AFM241 Chapter Notes - Chapter 4: Business Analytics, Data Warehouse, Unstructured Data
17 May 2018
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CHAPTER 4- Competing with analytics
News Based Context and Perspective
• Business Analytics in the Oil and Gas Industry
• High IT spending has been associated with firms and industries known for the high
information content of their products or services – e.g. higher for finance and banking
• But technological innovations can benefit all industries – e.g. in natural resources industries
• Analytical approaches that impact the success rate of finding or reducing the cost to
develop and produce oil and gas can make energy more affordable
4.1 INTRODUCTION
• Intense rivalry in the market state is called Hyper-competition, manifests in increased performance
variations and higher frequency of firms reporting losses. E.g. 15% loss in 1970 to over 40% in 2000s
• Success in this environment requires firm to seek new knowledge to predict what customers
want and deliver it while anticipating new competitors
• Staying competitive by investing POS systems, customer relationship management (CRM)
systems, supply chain management (SCM) systems, as well as social media and regular
media
• Structured data (POS, CRM, SCM) + unstructured data (social media, media) = big data (data that
comes in high volume, high velocity, and high variety)
• Volume: increase in data volume and storage for the used to e ostl. It’s heap o.
Now the problem is how to determine relevance within large data volumes and how to use
analytics to create value from relevant data
• Velocity: data is streaming in at unprecedented speed and must be dealt in a timely
manner. How to react quickly to deal with data velocity?
• Variety: managing, merging and governing different varieties of data is hard
• Data are organized into data warehouses and management analyze them to drive their business
strategies
• This set of methodologies, processes, architectures, and technologies that transform raw data into
meaningful and useful information used to enable more effective strategic and decision making is
known as business analytics (BA)
• With huge aout of data aout opa’s ustoers, suppliers, eploees, et. eed BA
• Software vendors such as IBM understand the need of BA and rushed to fill in the gap
• BA tools are more affordable and are put in front line decision makers now
• Field agents are leveraging BA in their planning, merchandising, and supply chain
management
• BA has increased the incidents of firms that face significant competition from unanticipated
competitors
4.2 Business analytics (BA)
4.2.1 business analytics and decision makers
• Decision makers should look at available data to see what happened in the past, present and what is
likely in the future to support their decision making
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• BA could help at the operational, managerial or tactical, and strategic level
• Operational level
• Front line employees leverage historical data
• E.g. call center employees have well structured information and rely on predetermined
routines/procedures in order to deal with routine transactions
• BA tools need to proide the ith ustoer’s profile, histor of purhases/oplaits,
available warehouse inventory, tracking systems, etc.
• Need tools to let them process data and generate information to help better service
customers
• Need a performance related dashboard to track individual performances and compare to
peers
• Tactical level
• Managers
• Rely on semi-structured information and summary reports generated by the operational
level
• E.g. use sales data to determine kinds of promotions, use supplier information to determine
preferred supplier status to a vendor
• Tatial deisios are related to the alloatio of fir’s resoures ad the ai is to achieve
the maximum return. Look at patterns and trends that will help them monitor success and
plan a short-term course of action
• Strategic level
• Senior executives e.g. CEO
• Rely on unstructured information, both internal and external, to deal with decisions that
affet the fir’s log ter suess
• Iteral sstes suh as arketig, operatios geerate reports leadig to a fir’s deisio
to enter into a new product market. This information will have to be complemented by
competitive intelligence, that aims to assess the state of its competitors and market analysis
for uderstadig ustoer’s preferees
4.2.2 Categories of Business Analytics
Classification Proposed by INFORMS
• Two popular ways of classifying and organizing BA
• First one introduced by INFORMS, proposes the classification of BA in three categories: descriptive,
predictive, and prescriptive analytics
• Descriptive analytics focuses on preparation and analysis of historical data in order to identify
patterns and report trends.
• Visualization is critical for firms that want to derive insight from their data that otherwise
could have been impossible
• predictive analytics focuses on the prediction of future probabilities and trends
• uncover trends that are not apparent based on descriptive analysis
• business analytics is likely to rely on either traditional statistical analysis or data mining in
order to predict behavior of a targeted audience
find more resources at oneclass.com
find more resources at oneclass.com
Document Summary
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