Chemical Engineering January 2022 - 30

ance, Frank et al., describe stages
of Industry 4.0 implementation patterns,
where vertical integration of
information systems is one of the elementary
steps to allow a company
to access more advanced scenarios.
In the first stage, most companies
focus on vertical system integration,
energy management and traceability
of materials. The second stage
will focus on automation and virtualization.
The last stage will focus
on making the production process
more flexible [10].
From different points of view, we
now see that the quality and integration
of data are essential for not
only improving the current operation,
but also for embarking on any
type of transformation project. As the
journey continues, establishing the
meaning of data quality is an integral
part of this quest.
What is data quality?
Throughout this article, the terms
data and information have both
been used. Although these terms
are used interchangeably from time
to time, there are clear differences.
Amongst academics, similar definitions
emerge. The word " data " is
defined as a discrete and objective
description of events - for examDimension
Accuracy
Reliability
Timeliness
Relevance
Completeness
Currency
Consistency
Flexibility
Precision
No.
cited
25
22
19
16
15
9
8
5
5
TABLE 1. DATA QUALITY DIMENSIONS [13 ]
Dimension
Format
Interpretability
Content
Efficiency
Importance
Sufficiency
Usability
Usefulness
Clarity
ple, a temperature reading for an
instrument in the facility or a listing
of dates for approved changes to
the facility. Information is defined as
data that has been transformed to
add value in a specific context by
condensing, categorizing and other
means to support the decision-making
process [11]. A good example in
this sense is to use the database
with recorded changes (MOC) to
find trends related to equipment. A
valid question to ask is: are specific
pieces of equipment more prone
to failure compared to others? The
answer could influence buying and
replacement decisions.
Another popular buzzword in this
discussion is " big " data. The obvious
point to look for big data appliNo.
cited
4
4
3
3
3
3
3
3
2
Dimension
Comparability
Conciseness
Freedom from bias
Informativeness
Level of detail
Quantitativeness
Scope
Understandability
No. cited
2
2
2
2
2
2
2
2
cations is along the lines of the automation
pyramid with technologies
such as predictive maintenance.
In the context of big data, there is
often a reference to the " five Vs, "
as follows:
1. Volume - Describes large
amounts of data either stored or in
transit
2. Variety - Speaks to both structured
and unstructured data and a
wide variety of data sources
3. Velocity - Describes the speed
at which the data volume increases,
often at a very high level
4. Veracity - Relates to the accuracy
and quality aspect of the data
5. Value - The value created by
the data for the organization [12]
Big data is not a state of being
that one can achieve, but rather
more of a framework for tools and
methods used with varying data
sources to deliver value from these
complex and large data sources.
While combining different data
FIGURE 2. This taxonomy gives an overview of the potential costs associated with poor
engineering information [9]
30
sources at large volumes with varying
complexity across structured
and unstructured data is at the core
of a big data project, it is easy to
see how important it is to have data
aligned both horizontally and vertically.
For many facilities, through the
years, data have been managed differently,
with the result that a simple
equipment tag is often labeled differently
in all the different systems.
Searching for information on a
specific pump becomes that much
more difficult, and users must navigate
between asset management,
drawings and SCADA systems to
find the information they are looking
for. Aside from system requirements
that force inconsistencies,
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