Category Archives: Master Data Management

Analytics Stories: A case study from UPMC

UPMC

As I have shared within the posts of this series, businesses are using analytics to improve their internal and external facing business processes and to strengthen their “right to win” within the markets that they operate. Like healthcare institutions across the country, UPMC is striving to improve its quality of care and business profitability. One educational healthcare CEO put it to me this way–“if we can improve our quality of service, we can reduce costs while we increase our pricing power”. In UPMC’s case, they believe that the vast majority of their costs are in a fraction of their patients, but they want to prove this with real data and then use this information drive their go forward business strategies.

Getting more predictive to improved outcomes and reduce cost

AnalyticsArmed with this knowledge, UPMC’s leadership wanted to use advanced analytic and predictive modeling to improve clinical and financial decision making. And taking this action was seen as producing better patient outcomes and reducing costs. A focus area for analysis involved creating “longitudinal records” for the complete cost of providing particular types of care. For those that aren’t versed in time series analysis, longitudinal analysis uses a series of observations obtained from many respondents over time to derive a relevant business insight. When I was also involved in healthcare, I used this type of analysis to interrelate employee and patient engagement results versus healthcare outcomes. In UPMC’s case, they wanted to use this type of analysis to understand for example the total end to end cost of a spinal surgery. UPMC wanted to look beyond the cost of surgery and account for the pre-surgery care and recovery-related costs. However, to do this for the entire hospital meant that it needed to bring together data from hundreds of sources across UPMC and outside entities, including labs and pharmacies. However, by having this information, UPMC’s leadership saw the potential to create an accurate and comprehensive view which could be used to benchmark future procedures. Additionally, UPMC saw the potential to automate the creation of patient problem lists or examine clinical practice variations. But like the other case studies that we have reviewed, these steps required trustworthy and authoritative data to be accessed with agility and ease.

UPMC’s starts with a large, multiyear investment

In October 2012, UPMC made a $100 million investment to establish an enterprise analytics initiative to bring together for the first time, clinical, financial, administrative, genomic and other information together in one place. Tom Davenport, the author of Competing on Analytics, suggests in his writing that establishing an enterprise analytics capability represents a major step forward because it allows enterprises to answer the big questions, to better tie strategy and analytics, and to finally rationalize applications interconnect and business intelligence spending. As UPMC put its plan together, it realized that it needed to impact more than 1200 applications. As well it realized that it needed one system manage with data integration, master data management, and eventually complex event processing capabilities. At the same time, it created the people side of things by creating a governance team to manage data integrity improvements, ensuring that trusted data populates enterprise analytics and provides transparency into data integrity challenges. One of UPMC’s goals was to provide self-service capabilities. According to Terri Mikol, a project leader, “We can’t have people coming to IT for every information request. We’re never going to cure cancer that way.” Here is an example of the promise that occurred within the first eight months of this project. Researchers were able to integrate—for the first time ever– clinical and genomic information on 140 patients previously treated for breast cancer. Traditionally, these data have resided in separate information systems, making it difficult—if not impossible—to integrate and analyze dozens of variables. The researchers found intriguing molecular differences in the makeup of pre-menopausal vs. post-menopausal breast cancer, findings which will be further explored. For UPMC, this initial cancer insight is just the starting point of their efforts to mine massive amounts of data in the pursuit of smarter medicines.

Building the UPMC Enterprise Analytics Capability

To create their enterprise analytics platform, UPMC determined it was critical to establish “a single, unified platform for data integration, data governance, and master data management,” according to Terri Mikol. The solution required a number of key building blocks. The first was data integration to collect and cleanses data from hundreds of sources and organizes them into repositories that would enable fast, easy analysis and reporting by and for end users.

Specifically, the UPMC enterprise analytics capability pulls clinical and operational data from a broad range of sources, including systems for managing hospital admissions, emergency room operations, patient claims, health plans, electronic health records, as well as external databases that hold registries of genomic and epidemiological data needed for crafting personalized and translational medicine therapies. UPMC has integrated quality checked source data in accordance with industry-standard healthcare information models. This effort included putting together capabilities around data integration, data quality and master data management to manage transformations and enforce consistent definitions of patients, providers, facilities and medical terminology.

As said, the cleansed and harmonized data is organized into specialized genomics databases, multidimensional warehouses, and data marts. The approach makes use of traditional data warehousing approaches as well as big data capabilities to handle unstructured data and natural language processing. UPMC has also deployed analytical tools that allow end users to exploit the data enabled from the Enterprise Analytics platform. The tools drive everything from predictive analytics, cohort tracking, and business and compliance reporting. And UPMC did not stop here. If their data had value then it needed to be secured. UPMC created data audits and data governance practices. As well, they implemented a dynamic data masking solution ensures data security and privacy.

Parting Remarks

As I have discussed, many firms are pushing point silo solutions into their environments, but as UPMC shows this limits their ability to ask the bigger business questions or in UPMC’s case to discover things that can change people’s live. Analytics are more and more a business enabler if they are organized as an enterprise analytics capability. As well, I have come to believe that analytics have become foundational capability to all firms’ right to win. It informs a coherent set of capabilities and establishes a firm’s go forward right to win. For this, UPMC is a shining example of getting things right.

Related links

Detailed UPMC Case Study

Related Blogs

Analytics Stories: A Banking Case Study

Analytics Stories: A Financial Services Case Study

Analytics Stories: A Healthcare Case Study

Who Owns Enterprise Analytics and Data? Competing on Analytics: A Follow Up to

Thomas H. Davenport’s Post in HBR

Thomas Davenport Book “Competing On Analytics”

Author Twitter: @MylesSuer

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Building an Impactful Data Governance – One Step at a Time

Let’s face it, building a Data Governance program is no overnight task.  As one CDO puts it:  ”data governance is a marathon, not a sprint”.  Why? Because data governance is a complex business function that encompasses technology, people and process, all of which have to work together effectively to ensure the success of the initiative.  Because of the scope of the program, Data Governance often calls for participants from different business units within an organization, and it can be disruptive at first.

Why bother then?  Given that data governance is complex, disruptive, and could potentially introduce additional cost to a company?  Well, the drivers for data governance can vary for different organizations.  Let’s take a close look at some of the motivations behind data governance program.

For companies in heavily regulated industries, establishing a formal data governance program is a mandate.  When a company is not compliant, consequences can be severe. Penalties could include hefty fines, brand damage, loss in revenue, and even potential jail time for the person who is held accountable for being noncompliance. In order to meet the on-going regulatory requirements, adhere to data security policies and standards, companies need to rely on clean, connected and trusted data to enable transparency, auditability in their reporting to meet mandatory requirements and answer critical questions from auditors.  Without a dedicated data governance program in place, the compliance initiative could become an on-going nightmare for companies in the regulated industry.

A data governance program can also be established to support customer centricity initiative. To make effective cross-sells and ups-sells to your customers and grow your business,  you need clear visibility into customer purchasing behaviors across multiple shopping channels and touch points. Customer’s shopping behaviors and their attributes are captured by the data, therefore, to gain thorough understanding of your customers and boost your sales, a holistic Data Governance program is essential.

Other reasons for companies to start a data governance program include improving efficiency and reducing operational cost, supporting better analytics and driving more innovations. As long as it’s a business critical area and data is at the core of the process, and the business case is loud and sound, then there is a compelling reason for launching a data governance program.

Now that we have identified the drivers for data governance, how do we start?  This rather loaded question really gets into the details of the implementation. A few critical elements come to consideration including: identifying and establishing various task forces such as steering committee, data governance team and business sponsors; identifying roles and responsibilities for the stakeholders involved in the program; defining metrics for tracking the results.  And soon you will find that on top of everything, communications, communications and more communications is probably the most important tactic of all for driving the initial success of the program.

A rule of thumb?  Start small, take one-step at a time and focus on producing something tangible.

Sounds easy, right? Think this is easy?!Well, let’s hear what the real-world practitioners have to say. Join us at this Informatica webinar to hear Michael Wodzinski, Director of Information Architecture, Lisa Bemis, Director of Master Data, Fabian Torres, Director of Project Management from Houghton Mifflin Harcourt, global leader in publishing, as well as David Lyle, VP of product strategy from Informatica to discuss how to implement  a successful data governance practice that brings business impact to an enterprise organization.

If you are currently kicking the tires on setting up data governance practice in your organization,  I’d like to invite you to visit a member-only website dedicated to Data Governance:  http://governyourdata.com/. This site currently has over 1,000 members and is designed to foster open communications on everything data governance. There you will find conversations on best practices, methodologies, frame works, tools and metrics.  I would also encourage you to take a data governance maturity assessment to see where you currently stand on the data governance maturity curve, and compare the result against industry benchmark.  More than 200 members have taken the assessment to gain better understanding of their current data governance program,  so why not give it a shot?

Governyourdata.com

Governyourdata.com

Data Governance is a journey, likely a never-ending one.  We wish you best of the luck on this effort and a joyful ride! We love to hear your stories.

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Guiding Your Way to Master Data Management Nirvana

Achieving and maintaining a single, semantically consistent version of master data is crucial for every organization. As many companies are moving from an account or product-centric approach to a customer-centric model, master data management is becoming an important part of their enterprise data management strategy. MDM provides the clean, consistent and connected information your organizations need for you to –

  1. Empower customer facing teams to capitalize on cross-sell and up-sell opportunities
  2. Create trusted information to improve employee productivity
  3. Be agile with data management so you can make confident decisions in a fast changing business landscape
  4. Improve information governance and be compliant with regulations

Master Data ManagementBut there are challenges ahead for the organizations. As Andrew White of Gartner very aptly wrote in a blog post, we are only half pregnant with Master Data Management. Andrew in his blog post talked about increasing number of inquiries he gets from organizations that are making some pretty simple mistakes in their approach to MDM without realizing the impact of those decisions on a long run.

Over last 10 years, I have seen many organizations struggle to implement MDM in a right way. Few MDM implementations have failed and many have taken more time and incurred cost before showing value.

So, what is the secret sauce?

A key factor for a successful MDM implementation lays in mapping your business objectives to features and functionalities offered by the product you are selecting. It is a phase where you ask right questions and get them answered. There are few great ways in which organizations can get this done and talking to analysts is one of them. The other option is to attend MDM focused events that allow you to talk to experts, learn from other customer’s experience and hear about best practices.

We at Informatica have been working hard to deliver you a flexible MDM platform that provides complete capabilities out of the box. But MDM journey is more than just technology and product features as we have learnt over the years. To ensure our customer success, we are sharing knowledge and best practices we have gained with hundreds of successful MDM and PIM implementations. The Informatica MDM Day, is a great opportunity for organizations where we will –

  • Share best practices and demonstrate our latest features and functionality
  • Show our product capabilities which will address your current and future master data challenges
  • Provide you opportunity to learn from other customer’s MDM and PIM journeys.
  • Share knowledge about MDM powered applications that can help you realize early benefits
  • Share our product roadmap and our vision
  • Provide you an opportunity to network with other like-minded MDM, PIM experts and practitioners

So, join us by registering today for our MDM Day event in New York on 24th February. We are excited to see you all there and walk with you towards MDM Nirvana.

~Prash
@MDMGeek
www.mdmgeek.com

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Asia-Pacific Ecommerce surpassed Europe and North America

With a total B2C e-commerce turnover of $567.3bn in 2013, Asia-Pacific was the strongest e-commerce region in the world in 2013, as it surpassed Europe ($482.3bn) and North America ($452.4bn). Online sales in Asia-Pacific expected to have reached $799.2 billion in 2014, due to latest report from the Ecommerce Foundation.

Revenue: China, followed by Japan and Australia
As a matter of fact, China was the second-largest e-commerce market in the world, only behind the US ($419.0 billion), and for 2014 it is estimated that China even surpassed the US ($537.0 billion vs. $456.0 billion). In terms of B2C e-commerce turnover, Japan ($136.7 billion) ranked second, followed by Australia ($35.7 billion), South Korea ($20.2 billion) and India ($10.7 billion).

On average, Asian-Pacific e-shoppers spent $1,268 online in 2013
Ecommerce Europe’s research reveals that 235.7 million consumers in Asia-Pacific purchased goods and services online in 2013. On average, APAC online consumers each spent $1,268 online in 2013. This is slightly lower than the global average of $1,304. At $2,167, Australian e-shopper were the biggest spenders online, followed by the Japanese ($1,808) and China ($1,087).

Mobile: Japan and Australia lead the pack
In the frequency of mobile purchasing Japan shows the highest adoption, followed by Japan. An interesting fact is that 50% of transactions are done at home, 20% at work and 10% on the go.

frequency mobile shopping APAC

You can download the full report here. What does this mean for your business opportunity? Read more on the omnichannel trends 2015, which are driving customer experience. Happy to discuss @benrund.

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Posted in CMO, Customer Acquisition & Retention, DaaS, Data Quality, Manufacturing, Master Data Management, PiM, Product Information Management, Retail | Tagged , , , , | Leave a comment

A True Love Quiz: Is Your Marketing Data Right For You?

questionnaire and computer mouseValentine’s Day is such a strange holiday.  It always seems to bring up more questions than answers.  And the internet always seems to have a quiz to find out the answer!  There’s the “Does he have a crush on you too – 10 simple ways to find out” quiz.  There’s the “What special gift should I get her this Valentine’s Day?” quiz.  And the ever popular “Why am I still single on Valentine’s Day?” quiz.

Well Marketers, it’s your lucky Valentine’s Day!  We have a quiz for you too!  It’s about your relationship with data.  Where do you stand?  Are you ready to take the next step?


Question 1:  Do you connect – I mean, really connect – with your data?
Connect My Data□ (A) Not really.  We just can’t seem to get it together and really connect.
□ (B) Sometimes.  We connect on some levels, but there are big gaps.
□ (C) Most of the time.  We usually connect, but we miss out on some things.
□ (D) We are a perfect match!  We connect about everything, no matter where, no matter when.

Translation:  Data ready marketers have access to the best possible data, no matter what form it is in, no matter what system it is in.  They are able to make decisions based everything the entire organization “knows” about their customer/partner/product – with a complete 360 degree view. And they are also able to connect to and integrate with data outside the bounds of their organization to achieve the sought-after 720 degree view.  They can integrate and react to social media comments, trends, and feedback – in real time – and to match it with an existing record whenever possible. And they can quickly and easily bring together any third party data sources they may need.


Question 2:  How good looking & clean is you data?
My Data is So Good Looking□ (A) Yikes, not very. But it’s what’s on the inside that counts right?
□ (B) It’s ok.  We’ve both let ourselves go a bit.
□ (C) It’s pretty cute.  Not supermodel hot, but definitely girl or boy next door cute.
□ (D) My data is HOT!  It’s perfect in every way!

Translation: Marketers need data that is reliable and clean. According to a recent Experian study, American companies believe that 25% of their data is inaccurate, the rest of the world isn’t much more confident. 90% of respondents said they suffer from common data errors, and 78% have problems with the quality of the data they gather from disparate channels.  Making marketing decisions based upon data that is inaccurate leads to poor decisions.  And what’s worse, many marketers have no idea how good or bad their data is, so they have no idea what impact it is having on their marketing programs and analysis.  The data ready marketer understands this and has a top tier data quality solution in place to make sure their data is in the best shape possible.


Question 3:  Do you feel safe when you’re with your data?
I Heart Safe Data□ (A) No, my data is pretty scary.  911 is on speed dial.
□ (B) I’m not sure actually. I think so?
□ (C) My date is mostly safe, but it’s got a little “bad boy” or “bad girl” streak.
□ (D) I protect my data, and it protects me back.  We keep each other safe and secure.

Translation: Marketers need to be able to trust the quality of their data, but they also need to trust the security of their data.  Is it protected or is it susceptible to theft and nefarious attacks like the ones that have been all over the news lately?  Nothing keeps a CMO and their PR team up at night like worrying they are going to be the next brand on the cover of a magazine for losing millions of personal customer records. But beyond a high profile data breach, marketers need to be concerned over data privacy.  Are you treating customer data in the way that is expected and demanded?  Are you using protected data in your marketing practices that you really shouldn’t be?  Are you marketing to people on excluded lists


Question 4:  Is your data adventurous and well-traveled, or is it more of a “home-body”?
Home is where my data is□ (A) My data is all over the place and it’s impossible to find.
□ (B) My data is all in one place.  I know we’re missing out on fun and exciting options, but it’s just easier this way.
□ (C) My data is in a few places and I keep fairly good tabs on it. We can find each other when we need to, but it takes some effort.
□ (D) My data is everywhere, but I have complete faith that I can get ahold of any source I might need, when and where I need it.

Translation: Marketing data is everywhere. Your marketing data warehouse, your CRM system, your marketing automation system.  It’s throughout your organization in finance, customer support, and sale systems. It’s in third party systems like social media and data aggregators. That means it’s in the cloud, it’s on premise, and everywhere in between.  Marketers need to be able to get to and integrate data no matter where it “lives”.


Question 5:  Does your data take forever to get ready when it’s time to go do so something together?
My data is ready on time□ (A) It takes forever to prepare my data for each new outing.  It’s definitely not “ready to go”.
□ (B) My data takes it’s time to get ready, but it’s worth the wait… usually!
□ (C) My data is fairly quick to get ready, but it does take a little time and effort.
□ (D) My data is always ready to go, whenever we need to go somewhere or do something.

Translation:  One of the reasons many marketers end up in marketing is because it is fast paced and every day is different. Nothing is the same from day-to-day, so you need to be ready to act at a moment’s notice, and change course on a dime.  Data ready marketers have a foundation of great data that they can point at any given problem, at any given time, without a lot of work to prepare it.  If it is taking you weeks or even days to pull data together to analyze something new or test out a new hunch, it’s too late – your competitors have already done it!


Question 6:  Can you believe the stories your data is telling you?
My data tells the truth□ (A) My data is wrong a lot.  It stretches the truth a lot, and I cannot rely on it.
□ (B) I really don’t know.  I question these stories – dare I say excused – but haven’t been able to prove it one way or the other.
□ (C) I believe what my data says most of the time. It rarely lets me down.
□ (D) My data is very trustworthy.  I believe it implicitly because we’ve earned each other’s trust.

Translation:  If your data is dirty, inaccurate, and/or incomplete, it is essentially “lying” to you. And if you cannot get to all of the data sources you need, your data is telling you “white lies”!  All of the work you’re putting into analysis and optimization is based on questionable data, and is giving you questionable results.  Data ready marketers understand this and ensure their data is clean, safe, and connected at all times.


Question 7:  Does your data help you around the house with your daily chores?
My data helps me out□ (A) My data just sits around on the couch watching TV.
□ (B) When I nag my data will help out occasionally.
□ (C) My data is pretty good about helping out. It doesn’t take imitative, but it helps out whenever I ask.
□ (D) My data is amazing.  It helps out whenever it can, however it can, even without being asked.

Translation:  Your marketing data can do so much. It should enable you be “customer ready” – helping you to understand everything there is to know about your customers so you can design amazing personalized campaigns that speak directly to them.  It should enable you to be “decision ready” – powering your analytics capabilities with great data so you can make great decisions and optimize your processes.  But it should also enable you to be “showcase ready” – giving you the proof points to demonstrate marketing’s actual impact on the bottom line.


Now for the fun part… It’s time to rate your  data relationship status
If you answered mostly (A):  You have a rocky relationship with your data.  You may need some data counseling!

If you answered mostly (B):  It’s time to decide if you want this data relationship to work.  There’s hope, but you’ve got some work to do.

If you answered mostly (C):  You and your data are at the beginning of a beautiful love affair.  Keep working at it because you’re getting close!

If you answered mostly (D): Congratulations, you have a strong data marriage that is based on clean, safe, and connected data.  You are making great business decisions because you are a data ready marketer!


Do You Love Your Data?
Learn to love your dataNo matter what your data relationship status, we’d love to hear from you.  Please take our survey about your use of data and technology.  The results are coming out soon so don’t miss your chance to be a part.  https://www.surveymonkey.com/s/DataMktg

Also, follow me on twitter – The Data Ready Marketer – for some of the latest & greatest news and insights on the world of data ready marketing.  And stay tuned because we have several new Data Ready Marketing pieces coming out soon – InfoGraphics, eBooks, SlideShares, and more!

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Posted in 5 Sales Plays, Business Impact / Benefits, CMO, Customers, Data First, Data Integration, Data masking, Data Privacy, Data Quality, Data Security, Intelligent Data Platform, Master Data Management, Operational Efficiency, Total Customer Relationship | Tagged , , , , , , , , | Leave a comment

Healthcare’s Love Hate Relationship with Data

Healthcare's Love Hate Relationship with Data

Healthcare’s Love Hate Relationship with Data

Healthcare and data have the makings of an epic love affair, but like most relationships, it’s not all roses. Data is playing a powerful role in finding a cure for cancer, informing cost reduction, targeting preventative treatments and engaging healthcare consumers in their own care. The downside? Data is needy. It requires investments in connectedness, cleanliness and safety to maximize its potential.

  • Data is ubiquitous…connect it.

4400 times the amount of information held at the Library of Congress – that’s how much data Kaiser Permanente alone has generated from its electronic medical record. Kaiser successfully makes every piece of information about each patient available to clinicians, including patient health history, diagnosis by other providers, lab results and prescriptions. As a result, Kaiser has seen marked improvements in outcomes: 26% reduction in office visits per member and a 57% reduction in medication errors.

Ongoing value, however, requires continuous investment in data. Investments in data integration and data quality ensure that information from the EMR is integrated with other sources (think claims, social, billing, supply chain) so that clinicians and decision makers have access in the format they need. Without this, self-service intelligence can be inhibited by duplicate data, poor quality data or application silos.

  • Data is popular…ensure it is clean.

Healthcare leaders can finally rely on electronic data to make strategic decisions. A CHRISTUS Health anecdote you might relate to – In a weekly meeting each executive reviews their strategic dashboard; these dashboards drive strategic decision making about CPOE adoption (computerized physician order entry), emergency room wait times and price per procedure. Powered by enterprise information management, these dashboards paint a reliable and consistent view across the system’s 60 hospitals. Previous to the implementation of an enterprise data platform, each executive was reliant on their own set of data.

In the pre-data investment era, seemingly common data elements from different sources did not mean the same thing. For example, “Admit Date” in one report reflected the emergency department admission date whereas “Admit Date” in another report referred to the inpatient admission date.

  •  Sharing data is necessary…make it safe.

To cure cancer, reduce costs and engage patients, care providers need access to data and not just the data they generate; it has to be shared for coordination of care through transitions of care and across settings, i.e. home care, long term care and behavioral health. Fortunately, Consumers and Clinicians agree on this, PWC reports that 56% of consumers and 30% of physicians are comfortable with data sharing for care coordination. Further progress is demonstrated by healthcare organizations willingly adopting cloud based applications –as of 2013, 40% of healthcare organizations were already storing protected health information (PHI) in the cloud.

Increased data access carries risk, leaving health data exposed, however. The threat of data breach or hacking is multiplied by the presence (in many cases necessary) of PHI on employee laptops and the fact that providers are provided increased access to PHI. Ponemon Institute, a security firm estimates that data breaches cost the industry $5.6 billion each year. Investments in data-centric security are necessary to assuage fear, protect personal health data and make secure data sharing a reality.

Early improvements in patient outcomes indicate that the relationship between data and healthcare is a valuable investment. The International Institute of Analytics supports this, reporting that although analytics and data maturity across healthcare lags other industries, the opportunity to positively impact clinical and operational outcomes is significant.

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Posted in Big Data, Data Integration, Data Quality, Healthcare, Master Data Management, Mergers and Acquisitions, Operational Efficiency | Tagged , | Leave a comment

All You Need is Love! (Well, That and Some Software)

Picture1That’s right, Valentine’s Day is upon us, the day that symbolizes the power of love and has the ability to strengthen relationships between people. I’ve personally experienced 53 Valentine’s Days so I believe I speak with no small measure of authority on the topic of how to make the best of it. Here are my top five suggestions for having a great day:

  1. Know everything you can about the people you have relationships with
  2. Quality matters
  3. ALL your relationships matter
  4. Uncover your hidden or anonymous relationships
  5. Treat your relationships with respect all year long

OK, I admit, this is not the most romantic list ever and might get you in more trouble with your significant other than actually forgetting Valentine’s Day altogether! But, what did you expect? I work for a software company, not eHarmony! :-)

Right. Software. Let’s put this list into the context of government agencies.

  1. Know everything – If your agency’s mission involves delivering services to citizens, likely, you have multiple “systems of record”, each with a supposed accurate record of all the people being tracked by each system. In reality though, it’s rare that the data about individuals is consistently accurate and complete from system to system. The ability to centralize all the data about individuals into a single, authoritative “record” is key to improving service delivery. Such a record will enable you to ensure the citizens you serve are able to take full advantage of all the services available to them. Further, having a single record for each citizen has the added benefit of reducing fraud, waste and abuse.
  2. Quality matters – Few things hinder the delivery of services more than bad data, data with errors, inconsistencies and gaps in completeness. It is difficult, at best, to make sound business decisions with bad data. At the individual level and at the macro level, agency decision makers need complete and accurate data to ensure each citizen is fully served.
  3. All relationships matter – In this context, going beyond having single records to represent people, it’s also important to have single, authoritative views of other entities – programs, services, providers, deliverables, places, etc.
  4. Uncover hidden relationships – Too often, in the complex eco-system of government programs and services, the inability to easily recognize relationships between people and the additional entities mentioned above creates inefficiencies in the “system”. For example, it can go unnoticed that a single parent is not enrolled in a special program designed for their unique life circumstances. Flipping the coin, not having a full view of hidden relationships also opens the door for the less scrupulous in society, giving them the ability to hide their fraudulent activities in plain sight.
  5. Treat relationships respectfully all year – Data hygiene is not a one-time endeavor. Having the right mindset, processes and tools to implement and automate the process of “mastering” data as an on-going process will better ensure the relationship between your agency and those it serves will remain positive and productive.

I may not win the “Cupid of the Year” award, but, I hope my light-hearted Valentine’s Day message has given you a thing or two to think about. Maybe Lennon and McCartney are right, between people, “Love is All You Need”. But, we at Informatica believe for Government-Citizen relationships, a little of the right software can go a long way.

Care to learn more?

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Healthcare Consumer Engagement

The signs that healthcare is becoming a more consumer (think patients, members, Healthcareproviders)  driven industry are evident all around us. I see provider and payer organizations clamoring for more data, specifically data that is actionable, relatable and has integrity. Armed with this data, healthcare organizations are able to differentiate around a member/patient-centric view.

These consumer-centric views convey the total value of the relationships healthcare organizations have with consumers. Understanding the total value creates a more comprehensive understanding of consumers because they deliver a complete picture of an individual’s critical relationships including: patient to primary care provider, member to household, provider to network and even members to legacy plans. This is the type of knowledge that informs new treatments, targets preventative care programs and improves outcomes.

Payer organizations are collecting and analyzing data to identify opportunities for  more informed care management and segmentation to reach new, high value customers in individual markets. By segmenting and targeting messaging to specific populations, health plans generate increased member satisfaction and cost effectively expands and manages provider networks.

How will they accomplish this? Enabling members to interact in health and wellness forums, analyzing member behavior and trends and informing care management programs with a 360 view of members… to name a few . Payers will also drive new member programs, member retention and member engagement marketing and sales programs by investigating complete views of member households and market segments.

In the provider space, this relationship building can be a little more challenging because often consumers as patients do not interact with their doctor unless they are sick, creating gaps in data. When provider organizations have a better understanding of their patients and providers, they can increase patient satisfaction and proactively offer preventative care to the sickest (and most likely to engage) of patients before an episode occurs. These activities result in increased market share and improved outcomes.

Where can providers start? By creating a 360 view of the patient, organizations can now improve care coordination, open new patient service centers and develop patient engagement programs.

Analyzing populations of patients, and fostering patient engagement based on Meaningful Use requirements or Accountable Care requirements, building out referral networks and developing physician relationships are essential ingredients in consumer engagement. Knowing your patients and providing a better patient experience than your competition will differentiate provider organizations.

You may say “This all sounds great, but how does it work?” An essential ingredient is clean, safe and connected data.  Clean, safe and connected data requires an investment in data as an asset… just like you invest in real estate and human capital, you must invest in the accessibility and quality of your data. To be successful, arm your team with tools to govern data –ensuring ongoing integrity and quality of data, removes duplicate records and dynamically incorporates data validation/quality rules. These tools include master data management, data quality, metadata management and are focused on information quality. Tools focused on information quality support a total customer relationship view of members, patients and providers.

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Posted in Customer Acquisition & Retention, Data Governance, Data Quality, Healthcare, Master Data Management | Tagged , , , , | Leave a comment

Managing a Vast Amount of Data Successfully

managedataI have two kids. In school. They generate a remarkable amount of paper. From math worksheets, permission slips, book reports (now called reading responses) to newsletters from the school. That’s a lot of paper. All of it is presented in different forms with different results – the math worksheets tell me how my child is doing in math, the permission slips tell me when my kids will be leaving school property and the book reports tell me what kind of books my child is interested in reading. I need to put the math worksheet information into a storage space so I can figure out how to prop up my kid if needed on the basic geometry constructs. The dates that permission slips are covering need to go into the calendar. The book reports can be used at the library to choose the next book.

We are facing a similar problem (albeit on a MUCH larger scale) in the insurance market. We are getting data from clinicians. Many of you are developing and deploying mobile applications to help patients manage their care, locate providers and improve their health. You may capture licensing data to assist pharmaceutical companies identify patients for inclusion in clinical trials. You have advanced analytics systems for fraud detection and to check the accuracy and consistency of claims. Possibly you are at the point of near real-time claim authorization.

The amount of data generated in our world is expected to increase significantly in the coming years. There are an estimated 50 petabytes of data in the Healthcare realm, which is predicted to grow by a factor of 50 to 25,000 petabytes by 2020. Healthcare payers already store and analyze some of this data. However in order to capture, integrate and interrogate large information sets, the scope of the payer information will have to increase significantly to include provider data, social data, government data, pharmaceutical and medical product manufacturers data, and information aggregator data.

Right now – you probably depend on a traditional data warehouse model and structured data analytics to access some of your data. This has worked adequately for you up to now, but with the amount of data that will be generated in the future, you need the processing capability to load and query multi-terabyte datasets in a timely fashion. You need the ability to manage both semi-structured and unstructured data.

Fortunately, a set of emerging technologies (called “Big Data”) may provide the technical foundation of a solution. Big Data usually includes data sets with sizes beyond the ability of commonly used software tools to capture, curate, manage and process data within a tolerable amount of time. While some existing technology may prove inadequate to future tasks, many of the information management methods of the past will prove to be as valuable as ever. Assembling successful Big Data solutions will require a fusion of new technology and old-school disciplines:

Which of these technologies do you have? Which of these technologies can integrate with on-premise AND cloud based solutions? On which of these technologies does your organization have knowledgeable resources that can utilize the capabilities to take advantage of Big Data?

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Posted in 5 Sales Plays, Big Data, Business Impact / Benefits, Data Integration, Data Warehousing, Healthcare, Master Data Management | Tagged , , , , , | Leave a comment

The Quality of the Ingredients Make the Dish-Applies to Data Quality

Data_Quality

Data Quality Leads to Other Integrated Benefits

In a previous life, I was a pastry chef in a now-defunct restaurant. One of the things I noticed while working there (and frankly while cooking at home) is that the better the ingredients, the better the final result. If we used poor quality apples in the apple tart, we ended up with a soupy, flavorless mess with a chewy crust.

The same analogy can be applied to Data Analytics. With poor quality data, you get poor results from your analytics projects. We all know that companies that can implement fantastic analytic solutions that can provide near real-time access to consumer trends are the same companies that can do successful targeted marketing campaigns that are of the minute. The Data Warehousing Institute estimates that data quality problems cost U.S. businesses more than $600 billion a year.

The business impact of poor data quality cannot be underestimated. If not identified and corrected early on, defective data can contaminate all downstream systems and information assets, jacking up costs, jeopardizing customer relationships, and causing imprecise forecasts and poor decisions.

  • To help you quantify: Let’s say your company receives 2 million claims per month with 377 data elements per claim. Even at an error rate of .001, the claims data contains more than 754,000 errors per month and more than 9.04 million errors per year! If you determine that 10 percent of the data elements are critical to your business decisions and processes, you still must fix almost 1 million errors each year!
  • What is your exposure to these errors? Let’s estimate the risk at $10 per error (including staff time required to fix the error downstream after a customer discovers it, the loss of customer trust and loyalty and erroneous payouts. Your company’s risk exposure to poor quality claims data is $10 million a year.

Once your company values quality data as a critical resource – it is much easier to perform high-value analytics that have an impact on your bottom line. Start with creation of a Data Quality program. Data is a critical asset in the information economy, and the quality of a company’s data is a good predictor of its future success.

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Posted in Business Impact / Benefits, Cloud Data Integration, Customer Services, Data Aggregation, Data Integration, Data Quality, Data Warehousing, Database Archiving, Healthcare, Master Data Management, Profiling, Scorecarding, Total Customer Relationship | Tagged , , , , | 2 Comments