Category Archives: Business Impact / Benefits
“Inaccurate, inconsistent and disconnected supplier information prohibits us from doing accurate supplier spend analysis, leveraging discounts, comparing and choosing the best prices, and enforcing corporate standards.”
This is quotation from a manufacturing company executive. It illustrates the negative impact that poorly managed supplier information can have on a company’s ability to cut costs and achieve revenue targets.
Many supply chain and procurement teams at large companies struggle to see the total relationship they have with suppliers across product lines, business units and regions. Why? Supplier information is scattered across dozens or hundreds of Enterprise Resource Planning (ERP) and Accounts Payable (AP) applications. Too much valuable time is spent manually reconciling inaccurate, inconsistent and disconnected supplier information in an effort to see the big picture. All this manual effort results in back office administrative costs that are higher than they should be.
Do these quotations from supply chain leaders and their teams sound familiar?
“We have 500,000 suppliers. 15-20% of our supplier records are duplicates. 5% are inaccurate.”
“I get 100 e-mails a day questioning which supplier to use.”
“To consolidate vendor reporting for a single supplier between divisions is really just a guess.”
“Every year 1099 tax mailings get returned to us because of invalid addresses, and we play a lot of Schedule B fines to the IRS.”
“Two years ago we spent a significant amount of time and money cleansing supplier data. Now we are back where we started.”
Please join me and Naveen Sharma, Director of the Master Data Management (MDM) Practice at Cognizant for a Webinar, Supercharge Your Supply Chain Applications with Better Supplier Information, on Tuesday, July 29th at 11 am PT.
During the Webinar, we’ll explain how better managing supplier information can help you achieve the following goals:
- Accelerate supplier onboarding
- Mitiate the risk of supply disruption
- Better manage supplier performance
- Streamline billing and payment processes
- Improve supplier relationship management and collaboration
- Make it easier to evaluate non-compliance with Service Level Agreements (SLAs)
- Decrease costs by negotiating favorable payment terms and SLAs
I hope you can join us for this upcoming Webinar!
By now, the business benefits of effectively leveraging big data have become well known. Enhanced analytical capabilities, greater understanding of customers, and ability to predict trends before they happen are just some of the advantages. But big data doesn’t just appear and present itself. It needs to be made tangible to the business. All too often, executives are intimidated by the concept of big data, thinking the only way to work with it is to have an advanced degree in statistics.
There are ways to make big data more than an abstract concept that can only be loved by data scientists. Four of these ways were recently covered in a report by David Stodder, director of business intelligence research for TDWI, as part of TDWI’s special report on What Works in Big Data.
The time is ripe for experimentation with real-time, interactive analytics technologies, Stodder says. The next major step in the movement toward big data is enabling real-time or near-real-time delivery of information. Real-time data has been a challenge with BI data for years, with limited success, Stodder says. The good news is that Hadoop framework, originally built for batch processing, now includes interactive querying and streaming applications, he reports. This opens the way for real-time processing of big data.
Design for self-service
Interest in self-service access to analytical data continues to grow. “Increasing users’ self-reliance and reducing their dependence on IT are broadly shared goals,” Stodder says. “Nontechnical users—those not well versed in writing queries or navigating data schemas—are requesting to do more on their own.” There is an impressive array of self-service tools and platforms now appearing on the market. “Many tools automate steps for underlying data access and integration, enabling users to do more source selection and transformation on their own, including for data from Hadoop files,” he says. “In addition, new tools are hitting the market that put greater emphasis on exploratory analytics over traditional BI reporting; these are aimed at the needs of users who want to access raw big data files, perform ad-hoc requests routinely, and invoke transformations after data extraction and loading (that is, ELT) rather than before.”
Nothing gets a point across faster than having data points visually displayed – decision-makers can draw inferences within seconds. “Data visualization has been an important component of BI and analytics for a long time, but it takes on added significance in the era of big data,” Stodder says. “As expressions of meaning, visualizations are becoming a critical way for users to collaborate on data; users can share visualizations linked to text annotations as well as other types of content, such as pictures, audio files, and maps to put together comprehensive, shared views.”
Unify views of data
Users are working with many different data types these days, and are looking to bring this information into a single view – “rather than having to move from one interface to another to view data in disparate silos,” says Stodder. Unstructured data – graphics and video files – can also provide a fuller context to reports, he adds.
1) It’s difficult to find and retain resource skills to staff big data projects
The biggest challenge by far that we see with Big Data is that it is difficult to find and retain the resources skills to staff Big Data projects. The fact that “Its expertise is scarce and expensive” is the #1 concern about using Big Data according to an Information Week survey of 541 business technology professionals (1). And according to Gartner by 2015, only a third of the 4.4 million big data related jobs will be filled (5)
2) It takes too long to deploy Big Data projects from ‘proof-of-concept’ to production
At Hadoop Summit in June 2014, one of the largest Big Data conferences in the world, Gartner stated in their keynote that only about 30% of Hadoop implementations are in production (4). This observation highlights the second challenge which is that it takes too long to deploy Big Data projects from the ‘proof-of-concept’ phase into production.
3) Big data technologies are evolving too quickly to adapt
With the related market projected to grow from $28.5 billion in 2014 to $50.1 billion in 2015 according to Wikibon (6), Big Data technologies are emerging and evolving extremely fast. This in turn becomes a barrier to innovation since these technologies evolve much too quickly for most organizations to adopt before the next big thing comes along.
4) Big Data projects fail to deliver the expected value
Too many Big Data projects start off as science experiments and fail to deliver the expected value primarily because of inaccurate scope. They underestimate what it takes to integrate, operationalize, and deliver actionable information at production scale. According to an InfoChimp survey of 300 IT professionals “55% of big data projects don’t get completed and many others fall short of their objectives” (3)
4) It’s difficult to make Big Data fit-for-purpose, assess trust, and ensure security
Uncertainty is inherent to Big Data when dealing with a wide variety of large data sets coming from external data sources such as social, mobile, and sensor devices. Therefore, organizations often struggle to make their data fit-for-purpose, assessing the level of trust, and ensuring data level security. According to Gartner, “Business leaders recognize that big data can help deliver better business results through valuable insights. Without an understanding of the trust implicit in the big data (and applying information trust models), organizations maybe be taking risks that undermine the value they seek.” (2)
For more information on “How Informatica Tackles the Top 5 Big Data Challenges,” see the blog post here.
- InformationWeek 2013 Analytics, Business Intelligence and Information Management Survey of 541 business technology professionals
- Big Data Governance From Truth to Trust, Gartner Research Note, July 2013
- “CIOs & Big Data: What Your IT Team Wants You to Know,“ – Infochimps conducted its survey of 300 IT staffers with assistance from enterprise software community site SSWUG.ORG. http://visual.ly/cios-big-data
- Gartner presentation, Hadoop Summit 2014
- Predicts 2013: Big Data and Information Infrastructure, Gartner, November 2012
- Wikibon Big Data Vendor Revenue and Market Forecast 2013-2017
“Not only do we underestimate the cost for projects up to 150%, but we overestimate the revenue it will generate.” This quotation from an Energy & Petroleum (E&P) company executive illustrates the negative impact of inaccurate, inconsistent and disconnected well data and asset data on revenue potential.
“Operational Excellence” is a common goal of many E&P company executives pursuing higher growth targets. But, inaccurate, inconsistent and disconnected well data and asset data may be holding them back. It obscures the complete picture of the well information lifecycle, making it difficult to maximize production efficiency, reduce Non-Productive Time (NPT), streamline the oilfield supply chain, calculate well by-well profitability, and mitigate risk.
To explain how E&P companies can better manage well data and asset data, we hosted a webinar, “Attention E&P Executives: Streamlining the Well Information Lifecycle.” Our well data experts Stephanie Wilkin, Senior Principal Consultant at Noah Consulting, and Stephan Zoder, Director of Value Engineering at Informatica shared some advice. E&P companies should reevaluate “throwing more bodies at a data cleanup project twice a year.” This approach does not support the pursuit of operational excellence.
In this interview, Stephanie shares details about the award-winning collaboration between Noah Consulting and Devon Energy to create a single trusted source of well data, which is standardized and mastered.
Q. Congratulations on winning the 2014 Innovation Award, Stephanie!
A. Thanks Jakki. It was really exciting working with Devon Energy. Together we put the technology and processes in place to manage and master well data in a central location and share it with downstream systems on an ongoing basis. We were proud to win the 2014 Innovation Award for Best Enterprise Data Platform.
Q. What was the business need for mastering well data?
A. As E&P companies grow so do their needs for business-critical well data. All departments need clean, consistent and connected well data to fuel their applications. We implemented a master data management (MDM) solution for well data with the goals of improving information management, business productivity, organizational efficiency, and reporting.
Q. How long did it take to implement the MDM solution for well data?
A. The Devon Energy project kicked off in May of 2012. Within five months we built the complete solution from gathering business requirements to development and testing.
Q. What were the steps in implementing the MDM solution?
A: The first and most important step was securing buy-in on a common definition for master well data or Unique Well Identifier (UWI). The key was to create a definition that would meet the needs of various business functions. Then we built the well master, which would be consistent across various systems, such as G&G, Drilling, Production, Finance, etc. We used the Professional Petroleum Data Management Association (PPDM) data model and created more than 70 unique attributes for the well, including Lahee Class, Fluid Direction, Trajectory, Role and Business Interest.
As part of the original go-live, we had three source systems of well data and two target systems connected to the MDM solution. Over the course of the next year, we added three additional source systems and four additional target systems. We did a cross-system analysis to make sure every department has the right wells and the right data about those wells. Now the company uses MDM as the single trusted source of well data, which is standardized and mastered, to do analysis and build reports.
Q. What’s been the traditional approach for managing well data?
A. Typically when a new well is created, employees spend time entering well data into their own systems. For example, one person enters well data into the G&G application. Another person enters the same well data into the Drilling application. A third person enters the same well data into the Finance application. According to statistics, it takes about 30 minutes to enter wells into a particular financial application.
So imagine if you need to add 500 new wells to your systems. This is common after a merger or acquisition. That translates to roughly 250 hours or 6.25 weeks of employee time saved on the well create process! By automating across systems, you not only save time, you eliminate redundant data entry and possible errors in the process.
Q. That sounds like a painfully slow and error-prone process.
A. It is! But that’s only half the problem. Without a single trusted source of well data, how do you get a complete picture of your wells? When you compare the well data in the G&G system to the well data in the Drilling or Finance systems, it’s typically inconsistent and difficult to reconcile. This leads to the question, “Which one of these systems has the best version of the truth?” Employees spend too much time manually reconciling well data for reporting and decision-making.
Q. So there is a lot to be gained by better managing well data.
A. That’s right. The CFO typically loves the ROI on a master well data project. It’s a huge opportunity to save time and money, boost productivity and get more accurate reporting.
Q: What were some of the business requirements for the MDM solution?
A: We couldn’t build a solution that was narrowly focused on meeting the company’s needs today. We had to keep the future in mind. Our goal was to build a framework that was scalable and supportable as the company’s business environment changed. This allows the company to add additional data domains or attributes to the well data model at any time.
Q: Why did you choose Informatica MDM?
A: The decision to use Informatica MDM for the MDM Trust Framework came down to the following capabilities:
- Match and Merge: With Informatica, we get a lot of flexibility. Some systems carry the API or well government ID, but some don’t. We can match and merge records differently based on the system.
- X-References: We keep a cross-reference between all the systems. We can go back to the master well data and find out where that data came from and when. We can see where changes have occurred because Informatica MDM tracks the history and lineage.
- Scalability: This was a key requirement. While we went live after only 5 months, we’ve been continually building out the well master based on the requiremets of the target systems.
- Flexibility: Down the road, if we want to add an additional facet or classification to the well master, the framework allows for that.
- Simple Integration: Instead of building point-to-point integrations, we use the hub model.
In addition to Informatica MDM, our Noah Consulting MDM Trust Framework includes Informatica PowerCenter for data integration, Informatica Data Quality for data cleansing and Informatica Data Virtualization.
Q: Can you give some examples of the business value gained by mastering well data?
A: One person said to me, “I’m so overwhelmed! We’ve never had one place to look at this well data before.” With MDM centrally managing master well data and fueling key business applications, many upstream processes can be optimized to achieve their full potential value.
People spend less time entering well data on the front end and reconciling well data on the back end. Well data is entered once and it’s automatically shared across all systems that need it. People can trust that it’s consistent across systems. Also, because the data across systems is now tied together, it provides business value they were unable to realize before, such as predictive analytics.
Q. What’s next?
A. There’s a lot of insight that can be gained by understanding the relationships between the well, and the people, equipment and facilities associated with it. Next, we’re planning to add the operational hierarchy. For example, we’ll be able to identify which production engineer, reservoir engineer and foreman are working on a particular well.
We’ve also started gathering business requirements for equipment and facilities to be tied to each well. There’s a lot more business value on the horizon as the company streamlines their well information lifecycle and the valuable relationships around the well.
If you missed the webinar, you can watch the replay now: Attention E&P Executives: Streamlining the Well Information Lifecycle.
In my last blog, I talked about the dreadful experience of cleaning raw data by hand as a former analyst a few years back. Well, the truth is, I was not alone. At a recent data mining Meetup event in San Francisco bay area, I asked a few analysts: “How much time do you spend on cleaning your data at work?” “More than 80% of my time” and “most my days” said the analysts, and “they are not fun”.
But check this out: There are over a dozen Meetup groups focused on data science and data mining here in the bay area I live. Those groups put on events multiple times a month, with topics often around hot, emerging technologies such as machine learning, graph analysis, real-time analytics, new algorithm on analyzing social media data, and of course, anything Big Data. Cools BI tools, new programming models and algorithms for better analysis are a big draw to data practitioners these days.
That got me thinking… if what analysts said to me is true, i.e., they spent 80% of their time on data prepping and 1/4 of that time analyzing the data and visualizing the results, which BTW, “is actually fun”, quoting a data analyst, then why are they drawn to the events focused on discussing the tools that can only help them 20% of the time? Why wouldn’t they want to explore technologies that can help address the dreadful 80% of the data scrubbing task they complain about?
Having been there myself, I thought perhaps a little self-reflection would help answer the question.
As a student of math, I love data and am fascinated about good stories I can discover from them. My two-year math program in graduate school was primarily focused on learning how to build fabulous math models to simulate the real events, and use those formula to predict the future, or look for meaningful patterns.
I used BI and statistical analysis tools while at school, and continued to use them at work after I graduated. Those software were great in that they helped me get to the results and see what’s in my data, and I can develop conclusions and make recommendations based on those insights for my clients. Without BI and visualization tools, I would not have delivered any results.
That was fun and glamorous part of my job as an analyst, but when I was not creating nice charts and presentations to tell the stories in my data, I was spending time, great amount of time, sometimes up to the wee hours cleaning and verifying my data, I was convinced that was part of my job and I just had to suck it up.
It was only a few months ago that I stumbled upon data quality software – it happened when I joined Informatica. At first I thought they were talking to the wrong person when they started pitching me data quality solutions.
Turns out, the concept of data quality automation is a highly relevant and extremely intuitive subject to me, and for anyone who is dealing with data on the regular basis. Data quality software offers an automated process for data cleansing and is much faster and delivers more accurate results than manual process. To put that in math context, if a data quality tool can reduce the data cleansing effort from 80% to 40% (btw, this is hardly a random number, some of our customers have reported much better results), that means analysts can now free up 40% of their time from scrubbing data, and use that times to do the things they like – playing with data in BI tools, building new models or running more scenarios, producing different views of the data and discovering things they may not be able to before, and do all of that with clean, trusted data. No more bored to death experience, what they are left with are improved productivity, more accurate and consistent results, compelling stories about data, and most important, they can focus on doing the things they like! Not too shabby right?
I am excited about trying out the data quality tools we have here at Informtica, my fellow analysts, you should start looking into them also. And I will check back in soon with more stories to share..
I have been in marketing for over two decades. As I meet people in social situations, on airplanes, and on the sidelines at children’s soccer games, and they ask what it is I do, I get responses that constantly amuse me and lead me to the conclusion that the general public has absolutely no idea what a marketer does. I am often asked things like “have you created any commercials that I might have seen?” and peppered with questions that evoke visions of Mad Men-esque 1960’s style agency work and late night creative martini-filled pitch sessions.
I admit I do love to catch the occasional Mad Men episode, and a few weeks ago, I stumbled upon one that had me chuckling. You may remember the one that Don Draper is pitching a lipstick advertisement and after persuading the executive to see things his way, he says something along the lines of, “We’ll never know, will we? It’s not a science.”
How the times have changed. I would argue that in today’s data-driven world, marketing is no longer an art and is now squarely a science.
Sure, great marketers still understand their buyers at a gut level, but their hunches are no longer the impetus of a marketing campaign. Their hunches are now the impetus for a data-driven, fact-finding mission, and only after the analysis has been completed and confirms or contradicts this hunch, is the campaign designed and launched.
This is only possible because today, marketers have access to enormous amounts of data – not just the basic demographics of years past. Most marketers realize that there is great promise in all of that data, but it’s just too complicated, time-consuming, and costly to truly harness it. How can you really ever make sense of the hundreds of data sources and tens of thousands of variables within these sources? Social media, web analytics, geo-targeting, internal customer and financial systems, in house marketing automation systems, third party data augmentation in the cloud… the list goes on and on!
How can marketers harness the right data, in the right way, right away? The answer starts with making the commitment that your marketing team – and hopefully your organization as a whole – will think “data first”. In the coming weeks, I will focus on what exactly thinking data first means, and how it will pay dividends to marketers.
In the mean time, I will make the personal commitment to be more patient about answering the silly questions and comments about marketers.
Now, it’s your turn to comment…
What are some of the most amusing misconceptions about marketers that you’ve encountered?
- and -
Do you agree? Is marketing an art? A science? Or somewhere in between?
Did I really compare data quality to flushing toilet paper? Yeah, I think I did. Makes me laugh when I read that, but still true. And yes, I am still playing with more data. This time it’s a location schedule for earthquake risk. I see a 26-story structure with a building value of only $136,000 built in who knows what year. I’d pull my hair out if it weren’t already shaved off.
So let’s talk about the six steps for data quality competency in underwriting. These six steps are standard in the enterprise. But, what we will discuss is how to tackle these in insurance underwriting. And more importantly, what is the business impact to effective adoption of the competency. It’s a repeating self-reinforcing cycle. And when done correctly can be intelligent and adaptive to changing business needs.
Profile – Effectively profile and discover data from multiple sources
We’ll start at the beginning, a very good place to start. First you need to understand your data. Where is it from and in what shape does it come? Whether internal or external sources, the profile step will help identify the problem areas. In underwriting, this will involve a lot of external submission data from brokers and MGAs. This is then combined with internal and service bureau data to get a full picture of the risk. Identify you key data points for underwriting and a desired state for that data. Once the data is profiled, you’ll get a very good sense of where your troubles are. And continually profile as you bring other sources online using the same standards of measurement. As a side, this will also help in remediating brokers that are not meeting the standard.
Measure – Establish data quality metrics and targets
As an underwriter you will need to determine what is the quality bar for the data you use. Usually this means flagging your most critical data fields for meeting underwriting guidelines. See where you are and where you want to be. Determine how you will measure the quality of the data as well as desired state. And by the way, actuarial and risk will likely do the same thing on the same or similar data. Over time it all comes together as a team.
Design – Quickly build comprehensive data quality rules
This is the meaty part of the cycle, and fun to boot. First look to your desired future state and your critical underwriting fields. For each one, determine the rules by which you normally fix errant data. Like what you do when you see a 30-story wood frame structure? How do you validate, cleanse and remediate that discrepancy? This may involve fuzzy logic or supporting data lookups, and can easily be captured. Do this, write it down, and catalog it to be codified in your data quality tool. As you go along you will see a growing library of data quality rules being compiled for broad use.
Deploy – Native data quality services across the enterprise
Once these rules are compiled and tested, they can be deployed for reuse in the organization. This is the beautiful magical thing that happens. Your institutional knowledge of your underwriting criteria can be captured and reused. This doesn’t mean just once, but reused to cleanse existing data, new data and everything going forward. Your analysts will love you, your actuaries and risk modelers will love you; you will be a hero.
Review – Assess performance against goals
Remember those goals you set for your quality when you started? Check and see how you’re doing. After a few weeks and months, you should be able to profile the data, run the reports and see that the needle will have moved. Remember that as part of the self-reinforcing cycle, you can now identify new issues to tackle and adjust those that aren’t working. One metric that you’ll want to measure over time is the increase of higher quote flow, better productivity and more competitive premium pricing.
Monitor – Proactively address critical issues
Now monitor constantly. As you bring new MGAs online, receive new underwriting guidelines or launch into new lines of business you will repeat this cycle. You will also utilize the same rule set as portfolios are acquired. It becomes a good way to sanity check the acquisition of business against your quality standards.
In case it wasn’t apparent your data quality plan is now more automated. With few manual exceptions you should not have to be remediating data the way you were in the past. In each of these steps there is obvious business value. In the end, it all adds up to better risk/cat modeling, more accurate risk pricing, cleaner data (for everyone in the organization) and more time doing the core business of underwriting. Imagine if you can increase your quote volume simply by not needing to muck around in data. Imagine if you can improve your quote to bind ratio through better quality data and pricing. The last time I checked, that’s just good insurance business.
And now for something completely different…cats on pianos. No, just kidding. But check here to learn more about Informatica’s insurance initiatives.
I was just looking at some data I found. Yes, real data, not fake demo stuff. Real hurricane location analysis with modeled loss numbers. At first glance, I thought it looked good. There are addresses, latitudes/longitudes, values, loss numbers and other goodies like year built and construction codes. Yes, just the sort of data that an underwriter would look at when writing a risk. But after skimming through the schedule of locations a few things start jumping out at me. So I dig deeper. I see a multi-million dollar structure in Palm Beach, Florida with $0 in modeled loss. That’s strange. And wait, some of these geocode resolutions look a little coarse. Are they tier one or tier two counties? Who would know? At least all of the construction and occupancy codes have values, albeit they look like defaults. Perhaps it’s time to talk about data quality.
This whole concept of data quality is a tricky one. As cost in acquiring good data is weighed against speed of underwriting/quoting and model correctness I’m sure some tradeoffs are made. But the impact can be huge. First, incomplete data will either force defaults in risk models and pricing or add mathematical uncertainty. Second, massively incomplete data chews up personnel resources to cleanse and enhance. And third, if not corrected, the risk profile will be wrong with potential impact to pricing and portfolio shape. And that’s just to name a few.
I’ll admit it’s daunting to think about. Imagine tens of thousands of submissions a month. Schedules of thousands of locations received every day. Can there even be a way out of this cave? The answer is yes, and that answer is a robust enterprise data quality infrastructure. But wait, you say, enterprise data quality is an IT problem. Yeah, I guess, just like trying to flush an entire roll of toilet paper in one go is the plumber’s problem. Data quality in underwriting is a business problem, a business opportunity and has real business impacts.
Join me in Part 2 as I outline the six steps for data quality competency in underwriting with tangible business benefits and enterprise impact. And now that I have you on the edge of your seats, get smart about the basics of enterprise data quality.
Before I joined Informatica I worked for a health plan in Boston. I managed several programs including CMS Five Start Quality Rating System and Risk Adjustment Redesign. We recognized the need for a robust diagnostic profile of our members in support of risk adjustment. However, because the information resides in multiple sources, gathering and connecting the data presented many challenges. I see the opportunity for health plans to transform risk adjustment.
As risk adjustment becomes an integral component in healthcare, I encourage health plans to create a core competency around the development of diagnostic profiles. This should be the case for health plans and ACO’s. This profile is the source of reimbursement for an individual. This profile is also the basis for clinical care management. Augmented with social and demographic data, the profile can create a roadmap for successfully engaging each member.
Why is risk adjustment important?
Risk Adjustment is increasingly entrenched in the healthcare ecosystem. Originating in Medicare Advantage, it is now applicable to other areas. Risk adjustment is mission critical to protect financial viability and identify a clinical baseline for members.
What are a few examples of the increasing importance of risk adjustment?
1) Centers for Medicare and Medicaid (CMS) continues to increase the focus on Risk Adjustment. They are evaluating the value provided to the Federal government and beneficiaries. CMS has questioned the efficacy of home assessments and challenged health plans to provide a value statement beyond the harvesting of diagnoses codes which result solely in revenue enhancement. Illustrating additional value has been a challenge. Integrating data across the health plan will help address this challenge and derive value.
2) Marketplace members will also require risk adjustment calculations. After the first three years, the three “R’s” will dwindle down to one ‘R”. When Reinsurance and Risk Corridors end, we will be left with Risk Adjustment. To succeed with this new population, health plans need a clear strategy to obtain, analyze and process data. CMS processing delays make risk adjustment even more difficult. A Health Plan’s ability to manage this information will be critical to success.
3) Dual Eligibles, Medicaid members and ACO’s also rely on risk management for profitability and improved quality.
With an enhanced diagnostic profile — one that is accurate, complete and shared — I believe it is possible to enhance care, deliver appropriate reimbursements and provide coordinated care.
How can payers better enable risk adjustment?
- Facilitate timely analysis of accurate data from a variety of sources, in any format.
- Integrate and reconcile data from initial receipt through adjudication and submission.
- Deliver clean and normalized data to business users.
- Provide an aggregated view of master data about members, providers and the relationships between them to reveal insights and enable a differentiated level of service.
- Apply natural language processing to capture insights otherwise trapped in text based notes.
With clean, safe and connected data, health plans can profile members and identify undocumented diagnoses. With this data, health plans will also be able to create reports identifying providers who would benefit from additional training and support (about coding accuracy and completeness).
What will clean, safe and connected data allow?
- Allow risk adjustment to become a core competency and source of differentiation. Revenue impacts are expanding to lines of business representing larger and increasingly complex populations.
- Educate, motivate and engage providers with accurate reporting. Obtaining and acting on diagnostic data is best done when the member/patient is meeting with the caregiver. Clear and trusted feedback to physicians will contribute to a strong partnership.
- Improve patient care, reduce medical cost, increase quality ratings and engage members.