This whitepaper gives an overview about the requirements and the approaches to make your compliance initiative count double. Not only to fulfill compliance but to go the next step bringing your documentation and knowledge handling to a stage where future projects can learn from previous successes and mistakes. This will make your R&D department ready for future challenges, faster markets and global partnerships.

1. www.process-relations.com
Process Relations GmbH
Make compliance fulfillment
count double
Abstract
The need for compliance fulfillment, like ISO quality assurance, information
governance, etc., has arrived in R&D. If you like it or not in more and more
industries the development of new products is regulated and you need to think early
on about how to fulfill all the documentation and other compliance requirements. But
this whitepaper is not about how to do that – it is about how you gain more out of the
compliance initiative in your company than just compliance fulfillment.
Process Development Execution Systems (PDES) borrow on concepts from
Manufacturing Executions Systems (MES) and Product Lifecycle Management
(PLM) and provide an infrastructure tailored to support and help compliance in R&D
projects. PDES can be used to organize and track the data and information
gathered during R&D efforts and therefore provide solid documentation for
compliance initiatives. But it is not only about compliant documentation, it provides
capabilities to load, to manage and to retrieve data from various sources. It allows
engineers to look at historical and current data and to make connections between
the results gathered. By doing so a PDES like XperiDesk by Process Relations
enhances data and converts it into information that can be used for new product
developments.
This whitepaper gives an overview about the requirements and the approaches to
make your compliance initiative count double. Not only to fulfill compliance but to go
the next step bringing your documentation and knowledge handling to a stage where
future projects can learn from previous successes and mistakes. This will make your
R&D department ready for future challenges, faster markets and global
partnerships.
Whitepaper

2. Whitepaper
Make compliance fulfillment count double Page 2/6
Table of Contents
Make compliance fulfillment count double..................................................................................................... 1
Abstract ......................................................................................................................................................... 1
The Challenge ............................................................................................................................................... 3
What is intelligent compliance? ..................................................................................................................... 4
Conclusions ................................................................................................................................................... 6

3. Whitepaper
Make compliance fulfillment count double Page 3/6
The Challenge
Let’s be honest, compliance fulfillment in R&D is mostly seen as a necessary evil. It is not liked because it
seems to limit the freedom of research & development and creates (at least at first) a documentation
overhead with perceived limited value. So let’s have a look at the documentation part of the compliance
first.
What you gain out of documentation and compliance should be:
 Storage of research data with (authorized) access from everywhere you may need it
 Search for research data (either full text or by certain criteria defined in the compliance initiative)
In R&D however this poses several problems:
 Data is available in structured (Tables with numbers and units) and unstructured (images, email,
documents) form. In fact approximately 80% of the digitized data in a typical company is in the
form of unstructured data as shown in Figure 1.
 The structured data may change daily or even hourly. New parameters to collect and monitor are
found and old ones deprecated.
 Search criteria and reports change with each project and even within a project.
 Full text search is not enough and it doesn’t deliver the context (In which project was this result
achieved? How was the component produced? Where else did we use the same material? Show
me all components that work in a range from -50°C to 120°C.)
 The context of the data in general is most often not kept or partly lost due to limitations in
categorization.
Figure 1: Ratio between structured and unstructured data
This leads to the undesirable result, that data is only used within one project or the live cycle of a
component. The learning for future projects is limited since only specialists or people who entered and
archived the data in the first place can find and reuse the gained information. And that is basically the
situation where the “I told you so” person comes and tells you, that compliance in R&D is of limited use
and it becomes more and more difficult to keep people motivated to continue the compliance conform
documentation of research projects.
As mentioned, the creation and storage of data today is a technical (solvable) issue. Truth is, with today’s
IT it is, in most cases, no big problem to store the large amount of data. An amount that is growing every

4. Whitepaper
Make compliance fulfillment count double Page 4/6
year by approximately 50%1. But that is the crux of it. The data can be stored. Many technologies and
methods used are optimized for storage… and are more than 30 years old. While that is fine for archiving
your results and thus fulfilling regulatory requirements, in R&D you want to gain information and
knowledge out of that data. But when you are drowning in that data with no effective way to retrieve it to
generate knowledge for informed decisions, you have a problem. A problem that increases by 50% every
year! Recent reports by IDC document that even today 40% of experiments are repeated due to
inappropriate storage and retrieval capabilities.
The question is now: How can we break that vicious circle? We can’t get around compliance! So the only
logical conclusion is to gain more from the compliance effort than just “documentation”. What we need is a
more intelligent form of compliance fulfillment that is geared towards the needs of R&D and provides for
collaborative knowledge management and learning.
What is intelligent compliance?
The documentation situation in development organizations today can be summarized by the following:
 Documentation on file servers, MS Excel, MS Sharepoint, paper notebooks, …
 Untraceable and undiscoverable R&D results (people leaving the company, one dimensional
search criteria, etc.)
 Limited formalized data (numerical data being parameter AND unit aware) available
 Formalized data is not really searchable if the units change (e.g. search for temperature in °C if it
is stored in K)
 Formalized data is “formalized” in different ways by different departments or even different
persons even if they describe the same facts
 R&D data is not interlinked / related, the context is missing in the documentation
Intelligent compliance needs to overcome these hurdles. But let’s look at some definitions first:
Figure 2: DIKW model2
 Data: Data is raw. It simply exists and has no significance beyond its existence (in and of itself). It
can exist in any form, usable or not. It does not have meaning of itself. In computer parlance, a
spreadsheet generally starts out by holding data.
1 Oracle: Information Management – Get control of your Information
2 Following the DIKW model: http://www.systems-thinking.org/dikw/dikw.htm

5. Whitepaper
Make compliance fulfillment count double Page 5/6
 Information: data that are processed to be useful; provides answers to "who", "what", "where",
and "when" questions. Information is Data that has been given meaning by way of relational
connection.
 Knowledge: application of Data and Information; answers "how" questions. Knowledge is the
appropriate collection of information, such that its intent is to be useful.
So what we are really looking for in R&D (ok, basically in every domain) is at least information or even
better knowledge. And intelligent, compliant documentation for R&D should deliver just that! But how do
we gain that? Looking in the production area this problem is handled by a multitude of tools from the
categories Manufacturing Execution System (MES), Product Lifecycle Management (PLM) and Enterprise
Resource Planning (ERP). These tools use a database as background to accumulate data, provide it for
evaluation and to derive appropriate actions out of these evaluations. So is this the answer? Simply use a
database instead of Excel and be able to search?
Unfortunately the answer is no. Contrary to production, in R&D the data is changing constantly. New
parameters to take care of are discovered daily and a big chunk of the data – such as images – is
inherently unstructured and not suitable for database storage. Another point that we learn from the
definitions is that information needs relational connections between the data points. These relations
represent the context of data and transform data into information. But these connections and relations are
not always known at the beginning of R&D projects.
A system to manage R&D data must be a comprehensive data repository for structured & unstructured
data. It must provide for audit trail with versioning capabilities. The versioning must be applicable for
structured (e.g. numerical parameter data) and unstructured (e.g. PowerPoint file) data. It must provide
the audit trail with a strong “what was edited” component for formalized data that goes down to the specific
parameter edited. This is a special need from R&D that “normal” audit trails don’t require. Comparison
between versions of e.g. a processing instruction must clearly show which parameter was changed.
But to handle R&D data it must offer more! It must provide multi-dimensional access and even graphical
navigation possibilities. A system to support R&D must cater for multi-disciplined working environments
(e.g., providing the electrical engineer with E-Test data and the mechanical engineer stress test data)
without media brake. Users must be allowed to simply manage and create relations between different
entities (like components, experiments, assessments, projects and processing instructions). By allowing
users to create relations between the different entities a semantic web is formed. This semantic web can
be used to perform powerful relations based searches (e.g., show all experiments within a project where a
certain processing instruction type was used and an assessment shows a certain electrical and another
assessment a certain mechanical property). Additionally a semantic web can be used to provide graphical
navigation through historical data (e.g., with tree and graph based views) and those enhance the visibility
and retrievability of the data (for example looking at the same data from a project manager’s perspective -
project driven - and from an engineer’s perspective - component driven).
Other important requirements to allow for data-discovery are sophisticated searching capabilities. These
need to allow searching in structured as well as in unstructured data such as documents. While text based
unstructured data / files can be searched relatively easily using advanced index services, the structured
data management needs to be equipped with physical awareness. This means that searching for a
Voltage only really searches Voltage parameters to comprehensively retrieve the information. Both means
of searching must be combinable and the system must also be able to use freely definable relations in the
searches.
The most important requirements for such a system are the im- and export capabilities. A data
management system for R&D must provide means to import data from existing data sources (Excel
sheets, File servers, SQL databases, etc.). It must allow to integrate data from diverse sources to provide
a comprehensive, holistic picture of all activities, data pieces, etc.. It also requires means to export e.g.
search results into other tools (Excel, Statistical software, ERP, MES, etc.) for further processing. It must
enable engineers to spend more time on evaluating than on collecting and managing data.
But the import capabilities play other important roles in the compliance initiative: Engineers can continue
to work with tools (like MS Excel) that they know! This makes the change for them gradual and the

6. Whitepaper
Make compliance fulfillment count double Page 6/6
transition into the new work methodology is eased. On the other hand the import also enables the system
to gather “historical” data. Thus the system is not empty when the work starts and reference data can be
used right from the start. The “why should I start with entering MY data” hurdle is considerably lowered.
PDES (Process Development Execution Systems) like XperiDesk from Process Relations aim to fill
exactly this role. They provide a centralized platform to collect, evaluate and export data in a
multidisciplinary research facility. PDES are geared to cope with the ever changing structured and
unstructured data that is experienced in R&D organizations.
Conclusions
We have to face the truth – if you want to survive your R&D needs to be compliant with certain rules and
documentation standards. Regulatory compliance initiatives can be used – if done right – to gain more
than just compliance fulfillment. Since you need to do the work to be compliant you can push changes
now that would not be able to be implemented by themselves. Bringing your documentation and
knowledge handling to a stage where future projects can learn from previous successes and mistakes will
make your R&D department ready for future challenges, faster markets and global partnerships.