As we enter the digital economy, it becomes increasingly transparent that the information and data ecosphere will continue to be a complex environment for the foreseeable future, with information being provided from a variety of internal and external sources in the form of files, messages, queries and streams. It would be foolish for any organization to place their bets on any one platform to be their platform of choice because it is incongruent to the thought patterns of the consumers, suppliers, regulators, partners and financiers who will participate in their information ecosphere through data feeds, information requests and a host of other interfaces. Rather, there is a role of each of these platforms which serve as the conduit for data and the transformation of data into information aligned with the value propositions of the organization. This writing is focused on the big data platform because there are some unique characteristics of the big data environment that require an approach different than many of the legacy environments that exist in organizations. Furthermore, while big data is the one environment that is new and requires these special handling characteristics, there will be future platforms with the same requirements as big data requires today, and hopefully lessons learned will be left to not revisit each of the challenges as the next transformational information ecosphere is made available. Figure 1 The Fourth Industrial Revolution, World Economic Forum, InfoSight Partners, 2016 This time is different, in that information is the catalyst to achieving value and the platform ideally suited to house information not optimal for storage in the form of rows and columns is the big data environment. Understanding which information is delivered with intended consequences and having the management prowess to tune information shared with customers, prospects, suppliers, partners, regulators and financiers is critical for the digital economy. Additionally, it is specific to understand the challenges each platform housing information bring to the equation. This writing will focus on big data.

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Information Economics and Big Data
Introduction
As we enter the digital economy, it becomes increasingly transparent that the information and data
ecosphere will continue to be a complex environment for the foreseeable future, with information being
provided from a variety of internal and external sources in the form of files, messages, queries and
streams. It would be foolish for any organization to place their bets on any one platform to be their
platform of choice because it is incongruent to the thought patterns of the consumers, suppliers,
regulators, partners and financiers who will participate in their information ecosphere through data
feeds, information requests and a host of other interfaces.
Rather, there is a role of each of these platforms which serve as the conduit for data and the
transformation of data into information aligned with the value propositions of the organization. This
writing is focused on the big data platform because there are some unique characteristics of the big data
environment that require an approach different than many of the legacy environments that exist in
organizations. Furthermore, while big data is the one environment that is new and requires these
special handling characteristics, there will be future platforms with the same requirements as big data
requires today, and hopefully lessons learned will be left to not revisit each of the challenges as the next
transformational information ecosphere is made available.
Figure 1 The Fourth Industrial Revolution, World Economic Forum, InfoSight Partners, 2016
This time is different, in that information is the catalyst to achieving value and the platform ideally
suited to house information not optimal for storage in the form of rows and columns is the big data
environment. Understanding which information is delivered with intended consequences and having
the management prowess to tune information shared with customers, prospects, suppliers, partners,
regulators and financiers is critical for the digital economy. Additionally, it is specific to understand the
challenges each platform housing information bring to the equation. This writing will focus on big data.

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Defining a business model and big data’s fit
Big data will serve as one of the platforms that deliver information to both people who are involved in
orchestrating the business model and people who are beneficiaries of the business model. The
participation of beneficiaries of the business model is defined in the vision storyboard, the orchestration
is defined in the processes used to manage business models and vision storyboards.
In most cases, data stored in the big data environment will be aligned to the sources providing
information (source data) and there is one more source that needs to be added to the big data
environment, that being the one that transforms source aligned data into consumption aligned
information.
Because we are describing big data’s role in information economics, we will acknowledge that there is a
cost preference to the big data environment, but that is a much smaller component to information
economics than the consumption of information to drive the value propositions of the organization and
the role that big data plays in this effort.
Figure 2| The information consumption conceptual framework, InfoSight Partners, 2016
Big Data is not the replacement for all the platforms that participate in information consumed by
business processes. It is one of the platforms that are available to organizations. To understand how
the big data environment fits into the overall information ecosphere, it is important to understand the
characteristics of big data and the attributes of big data that serve as challenges to its being the single
source of information to organizations. Big data can be the receptacle for information both defining the
characteristics of the information consumption conceptual framework (see figure 2) and the
transactions that encapsulate information as defined in the information consumption conceptual
framework. However, for information housed in the big data environment to be effective, all the

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information specific to the definition of the information consumption conceptual framework or the
transactions that are created through the information consumption conceptual framework must be
identifiable in total or will lead to erroneous results and hence not trusted.
Attribute Evolved Practices Big Data
Challenges
Remediation
Alternatives
Ability to find
information
Dictionary and catalog
services have matured in
databases, business
intelligence and other
platforms serving data and
information.
The big data
environment is
columnar and the
identity of the columns
is stripped without
some external interface.
An imbedded intelligent
catalog which serves the
identity of data and
information natively to
consumers is required.
Few exist today.
Ability to secure
information
Multi-layer security
protocols have been
devised to protect
information from
unintended intrusion.
There is no imbedded
security environment
which natively protects
data in the big data
environment today.
An imbedded security
layer which interfaces
all data requests from
the big data
environment is
required. None exists
today.
Supports a usage
resistance free
environment
Most organizations have
not embraced the need
for resistance free
environments, however
the underpinnings are in
place for such capabilities.
The big data
environment makes it
easier to ignore data
clutter, thereby
increasing usage
resistance.
A set of proactive
processes that identify
and eradicate clutter
from the environment is
required.
Ability to interface
massive amounts
of information
The ability to efficiently
interface and analyze
massive amounts of
information is a primary
reason for the existence of
big data. There are
practical limits in each of
the legacy platforms, but
even these are fading.
Big Data is specifically
devised to efficiently
serve massive amounts
of information at a
preferable price point.
However, there are
system dependent
activities required to
benefit.
Uncoupling the systems
activities from the usage
of big data is required to
accommodate the
ability for data scientists
to use massive amounts
of information in time
to matter during
disruptive scenarios.
Ability to respond
to market
disruptions at the
speed of business
Practices today call for a
general-purpose data
model devised to fit the
largest number of
business needs. This is
not optimal for market
disruptions.
Big Data environment
are easier to
accommodate changes,
but there are system
activities to make those
changes available to
data scientists.
A loosely coupled data
catalog which inherited
both the metadata and
security necessary to
support data scientists
at the speed of business
is required. None exists
today.
Figure 3| Big Data Framework challenges by attribute, InfoSight Partners, 2016

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Big data introduces challenges to yield value from information in three important areas:
1. All metadata, including technical metadata, is stripped from data housed in the big data
environment. Other platforms housing data have internal components devised to percolate
technical metadata (data element name, size, etc.) and have capabilities to percolate other
metadata (definitions, suitability, etc.).
2. Native security of data housed in the big data environment requires third party tools not native
to the big data environment.
3. It is cheaper to store data in the big data environment, which has given the appearance of
negating concerns about data clutter to many organizations by confusing storage costs and
accessibility costs.
Why Big Data Provides Little Value in Many Organizations
Big data has little value in many organizations because of resistance caused by lack of metadata and the
ability for data scientists to identify information housed in the big data environment due to metadata,
clutter and technical support necessary to provide passages to data housed in the big data environment.
It is clutter and the lack of metadata native to the big data environment that is the most likely candidate
for big data failures. The facts are that 80% of data scientist’s time, the experts in wielding information,
is spent either finding or reorganizing (and validating the results of the reorganization) data and
information to fit the scenarios underlying business processes requiring data. There is little time
available to expend this precious time to find and reorganize data, so other means of satisfying the
needs of business processes are sought, at the expense of big data initiatives.
It is the one fact that technologists latch onto the “more is better” proposition, trying to fit into a
general-purpose business model as much relationship and data to fit as many scenarios business
stakeholders can muster. Align this complexity with the clutter obfuscating information really required
and the masking of technical metadata required to identify information with some assurance, and no
wonder the value of big data initiatives is marginalized in many organizations.
If the big data environment was enabled through the catalog proposed by InfoSight Partners, the
alignment would be inherent in the linkages appearing in the catalog, thereby eradicating much if not all
of the preparation time required by data scientists to wield information.
There is a lag between requiring never previously sourced information and the availability of this
information in the big data environment. This lag is due to the processes that make this information
available in a form easily digested by non-technicians. While this lag will become less of an issue over
time as the big data environment matures, today, this is one of the sources of information use
resistance that exists when considering the big data platform for housing information that will be
potentially be consumed in highly disruptive business scenarios.

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Why the catalog attributes augment big data
The catalog serving the valuation of information serves additional roles critical to the use of information
accessible through the big data environment.
• The identity of information is made possible through the catalog. The metadata required to
specifically identify information is stored before it is stripped by the processes that include it in
the big data environment.
• The derivation of information is exposed through the catalog. The technical processes that
transform heard and learned inferences, knowledge and innovations into information made
available for consumption by business processes is stored in the catalog and available for
review.
• The information alignment to processes is exposed in the catalog, thereby reducing the effect of
clutter stored in the big data environment.
The catalog tracks the following categories of information required to perform the valuation processes
required of information economics.
• The data feeds and data streams subjected to machinery to transform source aligned data to
consumption aligned information.
• The technical processes that comprise the machinery to transform source aligned data to
consumption aligned information.
• The information made available for consumption.
• The actors who are accountable, responsible, consulted or informed in the processes that
achieve the value propositions identified in business models.
• The scenarios of each of the processes, which is important to information valuation.
• The playbooks comprised of vision storyboard, business models and risk canvas documents.
These are the materials used by the Chief Data Officer to architect the information process map
(IPAS).
o The vision storyboard is the vehicle used to identify how the processes that satisfy value
propositions work, what are the business conditions that trigger the processes and what
information is consumable at this process. Vision storyboards are for information
devised for internal consumption, information devised for automated consumption (i.e.,
information made available to the real-time advertising exchanges) and information
devised for consumption by customers, suppliers, vendors, regulators and financiers in
push and pull delivery models.
o The business model is the vehicle used to identify the specific characteristics that when
combined in a specific way are devised to deliver a specific value proposition.
o The risk canvas is the vehicle used to identify systemic and operational risks exposed in
the business model and vision storyboard. Many of these risks in the digital economy
are related to cybertheft activities, where personal identity information is exposed
through cookies (digital information packets storing information for the convenience of
digital content consumers and suppliers) and other means.

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Figure3| Conceptual Framework of the catalog used to manage and apply value to information, InfoSight Partners, 2016
• Risks to information consumption, particularly that information made available to customers,
suppliers, vendors, regulators and financiers in the form of content, must be managed so that
information is used as planned. 87% of the devices used by customers, suppliers, vendors,
regulators and financiers have personal information stored on the devices for the convenience
of commerce, theft of this information is a deterrent to the usage of the content. If the identity
of this information is stripped and it is obfuscated by appearing in unintended columns, then the
risks of unintended malicious information theft is heightened until these issues are addressed.
This material plus the workflow used to orchestrate the consumption registration and value
registration process are components of the Information Valuation Engine (IVE) process.
About the Author
Mark Albala is the President of InfoSight Partners, LLC, a business
consultancy which provides financial and technology advisory services
devised to facilitate focus into the value of information assets. InfoSight
Partners is led by Mark Albala, who has served in technology and thought
leadership roles and serves as an advisor to analyst organizations. Mark can
be reached at mark@infosightpartners.com.