Why IT Fumbles Analytics

Why IT Fumbles Analytics
By Donald A. Marchand and Joe Peppard

In their quest to extract insights from the massive amounts of data now available from internal and external sources, many companies are spending heavily on IT tools and hiring data scientists. Yet most are struggling to achieve a worthwhile return. That’s because they treat their big data and analytics projects the same way they treat all IT projects, not realizing that the two are completely different animals.

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Gartner viewpoint on Systems of Innovation and how they are different from Systems of Record

Accelerating Innovation by Adopting a Pace-Layered Application Strategy
Analyst(s): Yvonne Genovese

Gartner’s Pace-Layered Application Strategy is a methodology for categorizing, selecting, managing and governing applications to support business change, differentiation and innovation…

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How to use customers’ life stage changes for deepening customer loyalty?

Lokendra Devangan
David is an investment banker based in New York. At least twice every month he visits Redwood hypermarket to shop for groceries and other personal needs. Three months ago, he married his girlfriend from China.

David was lounging around on a lazy Saturday when he received an SMS from Redwood. The message offered 40% discount on woman fashion brands at a partner retailer and 30% discount at famous Chinese restaurants in central New York.

After seeing the offer David was impressed with Redwood.  He was also surprised as most of their earlier offers were bundled for grocery items. Why did Redwood think that these offers were relevant for him?  He then smiled and thought that he will definitely avail both the offers next month on his wife’s birthday. He will gift his wife a lovely handbag and take her to one of the restaurants for dinner.

How did Redwood figure out this highly relevant offer for David?

David is a loyal customer of Redwood. Redwood Customer Loyalty team uses advanced analytics method to identify the customers’ life stage. They track the customers’ basket size, product mix and frequency of visit to segment customers into groups such as large family, family with kids, family without kids, Single, occasional visitor etc. Loyalty team observed that in recent months David’s monthly spend at Redwood store has increased and the basket composition has changed. They noticed that apart from larger baskets, transactions for his household now routinely include women’s beauty and hygiene products.

Redwood employs automated analytics where the system captures such changes in behavior, and identifies opportunities to enhance marketing around this life stage changes.

The change in the product-mix in David’s basket can be permanent or temporary (a friend or a relative visiting). Redwood loyalty team can test the hypothesis that this is infact a change in relationship status and that the partner likes Asian food. Redwood to test their hypothesis has created a special personalized offer for David and extended discounts at partner fashion retailer and restaurant chain. They also plan to send such multiple offers over the period of next two months. If David responds to offers regularly, Redwood will be able to conclude that David is married. This information can be used for more personalized offers.

Redwood has realized the power of data analytics and used it efficiently to enhance the relationship with the loyal customers.

Insights

Need More Capacity for your Data Warehouse:  We can save you at least $990,000

Data volumes in your organization are exploding and your data warehouse is bursting at the seams. You have heard of all the hype about Hadoop but it is not well-suited for your workloads of real-time reporting. You don’t seem to have any other options other than to buy that dreaded expansion license (1 TB = $ 1 Million) for your data warehouse. Does this sound like your predicament? Read on, it’s worth at least $990,000.

For one of our customers utilizing the CoreCompete A3 service, we identified a model where they could move a majority of the newer, transient work-loads onto a cloud based data warehouse, while utilizing their current data warehouse for traditional workloads. Leveraging Amazon’s Redshift data warehouse as a service, we proved to them that data from syndicated data providers can be made ready for analysis by analysts in 1-2 weeks cutting down the 3-6 month process that is currently employed by the standard data warehousing process. This significantly increased their ability to respond to demand signals when they launched a new drug.

The cost of the AWS Redshift solution, was less than $10,000/ Terabyte. In addition, this system was integrated into other services such as AWS S3, that allowed the customer to store a significantly larger volume of data in an environment that is a fraction of a traditional data warehouse cost (1/10000th), while being able to bring it online for analysis in a matter of minutes.

One of the early concerns that the customer had was around the security model of the cloud based solution and potential risks around this. However, through a series of reviews, we were able to convince that our AWS based architecture was designed to address the demanding security expectations of the client.

We integrated AWS Redshift into the client’s SAS and Tableau environments. The enterprise class SAS Environment which was behind the client’s firewall, was able to send SQL pass-through commands to Redshift, allowing us to leverage the MPP capabilities of AWS and bring back summarized data into SAS for further analysis. The Tableau server environment was deployed directly on top of the Redshift database to enable real-time access to the data for the dashboards that are now widely used to understand launch performance.

Now, the client is also engaged with us for a variety of pilots with integrated delivery networks (IDN), where they bring the IDN data into the CoreCompete A3 Service to identify insights specific to the IDN to reduce re-admissions and increase Medicare related shared savings for the IDN.

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Forecast Health: How we built a Secure HIPAA Compliant Big Data Environment leveraging AWS

How does your doctor make sure that you don’t get re-admitted? Generally, they are awash with data about your health, but have very limited data on your behavior outside the health system. On top of it, they really don’t know how to synthesize all this information in the limited time they have with their patients to come up with an actionable and tailored set of actions.

Forecast Health, a Durham, NC based start-up precisely aims to address this problem. Synthesizing data from Electronic Health Records (EHR), Insurance Claims and more than 100 consumer behavior oriented data sets, they provide actionable predictive risk scores that are integrated directly into the EHR system that the physician already uses.

While what they do is amazing in its own right. What’s even more amazing is that they were able to get from the ground up and running with a lean staff in less than 60 days. Leveraging Amazon Web Services, CoreCompete built a HIPAA Compliant Cloud Environment that enabled the Forecast Health staff to bring large datasets from Center for Medicare Services (CMS), syndicated data providers that provided patient level identifiable data and from within the health system in a cost-effective manner.  In addition, leveraging the elasticity of the AWS cloud and the variety of on-demand services such as EMR (Hadoop) and RedShift, CoreCompete provided an environment that was designed to scale for the most highly demanded workloads. The entire environment was validated by external auditors.

“The CoreCompete team enabled us to get up and running very quickly allowing us to prove our capabilities very quickly to both our investors and our early customers. Without CoreCompete and the AWS cloud, our business would have never taken off”, said Dr. Michael Cousins, President, Forecast Health.

Insights

Can Your Bank Help You Keep Your Promise to a Loved One?

Lokendra Devangan
Steve was reading the newspaper and having his morning coffee.  It was 20th November. He realized that after three months on the same day, he will be celebrating his 10th wedding anniversary.  He remembered that he had promised to take his wife, Lisa, on an adventure trip to South Asia, to celebrate their 10th wedding anniversary.

He thought for a moment and said to himself “Better not to remind her, not sure if I can pull it off”.

Later that evening, he rushed home and said “Dear Lisa, we are going on adventure trip to South Asia to celebrate our 10th wedding anniversary”.

What happened in the 8-10 hours that Steve changed his decision?

Steve received an email from his bank stating that he could enjoy interest-free credit on purchases on his credit card for next six months and enjoy his wedding anniversary. Bank had also increased the line of credit for three months.

Steve thought he could avail this offer and pay it off when he gets his bonus in March. He’s been doing well at work and was expecting a nice bonus. But he wondered whether the offer being made in a timely manner was just a coincidence or something that the bank carefully orchestrated! Steve brought this up and wanted to know if it was possible that this was not a coincidence. I said “This is not coincidence but most likely based on the banks assessment of customer life-time value using the data generated by various banking relationships with customer. They also computed the risk score for their customers. These two numbers collectively portray the customers’ potential value and riskiness. They combined this with life events (such as a birthdays/ wedding anniversaries to make timely offers”.

“I can understand that bank maintains records such as date of birth, anniversaries etc but how do they know that I am expecting a good bonus?” Steve interrupted.

I smiled and explained “It is very easy, if the customers has salary account with the bank or if same bank is the primary bank for the customer.  Banks are able to deploy customer decision hubs that bring all the customer’s data into one place, and are able to proactively trigger marketing activities leveraging a 360-degree view of the customer’s financial and life events.”

Steve chuckled and said “I am glad even the bank thinks I will get a good bonus, better remind the boss.”

Insights

Kohl’s Makes My Day with a Birthday Gift: Good Practices in Retail Loyalty

Shiva Kommareddi
My birthday is coming up and Kohl’s sent me an offer in my email: “Here’s a $10 gift for you to spend at Kohl’s to get your celebration started”

This really touched my heart and I was wondering why this meant so much to me (especially given that a Kohl’s coupon is not the hardest thing to get, there are in fact three others in my mail box right now with different offers).

Here are some reasons I came up with:

While I probably have atleast a dozen or more loyalty club memberships and many of them take the trouble of sending an email to say Happy Birthday, this is really the first time someone sent me a gift. While an email (that in my mind doesn’t cost anything) from an impersonal entity (AAdvantage wishes you a happy birthday!) was something that I was almost insulted by, as I knew it was a form letter, this communication from Kohl’s seems like they really meant it
It was a gift not an offer. They did not say 10% off, they did not say buy $50 and get $10 off your next purchase, they just said here’s a gift no obligations. This does not sound like a marketing offer but a gift from a friend
They reminded me to use it to get the celebration started with this. I was not thinking about the celebration earlier, but now I am thinking: What should I treat myself to at Kohl’s (that second pair of sneakers that I have been thinking of?)

I would have thought that in the crowded world of loyalty, I would be bombarded with such offers. Nope. Just one offer so far, and unlikely that I will get any more.

A few things that Kohl’s could have done better:

It arrived on the Sunday before my birthday, Saturday AM would have been better timing
It did not have any creative relevant to me (man vs woman at least)

Anyway nice job Kohl’s and I will expect great things from you.

Insights

Social Media Analytics, a Blessing in Disguise for Traditional Market Research

Anurag Sachdev
Businesses across the globe are waking up to the need for putting their customers at the heart of every decision. The need to better channelize the innovation budget to address what customers really want, has never been more acute.

A PC giant with over $30BN in revenues, has been looking to incorporate customer-centricity deep into their product development and planning cycles. Traditionally very strong in the enterprise PC segment, they want to replicate and augment their success with the consumer PC segments – hence the need for customer-centricity and the call to CoreCompete.

Disclaimer: We don’t have Sherlock Holmes and Dr. Watson on our payroll…yet!

Dr. Watson: Sherlock, how do we get to know what customers want?
Sherlock Holmes: Well the old school would say – ask them. The traditional way has been to leverage market research campaigns to interview relevant candidates and categorize their responses.

In a typical campaign, you interview existing or potential customers, trying to understand their preferences, likes and dislikes to landscape potential innovation areas for the next product cycle. Towards the end of the survey, you go about identifying the behavioral type of the interviewee so as to slot their responses into relevant customer segments. A good example is Conjoint Analysis where the questions are designed to evoke customer responses to potential product profiles and feature trade-offs.

These campaigns also involve “tracking studies” that comprise of periodically conducted surveys. These are typically shorter and are good for maintenance of the preference models created earlier, just like you would service your car.

Dr. Watson: So, what’s the catch?
Sherlock Holmes: Nothing, it’s awesome but don’t you want to be more awesome. It’s got a few glaring limitations that have intensified in today’s fast paced world:

Dependence on the sample quality: The preference scores thus generated from survey responses inherently contain the noise caused by low sample quality
Lack of volume: In today’s world where even an abacus would generate gigabytes of data, a marketing research sample can rarely be large enough to be conclusive on all the questions you have
Expensive: Scaling up the sample size of the research effort is expensive with costs increasing linearly for every additional response

Dr. Watson: So is this “old school” a passé then?
Sherlock Holmes: Not at all. I am apparently old school too, remember? Well, this technique has existed eternally for a reason and iterations over time have only improved it statistically and economically. Maybe there is a way to best use its most powerful offering, which is, getting exact responses for limited but targeted questions. Hold on to that thought and we will come back to it.

Dr. Watson: OK. So what’s next?
Sherlock Holmes: The new age my friend – the art of listening to customers. The mention of “Big Data” instantly strikes up an image of bits and bytes zooming around at supersonic speeds to the dreamier folks. To the realists, it comprises of high volumes of data, being tapped at even higher frequencies and from a wide variety of sources. Whichever side of the personality trait you might be, one bitter truth that does exist still with big data is that the more you think you got it under control, the closer you inch towards a data fusion bomb, ready to explode. Only few analytical initiatives have been able to successfully harness its power so far, and Social Media Analytics is among them. By the way, this now is the cue to link back to the thought you so patiently held on to a couple of minutes back. So how about making the best of both worlds collaborate with each other.

Dr. Watson: But how do we do that?
Sherlock Holmes: Convergence! Traditional market research helps you categorize customers into meaningful segments and consequently provide the precious dimensions that one needs to know to understand the DNA of a customer segment. And this is where you leverage social media analytics to scale up massively! Calibrate these dimensions to build models on big data that categorize the voice of current and potential customers, based on what they have been expressing online across various social channels. Tap into what they have been tweeting, posting on Facebook, expressing on various blogs or through product reviews on different e-tailer websites. The mission is convergence, of knowing what each customer persona has been expressing online, everywhere. Scanning for everything that a customer segment writes about, starts yielding unimaginable level of granular insights. You get an ocean of knowledge on offer because you are not restricted by the limited number of questions in a conventional market research anymore. Even the “tracking studies” conducted for model maintenance can become less frequent now since you are already validating on the fly.

To sum up, use a short but crisp Market Research to discover and categorize, and Social Media Analytics to amplify.

Dr. Watson: Excellent!
Sherlock Holmes: Elementary, my dear Watson.

Insights

Chief Analytics Officer: Unbounded

You are a new Chief Analytics Officer and you have the mandate to create business impact using advanced analytics and create an analytics driven culture. You are then faced with the realities of budgets, timelines, competing IT priorities and the lack of resources to get things done fast. What do you do next?

In 2013, Anthony Volpe, joined Lenovo as the Corporate Chief Analytics Officer with a similar mandate. He said “I had the challenge of delivering measurable results within the first 12 months, I could not afford to spend 12 months getting my infrastructure and team figured out, we needed to move fast, and needed an approach that will allow us make some quick decisions and re-visit them as we gathered more information. However, we were not looking for a quick fix or a PoC environment, we needed something that could scale to our global needs if the pilots succeeded. After evaluating various alternatives, we concluded that CoreCompete’s A3 service really offered us the best of option.”

Leveraging Amazon Web Services (AWS), CoreCompete created an elastic analytical environment. CoreCompete is unique in the way that it’s team has skills not just in AWS but also in enterprise analytical tools like SAS, SPSS, Tableau and Hadoop and combines it with their experience in helping enterprises capture financial value from deploying predictive analytics.

In 30 days, CoreCompete delivered an infrastructure and team that allowed Lenovo to confidently execute on a range of initiatives. Anthony says “In my first six months, we were able to pilot initiatives on a big scale that involved understanding quality using social data, optimizing inventory using internal supply chain data, and doing what-if planning on our sourcing networks. We could not achieve this without the agility and flexibility we got through AWS and CoreCompete”.

Do you want to be a Chief Analytics Officer that is Unbounded?

Insights

Using Cloud for Analytics Driven Innovation

Kumar Majety
In an increasingly big data intensive environment, most organizations want to crack the code on analytics driven innovation. They’re eagerly trying to get big data projects off the ground, but often run into hurdles according to eWEEK Enterprise 2014 Big Data Outlook. The key challenges include supporting large data volumes and new types of data (53%) with their existing corporate IT infrastructures and budgets, complexity of software/data integration (54%) and making analytics easier for business users (53%).

So, what is required to deliver big value from analytics to your business? A well-defined use case is a good first step but not enough.  Setting high expectations for your data analytics team and asking them to quickly generate conclusions by working with the IT department seldom translates into success.  A clean sheet approach to the underlying technology infrastructure (including database, compute & storage) and enterprise analytics/visualization software tools combined with deployment agility and flexibility is essential for successful innovations as described by Martha Bennett of Forrester Research. However, these enterprise grade analytics platforms are often anything but agile and carry a hefty price tag of complexity and inflexibility.  This leads to Catch-22:  to get investment dollars, you need to show proof of value but to have an air-tight business case, you will need to pilot the initiative and see if the idea can really generate value.

How do Cloud Analytics fit into this equation? Cloud analytics provide an ideal solution to get your big data initiatives up and running quickly and inexpensively.  These solutions offer high scalability, reliability, and flexibility to run any vendor’s analytics tools without lock-in or the headaches of managing hardware and software.  They allow you to only pay for what you need, and easily scale up or down.  “Software as a Service Offerings” from Analytics or Database software vendors are not ideal for innovation projects as they lack the flexibility to support your specific needs or tools of choice.  Cloud analytics solutions on the other hand are a better fit for innovation projects as they address end-to-end analytics lifecycle needs on a pay-as-you-go basis.  That means, specific advice on turning your data into business insight, hardware and software architecture designed for your analytics/data/business requirements, pre-built templates for agile deployment, end-to-end administration of analytics infrastructure & applications, data management and integration of different data sources in the cloud, and enterprise security and compliance.  A great way to start would be to kick tires with a partner that offers try-n-buy of the complete cloud analytics platform and service at no cost to you.

Lenovo is running Analytics Innovation Studio on Cloud Analytics.  A global manufacturer with more than $30+ billion in revenues is using SAS’s advanced analytics, text mining and visual analytics tools deployed on CoreCompete’s Agile Analytics Platform.  In 2 months, CoreCompete delivered the cloud analytics solution (on AWS) that enabled the client to pilot several big data solutions that integrate internal and external data in a cloud environment and prove innovations in: Quality Analytics, Channel Analytics, Sourcing Analytics, and Social Media Analytics.  In addition, Lenovo has also leveraged the analytics innovation platform to successfully scale these for enterprise wide usage.

We hope that these examples gave you some ideas on advancing big data innovation projects in your organization.  E-mail us to see how we might be able to help you get started.

Analytics Data Science Insights

Generating Operational Insights from Social Media Analytics

Shiva Kommareddi
Willie Sutton, the famous bank robber, when asked “Why did he rob banks?” answered “That’s where the money is”. Business Executives who want to learn more about their customers are forced to listen closely to what their customers are saying in Social Channels, because that’s where they are sharing their unbiased feedback on their product and service experiences.

In most organizations, Social Media Engagement is confused with Social Media Analytics. It is worth distinguishing the two to truly benefit from the power of these ideas. Social Media Engagement is active participation in social communities to shape the dialogue and to utilize it as a customer service channel. Social Media Analytics, on the other hand is about analyzing data available through social channels (or created utilizing social channels) to improve business (including marketing but not limited to it).

The first generation of Social Media Analytics initiatives were squarely focused on understanding the trends in customer advocacy, i.e., creating variants of the Net Promoter Score(NPS) using Social data instead of survey data. Back in 2009, Dell started to tout the benefits of this approach. Our own conversations with current and former Dell employees suggest that while the promise was there, the idea of running the organization using NPS itself was new, it was not practical for them to muddy the measurement with other metrics that may distract from the real goal (which is improved advocacy, not innovations in measurement).

However, since then, Social Media Analytics have moved beyond being a poor augmenter of NPS into areas where it can truly, uniquely solve with the richness and timeliness that other data sources do not provide. We have provided a few examples to spur your own answer to the question “How can we apply analysis of Social Data to improve our own business operations?”

Lenovo asked How Can We Improve Product Quality? A global manufacturer with more than $30 billion in revenues, early detection of quality problems can save millions of dollars annually for Lenovo. Their tech-savvy customers are more likely to report an issue first in an online forum before they reach out to Lenovo’s official support organization. Lenovo decided to aggregate the feedback across these global channels to create consolidated views of the emerging quality problems. These highly operational dashboards enable internal teams to manage identify issues in a timely manner, and significantly reduce quality concerns and warranty costs. Lenovo leveraged SAS’s powerful text mining and visual analytics tools and deployed them on CoreCompete’s Agile Analytics Platform.

McDonald’s asked How Can We Improve Customer Experience? It might not be legal to text while driving, but it may be OK to post on Twitter while going through a drive-thru. McDonalds tracks vast amounts of data from social media in order to improve operations and boost customer experience. The company uses these insights to design its drive-thru outlets, list items on menus, plan order sizes and ordering patterns to customize their offerings to perfectly match the expectations of different micro markets.

Toyota asked How Do We Improve Our Sales Forecasts? CIO Magazine reports a number of interesting use-cases on how Toyota is thinking about social media analytics for operational improvements. One of the interesting examples they talk about is of improving sales forecasts based on trends in sentiment. They can further analyze these sentiments to see what type of demographic characteristics are leading to the improved/ decreasing sentiments.

We hope that these examples made you think of your own ideas that are relevant to your organization. It does not have to be hard or expensive to get started and add value to your organization. E-Mail us to see how we might be able to help you get started.

Insights

Big Data Analytics Paves Way for a New Era in Healthcare

Medical professionals, hospitals and related healthcare organizations are facing challenges to reduce costs, provide coordinated patient care, standardize healthcare quality and deliver effective patient outcomes. Standard medical practice is moving from relatively ad-hoc and subjective decision making to evidence-based healthcare. A McKinsey study identifies a set of converging trends in the healthcare industry to a tipping point where Big Data and Analytics will play a major role. The trends are:

Demand for better data owing to cost-reduction pressures
Availability of relevant data at scale that includes
Clinical data in the form of EMRs and information exchanges
Non-healthcare consumer data
Technical capability
Government enabling and catalyzing market change

As US healthcare providers have dramatically increased the usage of Electronic Health Record system, according to data shared by US Department of Health and Human Services, “More than half of eligible professionals and 80 percent of eligible hospitals have adopted these systems, which are critical to modernizing our health care system.” This digitization drive is leading to generation of large volumes of data. Additional sources adding on to healthcare data include:

Development of new technologies such as capturing devices, sensors, and mobile applications
Extraction of genomic information has become much cheaper and quicker
People have become very active on social media channels
Interactions with various healthcare organizations through digital forms are increasing
Enormous amounts of medical knowledge/discoveries are being accumulated

In 2012, worldwide digital healthcare data was estimated to be equal to 500 petabytes and is expected to reach 25,000 petabytes in 2020.

Building analytics competency can help healthcare organizations harness “big data” to create actionable insights, set their future vision, improve outcomes and reduce time to value.  Leading healthcare organizations use analytics to differentiate, see the future and drive revenue growth. In a series of interviews with healthcare executives conducted by IBM Institute for Business Value, health care professionals feel that three business objectives can be addressed by Analytics in the Healthcare industry:

Improve clinical effectiveness and patient satisfaction
Improve operational effectiveness
Improve financial and administrative performance

How are Health Care Organizations Realizing these Benefits?

Improved Quality of Patient Care: As Healthcare Quality is one of the primary concerns, Analytics can assist in maintaining high quality of patient care by analyzing health outcomes data for different services to pinpoint lags in providing effective treatments consistently across a patient population or geographic area.

GE Healthcare leverages SAS technology to analyze patient health datasets and look for specific patterns and trends that help hospitals prevent adverse medical events. The SAS software mines patient-related data and provides critical insights, best practices, and benchmarking to enable clinicians to make informed decisions aimed at reducing medical errors and improving the quality of care. GE’s Patient Safety Organization provides its members a single common medical event-reporting platform, with comprehensive data analytics and advisory support to identify the root causes of risk, and help hospitals make lasting safety improvements.

Baptist Health’s CHF reduction initiative is a successful example of combining data analysis with new approaches to care delivery to improve quality and reduce costs. Data was merged from multiple sources across Baptist Health to present a full picture of the causes of CHF (Congestive Heart Failure) re-admissions. This data was analyzed to identify at-risk patients, determine resource utilization rates, and assess progress on a set of quality benchmarks. Dashboards offered providers the tools needed to use this data at the point of care, and education affirmed new roles and responsibilities. As a result Baptist Health was able to achieve its goals and reduce re-admissions for CHF patients at relatively low costs.

Improved Revenue Cycle Management: Revenue cycle management (RCM) refers to the process of managing claims, payments, and revenue generation and relies heavily on a combination of claims data, clinical data, and analytics technology. Analytic tools can help healthcare organizations determine patient eligibility, validate coverage, authorize services, assess payment risk, manage submissions, and track performance.

In a recent SAS podcast, Graham Hughes, the chief medical officer of analytics provider SAS, talks about the difference analytics can make in examining the cost and quality of health care in the United States. Hughes discusses a new analytics program SAS has launched to collect and compare health care payer claims data. By collecting this information in a data warehouse, the software allows users, including state agencies, health care providers, researchers, and eventually consumers, to analyze and compare health care cost data within a county or state. This solution can go a long way in eradicating opaque medical bills and establish transparent pricing policy in healthcare.

Better Resource Utilization: Analytics can build effective processes which result in removing system bottlenecks and reducing wastage. By appropriately estimating patient volumes, length of stay, and/or waiting times, inventory control systems and supply chain management processes can be effectively redesigned. Real-time data interpretation helps tremendously in developing efficient and optimized workflows.

Fraud and Abuse Prevention: Fraud refers to a calculated misrepresentation of facts aimed at convincing payors to process a false claim for financial gain while Abuse refers to neglect of accepted business or medical practices resulting in higher reimbursements. Cost trending and forecasting, care utilization analysis, and actuarial and financial analysis are commonly used analytic applications for preventing such cases.

Population Health Management: Healthcare providers carry the responsibility to educate people, spread awareness about lifestyle changes leading to disease prevention as well as its treatment. Their timely action in reaching people before, during, and after they need specific medical attention can save many lives. Analytics plays a key role here by assisting healthcare organizations in recognizing populations consuming the most resources or at greatest risk for hospital readmissions, enabling them to target high-risk groups to reduce costs and improve outcomes. Real-time data insights can help identify trends in disease prevalence, compare the effectiveness of different treatment options, and derive best practices.

The Louisiana Department of Health and Hospitals, for example, recently teamed up with geographic information systems (GIS) software vendor ESRI to map epidemiological issues, such as babies with low birth weights. Using ESRI’s GIS mapping software, the LDHH plugged in 354 points of data for every live birth in Louisiana. Sophisticated algorithms identified clusters among locations and then generated a map based on this data. For example, by crunching low birth weight records with geospatial data, the LDHH discovered correlations between low birth weight rates and crime-riddled neighborhoods. By flagging neighborhoods with low birth weights, preventative healthcare measures can reduce the number of high-risk births, thereby cutting healthcare costs.

According to the CDC, 26 million Americans currently have asthma that costs $3,300 per person annually in treatment costs. Asthmapolis, one of a new generation of digital health startups, has designed snap-on, Bluetooth-enabled sensors that track how often people are using their inhalers (along with location and time-of-day), along with analytics and mobile apps to help them visualize and understand their triggers and trends while receiving personalized feedback. In turn, the data collected by the solution enables doctors to identify patients who are at risk or need more help controlling its symptoms. This allows them to potentially prevent attacks before they happen, saving them the cost of hospitalization or a trip to the emergency room. In fact, Asthmapolis’ early studies found that this access to real-time data was able to reduce the number of people with uncontrolled asthma (or those not regularly using inhalers) by 50 percent.

Despite the fact that there is a lot of data that the hospitals already have (through their EHR systems), there are important pieces of data that the hospital system does not have. e.g., they do not know if the patient is a smoker or not (unless the patient fills that in their form), the size of their households, their consumption of various luxury, health and vice-goods etc. HealthVue, a Raleigh, NC based start-up is bringing together data from consumer sources, geo-spatial data, CMS data and combine it with EHR data to deliver a comprehensive and timely view of the populations.

Big Data initiatives can transform the healthcare industry to make it more coordinated and streamlined and be readily available at the right time, saving millions of lives and making high-quality patient care the new norm.

Analytics Forecasting Healthcare Insights
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