DM1805

STRATEGY: ECM 2.0 Dm
The way ahead
Alan Pelz-Sharpe, founder of Deep Analysis, discusses how organisations can move
from ECM 1.0 to 2.0
When any technology sector
becomes 'mature' it gets into a
rut. It's as if there is a collective
question of "Why change things, aren't
we are all doing just fine?". Even so, over
the past few years, things have begun to
change, and the fundamentals of how an
optimal ECM system might work in the
future are being reimagined. The change
is due to interest in the potential of:
Artificial Intelligence and Machine
Learning
An increased regulatory burden
Easy access to cloud storage and
distributed processing capabilities
The need to manage, route, and
control multiple sources of inputs
Managing an overwhelming amount
of unstructured data
Increased need to process and act on
data quickly
The cumulative cost of storing
large volumes of unmanaged and
access data
The move to API and service-based
architectures
Together these factors provide a tipping
point for new technical approaches to
manage and draw value from both active
and dormant files to emerge. We
thought it would be a good idea to
provide some structure for this move
from ECM 1.0 to 2.0, and so we came up
with the diagram above.
ECM 2.0
ECM 2.0 systems will take a contrary
position to the 1.0 systems that focused
on the importance of a single repository.
ECM 2.0 systems will take for granted
that although some business documents
and files may be closely managed in a
closed repository, most will not and never
will be. 2.0 also accepts that there will be
multiple workflows and integration points
in play within a single organisation.
Again, rather than one workflow
system optimised for one repository
there will be multiple and (sometimes)
competing automation and process
systems. In our architecture outline, 2.0
also extensively utilises traditional
content analytics, AI and ML (and in
some cases Deep Learning) at three
layers in the stack.
CORPUS ANALYSIS
Most established firms have millions (in
some cases billions) of stored historic
documents sitting in legacy systems and
document repositories. Few have any real
insight into what is in these documents or
what value (or risk) that these documents
carry. Machine Learning and Deep
Learning systems can be trained to
analyse this corpus of data for legal
discovery, the identification of risks,
duplicates, and redundancy.
ACTIVE ANALYSIS
Active files and documents in ECM 2.0
leverage machine learning to ensure that
intelligent capture, document
classification, summarisation, and insight
are applied. It's here that we see most of
the current activity in the market
occurring, whether that be to improving
capture efficiencies or simply applying
accurate classifications to meet increasing
regulatory oversight needs.
ACTIVITY ANALYSIS
To date, external activity analysis has been
primarily concerned with web content
and there is a growing interest in
analysing who engaged with the content,
how they engaged with the content and
when, and in the process extracting key
business insights.
ECM 2.0 opens up a range of
possibilities to leverage the rich, yet
currently unloved legacy silos of data they
have accumulated, while also extracting
more value from new content and
automating many more activities down
the line. The shift will be a major but
worthwhile undertaking, and play out
over many years. But do not be
overwhelmed, as in many cases, the first
step may just be simply moving your files
to cloud storage. As the Chinese saying
goes, a journey of a thousand miles
begins with a single step. Carpe Diem!
If you would like a copy of our new report
"Intelligent Information Management -
from ECM 1.0 to 2.0" send us an email at
info@deep-analysis.net.
More info: www.deep-analysis.net
www.document-manager.com
May/June 2018
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