Chapter 11 – The Data Asset: Databases, Business Intelligence, and Competitive
- Big data: a general term use to describe massive amount of data available to
today’s managers. Big data are often unstructured and are too big and costly
to easily work through use of conventional databases, but new tools are
making these massive datasets available for analysis and insight.
- Business intelligence: a term combining aspects of reporting, data
exploration and ad hoc queries, and sophisticated data modeling and
- Analytics: describes the extensive use of data, statistical and quantitative
analysis, explanatory and predictive models, and fact-based management to
drive decisions and actions.
o Benefits are seen everywhere – gaining competitive advantage over
your competitors, growing your company, etc.etc.
- How do you get to the point where an organization can leverage its data ?
Many organization’s – it lies dormant and it is very hard to design a system to
pull it all together
- Data provides a strategic advantage when it’s rare, valuable, imperfectly
imitable, and lacking in substitutes
- Data is just raw facts and figures --- want to turn data into information –
want to present it in a context so that it can answer a question or support
o then combine information + manager’s knowledge = stronger
- database: lists of related data
o most organizations have several database
- database management systems: software that creates, maintains, and
- structured query language (SQL): most common language for creating and
- Common terms associated with databases
o Table or file: list of data
o Column or field: defines the data that a table can hold
o Row or record: represents a single instance of whatever the table
keeps track of.
o Key: the field used to relate tables in a database. EG student_ID
- Regional databases: the most common standard for expressing databases,
whereby tables (files) are related based on common keys.
- Where to get the data?
o Transaction processing systems: systems that record a transaction
(some form of business-related exchange), such as a cash register sale,
ATM withdrawal, or product return. EG using a a loyalty card to collect data on customers when
they pay in cash
o Enterprise software: categories that are not just CRM, but also all
aspect of the value chain, including supply chain management (SCM)
and enterprise resource planning (ERP) systems.
More integrated and standardized than the prior era of
o Surveys: can tell you what your cash register can’t – Zara,
o External sources: combine a firm’s data with data brought in from
- Data aggregators: firms that trawl for data and package them up for resale.
- Many firms are data rich but information poor
o Just b/c firms have data doesn’t mean it can be used --- some large
firms have legacy systems: outdated information systems that were
not designed to share data, aren’t compatible with newer
technologies, and aren’t aligned with the firm’s current business
- Running analytics against data can bog down systems/be very time
- Data repositories for reporting and analytics work
o Data warehouse: a set of databases designed to support decision
making in an organization.
o Data mart: a database focused on addressing the concerns of a
specific problem or business unit.
- Large scale data analytics projects need to have a clear vision with business-
- The issues for system design, development, deployment, and maintenance:
o Data relevance
o Data sourcing
o Data quantity
o Data quality
o Data hosting
o Data governance
- Hadoop – OS project created to analyze massive amounts of raw information
better than traditional, highly-structured databases.
o Some half-dozen separate software pieces and requires the
integration of these pieces to work.
Flexibility – c