Research Analysis Report: Data Analytics in Business Decision-Making

This report examines the application of data analytics within the context of business decision-making, using SAS Institute as a case study. It explores various data sources, including descriptive, diagnostic, predictive, and prescriptive analytics, and how these are used to identify trends and inform decisions. The report also delves into data visualization techniques, highlighting their role in simplifying complex information and facilitating quick decision-making. Furthermore, it identifies and discusses three key decision-making technologies and tools, such as Decision Support Systems and Leximancer, emphasizing their importance in extracting meaningful insights from data. The findings underscore the crucial role of data analytics in enhancing business productivity and enabling organizations to make more effective, data-driven decisions. The conclusion reinforces the value of both quantitative and qualitative techniques in improving business outcomes.

Research Analysis
Report

Table of Contents
INTRODUCTION...........................................................................................................................1
1. Sources of data and usage of data analytics for identification of trends within decision-
making.........................................................................................................................................1
2. Visualisation of decision-making process and analytics.........................................................3
3. Three decision-making technologies and tools.......................................................................4
4. Findings...................................................................................................................................5
Conclusion.......................................................................................................................................5
References........................................................................................................................................5

INTRODUCTION
Business analytics refers to techniques as well as methods that can be used by
organisation for measuring performance. Basically, they denotes statistical methods which can
be applied for specified project (Chae and Olson, 2013). Normally, they are being used for
evaluation of entire organisation. It denotes a visual, quantitative as well as systematic approach
that is being used for addressing as well as evaluation of crucial choices that are made by
businesses. Decision analysis makes use of wide range of tools for assessment of significant
information that assist within malformation of decision making process. This report is based on
SAS Institute which is an American multinational developer of analytics software. It is
responsible for development as well as markets a suite of analytics software that aids with in
assessment, management, analysis and report on data which will assist within decision-making.
This report comprises of sources of data for identification of trends along with visualisation and
analytics for supporting process of decision-making. In addition to this, different tools and
technologies with respect to this have been covered in this report.
1. Sources of data and usage of data analytics for identification of trends within decision-making.
The process of examination of data sets for drawing conclusions with respect to
information contained in them that aids within specialised software and system is referred to data
analytics. Generally, it denotes basic needs of business intelligence. OLAP (Online analytical
processing) as well different forms associated with advanced analytics. Management techniques
in which statistical tools or techniques like probabilistic forecasting, multivariate analysis and
decision tree analysis can be applied within mathematical models of real world problems refers
to decision analysis (Delen, 2014). SAS Institute makes use of data analytics applications within
both their external and internal sources. They comprises of demographic data or whether data or
any data which is required for carrying out their operations in an appropriate manner. SAS
Institute have options for making use of any kind of sources from these as per trends prevailing,
they have been illustrated below:
Descriptive analytics: Such kind of analysis will assist them (SAS Institute) to answer
questions associated with what has been happened (Chae and Olson, 2013). For an instance,
SAS Institute is responsible for development of statistical analysis system for different
departments, they can make use of information like how many patients are hospitalised in this
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week if it is for specific month, sales which have been incurred in given month or any other
information with respect to organisation SAS institute is serving. Through descriptive analytics
they can have data from diverse sources which gives insight into crucial details.
Diagnostic analytics: In this case, SAS Institute makes use of historical data for
answering questions related with why specified thing happened (4 Types of Data Analytics to
Improve Decision-making, 2019). For an instance, SAS Institute started to develop systems for
agricultural departments, they analysed the previous data for having details associated with
growth of specified crop or impact of weather on them. The diagnostic analytics assist them to
find out possibilities for drilling down as well as finding out dependencies and identification of
pattern. Normally, they opt for this as it provides deeper insight into diverse aspects associated
with specified problem.
Predictive analytics: It gives details associated with what is likely to happen. For an
instance weather prediction, it depends on entire information related with past like when
monsoons occur or anything. In this case, SAS Institute makes use of both the sources of
information, they are diagnostic as well as descriptive analytics for detection of tendencies,
exceptions and clusters for prediction of future trends (Xu, Frankwick and Ramirez, 2016). By
the usage of predictive as well as proactive approach, systems created by SAS Institute can
minimise the cost associated to remediate or can weigh risks associated with expansion and
various others.
Prescriptive analytics: It denotes what actions can be taken for elimination to get rid of
future problems so that vantage can be taken from promising trends. For an instance, SAS
Institute can assist multinational organisations to acknowledge opportunities associated with
purchasing behaviour of customers via usage of sales history. This needs both internal and
external information for carrying out their entire process due to nature of statistical algorithms.
Above certain sources of data and usage of data analytics for identifying patterns have
been mentioned. Depending on this, organisation can formulate their decisions. An instance can
be taken like within a supermarket people are opting for bread and butter in their breakfast, then
they make sure that appropriate stock has been maintained (Duan and Xiong, 2015). Based on
these trends they ensure that if these products are not available then they have significant
alternative for this. As per scenario A data, there exist different departments within organisation
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and each have relation with each other like finance department ensures that all other departments
within SAS Institute have enough funds for carrying out their operations.
In similar manner, all other departments are linked with one other for efficaciously
carrying out their responsibilities. Like in case of predictive analytics, the knowledge gained
from different sources is merged and provided to others depending upon requirements. This will
yield affirmative results and SAS Institute can have enhanced system.
2. Visualisation of decision-making process and analytics.
The graphical display of abstract information that can be utilised for data analysis as well
as communication is known as data visualisation (Kudyba, 2014). It is powerful tool that can be
utilised for discovering as well acknowledging entire information associated. With respect to
SAS Institute, data visualisation renders high tech means for preparation of crucial information
that will enable them to have relevant business choices. Through this, while formation of system,
executives can have bigger picture of trends as well as hot spots and areas which are likeable to
cause trouble. In context of SAS Institute, as they are involved within development of real time
business decisions depending upon statistical data. They will assist them within break down of
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Illustration 1: Scenario A

information within logical as well as meaningful parts which aids within improvisation in
decision-making. Different ways of visualisation have been specified below:
Quick response times: Big data acts as intense sources for business for directors,
marketing or sales managers that require crucial data in their fingertips for carrying out their
everyday jobs (Big data analytics, 2019). For an instance, manager of SAS Institute can have list
of key statistics from previous systems or software with respect to specified client for carrying
out sales and find out data related with this. Through this they can assist other organisations to
specify recent trends within the market. It enables them them within identification of issues
along with improvisation in response time.
Simplicity: Advancement within technology assist SAS Institute for collection of
information from their audiences as well as customers. From traditional sources like phone
numbers and mailing addresses to advanced options through which buying trends along with
with behaviour of customers can be analysed with respect to everyday information (Larson and
Chang, 2016). Through data visualisation management of SAS Institute can provide their
customers with broad picture of crucial details which they may opt to have.
Easier pattern visualisation: Dashboard or spreadsheets acts as a base for management
who tends to identify patterns in data while they opt for reviewing hundreds of lines within
spreadsheet. SAS Institute makes use of data visualisation for viewing new paths as well as
identification of new trends and patterns.
Team involvement: It denotes that SAS Institute can make use of data visualisation for
displaying information within real time as every department possess access to information
needed for better collaboration (Liebowitz, 2013). These infographics will assist them within
aggregation of organisation's metrics. It assist them to formulate strategies as per informations
and finding out hidden patterns within data. This aids them within identification of crucial
information.
3. Three decision-making technologies and tools.
There exist diverse techniques that can be utilised by SAS Institute for formulation of
decisions. Few of them have been illustrated below:
Decision Support System: This system can be utilised for formulation of supporting
process as well as expand their knowledge with respect to different aspects. For an instance like
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SAS Institute is responsible for development of analytic system it is necessary for them to have
information associated with different aspects.
Management information system: It is a computer system that comprises of software as
well as hardware components that acts as backbone for carrying out operations (Vidgen, Shaw
and Grant,2017). SAS Institute is responsible for acquiring data from diverse resources that aids
them develop systems according to their needs.
Leximancer system of content analysis: It is responsible for carrying out quantitative
analysis by the usage of different machine learning techniques. These systems will aid SAS
Institute to acknowledge major concepts and ways in which they are linked with one other. It
will provide them with frequency word count along with their co-occurrence.
As per the scenario B data, while SAS Institute can make use of Leximancer concept map
for attaining better outcomes, reduction within human biasness that means that their will be
enhancement within validity. This map assist them to generate a transparent data analytics model
which can be interpreted by analyst (Schläfke, Silvi and Möller,2013). They are rigorous
statistical techniques which can be utilised for extraction of data like in this case, themes have
been marked such as communication .
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Through the usage of discovery modes or by guided exploration meaningful concepts can
be identified from qualitative data. As per the requirements of system themes and concepts can
be extracted like communication in this case.
4. Findings
It has been found that data analytics has crucial role within formulation of decision
associated with diverse operations that have to be carried out within organisation. There are
different sources through which organisation like SAS Institute can gather information and
analyse it according to their requirements. Through concept of visualisation, concepts can be
understood in better way for an instance Leximancer concept system aids them to extract hidden
information. Along with this, diverse trends and patterns associated with this can be analysed in
an appropriate manner. This leads organisations to formulate effectual decisions by considering
trends within market.
Conclusion
From above, it can be concluded that data analytics denotes quantitative as well as
qualitative techniques that are being used for enhancement within business gain as well as
productivity. In this case data is categorised as well as extracted for identification of patterns. In
addition to this, data visualisation provides better results which leads them to have effectual
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Illustration 2: Scenario B, Data

results and identify patterns. Apart from this, there are different systems which can be utilised so
that effective decisions can be made.
References
Books & Journals
Chae, B., & Olson, D. L. (2013). Business analytics for supply chain: A dynamic-capabilities
framework. International Journal of Information Technology & Decision
Making, 12(01), 9-26.
Delen, D. (2014). Real-world data mining: Applied business analytics and decision making. FT
Press.
Duan, L., & Xiong, Y. (2015). Big data analytics and business analytics. Journal of Management
Analytics, 2(1), 1-21.
Kudyba, S. (2014). Big data, mining, and analytics: components of strategic decision making.
CRC Press.
Larson, D., & Chang, V. (2016). A review and future direction of agile, business intelligence,
analytics and data science. International Journal of Information Management, 36(5),
700-710.
Liebowitz, J. (Ed.). (2013). Big data and business analytics. CRC press.
Schläfke, M., Silvi, R., & Möller, K. (2013). A framework for business analytics in performance
management. International Journal of Productivity and Performance Management.
Vidgen, R., Shaw, S., & Grant, D. B. (2017). Management challenges in creating value from
business analytics. European Journal of Operational Research, 261(2), 626-639.
Xu, Z., Frankwick, G. L., & Ramirez, E. (2016). Effects of big data analytics and traditional
marketing analytics on new product success: A knowledge fusion perspective. Journal
of Business Research, 69(5), 1562-1566.
Online
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4 Types of Data Analytics to Improve Decision-making. 2019. [Online]. Available through:
<https://www.scnsoft.com/blog/4-types-of-data-analytics>.
Big data analytics. 2019. [Online]. Available through:
<https://searchbusinessanalytics.techtarget.com/definition/big-data-analytics>.
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