Page 13 - 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

Preface

This book has its roots in a chance meeting brought on when Ron responded to an article on
data science that Tom published. One short discussion led to another, quickly narrowing to a
common theme: we shared the experience that, in order to help companies and organizations
become better at exploiting data and statistical analysis, one needs something more than
technical brilliance. For both of us, our most successful and impactful projects resulted from
other factors, such as understanding the problem, narrowing the focus, delivering simple mes-
sages in powerful ways, being in the right spot at the right time, and building the trust of
decision‐makers. Conversely, our failures stemmed not from poor technical work but from a
failure to connect, on the right issues, with the right people, or in the right way.
We had both written, separately, on some aspects of these topics. Ron has studied how one
generates information quality with a framework labeled “InfoQ,” Tom has addressed data
quality and became known as “the Data Doc.” We wondered if we could help data scientists
who work in companies and other organizations enjoy more and larger successes and endure
fewer failures by putting our heads together.

Fad, Trend, or Fundamental Transformation?

It is no secret that “data,” broadly defined, is all the rage. And “data science,” including tradi-
tional statistics, Bayesian statistics, business intelligence, predictive analytics, big data, machine
learning, and artificial intelligence (AI) are enjoying the spotlight. There are plenty of great
successes, building on a rich tradition of statistics in government and industry, driven by
increasing business needs, more data powered by social media, the Internet of Things, and the
computer power to analyze it. Iconic new companies include Amazon, Facebook, Google, and
Uber. At the same time, there are enormous issues: the Facebook/Cambridge Analytica scan-
dals of early 2018 underscore threats to our privacy (Kenett et al. 2018), many fear that millions
of jobs will be lost to artificial intelligence, analytics projects still fail at a high rate, and the
tremendous damage that has resulted from some notable “successful” efforts, as described in
O’Neil (2016).
Will data and data science power the next great economic miracle? Will they make solid
contributions, more positive than negative? Or will they be just another fad confined to the
scrap heap of failed ideas? Even worse, will they put our entire social fabric at risk? It is
impossible to know.
We do know that data and data science can be truly transformative, improving customer
satisfaction, increasing profits, and empowering people – we have seen it with our own eyes.