Page 30 - 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

Learn the Business 15

unit. A business that emphasizes financial indicators like price and margin may disregard
market share for higher‐priced niche products. A list of objectives, targets, measurements,
and initiatives comes with each indicator. The saying “we manage what we measure” holds
true.
Company‐wide KPIs break down into business unit, department, even work‐team KPIs.
Finally, we distinguish between lagging, real‐time, and lead indicators of:

• past performance (lag indicators)
• current performance (real‐time indicators)
• future performance (lead indicators).

Lag indicators include traditional accounting indicators such as: profitability, sales, and share-
holder value; customer satisfaction; product and/or service quality; and employee satisfaction
(Kenett and Salini 2011). They are useful, because they indicate the overall health of the business.
At the same time, some liken using them to steering a car by looking in the rearview mirror.
Real‐time indicators help determine the current status of a project. Cost performance index
(CPI), for example, indicates the current status of funds expended. It measures budgeted cost
of work against actual cost of work performed. A ratio less than one indicates that the project
is overrunning its budget. Schedule performance index (SPI), as another example, indicates
the current schedule status. It compares budgeted cost of work performed (BCWP) to
budgeted cost of work scheduled (BCWS). A ratio less than one indicates that the work is
behind schedule. For examples see Kenett and Baker (2010).
Lead indicators, as opposed to lag and current indicators, are designed to predict future
performance. They are derived from:

• customer analyses (segments, motivations, unmet needs);
• competitor analyses (identity, strategic groups, performance, image, objectives, strategies,
culture, cost, structure, strengths, and weaknesses);
• market analysis (size, projected growth, entry barriers, cost structure, distribution systems,
trends, key success factors);
• environmental analyses (technological, governmental, economic, cultural, demographic, etc.).

Understanding the organizational vision, its key strategic initiatives, and the indicators used
to run the company is basic if data scientists want to be effective. They should also build their
networks and take a larger look at the company through their work, what we call “the data
lens.”

The Data Lens

As discussed above, one can look into an organization in a variety of ways: via its income
statement and balance sheet; via the leadership team; via its business plan; and so forth. These
projections are available to everyone.
The so‐called “data and information lens” is uniquely available to data scientists, and it
provides an extremely powerful end‐to‐end look. To employ it, one first examines the
movement and management of data and information as they wind their way across the orga-
nization. This lens reveals who touches them, how people and processes use them to add