1. Innovation and the evolution of the knowledge economy
By Dimokritos Amallos, M.Phil.
Co-author Panagis Fourniotis-Pavlatos, Ph.D.
“Once, in the days of the time immemorial, there was a king of Greece who had thirty-three
daughters. Each of these daughters rose up in revolt and murdered her husband. Perplexed as to
how he had bred such rebels, but not wanting to kill his own flesh and blood, their princely father
exiled them and set them adrift in a rudderless ship.
The ship was provisioned for six months. By the end of this period, the winds and tides had carried
them to the edge of the known earth. They landed on an island shrouded in mist. As it had no
name, the eldest of the killers gave it hers: Albina.”
This extract of the winner of the 2009 Man Booker Prize novel “Wolf Hall” by Hilary Mantel
reminded me of the feelings our potential clients might have had, as we started in Qualco our
journey to sell our innovative products and services to the other European countries and especially
to the Euro area and the UK. It took us more than six months, almost four years, when, after having
our second customer in UK we decided to establish our first subsidiary in London. A year later our
second subsidiary in Paris followed and now we feel almost confident that our European trip will
continue.
But trying to sell innovation abroad, when your own country is in deep crisis, and at the same time
almost everybody in the world is talking about Greece’s low productivity, falling competitiveness
and a huge debt created by the state while it was promoting consumption against investment: well
all these were not exactly the circumstances that would create an export oriented consciousness in
any company, even more in an innovative one. This has been an experience that could take me the
whole speech to talk about and I would rather leave it for the questions and answers period.
Getting back to the core of my subject Innovation and the Knowledge Economy I would start from
the perception that workers, and indeed national policies, favor jobs in which ideas are valued
more highly than products, and the question of how should the growing knowledge economy be
balanced with the on-going need for the hard products of the industrial and agricultural sectors
It is true that, in most cases, “hard” products and knowledge- or idea-based services look and feel
completely distinct; their production employs radically different techniques, constraints, priorities,
cost factors, overheads and risks.
However, a little digging into the “hardest” of products will reveal the developing knowledge and
ideas underneath. Examples:
o In the automotive sector, scientific and technological evolution (and some
revolutionary advances) have led to massive improvements in safety, efficiency,
comfort and on the environmental impact – and also have let the industry become

2. more adaptable, more flexible and more sensitive to market needs. This applies
equally to the “hard” technology of the production line, and to the “soft” technology
of management science and resource optimization.
o Even in the agricultural sector, advances in production tools and methods, fertilizers,
hybrid development, weather and climate modeling, and – in some areas – genetic
engineering have brought similar benefits, while also massively increasing
production.
None of the above would be possible without the output of “idea-based” jobs. More importantly,
none of it would be possible if “idea-based” jobs only produced ideas: the point of any industry is to
bring a product or service to market, and the knowledge economy is at its most obviously effective
when enabling advances through the practical application of ideas.
Just as the growth and eventual impact of a “hard” industry depends on its ability to “scale up” and
commoditize its products, so does the demand on “idea-based” industries require them to do the
same.
This is most obvious in the IT industry, which straddles both sectors: in order to grow, it has had to
commoditize its offerings – from custom-made computers to mass-market PCs and mobile
consumer devices, from custom-made software to “off-the-shelf” packages and cloud-based
offerings – and this transformation has been driven by a very focused kind of innovation.
In fact, it is this sort of innovation – rather than fundamental research, which is becoming
increasingly hard to fund in an industry setting – that drives a lot of the “idea-based” services and
offerings.
For example, the emerging field of “big data analytics” is focused on understanding interactions –
financial transactions, Web clicks, health records, etc. – essentially economic activity at its lowest
level – with the aimof optimizing product and service offerings;
And the field itself, traditionally the domain of statisticians and domain specialists, is increasingly
being led to commoditize its offerings and infrastructure, and make them available to other lines of
business in a packaged form.
An Example from the New York Times: Other than their owners, most small businesses have no
employees. In the U.S. there are 4.3 million companies that employ fewer than 20 people each. And
while these companies collectively produce roughly 15 percent of the nation’s economic output,
their activities aren’t captured by the official numbers in a timely or detailed way.
Yet this measurement shortfall in the small-business sector, and a series of other information gaps
in the economy, may be overcome by what experts say is an emerging data revolution — Big Data,
in the current catchphrase. The ever-expanding universe of digital signals of behavior, from
browsing and buying on the Web to cell phone location data, is grist for potential breakthroughs in

3. economic measurement. It could produce more accurate forecasting and more informed policy-
making — more science and less guesswork.
In the small-business case, 200,000 companies have allowed Intuit, the software maker, to gather
data on their use of Intuit’s online payroll or online accounting products for research on
employment and sales trends. The data are stripped of identifying information, and, in asking
permission, Intuit also emphasizes that it uses the data to improve its products.
Intuit began its research in 2004 on small businesses and has expanded its scope, including many
more companies and becoming more fine-grained in its data tracking as it has added products,
services and customers.
Researchers at the Bureau of Economic Analysis, the government’s statistical scorekeeper of
economic activity, are now experimenting with the Intuit data, seeking to tap it to improve the
official estimates.
The main tool for government statistics remains telephone and in-person surveys of households
and businesses — surveys that are costly and time-consuming.
Tracking behavior online can pull in far more data, more quickly — so that governments should be
able to see signs of inflation, deflation and employment trends sooner and adjust policy faster.
For example, the Intuit monthly employment data, based on online monitoring, is current. That is
eight months to a year ahead of the government’s best statistical look at the health of small
business, which is culled from quarterly surveys, state unemployment records and tax returns. The
monthly Intuit survey data have also proved accurate, almost mirroring the government results
when they are finally reported.
Yet whether more data, collected faster, will improve economic forecasting is uncertain. So far, the
results are mixed. An encouraging study, begun in 2009 and repeatedly updated, has used Google
searches to predict home sales and prices three months into the future. In the study, the higher the
frequency of search terms like “house prices,” “real estate agent” and “mortgage rates,” the more
likely the national housing market would heat up.
In the most recent version, their model using search data predicted future home sales 24 percent
more accurately than the forecasts by experts from the National Association of Realtors.
It should be noted however, that the influence is not one-way: “hard” products enable further
needs of both consumers and industry, and shape the directions in which the “ideas-based” sector
evolves.

4. Qualco’s experience, as an IT services and products provider, has been in the thick of this interplay,
and we have bet our continued success on this commoditization of innovation: we have aimed to
transform innovative ideas into products, a transformation which itself requires innovation to
succeed.
Examples: in our debt management product (QC), we have strived to offer ways to express
management experience and business practices, feed them into the product, automate their
execution, and offer actionable reporting on the results.
This has led to a number of modules being delivered through the product roadmap over the years
(Collections Strategies, Legal Processes, Settlement & Rescheduling Workflow, etc.), with emphasis
placed on adaptability (one product for multiple industry needs / sectors / priorities) as much as on
innovative functionality.
Similarly, our credit portfolio management product has focused on automating and commoditizing
the process of setting up, executing and monitoring diverse customer management campaigns –
minimizing the repetitive effort expended on each campaign and the costs incurred, without the
need for custom development.
In our future evolution, we will be introducing predictive analytics products as the next step on
from traditional monitoring of, and reporting on, daily operations; again, the focus will be on
productizing and commoditizing a traditionally specialized service with high and recurring
operational costs.
But Innovation does not work without selection. And this is where many companies fall short. Yes,
choices about which ideas are worth pursuing and which are not are continually being made, but
too few companies think about and organize selection deliberately, with a clear strategy in mind.
The selection of ideas thus becomes a subjective process, wherein political interests and personal
preferences determine which projects are funded and which are terminated.
Usually selection occurs at two points. The first is at the middle manager level. Invariably, these
managers do not select the proposals that they find most promising, but instead choose the
proposals they think their superiors would want to see. They fear that passing along a bold, risky
idea that their superiors might reject would be bad for their reputation. The second point starts
with this biased pool. Top managers pick the proposals they liked best—typically ideas that fit their
preconceived notions of what the company should and should not do. As a consequence, many
companies do not seem to suffer from a lack of variation at the ideation stage, they experience
problems with their innovation pipeline.
To avoid these pitfalls, executives need to focus on developing a process that systematically
manages selection in a way that aligns with their company’s strategy. For most companies, this is
uncharted territory. The following five steps, I have come across, can help guide their way.
1. Enable selection to happen. The first thing that top managers need to accept is that they
themselves should not decide which projects live or die. At least they must not decide alone

5. 2. Consult as many employees as possible. It’s not just senior executives who have something to
offer. Taking advantage of the insight and understanding of a wider group of employees can also
lead to better decisions.
3. Objective the process. Many decision makers hold on to a failing course of action because it
provided success in the past or because someone’s reputation is tied to it or simply because they
have “come this far already.” Companies need to objective the process and decouple it from
individual decision makers’ personal interests and emotions.
4. Let the evidence match the investment. Data also plays a key role in the next step. Executives
often rely on just one or two selection moments. But the most successful innovators view selection
as an ongoing process. As a project progresses and begins to demand increased investment, more
and more data becomes available. The information revealed at one decision point should guide the
next.
5. Give them boundaries. The variation/selection process works only if it takes place within the
boundaries of a clear and explicit strategic direction for the company—as established by top
management.
Letting a thousand flowers bloom will give your company the appearance of being innovative. But
investing in variation alone is not enough. You must have a deliberate system of selection to ensure
that the right ideas get the funding they need. Those that are truly best for your company’s
strategic direction and not those determined by personal preference and emotion.
What skills are needed by companies hungry for innovation and for being competitive in a global
market?
In the context of the above, companies hungry for innovation obviously need innovators, but they
need very specific, goal-oriented sorts of innovators: people who can assimilate and visualise the
big picture, or the revolutionary new concept, and methodically transform it into innovative,
commoditized product (or into a series of actions leading to that product). This may sound
derivative, but, as detailed previously, it is itself a process of innovation. Furthermore, it is one that
can be taught.
Regarding the global market: all it means in this context is that supply and demand for ideas and
resources is on the global stage. This does not necessarily favor established players with immense
resources; the global “information culture” promoted by the Internet has massively increased the
supply of innovative ideas, which can now be easily exploited by emerging players in synergy with
their own advantages (agility, low overheads etc.).
What does the future look like in terms of the formal and informal sectors of the economy?
How will the two sectors influence one another?
Both sides have something to offer:

6. The “informal” workforce generates fundamental theoretical advances, conceptual innovation, and
sometimes even actual services or commodities (e.g. the Linux operating system kernel) –
sometimes with the aim of just “scratching an itch”, rather than for immediate financial gain
The “formal” workforce takes these in and productizes them (e.g. commercial Linux distributions,
such as Red Hat Enterprise)
This creates a demand, that influences what the “informal” workforce will concentrate on in the
future;
It can also generate (formal) revenue which can find its way back to the “informal” sector in the
form of training, job openings, etc.
How can governmental policies towards labor, education, infrastructure, etc. encourage growth
and development in both the formal and informal sectors?
Education has traditionally been viewed as either theoretical or vocational. Industry has seen too
many graduates with a lot of theoretical knowledge and no practical skills, and has traditionally
pushed towards more vocational training (even at university level). Students often also look for
courses in “marketable skills”. However, vocational training as traditionally understood leaves
innovation out of the equation
What if universities taught more about the practical impact of their theoretical teachings? What’s
more, what if they taught more about the process of obtaining such practical impact? (e.g.
algorithms and its impact on predicting future trends). Not only would they be providing the skills
demanded by the innovative segments of the economy, they would also be closer to the
fundamental aim of education from society’s perspective, which is, to make the world a better
place.
I have prepared this essay along with my colleague and Qualco’s executive Dr Panayis Fourniotis-
Pavlatos, who has also been a Cambridge graduate but, alas, a few years later than me.