Ajilitee

Big data technologies in healthcare insurance (payers): nosql and MDM–part 1

Friday, April 20th, 2012

We were working with a client around Fraud, Waste and Abuse (FWA) recently and we needed to clean up the client’s Provider data to help us track longitudinal changes in fraud behavior. Some of the published reports suggest that FWA accounts for, minimally, $21B in Medicare payments that never should have been made. That’s a lot of money. I’ll blog more on FWA, analytics and how Ajilitee could help you under our Managed Analytics service offerings, but in this blog I wanted to focus on just one small aspect of managing Provider data at Payer companies.

Many healthcare insurance companies are not known for rapid innovation. But times have changed. The need to manage members using Customer Relationship Management (CRM) technologies has greatly increased and many Payers have started their CRM efforts. But, these CRM technologies have been in use other industries for 20 years.  The technologies needed to support CRM analytics and member point of care/point of service interactions are different then what many Payer organizations have in place today.

Where do bigdata technologies play? We have been asked that question here at Ajilitee and as part of working on our FWA products and internal R&D innovation, we have been using and looking at these technologies for awhile.

Bigdata technologies span many areas and have interesting names: hadoop, hbase, hive, pig, nosql, mahout and more. Nosql and hadoop in particular are core technologies that other layers build on. For example, hive and pig build on hadoop. Hadoop itself can build on nosql technologies. I will not repeat all of these concepts in this blog because a lot of content has already been written on these technologies. From here on, I’ll assume you are familiar with some of these terms or look them up easily.

Guiding Thoughts

Here’s a couple of principles that can help guide the conversation:

• Deep insights: The more you understand your domain specific problem, the easier it is to adapt new technologies to solve them in novel ways or to recognize trade-offs you are implicitly making with current technologies. You can find novel uses that solve a problem in different ways. Think Geoffrey Moore and disruption.
• Delamination: By rethinking the layers of technologies you use today, its possible to pick and choose the layers and how they combine together to create new solutions. Many nosql packages do not include compute engines within their data access and storage solution. So solutions such as hadoop must be layered on top of the nosql databases in order to obtain compute processing. There are a lot of variations to this thought but just think “delamination” can be helpful around innovation.
• Do something different: Unless you are really, really smart, a great way to learn about how to use the new technologies is try something and fail multiple times to solve a problem.  This learning by doing is key to innovation. Of course, you have to learn from your mistakes each time—failing by itself is pointless.

Scanning the bigdata Technical Landscape

There are many new technologies in the bigdata world. Let’s look at nosql. Many people question whether the technologies are mature enough for production use and whether they help them solve business problems faster, cheaper or better in some way.

Bigdata and nosql conversations usually start with explaining issues found in the world of managing and serving massive amounts of data needed for websites. But the technologies seem confusing at times and begin to wonder if they apply to our problems especially those in the healthcare world.

Nosql technologies are often quoted as having the following properties:

• No sql: This means there is no sql!
• Schema-less: There is no schema!
• Eventually consistent: Forget ACID. Forget transactions. Eventually your data is consistent.
• Fault tolerant, scalable, distributed: A whole number of really great architectural characteristics that sound good but you are not always sure apply to you.

Once you start looking at the bigdata technologies you are immediately struck by the fact that:

• With some nosql technologies you actually do define a schema and indicate what a column’s type and name is. Some nosql technologies also need to know how to sort columns so they need knowledge of how to compare keys or values. That sounds like a schema!
• With nearly all bigdata technologies, someone has already written a [insert technology name here] Query Language to you can run queries. This sounds like everyone still wants *SQL.
• With some nosql technologies, you have to specify properties such as coherency, which indicates that you can get the value you just wrote back out of the database. While not the full definition of ACID, it starts sounding close!
• There is a lot of parallelism everywhere. The file system is parallelized! Don’t forget that the job flow is parallelized as well! (assuming it fits a specific data parallel processing model). Everything is about scale-out—add more nodes and everything keeps running, the system stays up, and everyone has fast, guaranteed access!
• There appears to be several interfaces into the database some of which require programming in a language you may not be familiar with e.g. not SQL! How do you even load data?! You almost feel like you are programming at the lowest level of database programming possible. Wait a second, which layer are you programming? The filesystem level? The map-reduce level? Or both?

This seems very confusing. So let’s think through the issues. We want to avoid having everything look like a nail because we have a bigdata hammer.

Healthcare Example

In the Payer space, Master Data Management (MDM) is finally becoming a component of the business and architectural landscape.  In the Payer world, MDM means managing Providers, Members, Contracts and Products and other business entities that you can often touch and feel or that are really considered non-claims data.  Other types of Payer data include “event” data. This is data generated by interactions with Members from sales, service and care oriented interactions. Of course, there is also claims data. Claims data is the largest source of data, followed by “event” data then MDM. Are some of the bigdata technologies relevant for even the small datasets such as MDM datasets? Small in this case means a few million rows and typically much less. Of course, this links back to our FWA problem statement at the beginning of the blog and the need to clean up our Provider list to perform Fraud analysis. We’ll illustrate some of our points with industry specifics.

• Schema-less:
  • The NPEES Provider list from CMS changes multiple times a year. The list contains all of the Medicare Providers and some, but not all, of their demographics.  The columns of data change although not frequently. New providers are added or removed as appropriate. Provider data inside a Payer typically originates in several systems–sometimes up to 20.  So it would seem that a technology that claims to be “schema-less” would be useful. But schema-less does not mean that you do not need to specify the data types of the data.  You have to specify the format somewhere so external tools can use the data. The NPEES file has several sub-entities in it like addresses and other codes indicating the Provider’s specialty or whether the Provider is an individual or an organization. Shouldn’t we pull these sub-entities out and make them their own table? Shouldn’t we also try to specify where and how the data should be loaded to be efficiently accessed, perhaps by using table partitions, or striped volumes or other typical database designs? These are normal database design concepts.
    • Part of the value of being schemaless is that you tend to concentrate data together into denormalized structures and use it to answer a smaller set of business questions such as “what data changed between NPPES files each month?” And the data you load may be very dirty, so lets load it all as strings, then convert the data in the database itself. We don’t have to work to hard to specify types, but we must specify some. We can also ignore doing detailed table design because most nosql database are designed to scale out. Bigdata can help us push aside this operational complexity.
    • MDM data changes over time. For example, you may choose to append one external data vendor’s Provider demographic data one year, then switch the vendor the next. That’s a whole new set of data structures in the traditional world. Being schemaless allows us to manage data changing over time without having to reload or re-baseline to achieve acceptable performance. Hence, you can evolve the schema more easily and that’s a great reduction in operational complexity.
    • No-sql: We clearly need to write a query to determine what changed between different NPPES file releases. We have to write a query. The entire claim of nosql must be false! The answer is more subtle than that. The claim of nosql is really one of not having many characteristics of traditional RDBMS databases built into the database layer. For example, you will not see nosql databases implementing referential integrity through sql statement such as foreign keys, etc. You do have to specify primary keys for some nosql database just to help with managing the data.  In fact, many systems today, whether a data warehouse or a transactional system, actually implement integrity in the processing layer above the database these days. This is neither wrong nor right, but just where it is often happening. Hence by saying its nosql, you are really saying that the data architecture is one where the data is more concentrated, where integrity is implemented in a processing layer and not the database, and where the data access interface makes as few assumptions about the data as possible in terms of its structure.
      • There is often another implication of schemaless that is less often recognized. Because the nosql database essentially delaminate the database stack to some degree. Mathematical processing occurs outside the layer.. While nosql creates uniform access performance by keeping the interface simple and scaling out, it also does not allow computations to be automatically pushed down to an individual node for parallel processing. That’s where hadoop steps in. By teasing apart the computing part from the data access layer, you have to now choose where computing occurs. In the case of hadoop, that processing can occur on a node where the data lives (there is a Cassandra+Hadoop integration layer) or you can process the data controlling using uniform access performance to avoid overloading the compute server. This also means there are really not any stored procedures in nosql databases.
      • Eventually Consistent: In our specific case, eventually consistent is fine. Since we are loading the data and deduping and cleansing it initially, that’s not a big deal. But…
        • Let’s also think through the case of Provider MDM. In an enterprise MDM system, all transactional systems should reference, in real time, the MDM system to obtain authoritative data when it needs it. The MDM data should be consistent. However, even in Payers today, the MDM data is not immediately consistent. There is an acceptable lag between one transactional system authoring some master data and another system in being able to access it. Typically, the lag is a few hours or a day or a few days. In fact, if we look at nosql databases like Cassandra, its quite possible to improve the time to consistency at a significant lower cost structure. For example, a social media site can tune its consistency which means it can tune how fast you can see your new “friends” post. You will also want to tune the consistency you want for MDM and scale it up or down. You can do this in nosql technologies without incurring additional development time or complexity all using the same database. That’s huge and compelling. Because enterprise MDM makes the MDM system an operational imperative, you have almost immediately solved some very vexing architecture problems at an incredible inexpensive cost point.
        • Cool architecture: The previous bullets have already pointed out the need for scale and robustness so I will not repeat that here. But an additional thought may be worth pointing out. If you think about the data access patterns, where the architecture is concentrated to have an MDM hub that selves up authoritative data to many, many consuming applications (many reads, few writes) and this all happening in relatively real-time, then scaling and fault-tolerance are actually key. In the MDM vision landscape, cool architecture is actually really important and your MDM hub does look more and more like a website serving up data. You need a transactional OD.  Its also important to realize that you can only take advantage of the cool architecture if the other parts of your architecture are also simplified. Your mileage may vary if all you are doing is plugging in new technology into the same old landscape without any changes anywhere.
          • Because a cool architecture can be delaminated, we have to plan for how computations (queries) will be executed. You cannot automatically have the computations pushed down to a node without using hadoop or something similar. Otherwise all computation and IO gets throttled on the node you issue the query from. That’s one are of review and choice you have to think through and one place where hadoop, hive and pig try to help you think through. Other database engines that distribute the data and computations might make this much easier but you may have to make other architecture choices to use those tools. Think deeply and carefully about cool architecture.

So based on some deeper thinking, it appears that the bigdata and nosql world can offer something of value even for a Provider MDM problem which seems like an ill-fit to begin with.

Summary

It appears that if the problem you are trying to solve is important enough to use these other technologies, there is some benefit to using them in the right mix and in the right proportions to your existing architecture. They are viable and based on our experience at Ajilitee, can be made production ready. In some cases, they can dramatically reduce operating complexity despite their seemingly lack of maturity around tooling. In many Payers, reducing operating complexity is a huge win.

In the next blog we will demonstrate learning by doing using bigdata technologies on larger Provider datasets and common healthcare processing analytical patterns. I’ll also return to the FWA theme.

As a treat, John Bair our CTO is speaking at TDWI’s Cool BI Forum in Chicago on May 8. He’ll be talking about these technologies and how they can help you. His talk is based on direct experiences from building products and solutions for our clients.

Are system integrators poised to play a new role as analytics innovator?

Wednesday, May 18th, 2011

We were briefing Gartner analysts John Hagerty and Kurt Schlegel on Ajilitee strategy, plans, and client work. We shared with them our belief that markets, especially mid-markets, are moving towards a world of managed analytics–analytics that are performed by third parties like Ajilitee and which are tuned to a specific business process and contracted through a managed services agreement.

Our sister division Discovery Health Partners (DHP) is a great proof-point on how managed analytics can deliver a fully outsourced business process in a SaaS model, in this case for healthcare payers who want to recover claim overpayments and subrogate third-party liability. The DHP Intelligent Cost Containment platform runs completely on Amazon Web Services. We also shared that Ajilitee will deliver the entire information management stack to one healthcare company and will provide process-specific reference data to support business rule processing in “smart” claims processing to a second—both using Amazon’s cloud.

Near the end of the conversation, Gartner posed some questions for us. Do we think that traditional system integrators, such as the large IT consultancies, are all poised to become a new type of analytics provider to companies?  Well, we think the answer is no, and of course, not surprisingly, it’s also a yes. Here’s why.

NO

On the one hand, systems integrators have been trying to integrate analytics all along as part of building an annuity revenue stream and as an extension of their efforts to integrate applications and business process. In neither case have these efforts changed the game. Let me explain.

First of all, large consultancies have thrived on large packaged-application implementations: SAP, Oracle and the like.  These are huge, complex projects that typically span years, continents, and literally hundreds of millions of dollars. This is revenue they can count on, year after year—the concept of a revenue annuity. Instead of selling a new consulting project each year, which is a lot of work, why not have something locked in for a multi-year contract?

The packaged-application projects have almost always been about improving, standardizing, centralizing or otherwise improving business processes. To answer client demands in these multi-year commitments, the large application vendors have spent millions, perhaps billions, developing footprint extensions in the analytics space to try and help clients understand the business process metrics as well as the end results of the business process, e.g., how much money did you make last quarter?  Some packages have even integrated more advanced data mining components. My old company, Thinking Machines, had a data mining product called Darwin that was sold to Oracle for just that reason.

So the idea of analytics closely coordinated with the business process software has always been an area of interest, an intention. However, while the analytics packages are okay, they are not always best-of-breed or useful in standalone situations. They are often considered footprint extensions of the main ERP application–they are not a focus.

Secondly, system integrators have always tried to build “solution sets” representing preintegrated business process solutions.  After all, system integrators are in a great position to understand the nuances of a client’s business process and inject analytics at the right place, in the right way, using the right tools. For years, going back to the time that I was at PricewaterhouseCoopers (PwC), producing these solution sets was a goal. Since I was in the management analytics (data mining) practice at the time, the idea was to produce analytical products that could be resold. We did not have a lot of tools, infrastructure was always hard to get allocated, and generally there were logistical issues that prevented us from realizing our dream.  The solution sets didn’t get built.

So, in one sense, the answer is no, system integrators are not a new type of analytics provider in the market. They have been trying to do this all along–for the annuity revenue and as an extension of what they had always been working on with applications and the business process. Analytics has been on the agenda for awhile.

YES

Alright, because we are also consultants, we also have to say yes. Here’s why.

System integrators are also a new analytics provider to companies because the tools and technologies, whether open source or commercial, are now becoming available at a scale that allows a system integrator to create, evolve and manage complex applications more easily and cheaper.  In short, creating analytic solutions has become a viable business for them.

Several enablers make this possible. Cloud computing has greatly enhanced the capability to create sandboxes, analysis environments and scalable analytical production systems, like scoring engines. Open source and commercial analytics software have more flexible licenses or architectures–there is probably even a hadoop app for your iPhone coming out soon that taps into your top-10 friends’ smartphones. And of course, the web has opened new analytical output delivery mechanisms that were not envisioned a decade ago. Taken together, these evolutions make it easier to deliver results.

So there is also a “yes” answer to the question because, at long last, new technologies allow a system integrator to engage in this area in a way that makes economic sense to the clients.

Inside client organizations, challenges may be creating more opportunities for system integrators to step in as analytics partner.  Analytical talent is still a scarce commodity and is shifting overseas.  Creating analytics capabilities inside a company has been harder than clients envisioned, given budget, skillsets, and focus. At the same time, time to market, the speed of delivery, required to make an improvement in a company’s top or bottom line has increased.  Data integration and data management, which are core competencies to power industrial-sized analytics—is hard work.

Considering Gartner’s question makes me smile. Yes, system integrators are making major noise in this space, and they are buying some companies here and there and forming internal groups to harvest their clients’ intellectual property and produce products. While they haven’t succeeded on a large scale in the past, things may be different in our evolving environment.

Ajilitee is already in this space and we are innovating.  We do this in the healthcare market today where security and privacy are paramount and we do it on a large scale across multiple clients. We do it with cloud computing technologies and our information management stack.  We do it because we have the business and technical talent to advise clients on how to make analytics work in their business process. We do it because we passionately believe that this approach helps our clients compete and improve their results. That’s what we are about.

Can Groupon Deliver for B2B?

Tuesday, March 1st, 2011

That’s the question Ajilitee is asking in an unusual Deal of the Day running right now on Groupon through its Groupon Stores.   You know Groupon, where you can find deeply discounted deals of the day in your home town on “things to do, eat, see and buy.”  The one recently featured on the cover of Forbes Magazine as the fastest growing company ever?  The company that just turned down Google’s acquisition offer of $6 billion?  Yes, that Groupon, the company that connects sellers and buyers in a fresh new B2C channel and taps into our bargain-hunting culture. The question is:  will it work for B2B?

We decided to find out by testing an offer on Groupon Stores, which is a new self-service platform for B2C businesses in which a business can construct an offer and use Groupon’s platform to market it to a wide audience.  Can a B2B business adapt a B2C channel to move its products and services, especially when those products and services are complex like consulting services? What will those Marketing people dream up next?

We acknowledge this is an unusual go-to-market strategy for IT consulting services. In fact, to our knowledge, we’re the first enterprise consulting firm to try it.  Yet, we’re intrigued by the way companies like Groupon and Living Social are changing the buy-sell model at the consumer level and can’t help wondering how (or when) this model could be viable for B2B.  At Ajilitee, innovation is part of our mission, and as marketers, our job is to respond to trends and changing behaviors in new ways to see what’s possible. Nothing ventured, nothing gained.

So here we have it, now running at Groupon Stores near you:  Ajilitee is sponsoring two offers for 50%-off consulting engagements.   These are very good deals, actually, for half off a week-long $25,000 engagement with two master-level consultants. The first is a Cloud Opportunity Map, which helps an organization identify the right area to pilot a cloud application. The second is a BI Best Practices Audit, which lets you analyze your BI operation against best practices.  You don’t have to pull the trigger on the buy until you talk to us to be sure it’s what you want, and you have a whole year to exercise it once you do.  Good value, minimal risk, big savings.  Check out our merchant page at Groupon here, and more details are also available on our own website at www.ajilitee.com/groupon (no longer accessible).  

So what do you think? Is it wishful thinking to believe consumer behaviors are valid in a B2B environment? Can B2B buyers and sellers wrap their heads around a Groupon Stores selling professional services, big-ticket hardware, or other complex B2B products?  Let us hear from you…and stay tuned for updates on Ajilitee’s little experiment.

Deep Dive on Analytical Styles: The Future Style

Tuesday, December 21st, 2010

You need this style today. You probably do not have it. Thirty years ago, statisticians starting using regression to improve response rates to direct mail campaigns. Ten years ago, artificial intelligences (AI) academics discovered new (and old) approaches to predicting the future. Five years ago, businesses saw the benefits and started incorporating future-looking analytics into the environment cost effectively. One year ago the recession hit and everyone said they were surprised (well, except a few people).

Predicting the future is hard. At the economic level it is really hard. But predicting the future at a customer level, while hard, is a much more bounded problem.

The Future Style of analytics uses your corporate customer data (touchpoints, surveys, purchase transactions) and combines with it with any other available data such as demographics, firmagraphics, behavioral and attitudinal to create a prediction. Perhaps you need to predict when a customer will churn from their cellular company, when a customer will fall into quiet mode for their DVD rentals, what they may need or want to purchase next or when they will move from the student segment to the affluent segment.
The Future Style gives you predictions at all levels, then helps you anticipate changes and provides a window, sometimes small, to do something about it. It’s up to you to act.

The Future style is not easy. It never is.

You need a large amount of past historical data. You have to assume that past behavior can reasonably predict future behavior. You also need the right math and science working on the data to make the prediction of the future with confidence. You need to take into account all of the variables that you think could influence the prediction. That’s a lot of machinery, a lot of infrastructure. It takes a coordinated effort to make this happen well, happen quickly, and most importantly integrate it into your business process.

Lets face it, if you can predict the future but can’t do anything about influencing the present to make money, there’s no point. Stop wasting time. The biggest, and I mean the biggest, issue with the Future Style is using a prediction to take action today. A lot of clients have spent their money to create a Future Style capability but they could not get their “customer channel” to change their business process and the way they interact with the company’s customers (despite my best efforts to address this with them). I said “company’s customers” because if your SBU or functional group believes they own the customer, you have to work with them (or go to the CEO) as a partner to implement actions to make the predictions valuable. That’s hard. Maybe it should not be hard, but it is.

The key to the Future Style is, yes, to have the capability to predict but also the ability to intervene to your benefit.

Analytically enabled agile companies balance all three analytical styles and use them where needed. Knowing which style to invoke in which circumstance means knowing what you want to measure—back to our strategy once again. The other necessity for analytic enablement is to have the information infrastructure behind you to support flexible, reliable and efficient access to the data and an ability to manipulate it for insight.

In the next series of blogs, I will look at what it takes from the technology perspective to be an analytically enabled agile enterprise.

Deep Dive on Analytical Styles: The Real-Time Style

Friday, November 12th, 2010

I was walking down the main street in my town the other day and I was struck by how many signs there were. Signs advertising places of business, places to eat, drink, have fun, buy things, get broken things fixed… the signs were endless. Many were advertising the week’s specials: buy now and save 20%. Each Sunday, I receive a stack of advertisements for the week. I also receive daily emails from merchants with that day’s special deal or a special thought about how to do something. On occasion, I receive a text message with an advertisement. When I go to work, I like to look at my operational reports—the status of my client projects, whether they are falling behind or are ahead of schedule.

All of these scenarios speak to the Real-Time Style. What real-time means is dependent on the business process and the receiver (in the examples above—me). Real-time sounds cool, it sounds hip. I’m real-time, you’re real-time.

In some cases, Real-Time Style means I get a text message as I am walking down the street. Sometimes it is a weekly flyer on Sunday since I do a lot of my buying on Sunday. Sometimes real-time means that as I am talking to a customer service rep, predictions are being made about how valuable a customer I am using information recorded on the call–then deciding how to tier me so I get the right level of service–not too much that I become unprofitable and not too little that I churn or attrite.
Real-Time Style is very contextual: Where I am or what I am doing when I make a decision makes a difference. This is important.

If the Real-Time Style has one critical element to be successful, it is that real-time analytics must have a context in which to make decision. If you need to build the infrastructure to have real-time analytics, and it is not always cheap or easy, then the target of the analytics, whether an executive, a manager or a customer, has to be in a state where a decision can be made. Imagine the entire company, completely organized behind influencing the decision of a single customer or person at an instant of time. If you use this customer-centric view, you will instantly realize the incredible amount of organization and collaboration to do this successfully.

This idea of the “context” actually gives us the definition of Real-Time Style: analytics designed to influence a decision within a specific context at the point of decision making.

Breaking down what a person does into smaller steps is key. Knowing how decisions are made and where they are made is paramount. What is your customer lifecycle? What does a customer service event look like? You have to take that person’s view, then align your analytical capabilities to support it. If you want to target a person and influence them, those capabilities are the “wood behind the arrow.”

Coming next: the third and final analytical style, the Future Style.