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Informatics are used to drive productivity, effi-ciency, quality and innovation, and now theyare set to help provide insights from oceans of

disparate data – ‘Big Data’. However, it is not just the data – social media

and mobile devices have transformed communica-tions, data acquisition and information and datasharing. The mobile devices we carry at all timesgive us unprecedented access to information inreal-time. Booking a restaurant, setting your TV,finding a car mechanic or calling a plumber can allbe done in seconds. It seems in our working lives,we don’t always enjoy the same level of access toinformation to help us make the correct decisionsfaster and be more productive. But things arechanging, and the tools and way we use them inour home lives are starting to be transferred to ourworking lives.

This article looks at the data and informaticslandscape in R&D and considers what and howthese developments might impact the way we workand utilise data over the coming years.

Look back to go forward To see how the future may look, it is often usefulto reflect back on where we have been. Twentyyears ago the pharmaceutical R&D IT landscape

was relatively simple and mainly centred aroundcheminformatics. There were tools for drawingchemical structures, databases for registering thosestructures and systems for storing associated data.The challenges then were accessing data and howto cope with increasing data volumes.

Most processes were analogue, not digital, andthe majority of decision-making happened in thelabs or at project meetings around an overheadprojector using acetate handwritten ‘slides’.Fortunately, project teams and individual scientistswere dealing with relatively small numbers of sam-ples so it was relatively easy for team leaders tokeep much of the data in their heads. Most of ITwas writing large systems for corporate data orbespoke data warehouses. Local IT groups werepopulated by ‘scientist developers’ creating appli-cations to solve point problems or to enable newtechniques. For example, parallel synthesis (orcombinatorial chemistry, as it was known then) ona worthwhile scale was not possible without soft-ware or for that matter automated preparation andstorage. The same was true of many in-house HighThroughput Screening (HTS) data systems.

Some things have moved a long way since thoseearly days – but others not so much. In HTS andcombinatorial chemistry, massive industrial change

By Glyn Williams and Dr Paul

Denny-Gouldson

INFORMATICSthe same old problems,just bigger

Today’s information technologies profoundly affect the way we conductresearch and development (R&D), and the last 20 years have seen greatadvances in information technology across both our work and home lives.From representing complex material structures to rapidly sequencing andannotating genomes, informatics continues to revolutionise the R&D arena.

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was driven by instrument technology enablingeveryone to create higher volumes of more com-plex data. This was supported by new softwarewhich was developed to analyse the data more cre-atively and quickly.

Here’s how the HTS story went: advances inhardware and reagents drove increased through-put; these became so high that no-one could eye-ball every result, so the industry began to employtechniques to analyse data quality (z’ analysis ofcontrol deviation) and select hits (robust statis-tics of control and test populations).What wasneeded then was software to collect and performthe analysis. Then we needed new ways to visu-alise and consume the data that had beenanalysed. 96-well plates became 384 whichbecame 1534, HTS became ultra-HTS and robot-ics drove the wheel even faster. Allied to this wasthe logistical need to order and create test platesready for the robots, which became inventory-hungry monsters. All this drove the ability toautomate by making the instruments talk to thesoftware, and we started to shift the dynamicfrom having isolated pockets of data to a morejoined-up world.

The human IT landscape has also changed dra-matically. Pharmaceutical companies now have rel-atively few developers compared with the past andthe modern philosophy is to buy as opposed tobuild. This is also helped by the number of soft-ware providers in this space.

Bigger and more varied challengesThe information challenges facing R&D now arestill there but are far bigger and more varied. Datavolumes and the rate they are produced are stillincreasing as in the days of HTS, and althoughnow we call it Big Data, it is all relative.Yesterday’s Big Data was a challenge because wewere limited by processor power and storagecapacity. We can now compute and store muchmore than we could have dreamed about in thepre-internet days but the same old problem stillexists. Irrespective of the amount of data beinggenerated, how do we make sense of it all?

We currently create more data than we can con-sume, and this is only set to increase. We needways of triaging the information we receive toensure what we get is what we need when we needit. In addition, high context data is essential tobuilding a valuable knowledge ecosystem. It mustbe stored alongside its ontology and provenance.This is what enables it to be compared and usedeffectively, weighted against other competing dataproperly and quality controlled.

In science a lot of what we do iscomplex – it’s a factToday we can do so much more. It is possible tohave reams of information at our fingertips butthis can easily lead to complexity and we canlose ourselves in a maelstrom of data. Softwareand infrastructure can help to control and avoidsuch confusion and help us better manage rou-tine complexity.

There is also complexity in the number of differ-ent systems an organisation needs to use. Thesesystems are useful in their own right, but when wecan share and link data between different systemsthen the potential usefulness increases exponential-ly. This is particularly the case if systems can talkto each other in a way that enables you to stay inyour application of choice and bring in data fromother systems as and when needed.

Take the analogy of the iPhone. It is a telephonewhich also has a camera. Dependant on what peo-ple do, they may only require this camera andnever use one that is ‘phone-free’. The purist, how-ever, wouldn’t take a prized family portrait withtheir phone and, when buying their next high endSLR, wouldn’t think of asking the salesperson if itcomes with a jazzy MP3 ringtone. At the end of theday, it comes down to which tools you need to dothe job you want to do.

The iPhone example has similarities to the R&Dinformatics arena. With the increased amount ofsoftware available, the lines between system capa-bilities have become blurred and the acronyms everless relevant. Does anybody get really excitedabout whether they are using an ERP, PLM, ELN,LIMS, SDMS, MES, LES, CDS, CTMS, LIS,

Relative importance ofattributes when selecting R&Dinformatics software

Ease of use38%

Price19%

Security 15%

Other17%

Ease of installation

11%

© IDBS Customer Survey 2011

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Chemistry Registration or Biological Registration– so long as they can get their R&D done quickly,correctly and without a shelf-full of manuals?

However, the competition between vendorsand the enterprise nature of some of these sys-tems can create complications. For example,Laboratory Information Management Systems(LIMS) can be generic, specific or both, andElectronic Laboratory Notebooks (ELN) canintegrate with LIMS or have LIMS features andcan overlap with other acronymic systems suchas SDMS, LES and CDS. This is often harderthan it should be, as we all love to classify things– particularly as scientists – thanks Darwin! It isa good thing that the consumer phone world isnot quite as confusing.

The underlying essence has to be: what do weneed the software to do? It is very easy when youare looking for systems to do specific tasks, butwhen you are looking across the enterprise youhave many more people to please. Compromise isessential but does that lead to a sub-optimal sys-tem? Possibly, but the Total Cost of Ownership(TCO) of a compromise solution can often out-weigh the lower Return on Investment (ROI) forindividual functions or groups.

Opting for a platform approach makes a lot ofsense, but as we mention above it is vital that plat-forms can link and share data with other systems.Most modern software therefore now comes withcomprehensive Application Programming Interfaces(APIs) and web services (RESTful and SOAPdepending on the nature of the integration), and ifthey don’t they should. These allow integration andconfiguration to cater for those specific edge cases and specialist systems to be integrated whereapplicable.

How can we bring our home lifeexperiences into the lab? In science a lot of what is done is complex, but wenow have an expectation that these complex thingscan be done simply – search the web and find everyplace where the phrase ‘regenerative medicine’exists in any language – and give me the results inless than a second and show me the context tooand how it links to other things!

This expectation has been steadily rising and,having been conditioned by the likes of the iPhone,we now believe that we should be able to do every-thing from phone calls, email, photos, speed-datingand internet access all with the swipe of a finger.

Scientists are getting social too!

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However, while most of these applications help usconsume and use data really well, they only offerus extremely simplistic ways to capture and securedata and information.

Pick up the phone and ask for help –you must be joking! Another factor in the expectation of users is thatnew technology has subtly changed the way wecommunicate. One of the important factors in thedata-information-knowledge paradigm is howinformation flows. Applications such as Facebookhave changed how information flows between peo-ple and how we share our information.

Recently I was sitting in a restaurant in Bostonwith colleagues and conversation got on to Fellinimovies, so my colleague typed into Google ‘LaDolce Vita’ hoping to get a link to the film.However, the first page was filled with restaurantsor establishments in the local area called La DolceVita. The name, which is pretty common in theNorth end of Boston, had to be prefixed withFellini to get a more pertinent set of results. Whileit was impressive that Google knew where wewere, due to GPS, it wrongly assumed what wewere looking for. The phone was obviously notaware that we (well some of us) were a bunch ofart house movie bores.

There is every expectation today that applica-tions will ‘know’ such things by linking systemstogether – link your Facebook profile to yourGoogle profile perhaps – or combining your news-feeds to assess your interests or community.Similarly, the ‘if you like this’ feature in Amazon isanother way in which we see things we may nothave been looking for. What is of interest in ourmovie search story is that the software on thephone is using the data it has about where we areand modifying the search results accordingly.These ‘m-apps’ are already heavily used by retailmarketers offering promotions and feedback fromthe stores you are walking towards or even out of,creating an ‘always on’ engagement with their cus-tomers. So, today’s systems can estimate, based onalgorithms, what we may be interested in ratherthan us having to look for it. They may not alwaysbe right, but by monitoring the choices we make,the systems can learn more about us and serve thedata we need or in our personal lives – the advertscompanies want us to see.

In essence the software deems what is appropri-ate or of particular interest to the phone’s owner oruser – herein lies another potential issue that ishighlighted later. This is a great opportunity forthese types of approaches in R&D, but the context

will need to be in terms of ‘where you are scientif-ically in your work’ not just where you are locationwise. This is a far more complex problem to solve– a tractable one no doubt – for those clever peo-ple out there.

We all consume data and information differentlyAs we subtly change the type of data we canaccess, and subsequently how we communicatethis then leads into changes in how we use thedata. While the flow of information (communica-tion) is important it is what people do with theinformation that potentially turns it into knowl-edge. As Peter Drucker said: “You can’t manageknowledge. Knowledge is between two ears, andonly between two ears.”1 Many of the social net-working features we use help share informationand make data more accessible, relevant, com-mentable and opinion-based. This is potentiallyvery useful in the lab and in capturing the intellectof the R&D community. However, it is importantthat the right people are commenting or ‘likingthis’ and have the context to make knowledgeableresponses that lead to good decisions.

To take a non-science related example, throughFacebook I learned that one of my friends wasgoing to buy some medium density fibreboard(MDF), not something I would share publicly butthere you go. What surprised me was the amountof people who commented on this, some with asemblance of knowledge and others without. WhatI now know is if I ever have the need to buy someMDF, I know who in my ‘community’ to ask. The

How we consume data is changing!

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likelihood is that I won’t be buying MDF, whichdemonstrates the counterpoint to serving upknowledge in a work environment. You want peo-ple to have access to what is going to be useful tothem to make the right decisions. They do notwant to be bombarded with superfluous informa-tion that consumes their day.

Pharmaceutical companies employ clever peo-ple, with specific skills and domain expertise. Ifonly two people out of 100 say they ‘like this’ onFacebook in our home lives, it may not be statis-tically valid based on a numbers game. But, in thelab it may be hugely scientifically valid if it is theright two people who comment. As well as pro-viding productivity, efficiency and better context,integrated knowledge informatics could alsobring people with complementary knowledgetogether. This is where the software having thecapability to know what will be of interest, andmore importantly, what won’t become important.This is a fundamental requirement so people arespending more time on what will add value totheir work.

Scientists and researchers have opinions aboutmost things. While the social networking featuresmay have many things to offer in the context of sci-entifically-based R&D, it will likely take somemodification and control to apply them and tomake them valuable tools in the day-to-day life ofa scientist or engineer.

We are mobile scientists – but are wereally touchy-feely scientists?So, most of us have some device that allows us tobe constantly connected to the world, and manycompanies see mobility and tablets as the way for-ward for their scientists. While there is no doubtthat this has great promise, it is not necessarily thegreat panacea for science: well not yet anyway.These devices allow you to capture and consumevery simple pieces of information.

The ‘m-approach’ consists of relatively smallapplications that talk to each other rather than anenterprise approach of large scale applications.This paradigm is effective when we may be justwanting to do specific tasks such as reviewinginformation, commenting, tagging or capturing aspecific piece of data in a remote lab or place (egbalance reading). This can complement enterpriseapplications but does not necessarily replace them.We have already seen the use of print to/send to inmost modern applications – now it needs to workwith snippets of data rather than big lumps and beable to put it into the right place to make the infor-mation useful.

The opportunities are endless... so we need to be pragmaticUndoubtedly we will see the rapid evolution ofsoftware and hardware usage in this space – lookat how far we have already come in 20 years. Data-driven change is a fundamental part of life, both inthe home and at work. It is a given for society andscience to evolve.

Computer science has evolved to a point wherewe have the power and the accessibility to do mostthings we need and want to do. What is changingis the way we interact with hardware and software,and indeed information. It is a ‘people thing’. It ishow we access and consume information that isimportant. The information is out there and thereis so much of it, that just asking the questions isnot going to work, as this is based on the assump-tion that we are asking the right question in thefirst place. Therefore it is a combination of askingfor the data we need, having relevant informationserved to us and enabling access to people with theright information or knowledge to lead us to betterdecisions. A great example of this stems from thepersonalised medicine world – where the vision isthat based on data (your genome, proteome, fami-ly history, symptoms and lifestyle) preventativeadvice and specific treatments can be prescribed.This requires a whole host of data to come togeth-er, but the advances made already show its poten-tial. Some cancer patients do not respond to certaindrugs and this can be linked to the patient’sgenome or the cancer’s genome – this is nowknown and used to help make clinical decisions inhospitals. Even the tools that remind you to takeyour medicines are changing outcomes.

Where will this all end up? Technology moves atsuch a pace but what is important is how it isapplied in the context of what you are working on.NASA is looking at 3D printing to make pizzas inspace. If they can do that then what does it meanfor R&D companies making ‘stuff’? Will it all bedone on computers and 3D printers in the future?Maybe it is far-fetched, but then I never expectedmy computer to recommend ‘Pretty Little LiarsSeason 3’ to me (it doesn’t know my daughter usesmy Amazon account). Perhaps real time geneticfingerprinting does have its place! DDW

Glyn Williams is VP Product Delivery at IDBS.

Dr Paul Denny-Gouldson is VP of TranslationalMedicine at IDBS.

References1 Drucker, PF (1969). The ageof discontinuity: guidelines toour changing society. NewYork, NY: Harper and Row.