We make a stronger case for open data
when we situate our argument within the larger picture of the global data economy, and
then place developing countries within this economy to make the case how and why effective data management is the most efficient engine of sustainable growth and
development.
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| Global Data Economy Map: Bah 2015 |
The global data
economy comprises seven commercial data brokerage industries. They are those involved with:
1. Data creation: All sectors of
society, particularly government, create data in their day-to-day activities. People Data is what Steve Adler calls the non-government aspects of this process. The creation may be passive or
active, known and unknown, local or international—e.g. social media
applications widely used in developing countries, such as Facebook, Twitter,
WhatsApp and Viber.
2. Data collection: Companies, in
concert with government ministries, departments and agencies (MDAs), non-profits and other companies, provide products and/or
services geared toward facilitating efficient data collection. In other words, in a way that would indicate the data’s level of integrity
(i.e. reliability, validity and comprehensiveness) and actionability (i.e. in formats that
facilitate quick and effective use of the data for their intended purposes).
3.
Data storage: Companies that provide hardware and software
products and services to facilitate data storage to MDAs, businesses and
non-profits . Cloud computing services typically fit here.
4. Data sharing: Companies and MDAs that provide products
and services to facilitate data sharing.
5. Data archiving :
Companies, non-profits and MDAs that provide
products and services for the archiving of government and business data, per
statutory requirements.
6. Data destruction
: Companies,
non-profits and MDAs that provide products and services for the destruction of
government, non-profit and business data per statutory requirements. E.g.
Paper-shredding products and multi-billion-dollar data/document shredding
companies such as Iron Mountain.
7. Data safety
& security: Companies
that provide products and services to individuals, MDAs and businesses to verify
data integrity, prevent data breach, and/or prevent or mitigate harm borne of
data breach (e.g. identity theft and ransomewaring). This
category serves as the fulcrum to the rest. Some of the major big businesses in this sector are LifeLock and the three
major credit bureaus in the United States (i.e. Equifax, Experian and
TransUnion) with many subsidiaries overseas. A significant revenue source of
these companies is now the various credit-monitoring services that they provide
to individuals and companies.
Admittedly, the categorization
above of the trillion-dollar global data economy is simplistic, serving merely to facilitate description
and comprehension. In reality, the vast majority of the companies fall in more
than one category, depend on one another to thrive and in many cases serve as
B2Bs to one-another.
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Risk assessment would be the two-word expression that best describes what dictates what we do, with whom, when, where, how, why. Just
like the economy. The greater the perceived risk, the less inclined we tend to
be to take such risk. Or when we do, the higher ROI margin we demand. And how
do we well assess (the) risk? With data that pass the efficacy test. In other words, with data that are valid, reliable, comprehensive and
actionable. Reflect a minute on the compendium of regulation, policies,
procedures and institutions governing the financial services industry in
developed countries; almost all of them deal with data management in one form
or the other, and all geared toward assessing investment risk. Of the
individual (e.g. individual credit reference bureaus), the company (e.g. the various
stock exchanges), the institution and the state (e.g. credit ratings agencies such
as Fitch, Moody’s and Standard & Poor’s).
Impressive economic growth worldwide,
attributable in no small measure to the BRIC, MINT and UAE
economies, leaves the world flush with financial capital in need of ventures,
mostly in developing countries, and mostly in Africa South of the Sahara. “China’s
World Bank” all but guarantees this to remain so in the foreseeable future.
What is preventing infusion of private investment capital into these regions is
not the purse-holders’ inability to see the huge market potential. Nor the
erstwhile inhibitors that cluster around the phrase “inadequate infrastructure";
we are now so far technologically advanced that companies from any part of the
world could offer well-heeled individuals living in any other part of the world
the wherewithal to live off the oft dysfunctional government grid. Think solar panels for electricity, boreholes
for water, and Facebook’s
drones for internet connectivity.
Nor is the problem investors’ inability
to accurately assess risk. Rather, it is their inability to access efficacious
data on, within and from these countries to enable them to estimate and monitor
at their own level of confidence, the (potential) risks associated with their
(potential) investment. We’re talking here, for example, about open data such
as court records to show liens and other forms of encumbrances by lending
agencies to protect their investment. We are also talking about shared data
such as individual credit reports to help lending institutions assess
individuals’ likelihood of defaulting.
My point is, there’s a lot of
data-management-related activities going on in developing countries. These
activities span all scope and types of data--open, shared, closed and permutations
between and betwixt. We make a stronger case for open data when we situate our argument within the larger picture of the global data economy, and then place developing
countries within this economy to make the case how and why effective data
management is the most efficient engine of growth and development.
I list four benefits of a comprehensive
national data management blueprint geared toward optimal competitiveness in the
global data economy:
1.
Generation
of consistent revenue flow for the country’s coffers from both international
and local sources. The government of Sierra Leone is a typical example of developing countries currently losing tens of of millions of dollars annually due to
lack of effective national data management regulation, policies, procedures and
practices. One main revenue source is “copy fees” typically levied by MDAs when fulfilling FOIA requests.
Another is licensing fees paid to MDAs by legitimate businesses such as
insurance agencies, for access to non-open data such as driving records.
2.
Support
and growth of local innovation and entrepreneurship in data brokerage.
3.
Job
and overall economic growth, borne primarily of #2 above and of making the country
much more business-friendly for local, regional and international investors.
4.
Greater
transparency and good governance.
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Something
to consider.
