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Data Secrets to A Successful Drug Launch

Ankur Gupta, Sr. Product Marketing Manager, Reltio

Value from pharma should be measured in terms of clinical outcomes, patient satisfaction, and cost reduction. Using data, pharma companies can enhance value for patients along the entire lifecycle of a drug, from drug discovery to commercialization to end of exclusivity.

From the perspective of business strategy, value delivery can be seen as a three-step process as illustrated by David Ormesher, CEO of closerlook, in his PharmExec.com post.

  • Value Creation (discovery)

  • Value Capture (commercialization)

  • Value Extraction (end of exclusivity)

Discovery Phase: Value Creation via Data

It is important to capture unique customer insight to inform drug innovation. The drug should be relevant (to an urgent disease burden) as well as differentiated (relative to alternate therapies). These two factors will largely determine market access, provider endorsement and patient acceptance for a new drug. However, departmental silos between medical affairs and commercial side of the business, and lack of access to quality data lead to incomplete understanding of competition and the market.

A Self-Learning Data Platform goes beyond a traditional master data management (MDM) offering and brings together patient, provider, payer, and plan data from internal, third party, and public sources to cleanse, match, merge, un-merge, and relate in real time. Platform’s multi-domain data organization capability helps perform deeper analysis to better understand the needs of patients, providers, payers, and relationships among these players. A Self-Learning Data Platform breaks down silos among medical affairs, marketing, business intelligence and manufacturing, and helps develop a common understanding of customer data and market insight across all departments.

Research indicates that 81% of future drug sales performance is determined by actions taken during clinical development and early commercialization phase. It’s even more critical for a pre-commercial pharma which is planning to bring its first drug to the market. Early adoption of a Self-Learning Data Platform helps a pre-commercial pharma develop future-proof commercial infrastructure and put up business processes to launch their first drug with safety, efficacy, and desired formulary placement in place. Read the pre-commercial pharma success stories about how they successfully launched their first drug with the help of a Self-Learning Data Platform.

Commercialization Phase: Value Capture via Data

A new product’s commercial performance during the first six months after FDA approval is often considered a very important indicator for how the product will do over the course of its patent life. During Value Capture or commercialization phase, the purpose of data is to build trust and respect via data-driven personalization and engagement. However, pharma companies are unable to recognize prescribers and patients consistently across multiple channels and touchpoints. They often fail to increase content speed to market in their customers’ preferred channel. This leads to negative Net Promoter Score (NPS), increased defection to competitors, and loss of revenue and market share.

The more you know about your customers – the physicians who can write the product – and what they care about, the more you’re able to build an effective campaign around a new product. What you need – an out-of-the-box, data-driven affiliation management application, with built-in MDM, for managing all relationships within and across HCOs and HCPs to support commercial operations, identify the right key opinion leaders (KOLs), and understand their influence.

A Self-Learning Data Platform helps you organize launch as a micro-battle (See the Infographic “Make Your Drug Launch Truly Take Off”, Bain Insights, September, 2017), gather continuous front-line feedback from sales reps before, during and after the launch, and make rapid adjustments as needed to the launch strategy. It helps you make quick decisions on messaging, targeting and marketing investments. Such platform powers reliable advanced analytics by enabling master data profiles and graph relationships to be seamlessly combined with real-time interactions and analyzed in Spark. For example, when a new drug is launched, it helps track sales performance compared to projections so that you can adjust strategies whenever needed.

Read the success story of a French multinational pharmaceutical company that built Customer 360 on top of a Self-learning Data Platform to support their account-centric field operations and personalized engagement.

Loss of Exclusivity Phase: Value Extraction via Data

At the point when a drug loses its patent protection, its price typically drops quickly as generic competitors enter the market. During this phase, there is often enormous pricing pressure from competitive products and health insurers. In addition to these external pressures, there is also internal competition for attention and resources, usually from a promising new product.

The business strategy during Value Extraction is to increase efficiency via operational excellence. The main cost now is sales and marketing. This is where digital can play a very strategic role. Digital sales and marketing through non-personal promotion can become an effective substitute for sales rep promotion. By replacing expensive personnel costs with lower cost digital channels, we can reduce overhead costs but still maintain market share.

Read the success story of one of the oldest and largest global pharma that consolidated customer profile across all business functions to improve customer experience across all digital touchpoints, and better engage high-value customers.

Successful pharma companies use data as a competitive weapon to develop new sources of differentiation, focus on building superior customer experiences and treat drug launches as a micro-battle. How did your last launch perform vs. expectations, and what were the reasons for under-performance or over-performance? Which interactions matter most for your target physicians, and do you provide a superior customer experience? What are the three largest internal challenges your launch team faces, and what would it take to eliminate them?

Read more Pharma Commercial Success Stories

Final Results are in! The Winners of Data-driven Madness 2016 are …

========== April 18, 2016 ==========

And the final results are in! The winning business initiative or technology of the 2016 Data-driven Madness tournament is …

Reliable High Quality Data

winning easily over Master Data Management in the technology region. Final results and full bracket below. Thank you everyone for participating. In summary, this tournament indicated that Reliable High Quality Data is a business imperative, that is crucial as the foundation for any predictive analytics or machine learning for relevant insights. It also showed that Master Data Management, a stalwart technology and discipline for over 10 years cannot stand alone as the only offering to meet business demands. In fact, for many Reliable High Quality Data now goes beyond just MDM. 

We will be publishing a full interactive infographic of the tournament shortly. Contact us to indicate your interest in receiving a copy.

The winner of the apple watch is Ryan McCormick with a narrow 3 point advantage over ericbless who receives a $50 amazon gift card for finishing second. As does Charles Joseph who won the random drawing among all participants. Thank you all for taking part in Reltio Data-driven Madness 2016!


========== April 11, 2016 ==========

The championship game is here! 

Anyone can still enter because Championship correct selections score 10 points each! Also as a bonus, a random drawing will occur on everyone who votes for the championship winner for another mystery prize.

Here is the near final look at the bracket the voting percentages and participant leader boards. Scroll down for the highlights of the round and instructions on how to play. Click here to enter your selections for the Championship game.

And the updated leaderboard. It’s a 3 horse race, with Ryan McCormick taking the lead.  ericbless in second and is only 3 points away. Big Dan scored 0 in the final four and drops to 3rd.


Highlights from the Final Four round (Feel free to provide your thoughts in comments below)

Business Region 1 vs Region 2

  • #1 Reliable High Quality Data won easily over #1 Relevant Insights Delivered. Indicating that accurate data was a prerequisite for insights.

Technical Region 1 vs Region 2

  • #1 Master Data Management narrowly squeezed by #1 Predictive Insights showing that a changing of the guard is nearly here. Many expect MDM to be standard moving forward, so the votes reflected that assumption. However MDM was still too critical and not widely adopted to be beaten this year

Preview of Championship game

It’s all about data quality and reliability in the final match up. The question remaining to be answered is that if business expects #1 Reliable High Quality Data, is #1 Master Data Management the only and the best way to deliver in this modern data management era.

Results will be published and the champion crowned at the end of the week. Voting closes Friday 12am PT.

========== April 6, 2016 ==========

Even though March Madness is in the books,  Reltio Data-driven Madness 2016 continues and for the first time in history, all #1 seeds have made it through to the final four!

Anyone can still enter because Final Four correct selections score 5 points each.

Here is the latest look at the bracket the voting percentages and participant leader boards. Scroll down for the highlights of the round and instructions on how to play. Click here to enter your selections for the Final Four.

And the updated leaderboard with “Big Dan” still holding serve with a slim 2 point advantage over Ryan McCormick.  Fernando drops to 4th with a DNP in the last round, ericbless gains ground and is only 5 points away. Charles Joseph is still mathematically in the running for the Apple Watch!

Some highlights from the Elite Eight round (Feel free to provide your thoughts in comments below)

Business Region 1

  • #1 Reliable High Quality Data blanked #2 Always Available in a Villanova vs. Oklahoma style beat down

Business Region 2

  • #1 Relevant Insights pulled away from #6 Recommended Actions highlighting that people want to know what’s going on with the data

Preview of Final Four Business Region 1 vs Region 2

Battle of #1 seeds in the business region begs the question. Is it enough to get #1 Relevant Insight? Or does the data have to be #1 Reliable and High Quality for the insights to be correct and matter?

Tech Region 3

  • #1 Master Data Management  trumped #6. Graph Database but not by an overwhelming margin. Many of the crowd realized that some of the best MDM platforms today already have Graph embedded  

Tech Region 4

  • #1 Predictive Analytics squeezes past #2 Data-driven Apps showing some indecision. Do you want just analytics and the #1 seed? Or #2 Data-driven Apps that are both analytical and operational in nature? This was one of the best games of the tournament with analytics winning out.

Preview of Final Four Tech Region 1 vs Region 2

In almost a carbon copy of the Business Region semi-finals, the Battle of Tech Region #1 seeds begs the question. Is it enough to use #1 Predictive Analytics? Or does the data have to be based on a #1 MDM foundation for reliable data quality for the insights to be correct and matter?

========== April 1, 2016 ==========

It’s down to the Elite Eight in Reltio Data-driven Madness 2016. Surprisingly all #1 seeds have made it through so far. Now it’s crunch time.

Anyone can still enter because Round 4 correct selections score 4 points each.

Here is the latest look at the bracket the voting percentages and participant leader boards. Scroll down for the highlights of the round and instructions on how to play. Click here to enter your selections for the Elite Eight.

And the updated leaderboard with “Big Dan” still holding serve with a slim 1 point advantage over Fernando. Ryan McCormick has dropped down to 5th (UPDATED due to a scoring/identification error Ryan’s Sweet Sixteen score has been updated and he is still in the running), ericbless and Charles Joseph are all within striking distance of the Apple Watch!

Some highlights from the Sweet Sixteen round (Feel free to provide your thoughts in comments below)

Business Region 1

  • #1 Reliable High Quality Data cruised into the Elite Eight with a dominant performance over cinderella #13 Team Collaboration
  • #2 Always Available survived a scare from #3 Closed-loop ROI

The chance for a final four berth pits #1 vs #2 in a fascinating conundrum. Is access to data anytime, anywhere more important? Or does that data have to be reliable in order for it to matter? 

Business Region 2

  • #1 Relevant Insights crushed #5 Easy-to-use with their starters resting in the 2nd half for the potential face off with #6 Recommended Actions
  • #6 Recommended Actions trumped #2 Real-time delivery in an upset, proving that getting support and guided help wins over speed on insight in this case

#1 vs #6  match-up here is almost too close to call. Do you need relevant insight first, before you get recommended actions? Or will you trust recommended actions to be based on relevant data and insight?

Tech Region 3

  • #1 Master Data Management a crowd voting favorite shimmied into the Elite Eight, over underdog #13 Semantic Analysis.  Chants of “MDM, MDM” could be heard in the crowd. Coach of #13 Semantic Analysis was quoted as saying “We’re a young technology, we’ll be back next year”
  • #6. Graph Database outlasted #7. Machine Learning in OT, with 7 lead changes in the final minutes.

In another #1 vs #6 game, the analysts feel that #1 MDM has the edge because all modern data management platforms have built-in Graph capabilities. Meaning that #6 Graph Database can’t possibly win. However those who are on legacy MDM systems, might feel they have to go for #6, but they may encounter scalability issues with typical Graph DB technology when they hit higher volumes.

Tech Region 4

  • #1 Predictive Analytics shut out #4 Elastic Scalability in an absolute drubbing
  • #2 Data-driven Apps won easily over #3 Hybrid Cloud, showing that IT values delivering business facing capabilities over infrastructure preferences

This final tech region 4 game is a doozie. Again who you favor depends on your perspective. Do you want just analytics and the #1 seed? Or #2 Data-driven Apps that are both analytical and operational in nature? This is one of the most anticipated games of the tournament.

========== March 24th, 2016 ==========

It’s Sweet Sixteen time in Reltio Data-driven Madness 2016. Not surprisingly all #1 seeds have made it through so far. Now it becomes a true test.

Anyone can still enter because Round 3 correct selections score 3 points each.

Here is the latest look at the bracket the voting percentages and participant leader boards. Scroll down for the highlights of the round and instructions on how to play. 

And the updated leaderboard with “Big Dan” holding a slim 1 point advantage over Fernando. Ryan McCormick, ericbless and Charles Joseph are all within striking distance of the Apple Watch!

Some highlights from the second round (Feel free to provide your thoughts in comments below)

Business Region 1

  • #2 Always Available had to survive a massive scare to beat out upstart #7 Free Text Search. It was looking like a lost cause but pulled off a Texas A&M style comeback to win in double OT
  • #13 Team Collaboration’s Cinderella run continues! Easily blowing away #12 Role-based Access, who in the last round pulled off their own upset.

Business Region 2

  • #1 Relevant Insight Delivered won easily over #8 Self-service Reporting. The crowd was heard chanting “You got Served!” at the #8 seed, proving that the fans want things automatically delivered on a platter
  • #6 Recommended Actions Provided was taken to overtime by favorite #3 Third-party Data Sources setting up a clash with #2 Real-time Delivery in the Sweet Sixteen

Tech Region 3

  • #13 Semantic Analysis a relatively new first timer to the tournament beat crowd favorite #12 Columnar Database in a sort of semi-structured vs. structured showdown
  • #6 Graph database continued to thump the competition, pushing aside #14 Schema-less
  • #7 Machine Learning ousted #2 Workflow with a barrage of well judged shots, that not surprisingly got better as the game wore on, and they received more data 

Tech Region 4

  • #1 Predictive analytics continued it’s date with destiny, even predicting in the post game conference that it would cruise into the final four
  • #3 Hybrid cloud showed that it was twice as nice by beating #6 Public cloud
  • #2 Data-driven apps once again destroyed its opponent and it wasn’t even close

========== March 20th, 2016 ==========

Round 1 of Data-driven Madness is complete! While there were some tough matchups, there were relatively few upsets. Most of the seeded teams moved on to the second round, where competition will now prove to be tougher.

You can still enter because Round 2 correct selections score 2 points each.

Here is the latest look at the bracket the voting percentages and participant leader boards. Scroll down for instructions on how to play. 

And here is the leaderboard …

Some highlights from the first round (Feel free to provide your thoughts in comments below)

Business Region 1

  • #13 Team Collaboration pulled a mild upset over #4 Structured Processes proving that democratization and input rules
  • #7 Desktop Access beat #10 Mobile access in a last minute thriller

Business Region 2

  • #3 Third party sources won easily over #Social Media/Public sources indicating that everyone still as a little distrust over uncurated data

Tech Region 3

  • #12 Columnar Database beat #5 Relational Database handily in an upset, signalling a changing of the guard
  • #6 Graph Database thumped #11 Document Database in a rout, in a battle of newcomers
  • #14 Schema-less handily beat #3 Fixed Data Model, showing that flexibility is king

Tech Region 4

  • #1 Predictive Analytics moved on by beating #16 Business Intelligence, illustrating that it’s better to look forward, than back
  • #8 Microservices crushed #9 ETL, indicating that given the horse power batch isn’t really relevant anymore
  • #6 Public Cloud beat out #11 Private Cloud. Next round’s match up between #6 Public cloud vs #3 Hybrid cloud (who vanquished #14 On-premises) should be fascinating
  • #2 Data-driven Apps destroyed #15 Process-driven Apps, no surprise as they are the future of the enterprise and strong favorites in the tournament

========== Challenge kickoff and instructions March 12th, 2016 ==========

It’s that time of the year. Just like March Madness, Data-driven Madness has brackets, and an ultimate champion. Some of the head-to-head match ups are “lay-ups” (pun intended), and others are a little “I’m on a desert island and I have to choose one” hard.

To play:

1. Fill in the final selection championship round here. Select the winner of each of the head-to-heads by forecasting and selecting the winner of the second round. Your selections will also cast a vote for each selection, and the highest number of votes for each will move on to the next round.

2. You get a point each time your selection moves on. After each round is completed, you will be given the chance to select the winners of the next head-to-head rounds. Second round winners count for 2 points and so on.

3. The winner of the Apple Watch will be the person with the most number of points at the end of the tournament. Unlike March Madness, you can join at any time until the end of the tournament when all the results will be tabulated.

4. Tie breaker on points will be determined by the person who selected the Champion, and then further tie breakers will be by the number of correct entries in the final four and previous rounds.

Surprising Findings from Master Data Management EU Summit

Earlier this week I presented a 3 hour workshop at the MDM EU Summit in London titled MDM Comes of Age with Big Data and Data-driven Applications”, covering best practices, case studies and technology considerations discussing the following topics and more: 

  • Leveraging enterprise multichannel data to enable ‘inside-out’ client view via MDM
  • Understanding the business value of Big Data, NoSQL vs. RDBMS vs. Data Warehouse, Hadoop (HDFS & MapReduce)
  • Discussion of IoT, Machine Learning and Data Monetization
  • Establishing the business case for MDM & real-time data-driven applications  

I also presented the next day a session Leveraging MDM for M&A – The “Ultimate” Master Data Challenge with a case study discussing how to:

  • Agglomerating M&A data together from the merging parties into a single Cloud environment to rapid deploy Big Data analytics
  • Creating a pre-merger information model that clearly shows synergies between merging parties while providing post-merger foundation for consolidation & business growth
  • Masterminding data-driven applications to simultaneously support downstream needs of hundreds of legacy applications across hundreds of operating divisions/business units

Please email me at ramon.chen@reltio.com if you’d like a copy of either presentation.

Both presentations were extremely interactive, with lots of great questions from the audience. During the workshop, and throughout the conference via Twitter and direct emails, I conducted an anonymous poll of the MDM EU Summit attendees. The latest results are summarized in the table below. If you would like to add to the survey results, feel free to take the poll at www.reltio.com/fullpoll:


  • #1 Experts and Beginners dominate (why come to a conference otherwise)
  • #2 Companies are starting to demand multi-domain as table-stakes
  • #3 Match and Merge flexibility rated most prized
  • #5 Big Data awareness and experience mostly at beginner level
  • #8 Data Quality rated Highest Challenge or Need


  • #4 Easier/faster upgrades or equivalent mentions (the question allowed for free format responses) rated highest, perhaps reflecting interest in cloud and more agile MDM
  • #6 Interest in all elements of social, mobile, analytics and cloud
  • #7 100% feel that MDM and Big Data initiatives should be combined, even though discussions with attendees indicated they were not responsible for Big Data projects
  • #8 Even though data quality was the main concern, time to business value was called out, reflecting sensitivity by IT to business user needs
  • #10 Machine learning was of interest

OBSERVATIONS DURING AAron Zornes MDM Vendor Evaluation Session:

The mention and focus on Graph databases continued in a panel hosted by Aaron, which selected Graphs as the #1 topic for discussion. This is not a surprising trend since Aaron himself tweeted out at NYC’s MDM Summit last year.

My workshop contained an in-depth discussion around graphs and their impact in the world of MDM

It was a great event, well organized with lots of excellent presentations and interactions. I concluded that many companies are just starting their MDM journey, and thanks to the pioneers, they can avoid the mistakes of years past. Judging by the interest level at the Summit in technologies beyond MDM, such as graph databases, cloud, machine learning, they are focused on doing so.