Category Archives: Life Sciences
Maybe the word “death” is a bit strong, so let’s say “demise” instead. Recently I read an article in the Harvard Business Review around how Big Data and Data Scientists will rule the world of the 21st century corporation and how they have to operate for maximum value. The thing I found rather disturbing was that it takes a PhD – probably a few of them – in a variety of math areas to give executives the necessary insight to make better decisions ranging from what product to develop next to who to sell it to and where.
Don’t get me wrong – this is mixed news for any enterprise software firm helping businesses locate, acquire, contextually link, understand and distribute high-quality data. The existence of such a high-value role validates product development but it also limits adoption. It is also great news that data has finally gathered the attention it deserves. But I am starting to ask myself why it always takes individuals with a “one-in-a-million” skill set to add value. What happened to the democratization of software? Why is the design starting point for enterprise software not always similar to B2C applications, like an iPhone app, i.e. simpler is better? Why is it always such a gradual “Cold War” evolution instead of a near-instant French Revolution?
Why do development environments for Big Data not accommodate limited or existing skills but always accommodate the most complex scenarios? Well, the answer could be that the first customers will be very large, very complex organizations with super complex problems, which they were unable to solve so far. If analytical apps have become a self-service proposition for business users, data integration should be as well. So why does access to a lot of fast moving and diverse data require scarce PIG or Cassandra developers to get the data into an analyzable shape and a PhD to query and interpret patterns?
I realize new technologies start with a foundation and as they spread supply will attempt to catch up to create an equilibrium. However, this is about a problem, which has existed for decades in many industries, such as the oil & gas, telecommunication, public and retail sector. Whenever I talk to architects and business leaders in these industries, they chuckle at “Big Data” and tell me “yes, we got that – and by the way, we have been dealing with this reality for a long time”. By now I would have expected that the skill (cost) side of turning data into a meaningful insight would have been driven down more significantly.
Informatica has made a tremendous push in this regard with its “Map Once, Deploy Anywhere” paradigm. I cannot wait to see what’s next – and I just saw something recently that got me very excited. Why you ask? Because at some point I would like to have at least a business-super user pummel terabytes of transaction and interaction data into an environment (Hadoop cluster, in memory DB…) and massage it so that his self-created dashboard gets him/her where (s)he needs to go. This should include concepts like; “where is the data I need for this insight?’, “what is missing and how do I get to that piece in the best way?”, “how do I want it to look to share it?” All that is required should be a semi-experienced knowledge of Excel and PowerPoint to get your hands on advanced Big Data analytics. Don’t you think? Do you believe that this role will disappear as quickly as it has surfaced?
I believe that most in the software business believe that it is tough enough to calculate and hence financially justify the purchase or build of an application - especially middleware – to a business leader or even a CIO. Most of business-centric IT initiatives involve improving processes (order, billing, service) and visualization (scorecarding, trending) for end users to be more efficient in engaging accounts. Some of these have actually migrated to targeting improvements towards customers rather than their logical placeholders like accounts. Similar strides have been made in the realm of other party-type (vendor, employee) as well as product data. They also tackle analyzing larger or smaller data sets and providing a visual set of clues on how to interpret historical or predictive trends on orders, bills, usage, clicks, conversions, etc.
If you think this is a tough enough proposition in itself, imagine the challenge of quantifying the financial benefit derived from understanding where your “hardware” is physically located, how it is configured, who maintained it, when and how. Depending on the business model you may even have to figure out who built it or owns it. All of this has bottom-line effects on how, who and when expenses are paid and revenues get realized and recognized. And then there is the added complication that these dimensions of hardware are often fairly dynamic as they can also change ownership and/or physical location and hence, tax treatment, insurance risk, etc.
Such hardware could be a pump, a valve, a compressor, a substation, a cell tower, a truck or components within these assets. Over time, with new technologies and acquisitions coming about, the systems that plan for, install and maintain these assets become very departmentalized in terms of scope and specialized in terms of function. The same application that designs an asset for department A or region B, is not the same as the one accounting for its value, which is not the same as the one reading its operational status, which is not the one scheduling maintenance, which is not the same as the one billing for any repairs or replacement. The same folks who said the Data Warehouse is the “Golden Copy” now say the “new ERP system” is the new central source for everything. Practitioners know that this is either naiveté or maliciousness. And then there are manual adjustments….
Moreover, to truly take squeeze value out of these assets being installed and upgraded, the massive amounts of data they generate in a myriad of formats and intervals need to be understood, moved, formatted, fixed, interpreted at the right time and stored for future use in a cost-sensitive, easy-to-access and contextual meaningful way.
I wish I could tell you one application does it all but the unsurprising reality is that it takes a concoction of multiple. None or very few asset life cycle-supporting legacy applications will be retired as they often house data in formats commensurate with the age of the assets they were built for. It makes little financial sense to shut down these systems in a big bang approach but rather migrate region after region and process after process to the new system. After all, some of the assets have been in service for 50 or more years and the institutional knowledge tied to them is becoming nearly as old. Also, it is probably easier to engage in often required manual data fixes (hopefully only outliers) bit-by-bit, especially to accommodate imminent audits.
So what do you do in the meantime until all the relevant data is in a single system to get an enterprise-level way to fix your asset tower of Babel and leverage the data volume rather than treat it like an unwanted step child? Most companies, which operate in asset, fixed-cost heavy business models do not want to create a disruption but a steady tuning effect (squeezing the data orange), something rather unsexy in this internet day and age. This is especially true in “older” industries where data is still considered a necessary evil, not an opportunity ready to exploit. Fact is though; that in order to improve the bottom line, we better get going, even if it is with baby steps.
If you are aware of business models and their difficulties to leverage data, write to me. If you even know about an annoying, peculiar or esoteric data “domain”, which does not lend itself to be easily leveraged, share your thoughts. Next time, I will share some examples on how certain industries try to work in this environment, what they envision and how they go about getting there.
The Physician Payments Sunshine Act shines a spotlight on the disorganized state of physician information, which is scattered across systems, often incomplete, inaccurate and inconsistent in most pharmaceutical and medical device manufacturing companies.
According to the recent Wall Street Journal article Doctors Face New Scrutiny over Gifts, “Drug companies collectively pay hundreds of millions of dollars in fees and gifts to doctors every year. In 2012, Pfizer Inc., the biggest drug maker by sales, paid $173.2 million to U.S. health-care professionals.”
The Risks of Creating Reports with Inaccurate Physician Information
There are serious risks of filing inaccurate reports. Just imagine dealing with:
- An angry call from a physician who received a $25 meal, which was inaccurately reported as $250 or who reportedly, received a gift that actually went to someone with a similar name.
- Hefty fines and increased scrutiny from the Centers for Medicare and Medicaid Services (CMS). Fines range from $1,000 to $10,000 for each transaction with a maximum penalty of maximum $1.15 million.
- Negative media attention. Reports will be available for anyone to access on a publicly accessible website.
How prepared are manufacturers to track and report physician payment information?
One of the major obstacles is getting a complete picture of the total payments made to one physician. Manufacturers need to know if Dr. Sriram Mennon and Dr. Sri Menon are one and the same.
On top of that, they need to understand the complicated connections between Dr. Sriram Menon, sales representatives’ expense report spreadsheets (T&E), marketing and R&D expenses, event data, and accounts payable data.
3 Steps to Ensure Physician Information is Accurate
In recent years, some pharmaceutical manufacturers and medical device manufacturers were required to respond to “Sunshine Act” type laws in states like California and Massachusetts. To simplify, automate and ensure physician payment reports are filed correctly and on time, they use an Aggregate Spend Repository or Physician Spend Management solution.
They also use these solutions to proactively track and review physician payments on a regular basis to ensure mandated thresholds are met before reports are due. Aggregate Spend Repository and Physician Spend Management solutions rely on a foundation of data integration, data quality, and master data management (MDM) software to better manage physician information.
For those manufacturers who want to avoid the risk of losing valuable physician relationships, paying hefty fines, and receiving scrutiny from CMS and negative media attention, here are three steps to ensure accurate physician information:
- Bring all your scattered physician information, including identifiers, addresses and specialties into a central place to fix incorrect, missing or inconsistent information and uniquely identify each physician.
- Identify connections between physicians and the hospitals and clinics where they work to help aggregate accurate payment information for each physician.
- Standardize transaction information so it’s easy to identify the purpose of payments and related products and link transaction information to physician information.
Physicians Will Review Reports for Accuracy in January 2014
In January 2014, after physicians review the federally mandated financial disclosures, they may question the accuracy of reported payments. Within two months manufacturers will need to fix any discrepancies and file their Sunshine Act reports, which will become part of a permanent archive. Time is precious for those companies who haven’t built an Aggregate Spend Repository or Physician Spend Management solution to drive their Sunshine Act compliance reports.
If you work for one of the pharmaceutical or medical device manufacturing companies already using an Aggregate Spend Repository or Physician Spend Management solution, please share your tips and tricks with others who are behind.
Tick tock, tick tock….
I’m astounded by the incredible turnout and response to MDM Day and other MDM-related events at Informatica World, and again, I see this as a sign of MDM’s importance in the business world. Attendees told their stories, swapped best-practices, and shared their visions of using MDM to improve up-sell, cross-sell, and other important business metrics. But now let’s keep the momentum going. Here I want to tell you about three free webinars that will help you to dive more deeply into MDM, and take your initiatives to the next level. The first is for any large organization, and the other two are for pharmaceutical companies. (more…)