Database Reference
In-Depth Information
Data products have changed rapidly with technology. Today, data products
may be automated, like a website that shows you reports in real time, or they
may be built by hand, like a PowerPoint presentation. They can be raw data
extracts—perhaps a CSV file containing sales details, or they can be highly
processed, like a complex, interactive data visualization.
Consider a large healthcare company, a client of ours. This client captures data
on hospitals from around the country and often uses Center for Medicare
and Medicaid Services (CMS) data to populate important hospital data fields
including addresses, total Medicare expenditures per hospital, and the total
number of Medicare patients served. CMS, a data source, provides a consis-
tent record of these publicly available events. After the CMS data is uploaded
into the healthcare company's Salesforce.com database, hospital names are
matched to unique CMS identification numbers so that useful reports can
be generated. The sales team prioritizes its prospects based on Medicare
spending—and works from a Salesforce.com report, a data product, which
lists hospitals accordingly. The following table gives examples of the data
source, data, and data product for the preceding film and healthcare cases.
Data Source
Data
Data ProDuct
Skyline
Photos
Empire , the film
CMS CSV file
Hospital address, Medicare
expenditure, number of
patients
Prioritized Salesforce.com
Report (Hospitals by Medicare
expenditure)
Data products can be organized and characterized by a series of continuums
that describe the nature of the data and how it is presented (Figure 4-3). Data
can range from being raw to processed, granular to summarized, and textual
to quantitative. Data presentations might be static or dynamic, small or mas-
sive, or anywhere in between (Figure 4-3).
CSV file
PPT presentation
Raw
Processed
List of purchases
KPIs
Granular
Summarized
Product review text
Product review scores
Textual
Quantitative
PDF report
Interactive dashboard
Static
Dynamic
Survey results
Web log files
Small
Massive
Figure 4-3 Characteristics of data products
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