The document discusses the importance of conducting thorough content and metadata analyses before selecting a digital asset management (DAM) system. It recommends performing a content audit to understand asset types, locations, relationships and versions. It also recommends analyzing metadata needs such as existing metadata, required additional metadata, and whether fields should have restricted data entry choices. The analyses are essential to define requirements that capture organizational needs and avoid selecting a DAM system that does not fit requirements.
Digital Asset Management What to know before you go.pdfHeyEmbedMe
There may be many good reasons to implement a
DAM system within your organization; not the least of
which is identifying, centralizing, and making accessible
valuable assets for use and reuse within an organization.
And while that is a worthy and most formidable goal to
assume, there must be an opportunity to stand back
and ensure the problems are being solved with this
DAM solution. Knowing your problem to solve will be
your greatest starting point on your DAM journey, and
from there more questions may then be formed. First
and foremost, the problem of who is your audience
and what are their problems to be solved is paramount
to success. Take the time to understand the usage
scenarios; who will be using the DAM and what
procedures / output do they need?
A fully integrated DAM can be much more. It’s a critical
component of your martech ecosystem. It enables
you to present a consistent brand to the world. Your
DAM encourages the use of assets along new, often
consumer-defined channels and content lifecycles while
protecting critical content where needed.
DAM is not a project, a temporary assignment to IT or
Marketing whereby temporary resources and monies
are assigned to try and fix something. DAM is much
more than this. It’s an operational asset to be financed,
resourced and managed like any other critical product
and/or service found in an organization
Become Data Driven With Hadoop as-a-ServiceMammoth Data
This presentation gives an overview of what it means to be a data driven company, all of the pros and cons of becoming data driven, and a few softwares used in data management.
Metadata is more than a search enabler. It is the value added to the workflow.Metadata as a Service (MaaS) means metadata that is hosted, managed and validated at a central source and syndicated in
the form of templates and panels to users to embed metadata into their assets.
Key Factors to Consider When Hosting DAM in the CloudCognizant
Detailed evaluation of various digital asset management (DAM) hosting service models - such as infrastructure as a service (IaaS) and software as a service (SaaS) - including costs, advantages and disadvantages, a set of prospective questions for DAM vendors and a use case example
Computing the ROI for an organization adopting a media asset management (MAM) or digital asset management (DAM) system is best done by computing cost avoidances. We offer a model and framework for calculating such cost avoidances associated with MAM/DAM systems, including cost bases and factors, covering the areas of distribution, reuse, management, storage and infrastructure.
Digital Asset Management What to know before you go.pdfHeyEmbedMe
There may be many good reasons to implement a
DAM system within your organization; not the least of
which is identifying, centralizing, and making accessible
valuable assets for use and reuse within an organization.
And while that is a worthy and most formidable goal to
assume, there must be an opportunity to stand back
and ensure the problems are being solved with this
DAM solution. Knowing your problem to solve will be
your greatest starting point on your DAM journey, and
from there more questions may then be formed. First
and foremost, the problem of who is your audience
and what are their problems to be solved is paramount
to success. Take the time to understand the usage
scenarios; who will be using the DAM and what
procedures / output do they need?
A fully integrated DAM can be much more. It’s a critical
component of your martech ecosystem. It enables
you to present a consistent brand to the world. Your
DAM encourages the use of assets along new, often
consumer-defined channels and content lifecycles while
protecting critical content where needed.
DAM is not a project, a temporary assignment to IT or
Marketing whereby temporary resources and monies
are assigned to try and fix something. DAM is much
more than this. It’s an operational asset to be financed,
resourced and managed like any other critical product
and/or service found in an organization
Become Data Driven With Hadoop as-a-ServiceMammoth Data
This presentation gives an overview of what it means to be a data driven company, all of the pros and cons of becoming data driven, and a few softwares used in data management.
Metadata is more than a search enabler. It is the value added to the workflow.Metadata as a Service (MaaS) means metadata that is hosted, managed and validated at a central source and syndicated in
the form of templates and panels to users to embed metadata into their assets.
Key Factors to Consider When Hosting DAM in the CloudCognizant
Detailed evaluation of various digital asset management (DAM) hosting service models - such as infrastructure as a service (IaaS) and software as a service (SaaS) - including costs, advantages and disadvantages, a set of prospective questions for DAM vendors and a use case example
Computing the ROI for an organization adopting a media asset management (MAM) or digital asset management (DAM) system is best done by computing cost avoidances. We offer a model and framework for calculating such cost avoidances associated with MAM/DAM systems, including cost bases and factors, covering the areas of distribution, reuse, management, storage and infrastructure.
This essay presents a new framework to analyze the impact of AI and ML on work. Its premise is that AI and ML have already been adopted in many firms. Now, efforts are underway to simplify the next stage of adoption by removing the complex requirement to create well-formulated algorithms.
This innovation is automating the deployment of ML ecosystems. Early adopters report substantial gains in new revenues, additional efficiencies in operations and a changed mindset for employees. One example of the latter is LinkedIn’s efforts to establish a “culture of data,” where data serves as the foundation for corporate strategy and data analytics-based operations. This essay contends that by lifting earlier roadblocks to adoption, growth of ML and AI systems will increase, greater attention will be paid to obtaining and structuring data resources, and more ML systems can be applied to evaluating strategic and financial decisions.
Architecting the Framework for Compliance & Risk Managementjadams6
Privacy and protection of personal information is a hot topic in data governance. However, the compliance challenge is in creating audit defensibility that ensures practices are compliant and performed in a way that is scalable, transparent, and defensible; thus creating “Audit Resilience.” Data practitioners often struggle with viewing the world from the auditor’s perspective. This presentation focuses on how to create the foundational governance framework supporting a data control model required to produce clean audit findings. These capabilities are critical in a world where due diligence and compliance with best practices are critical in addressing the impacts of security and privacy breaches. The companies in the news recently drive home these points.
Five Things to Consider About Data Mesh and Data GovernanceDATAVERSITY
Data mesh was among the most discussed and controversial enterprise data management topics of 2021. One of the reasons people struggle with data mesh concepts is we still have a lot of open questions that we are not thinking about:
Are you thinking beyond analytics? Are you thinking about all possible stakeholders? Are you thinking about how to be agile? Are you thinking about standardization and policies? Are you thinking about organizational structures and roles?
Join data.world VP of Product Tim Gasper and Principal Scientist Juan Sequeda for an honest, no-bs discussion about data mesh and its role in data governance.
In recent years, the need to manage digital assets has become critical - compelling organizations to focus more on how digital asset management (DAM) systems can be leveraged across the digital supply chain, rather than simply archived. DAMification employs DAM concepts and systems to automate, integrate and enhance workflows, processes and applications - all within an adaptable, technology-agnostic framework that utilizes best-of-breed solutions and supports positive business growth.
Semantic 'Radar' Steers Users to Insights in the Data LakeCognizant
By infusing information with intelligence, users can discover meaning in the digital data that envelops people, organizations, processes, products and things.
Data Mesh is the decentralized architecture where your units of architecture is a domain driven data set that is treated as a product owned by domains or teams that most intimately know that data either creating it or they are consuming it and re-sharing it and allocated specific roles that have the accountability and the responsibility to provide that data as a product abstracting away complexity into infrastructure layer a self-serve infrastructure layer so that create these products more much more easily.
Learn how the Perfect & Merge tool from DQ Global allows Microsoft Dynamics CRM users to identify, perfect and merge multiple duplicates from any business entity into an easy to use browser based environment.
Semantic 'Radar' Steers Users to Insights in the Data LakeThomas Kelly, PMP
By infusing information with intelligence, users can discover meaning in the digital data that envelops people, organizations, processes, products and things.
4DAlert data house platform is a sophisticated and user-friendly solution that enables efficient data management for any organization. Visit: https://medium.com/@nihar.rout_analytics/what-is-data-observability-ece66dcf0081
Big Data Matching - How to Find Two Similar Needles in a Really Big HaystackPrecisely
When consolidating multiple sources of information from across your organization, how do you find the records that relate to the same customer, the same company or the same product? This is the challenge faced by many businesses today when putting a data lake to work. The problem is made far worse when different systems may not have the same contact entered the same way. Is Bob Smith the same as Robert Smith? How about Dr. Robert L. Smith - is he the same person? What about Syncsort, Inc and Sinksort Corp.? Are those the same company? One must compare each individual record to every other record in the dataset with some very sophisticated matching algorithms to determine who is who, and you may have to compare the data multiple times in multiple ways to resolve each entity.
Just to add to the difficulty, let’s say your organization has very large volumes of records in your data lake - you don’t have to compare a thousand records to a thousand other records multiple times - you must compare a million to a million, or 100 million to 100 million. This kind of compute intensive comparison can bring even a powerful cluster to its knees.
This is a problem Syncsort customers must solve, and we have developed some very powerful and intelligent software to tackle it.
View this presentation as we discuss the challenges of entity resolution at scale, how Syncsort’s Trillium data quality software line has tackled them successfully in production clusters and see a demonstration of this software in action.
A lack of trust is inhibiting the adoption of #AI. This presentation discusses approaches to delivering trusted data pipelines for AI and machine learning
What is OLAP -Data Warehouse Concepts - IT Online Training @ NewyorksysNEWYORKSYS-IT SOLUTIONS
NEWYORKSYSTRAINING are destined to offer quality IT online training and comprehensive IT consulting services with complete business service delivery orientation.
Machine Learning in Autonomous Data WarehouseSandesh Rao
Machine Learning in Autonomous Data Warehouse: One can use Oracle Autonomous Data Warehouse for machine learning. There are several ways to do this. This presentation explores these different but related options for performing machine learning. Each of these options enables people with different backgrounds to engage with building machine learning solutions on their data. At the end of the session, you will know which option will work best for you
This is from the Bay area Cloud Computing event https://www.meetup.com/All-Things-Cloud-Computing-Bay-Area/events/271017950/
Introduction to Machine Learning and Data Science using Autonomous Database ...Sandesh Rao
This session will focus on basics of what Machine Learning is , different types of Machine Learning and Neural Networks , supervised and unsupervised machine learning , autoML for training models and this ends with an example of how to predict workloads using Average Active sessions and different algorithms as an example and also how to predict maintenance windows for your databases. We will also use different open source frameworks as well as some of the tools in the Autonomous Database cloud to do this. If you are a DBA and want to learn something about machine learning and use the tools to perform your tasks more efficiently and automaticall
While many enterprises consider cloud computing the savior of their data strategy, there is a process they should be following when looking to leveraging database-as-a-service. This includes understanding their own data requirements, selecting the right cloud computing candidate, and then planning for the migration and operations. A huge number of issues and obstacles will inevitably arise, but fortunately best practices are emerging. This presentation will take you through the process of moving data to cloud computing providers.
Datalayer Best Practices with ObservepointMike Plant
Building a dynamic data layer is not easy, it has various complexities based on your websites design, back end frameworks and libraries and present difficulty in figuring out when and how to present it to the browser.
This essay presents a new framework to analyze the impact of AI and ML on work. Its premise is that AI and ML have already been adopted in many firms. Now, efforts are underway to simplify the next stage of adoption by removing the complex requirement to create well-formulated algorithms.
This innovation is automating the deployment of ML ecosystems. Early adopters report substantial gains in new revenues, additional efficiencies in operations and a changed mindset for employees. One example of the latter is LinkedIn’s efforts to establish a “culture of data,” where data serves as the foundation for corporate strategy and data analytics-based operations. This essay contends that by lifting earlier roadblocks to adoption, growth of ML and AI systems will increase, greater attention will be paid to obtaining and structuring data resources, and more ML systems can be applied to evaluating strategic and financial decisions.
Architecting the Framework for Compliance & Risk Managementjadams6
Privacy and protection of personal information is a hot topic in data governance. However, the compliance challenge is in creating audit defensibility that ensures practices are compliant and performed in a way that is scalable, transparent, and defensible; thus creating “Audit Resilience.” Data practitioners often struggle with viewing the world from the auditor’s perspective. This presentation focuses on how to create the foundational governance framework supporting a data control model required to produce clean audit findings. These capabilities are critical in a world where due diligence and compliance with best practices are critical in addressing the impacts of security and privacy breaches. The companies in the news recently drive home these points.
Five Things to Consider About Data Mesh and Data GovernanceDATAVERSITY
Data mesh was among the most discussed and controversial enterprise data management topics of 2021. One of the reasons people struggle with data mesh concepts is we still have a lot of open questions that we are not thinking about:
Are you thinking beyond analytics? Are you thinking about all possible stakeholders? Are you thinking about how to be agile? Are you thinking about standardization and policies? Are you thinking about organizational structures and roles?
Join data.world VP of Product Tim Gasper and Principal Scientist Juan Sequeda for an honest, no-bs discussion about data mesh and its role in data governance.
In recent years, the need to manage digital assets has become critical - compelling organizations to focus more on how digital asset management (DAM) systems can be leveraged across the digital supply chain, rather than simply archived. DAMification employs DAM concepts and systems to automate, integrate and enhance workflows, processes and applications - all within an adaptable, technology-agnostic framework that utilizes best-of-breed solutions and supports positive business growth.
Semantic 'Radar' Steers Users to Insights in the Data LakeCognizant
By infusing information with intelligence, users can discover meaning in the digital data that envelops people, organizations, processes, products and things.
Data Mesh is the decentralized architecture where your units of architecture is a domain driven data set that is treated as a product owned by domains or teams that most intimately know that data either creating it or they are consuming it and re-sharing it and allocated specific roles that have the accountability and the responsibility to provide that data as a product abstracting away complexity into infrastructure layer a self-serve infrastructure layer so that create these products more much more easily.
Learn how the Perfect & Merge tool from DQ Global allows Microsoft Dynamics CRM users to identify, perfect and merge multiple duplicates from any business entity into an easy to use browser based environment.
Semantic 'Radar' Steers Users to Insights in the Data LakeThomas Kelly, PMP
By infusing information with intelligence, users can discover meaning in the digital data that envelops people, organizations, processes, products and things.
4DAlert data house platform is a sophisticated and user-friendly solution that enables efficient data management for any organization. Visit: https://medium.com/@nihar.rout_analytics/what-is-data-observability-ece66dcf0081
Big Data Matching - How to Find Two Similar Needles in a Really Big HaystackPrecisely
When consolidating multiple sources of information from across your organization, how do you find the records that relate to the same customer, the same company or the same product? This is the challenge faced by many businesses today when putting a data lake to work. The problem is made far worse when different systems may not have the same contact entered the same way. Is Bob Smith the same as Robert Smith? How about Dr. Robert L. Smith - is he the same person? What about Syncsort, Inc and Sinksort Corp.? Are those the same company? One must compare each individual record to every other record in the dataset with some very sophisticated matching algorithms to determine who is who, and you may have to compare the data multiple times in multiple ways to resolve each entity.
Just to add to the difficulty, let’s say your organization has very large volumes of records in your data lake - you don’t have to compare a thousand records to a thousand other records multiple times - you must compare a million to a million, or 100 million to 100 million. This kind of compute intensive comparison can bring even a powerful cluster to its knees.
This is a problem Syncsort customers must solve, and we have developed some very powerful and intelligent software to tackle it.
View this presentation as we discuss the challenges of entity resolution at scale, how Syncsort’s Trillium data quality software line has tackled them successfully in production clusters and see a demonstration of this software in action.
A lack of trust is inhibiting the adoption of #AI. This presentation discusses approaches to delivering trusted data pipelines for AI and machine learning
What is OLAP -Data Warehouse Concepts - IT Online Training @ NewyorksysNEWYORKSYS-IT SOLUTIONS
NEWYORKSYSTRAINING are destined to offer quality IT online training and comprehensive IT consulting services with complete business service delivery orientation.
Machine Learning in Autonomous Data WarehouseSandesh Rao
Machine Learning in Autonomous Data Warehouse: One can use Oracle Autonomous Data Warehouse for machine learning. There are several ways to do this. This presentation explores these different but related options for performing machine learning. Each of these options enables people with different backgrounds to engage with building machine learning solutions on their data. At the end of the session, you will know which option will work best for you
This is from the Bay area Cloud Computing event https://www.meetup.com/All-Things-Cloud-Computing-Bay-Area/events/271017950/
Introduction to Machine Learning and Data Science using Autonomous Database ...Sandesh Rao
This session will focus on basics of what Machine Learning is , different types of Machine Learning and Neural Networks , supervised and unsupervised machine learning , autoML for training models and this ends with an example of how to predict workloads using Average Active sessions and different algorithms as an example and also how to predict maintenance windows for your databases. We will also use different open source frameworks as well as some of the tools in the Autonomous Database cloud to do this. If you are a DBA and want to learn something about machine learning and use the tools to perform your tasks more efficiently and automaticall
While many enterprises consider cloud computing the savior of their data strategy, there is a process they should be following when looking to leveraging database-as-a-service. This includes understanding their own data requirements, selecting the right cloud computing candidate, and then planning for the migration and operations. A huge number of issues and obstacles will inevitably arise, but fortunately best practices are emerging. This presentation will take you through the process of moving data to cloud computing providers.
Datalayer Best Practices with ObservepointMike Plant
Building a dynamic data layer is not easy, it has various complexities based on your websites design, back end frameworks and libraries and present difficulty in figuring out when and how to present it to the browser.
1.
Washington,
DC
|
Brussels
|
London
|
Los
Angeles
|
New
York
|
Zurich
1100
Glendon
Avenue,
Suite
925
|
Los
Angeles,
CA
90024
|
310.954.2980
www.optimityadvisors.com
Don’t
Cut
the
DAM
Check
Yet!
Content
and
Metadata
Analysis
are
Fundamental
Requirements
Before
Selecting
a
DAM
By
Julia
Goodwin
There
are
a
wide
array
of
DAM
vendors
from
on
prem
to
cloud
to
hybrid
that
can
provide
a
variety
of
asset
management
capabilities
for
your
organization.
They
have
a
wide
range
of
features
and
prices.
Most
are
extremely
slick
looking
and
it’s
easy
to
fall
in
love.
Many
times,
companies
go
straight
to
the
chase
and
purchase
the
DAM
system
they’re
interested
in.
Worse,
they
make
their
decision
with
a
list
of
requirements
that
may
not
adequately
consider
file
formats,
workflow,
and
logical
and
“physical”
asset
metadata.
Before
choosing
a
DAM
system,
don’t
make
the
mistake
of
leaving
holistic
content
and
metadata
analysis
out
of
your
requirements.
Thorough
analyses
of
content
and
metadata
are
important
for
defining
DAM
system
requirements
that
capture
a
complete
picture
of
an
organization’s
needs.
Here
are
some
important
analyses
to
conduct
when
defining
requirements
for
evaluating
DAM
systems.
Without
performing
these,
you
may
end
up
with
a
solution
that
does
not
fit
your
organization
and
may
require
costly
enhancement
charges.
I
have
worked
with
companies
in
the
past
that
bought
a
DAM
system
only
to
find
out
later
that
it
could
not
accommodate
their
content
relationships
or
their
metadata
requirements
in
the
way
their
business
needed.
Content
Analysis
–
This
is
best
performed
through
a
content
audit
addressing
the
considerations
below
and
integrating
the
findings
to
your
requirements
list
to
make
sure
the
DAM
can
handle
it.
• Which
of
your
assets
are
essential
to
store
in
the
DAM?
Can
you
phase
their
addition
to
the
DAM
system?
• Where
are
all
the
asset
types
currently
stored?
Flesh
out
and
document
file
directories,
personal
hard
drives,
Cloud
drives
like
Box
or
Dropbox,
other
repositories
such
as
CMS,
MAM’s
or
PAM’s.
This
list
is
something
you
will
use
again
and
again.
It
will
also
help
you
prioritize
what
goes
into
the
DAM,
who
creates
it,
who
approves
it
and
where
it
needs
to
go.
It
will
also
tell
you
how
much
information
(metadata)
is
known
about
that
asset.
• Do
the
assets
have
relationships
(Parent-‐Child,
or
Child-‐Cousin)
that
you
need
to
maintain
and
track
in
the
new
DAM?
• Will
you
include
asset
versions
or
only
final
assets
in
the
DAM?
If
you
include
versions,
how
will
the
system
manage
this?
• Do
your
assets
have
a
Unique
Identifier
that
you
need
to
import?
Or
do
you
have
to
create
one
and
have
the
DAM
or
staff
link
any
asset
relationships?
Does
this
UID
need
to
conform
to
an
industry
standard
like
EIDR?
2.
Washington,
DC
|
Brussels
|
London
|
Los
Angeles
|
New
York
|
Zurich
1100
Glendon
Avenue,
Suite
925
|
Los
Angeles,
CA
90024
|
310.954.2980
www.optimityadvisors.com
Add
the
findings
from
the
analysis
above
to
your
DAM
System
Requirements
List.
Metadata
Analysis
–
One
common
failing
when
a
DAM
system
goes
live
is
that
the
information
users
need
is
not
where
they
need
it
or
further
investigation
outside
the
DAM
is
required
of
users
to
determine
if
they
have
found
the
right
assets.
This
is
how
Search
may
breakdown
in
a
beautiful
new
system.
Here
are
some
questions
to
ask
yourself
about
your
organization’s
metadata
needs
to
mitigate
this
outcome:
• For
each
asset
type
you
have
determined
to
bring
into
your
DAM,
what
metadata
currently
exists?
File
name
only?
More
than
that?
Is
additional
metadata
needed,
if
so,
what?
Is
the
metadata
consistent
with
what
others
in
the
organization
use?
If
not,
you
may
need
to
collaborate
across
teams
to
accept
a
common
Taxonomy
and
Metadata
Model,
especially
if
you
are
planning
on
integrating
your
DAM
to
other
systems.
Don’t
forget
any
technical
metadata
(format,
resolution,
file
format,
file
size,
etc.)
or
administrative
data
(created
by,
last
changed
by,
last
updated
by,
etc.)
• If
metadata
is
the
fields
of
information
you
will
use
to
describe
your
asset,
you
also
have
to
consider
if
those
fields
should
have
restricted
choices
on
data
entry
to
reduce
errors.
For
each
field,
list
these
restricted
values
and
get
approval
from
your
stakeholders.
• Note
that
some
asset
types
may
have
different
metadata
fields
and
values.
Can
the
DAM
support
this
by
only
displaying
needed
fields
by
asset
type?
Can
the
system
accomodate
dropdown
lists
for
specific
fields?
• Do
you
need
to
have
the
ability
to
select
one
value
from
a
field,
that
in
turn
determines
what
appears
in
the
next
field,
and
so
on?
This
is
called
cascading
metadata
and
when
it
exists,
it
greatly
reduces
input
errors.
If
so,
carefully
document
those
scenarios
that
exist.
• Will
metadata
templates
be
needed?
For
some
assets,
data
entry
can
be
minimized
when
certain
fields
are
default-‐entered
by
the
system
based
on
asset
type,
some
other
user
selection,
or
when
the
assets
are
coming
from
another
system.
Determine
if
this
is
needed
and
that
the
DAM
can
accommodate
it.
• Where
do
the
assets
need
to
go
and
what
metadata
needs
to
go
with
them?
This
is
a
final
check
to
make
sure
you’re
not
forgetting
anyone
downstream
that
requires
certain
assets
and
their
metadata
for
specific
purposes.
Workflow
Maps
While
not
always
required,
I’m
a
huge
fan
of
swim
lane
workflows
so
that
end
users
can
see
visually
the
interplay
of
assets
and
data
as
they
move
through
their
processes.
These
visual
workflows
may
also
tease
out
additional
requirements
or
“ah
ha!”
moments
and
also
confirm
that
your
understanding
of
their
asset
processes
are
accurate.
These
workflows
will
also
be
a
huge
help
to
your
selected
DAM
vendor,
along
with
the
analysis
described
above,
and
can
be
retooled
for
DAM
training
later.
3.
Washington,
DC
|
Brussels
|
London
|
Los
Angeles
|
New
York
|
Zurich
1100
Glendon
Avenue,
Suite
925
|
Los
Angeles,
CA
90024
|
310.954.2980
www.optimityadvisors.com
Define
demo
scenarios
around
your
DAM
system
requirements
Finally,
when
it
comes
to
DAM
selection
time,
be
strict
about
asking
your
final
vendor
selections
to
demonstrate
YOUR
workflows
with
YOUR
data.
Give
them
enough
notice
to
do
this
properly.
If
the
vendor
tries
to
sidestep
this,
it
should
tell
you
something:
they’re
interested
in
selling
their
product,
not
demonstrating
that
their
product
will
be
a
success
for
YOU.
Julia
Goodwin
is
a
Senior
Manager
within
the
Information
Management
practice
at
Optimity
Advisors.