People in science-driven companies make decisions constantly—from routine day-to-day choices to
strategic decisions that may affect the
direction of the entire business. With so
much at stake, how do company leaders
ensure that the right decisions are made?
The good news is the basis for making
smart decisions already exists within these
organizations and their external partner
ecosystems. If used properly, data generated throughout the product lifecycle can
produce actionable business intelligence that
can help define strategy, stimulate innovation, inspire new products, enhance customer relationships and bolster operations.
The actual value of an organization
does not reside in the products it produces, but in its intellectual property—its
knowledge. For example, the core value
in a manufacturing company is expertise
in the way it manufactures products.
Companies have plenty of data, which
together with its context (provided by
metadata) provides valuable knowledge. The problem is, many companies
don’t leverage data effectively.
Data originates from many sources.
Discovery and research, process and
product development, production and
quality systems, virtual and physical
testing, feedback from consumers and
patients, internal applications and
external communications all contribute to
the large pool of enterprise data. Among
those sources, data that comes from
laboratories and their related processes is
especially important for decision-making in
The information laboratory staff create
through experiments, testing, instruments
and observations has an impact through-
out the entire lifecycle of a product and
across the enterprise. Decisions about
product development, production pro-
cesses, testing methods and much more
are influenced by data that originates in
the lab. Accordingly, lab managers should
consider fundamental questions: Are we
using the right data in the right way? Can
we rely on the data? Can we make deci-
sions based on analytics from this data?
Well-managed laboratory data tends to
be high quality data that is easy to access
and fosters a culture of data-driven analytics and smart decision-making.
Four Factors Ensure Smart Data
To use laboratory data effectively for
smart decision-making, lab managers
must ensure it is reliable, relevant and
applicable. Four major factors contribute
to this: data quality, data integrity, data
governance and data analytics.
It all starts with data quality. You’ve
probably heard the phrase: garbage in,
garbage out. If you have poor data quality,
the decisions you make are likely to be
bad. High-quality data means data that is
complete, consistent and correct. It also
means data that has been contextualized.
Without context provided by metadata, the
data cannot be used for decision-making.
When people in an organization make
decisions—from small changes in produc-
tion to something transformative for the
direction of the business—it is important
that they can rely on the data. For that
data integrity is required. The data used in
making decisions must be the original data
as it was captured, not data that has been
changed in any way. It must be accurate
and consistent through its entire lifecycle.
Regulatory agencies focus closely
on this from a public health and safety
perspective. Regulations enforce data
integrity to ensure that the data has not
been altered or manipulated. Data must
be attributable, legible, contemporaneous,
original and accurate. Data integrity is
also important from the perspectives of
preserving intellectual property, supporting patents, maintaining company
reputation and upholding proper scientific
Trust in data as the basis for decision-making begins with high-quality, contextualized data that has full integrity and
has not been tampered with. To that end,
procedures for data governance must be
for Smart Decisions
Quality of data in science-driven companies directly impacts
by Dr. Daniela Jansen, Director, Product Marketing, Dassault Systèmes BIOVIA
Figure 1: Smart data is high quality, has integrity, is managed properly, and is reused and leveraged with the right analytical tools.