Page 79 - The Real Work Of Data Science Turning Data Into Information, Better Decisions, And Stronger Organizations by Ron S. Kenett, Thomas C. Redman (z-lib.org)_Neat

Educating Senior Leaders 69

Similarly, technology is the new engine. The engine powers the car and, without technolog-
ical advances, a data‐ and analytics‐led transformation would not be possible. Technologies
include databases, communications equipment and protocols, applications that support the
storage and processing of data, and the raw computing horsepower, much of it now in the
“cloud,” to drive it all. It is odd that this infrastructure is referred to as the “cloud.” After all,
much of it consists of fiberoptic cable, deep underwater.
In the analogy, we liken the data‐driven concept to the car’s GPS. As discussed in Chapter 10,
“data‐driven” means bringing as much data as you can to support decisions. GPS does just that
for drivers, helping them navigate to their destinations using the latest traffic and accident
reports.
Continuing, the Internet of Things is a catchall term for embedded and connected devices,
built into products and manufacturing lines, that create new data and effect control. The
Nest home thermometer is an example, and in the analogy we liken them to devices built
into cars that make them easier to maintain. Eventually, one can implement conditioned‐
based maintenance (CBM), where the maintenance schedule reflects the car condition and
driving patterns, not a one‐size‐fits‐all approach. CBM can both reduce cost and improve
safety. It requires, however, sensor data and analytic models with predictive capabilities.
Similarly, we liken privacy – the notion that people have rights to control the use of facts
about themselves – to tinted glass, which can keep the identities of those inside the car from
others. Frankly, this analogy is weak. Privacy is complex, as different people feel very differently
about their privacy. The situation is rapidly changing, and the European GDPR portends
further change all over the world (see Appendix D).
It bears mention that cars are increasingly online, integrated, and autonomous. So the
security, data, control, and privacy concerns in the car mirror those of any other company. For
example, security is not just airbags and bumpers – the real fear is cybersecurity – someone
hacking into and hijacking the car.
Other important concepts in the data space parts lead to analogies involving design and
manufacture, marketing and sales, and running the company. First, manufacturing. In the data
space, “digitization” or “digital transformation” refers to the application of digital technologies,
wherever possible, to operations and decision‐making. Digitization builds on existing tech-
nologies with the goal of increasing scale and decreasing unit cost. We liken it to the use of
advanced robotics in automobile manufacturing. Chapter 17 is about the industrial evolution
toward advanced manufacturing.
Just as manufacturing is well controlled, the concept behind data governance is that all
aspects of data operations, including moving it around, its usage, creating it, and changing it,
should be well controlled.
Next up is metadata, which we have already mentioned. Metadata is data that assists in the
interpretation of other data. Examples include data definitions and data models. The topic can
be incredibly esoteric. We mention metadata here because so many issues, such as the inability
to provide consistent answers to basic questions from three systems, stem from inadequate
metadata and, in turn, from imprecise business language. We liken it to the inventory
management system in the factory.
One topic that we feel to be underappreciated is proprietary data. Unlike most employees, who
can be hired away, or capital – your dollar is the same as everyone else’s – your data is uniquely
your own. If you manage well, some of it may gain “proprietary status” and will be a great
source of competitive advantage. Well‐known examples include Facebook’s friend, LinkedIn’s