IDBS Data Management Blog

This is part II of a series of blogs analyzing the results of our recent R&D Collaboration trends survey. See R&D Informatics – are you ready for 2012? for the first post.

Reality of the R&D Informatics landscape

It is often easy to assume the majority of organizations have had the opportunity to address basic data management and reporting requirements. For example, report production and tools for compiling data are a “must have” for today’s organizations who want to stave off the competition. The general assumption is that everyone has solved this problem, but the survey reflects a different reality. There is strong need for enterprise-level data management systems that collect and store information and make it readily accessible to multiple layers of users within an organization. Read on…

What are researchers concerned about?

It’s not just the voice of analyst and executive management, but this survey exposes that at researcher level three main themes cause respondents the most concern:

80% think the IT environment is too complex – they cited issues with existing tools, too many systems and lack of informatics staff as causing the most concern. There is a clear need for the capability to better manage data consistency across multiple domains.

Comment: This means organizations must simplify the desktop and make the data ecosystem less complex.

55% cannot collaborate effectively – they cited the ability to effectively share and collaborate through data, both internally & externally as an area of concern.

Comment: This means organizations must make data searchable, rich in context and digital – delivering the ability to align data, gain insight and make it usable across the enterprise.

37% believe that they are losing intellectual property – they voiced concerns about effective IP capture, management and security.

Comment: This means making sure researchers capture and securely share their innovation, without having to add to their workload. Their systems should seamlessly capture innovation and make IP auditable.

Top collaboration challenges:

Drilling down the topic of collaboration, the survey found that a staggering 91% of respondents surveyed reported their number one collaboration challenge is managing data to ensure the consistency of research results and avoid rework.

The top three collaboration challenges reported were:

1. 91% are worried that they cannot align results with their colleagues
2. 80% are worried that they cannot share their data with their colleagues
3. 78% had problems recording and tracking intellectual property

Comment: If you can’t manage and align data easily then you can’t collaborate with it or secure the IP – which becomes a vicious circle. Document centric approaches just make this problem worse.

What are people’s responses to these issues?

In the next year, R&D organizations are planning to address these data challenges by creating a simplified environment which captures innovation, enables collaboration and provides insight.  Initiatives organizations are focusing on include:

• External collaboration
• Systems consolidation
• Open innovation
• IP capture
• R&D governance

So now we know the issues, what are we going to do about it?

Discover more at IDBS seminars “R&D Informatics: Strategies for 2012” during November in Paris, Frankfurt & Boston, or attend our webinar on November 17.

In July 2011 we worked with Scientific Computing, R&D, and Drug Discovery & Development magazines to survey almost 700 R&D professionals.

The survey aimed to uncover the top challenges R&D centric organizations face in the quest for R&D collaboration, IP protection, and effective data management. It also explored the key initiatives planned to support R&D Collaboration and Data Management in the coming year.

EXPLORING THE SURVEY FINDINGS

88% of R&D organizations lack adequate systems or practices to automatically collect data for reporting, analysis and decision making. An essential part of collaboration is how effectively individuals and organizations share data. The survey found that today’s researchers still rely on manual processes and non-scientific applications, such as Microsoft Office applications, for report production. The task is particularly difficult in the Defense & Aerospace, Manufacturing & Engineering, Energy & Utility, and Academia and Government sectors.

Comment: The R&D world is moving to a data centric environment rather than a document centric environment. Report writing is on average taking 25% of researchers’ time. It’s not about reporting it is about developing insights and understanding from other’s data, enabling everyone to be more productive.

As Jay Galeota, SVP Strategy & Business Development, Global Human Health, Merck explains in a recent Ernst & Young report*: “The most important thing is what you can actually do with the data. It’s one thing to have interesting information, but it’s the insights that are important to guide smarter, better decisions…”

6 out of 10 respondents relied on manual compiling and searching of data. The survey explored how researchers shared data and found that 60% are unable to compile relevant R&D data without manually searching through documents and reports. This results in a static document-centric view of the data.

Comment: This results in a document rather than data centric approach. Each respondent is both a consumer and generator of data – the collaboration challenge is as much about internal person-to-person collaboration, as external business-to-business sharing of data. It also results in significant  time lost/taken away from actual research because people are busy searching through documents for info. For streamlined research, people need to put hands on relevant data immediately, not spend time recreating it and certainly not spend hours looking for it.

57% R&D organizations are relying on in-house systems to manage R&D data. Survey respondents across all sectors are predominantly using legacy in-house, home grown/built, solutions.

Comment: We know that change occurs in R&D organizations on a 12 month basis, but it is fair to say that few in-house systems can effectively evolve or be upgraded in synch with this timeframe. This causes an ongoing burden to businesses and their IT support systems. In talking with industry insiders we hear that this is leaving an unsustainable graveyard of systems. Where software development in-house does have a role is in configuring best in class systems to confer a competitive advantage. Does any R&D organization have building software as part of their vision?

As Chris Thoen, MD Global Innovation Office, Proctor & Gamble commented*: “Only do what only you can do.”

Less than 25% of responding organizations have deployed foundation technologies such as ELNs, LIMS, KM, and PLM systems. This leads to a R&D data ecosystem – data flowing from idea to product – that is fragmented and siloed. The survey reflects the proliferation of third party process applications.

Comment: Many organizations are using a combination of multiple data and knowledge management applications across their business. This makes management of IP/data from early research through to commercialization challenging. And while point solutions have been implemented to solve particular issues, there is a resulting duplication within the IT landscape (e.g. data analysis and reporting tools). In many organizations, multiple similar category products are used by different parts of the enterprise.The result is that there is no end to end solution for data management that integrates into existing data silos

Discover more at IDBS seminars “R&D Informatics: Strategies for 2012” during November in Paris, Frankfurt & Boston, or attend our webinar on November 17.

Watch out for the next blog post in this series – I will explore what the R&D Collaboration survey tells us about IT complexity, collaboration & intellectual property protection.

* Source: Progressions: Building Pharma 3.0 – Global Pharmaceutical; Industry Report 2011 from Ernst & Young.

“We’ll always need to record and search through our research data but how we do this will never be the same.”  This is probably how the Apple iPad advert for an “iResearch” app would run. How true it will become only time will tell.

There are certainly advantages to having access to your research data on the move where a truly mobile, always-on device like the iPad is a big plus. However, where I think devices like the iPad will take us in our marketplace is toward better and more intuitive usability in our research software.

Why do I say this?

I think many in the software industry have become lazy.  With modern software development it is easy to add a button here, a nested menu there and expose an ever increasing list of functionality. But making development so easy has stopped us thinking. Where usability is concerned, less is often more.

What I found developing the ChemJuice and ChemJuice Grande applications for the iPhone and iPad is that we really had to think hard about the user experience.

Why? Because the device and its gesture based paradigm force you to think differently. Everything has to become an extension of your fingertips and screen space is a premium.

Gone are all the things you took for granted – the Windows menu and toolbar system, the right click menu. So you have no choice but to think differently and it makes you think user experience.

It has been a thoroughly invigorating experience and I am getting more and more excited about where it can take us in the future.

See below for a demo of Chemjuice from IDBS.

Say what you’re not saying, don’t say it, say what you didn’t say

Last time I blogged that modelling is not limited to software engineering, play and simulation; but is universal in human endeavour. I mentioned that considering accuracy is important but not sufficient in assessing a model. What other considerations are there?

My favourite lens for looking at a model is abstraction. In philosophical terminology, abstraction is about grouping concepts together at decreasing levels of detail. So, a duck is a duck and no other thing is a duck (no matter how it looks or walks or sounds); but applying abstraction allows us to talk about birds and say useful things, which might be rather exasperating if we had to list every bird in the world to say them. This kind of classification is a particular feature of object-oriented programming languages (which may or may not be a good thing).

Leaving it out

A modeller, not saying

However another way of considering abstraction is to pause before asking what a model is saying, and ask: what is this model not saying?

The model of biological change that we call evolution has incredible empirical support, so that its application has great explanatory and predictive power (some would even say that we don’t apply it enough). Strangely, though, it seems to cause an awful lot of consternation to those who subscribe to another model called creationism.

Why strange? At first sight, both of these models deal with how the world came to be the way it is. But evolution models a process, and has nothing whatsoever to say about how that process began, or why it began, or who began it. Conversely, creationism says nothing about how its proposed agent went about his craft (well, usually). He just did it. Apples and oranges.

Putting it back in

Any critical analysis or use of a model has to carefully stick to assessing or building upon what it actually models. This might sound simple, but humans find it remarkably tricky. We are fond of making cultural and doctrinal assumptions and applying intuitions without knowing about it. (In a black alley, a black cat spies a black rat. How?*)  Unfortunately this is not only inevitable, it’s usually necessary.

Why so? Models almost always rely on background information. Of particular interest in computer science and artificial intelligence is the notion of semantics: the meaning of symbols. Tell a robot to fetch you a cuppa, and it may suffer the same semantic confusion as is now affecting US readers: a cuppa what?