patient's medical core could be an indication that a patient
is particularly at risk for a dangerous fall and therefore requires
extra precautions.
Finding these indicators, which could be individual data
points or subtle patterns of data, is a perfect use case for
machine learning. But building the models isn't a simple job.
"I got bombarded with questions about predictions," says
Nathan Patrick Taylor, director of data science and analytics at
Symphony. "Even if I spend every waking moment building machine
learning models, there's no way I can do all that."
So the company hired two more data scientists. "And they're
not cheap," says Taylor. "But we weren't getting the return we
needed. It was very difficult and costly."
So two years ago, Symphony started looking at commercial
alternatives, vendors who already had the machine learning models
built and ready to go. Now the company takes the data already
collected in its data warehouse, sends it through the cloud-based
AI engines from its vendor, DataRobot, and the results are funneled
back into the company's Microsoft PowerBI dashboards every four
hours. "Right away, I got it, my CIO got it, and we were just blown
away," says Taylor. "It looked like magic."
Today, 240 doctors and nurses get the predictions and
recommendations right in their PowerBI dashboards, which they can
access through tablets and smartphones. So, for example, patients
at higher risk of falling are automatically flagged with a
staircase icon. Patients at high risk of re- admittance are flagged
with an ambulance icon.
Injecting AI into BI
Re-admission rates are a big deal for Symphony, Taylor says.
Hospitals and insurance companies look at readmission rates, and
each readmission ends up costing the company $13,500. "That's not
an insignificant amount of money," he says.
To find out whether the DataRobot predictions were useful or
not, Symphony originally rolled out the DataRobot feeds to just
some of its facilities, and ran a six-month study to see whether
there was a difference in the readmission rates. "If you can move
it 1 percent, you're doing really well," he says.
And the rates did improve, Taylor says — from 21 percent to
about 18.8 percent. "That's a significant improvement," he says.
"That won over our CEO."
Today, the company is starting to use the same approach to
look at contracts with insurance companies. "If we're not billing
properly for services, that's money we leave on the table," he
says.
The initial installation took about 20 hours, and involved
connecting the data feeds and setting up the learning models. Now,
if someone wants new types of predictions, a brand-new learning
model takes about six to eight hours to set up, he says, spread
across three working days.
In addition, existing models can be retrained at any time, he
says. For example, regulations might change, or medical staff could
start using new procedures. In addition, a model might drift over
time. Taylor retrains the models every three months, or whenever
there's a major policy shift. If there's a big change, the learning
model might need to be trained only on data that came in after the
new policies went into effect.
Managing the system no longer requires a highly trained data
scientist, he says, but it does require someone who has a basic
knowledge of statistics. At Symphony, the company also uses R code
to set up the models.
DataRobot also supports Python out of the box. Customers who
use other languages can also use any Rest API aware language to
call the DataRobot Rest API, including Java, C#, SAS, JavaScript,
and Visual Basic, according to Colin Priest, DataRobot's director
of product marketing.
AI's next step is self-service
"AI has been democratized," says Boris Evelson, vice president
and principal analyst at Forrester Research. "Until recently, it
required a data scientist to write code. Today, with these business
intelligence systems, I can point and click at a few data points,
choose the variable I want to predict — like a customer's
propensity to buy — and these predictive models are going to be
automatically generated."
Something that used to take a data science professional months
to put together can now be put together in a few days by someone
who can understand data and work with Excel, he says.
"Marketers are using this to predict and act on customer
behavior, business managers are using it to look at and predict
risk, supply-chain people are using it to look at and optimize
logistics," he says.
According to a recent Forrester survey of global decision
makers, improving data, analytics or insights platforms is one of
the top three use cases for artificial intelligence technologies.
And all the major BI vendors, including IBM, Oracle and Microsoft,
are hard at work in this area.
And if eight hours sounds like too long to set up a new
machine learning model, easier options are on their way. Soon,
users will be able to get the most common types of predictions
automatically, get recommendations, and have built-in image
recognition and natural language processing, as Gartner predicts
that natural-language generation and artificial intelligence will
be a standard feature of 90 percent of modern business intelligence
platforms within the next two years.
Equipping BI for textual and visual analysis
Natural language processing will enable users to ask plain
English questions, in the moment, when they need the information,
says Bruce Molloy, CEO at SpringBoard.ai. "I think it's a natural
evolution."
The narrower the domain, and the more relevant data a platform
has access to, the easier it will be for the vendor to add AI
capabilities, he says. Accounting platforms, or customer
relationship management systems like Salesforce, already have the
data they need, and there are certain questions that users are most
likely to ask. "With Salesforce, it will be very interesting to see
what they do," he says. "It's already constrained, and the work is
already partially done. They've already determined that these views
are important, and they can layer in the AI capabilities on top of
that."
The push to artificial intelligence is driven by increased
processing power, smarter algorithms, cloud computing and standard
interfaces. DataRobot, for example, takes advantage of both cloud
computing and standard Rest APIs, allowing it to support Trifacta,
Alteryx, and Domino Data Labs business intelligence systems, in
addition to PowerBI, Tableau, Qlik, Excel, R Shiny, and many other
dashboard tools.
AI-powered business intelligence dashboards can also process a
much wider variety of data than before. Symphony, for example,
doesn't just look at the hard numbers in the patient records, but
also at the patient progress notes made by doctors and
nurses.
There's a lot of information saved in unstructured formats,
information that could lead to useful insights or predictions, says
Josh Sutton, global head of data and artificial intelligence at
consulting firm Publicis.Sapient. And it's not just text.
"One of the largest sources of unstructured data that is a
source of business intelligence is visual imagery," Sutton says.
Marketing departments, for example, could benefit from analysis of
how their customers are interacting with products based on the
photos they share on social media.
Moving beyond descriptive analytics
But predictions and insights are just the first step of what
AI can add to business intelligence dashboards, says David
Schubmehl, research director for cognitive and artificial
intelligent systems at International Data. AI-powered dashboards
can also provide advice or suggest specific actions that users
should do next — or even offer carry out those actions for the
users.
"If widgets [sales] numbers are dropping, it could say what
this would mean for the future, and what you should do about it
now," he says.
That makes BI much more valuable.
"I think that's why so many people are adopting these kinds of
tools," he says. For example, Salesforce just made a big
announcement that its Einstein predictions had recently crossed
over a billion predictions a day in terms of prescriptive
intelligence helping people close new business, identifying new
leads, creating action-oriented capabilities. I think that's an
indicator that people want more than just descriptive business
analytics."
And we're still in the very early stages, he says. "Sometime
over the next two to three years, we'll probably hit full maturity.
People are just beginning to understand what the possibilities are
with artificial intelligence and machine learning."
In particular, AI still doesn't have common sense, says Rumman
Chowdhury, senior principal and global lead for responsible
artificial intelligence at Accenture.
"We're in the world of narrow AI," she says. Even if a
particular platform has an AI model built in and ready to go, the
user still has to understand the data that is being used, and its
relevance to the question at hand.
"You have to make sure it's appropriate for the output you
think you're getting," she says. "And I don't know if we will fully
replace human judgment in some of these areas. I don't know if we
can fully automate real decision making — or even if we
should."
General questions to consider for this Case
• What is the main theme in this article and how is the theme
related to this course?
• What are the principal implications for business
organizations?
• What are your predictions for the evolution/impact of any
technology listed in this article?
(Support your predictions with content from corresponding
chapters in the textbook, or from any reliable publications such as
Harvard Business Review, The Economist, Bloomberg- Businessweek,
CIO Magazine, PC Magazine, Wired, BBC News, Forbes, etc.)
please answer the questions :
1- What is the actual problem?
2- What are the known facts?
3- What decision is to be made?
4- How the problem ought to be solved?
5- What are the alternatives?
6- What are your recommendations?
7- What are your predictions for the evolutions/impact of any
technology listed in this article?