COIT20253: Business Intelligence using Big Data Strategy Report

This report, prepared for COIT20253, focuses on a big data strategy for Wavesound, a publisher of large prints and the Zinio magazine app. It begins with an introduction to decision support systems and how big data fits into an organization's decision-making process. The report defines key terms like market research and business intelligence, then explores the big data value creation process and presents a relevant use case for sales and marketing. The core of the report is a proposed big data strategy for Wavesound, addressing the company's challenges in meeting customer satisfaction and managing overproduction. It outlines business initiatives, objectives, and tasks, along with the necessary technology stack, including data analytics, master data management, and NoSQL databases. The role of social media in Wavesound's decision-making process is also discussed. The report concludes with reflections, recommendations, and a balanced view of the benefits and drawbacks of implementing a big data DSS system for Wavesound, emphasizing the importance of data governance and reader analytics to improve customer satisfaction and drive profit. The report is a comprehensive analysis, aiming to guide Wavesound in leveraging big data to enhance its business operations.

Gizzle Bautista
CC53 MIS - S0261553
COIT20253 Business Intelligence using Big Data
Assignment 2
Due Date: 31 May 2016

Table of Contents
Introduction 3
Definition of Terms 3
Big Data Value Creation Process 4
Big Data Use Case 5
Big Data Strategy for Wavesound 6
Business Initiatives, Objectives and Tasks 6
Technology Stack 7
Data Analytics and MDM 8
NoSQL 8
Role of Social Media in Wavesound’s Decision Making Process 9
Reflections and Conclusions 9
References 11

Bautista, Gizzle
Introduction
Decision support systems (DSS) are created to help organizations make
fact-based business decisions. This report aims to do a market research on how
Big Data fits in an organization’s decision making process. Specifically, this
report will discuss how big data can be used by decision support systems that is
meant for traditional publishers. Relevant definitions and terminologies will be
presented to provide a clear context of the report.
Wavesound Pty. Ltd., being a publisher of large prints to Australian
libraries and vendor of Zinio magazine app, is the organization chosen for
discussion. This report will also analyze a value chain and use case which
Wavesound could use. With the value chain and use case presented, the Big
Data Strategy created for Wavesound will then be discussed which aims to solve
a current business problem.
Also, the roles of data analytics, master data management, NoSQL, and
social media in the decision making practice of the organization will be tackled.
Finally, this report aims to provide a recommendation for Wavesound and
present a conclusive finding, with positive and negative view points, on the use
of Big Data DSS systems.
Definition of Terms
Market research is defined by ICC/ESOMAR (2008) as a systematic way of
collecting and interpreting data, using statistics and analytics, to support in
decision making. Big Data refers to the combination of data from several sources
that could lead to better decision making strategies (Press 2014). While
Blackman (2016) cited Gartner’s definition of business intelligence as “an
umbrella term that includes the applications, infrastructure and tools, and best
practices that enable access to and analysis of information to improve and
optimize decisions and performance.”
According to Le (2015), BI is the successor of DSS with aims of solving
business problems with less algorithm involved. For Tay (2013), value chain
identification is a way for organizations to find its weak points and recognize its
strong activities. Then, a use case diagram, as per SourceMaking (n.d.), involves
actors, relationships and business use cases or activities. “Big data analytics
examines large amounts of data to uncover hidden patterns, correlations and
other insights” with the capability of uncovering insights for immediate actions
(SAS Institute n.d.). While, NoSQL databases means it’s not reliant with
Structured Query Language schema (Vaughan 2013). Lastly, Oracle (2013)
defined Master Data Management as the consolidation of technologies,
applications, and master data that will result to fact-based decision making.
3

Bautista, Gizzle
Big Data Value Creation Process
In creating business strategies, value chain identification can serve as a
guide (Hertog 2014). According to Tay (2013), starting with the identification of
the value chain in creating business strategies is a good idea since it forms a
systematic view of the whole company. As a sample value chain, the figure
below displays Amazon com inc’s value chain which could help in converting
inputs into products or services (OS Financial Trading System 2011).
Source: OS Financial Trading System 2011
4

Bautista, Gizzle
Big Data Use Case
After identifying the value streams in the organization and making sure
that the processes are streamlined and analyzed, the process evaluation of
where big data technology could be mostly benefited should start. With this,
creating a use case for the processes could help in providing operational
snapshots thus lead to choosing the right business intelligence analytics tools to
use (Sherman 2015).
For the sales and marketing value stream, the figure below shows the use
case for relevancy and retention boost (Search Technologies n.d.). Search
Technologies (n.d.) explains the figure below as how, “a powerful search engine
helps clean and enrich research documents’ metadata to ensure users find the
most relevant content and explore related content easily. Then, through machine
learning and predictive analytics, the publisher will be able to serve content in a
particular order in which the user’s most favorite content appear in the top
results. How do they know for sure? Because they can repeatedly test and score
the search engine’s performance offline to predict search accuracy and
abandonment rates before putting the engine into production on the live
website.”
Source: Search Technologies n.d.
5

Bautista, Gizzle
Big Data Strategy for Wavesound
In creating a big data strategy, a problem must be identified first. Which
would have been achieved by doing the value chain and process analysis via the
creation of use case. Having been employed by Wavesound for six months, I
have realized that meeting customer satisfaction is one of the company’s
weaknesses as it often times fail to meet its’ delivery due dates, meet its sales
quota (as the Warehouse Manager would often remind the servicing staff), and
have troubles with over production (disposing tons of large print copies as a
resort).
Thus, the business strategy, initiatives, objectives and tasks below were
specifically created to answer that problem.
Business Initiatives, Objectives and Tasks
The business initiatives are what the organization hopes to achieve. As for
the Outcome, Critical Success Factor (CSFs), & Tasks states what needs to be
done.
BUSINESS STRATEGY:
Improve Customer Satisfaction to Meet Monthly Sales Quota and Reduce Over
Production
BUSINESS INITIATIVES:
1. Increase customer engagement for better understanding of library and reader
needs
2. Efficient content recommendation for production control and delivery
3. Efficient use of social media and website for market forecasting to drive
production and order requests
OUTCOME & CRITICAL SUCCESS FACTOR:
1. Develop detailed knowledge & predictive insights into reading patterns,
readers' behaviour & author's success factor
2. Uncover & integrate customer-specific insights back into operational,
marketing & loyalty systems
3. Expand collection of user data leveraging subscribers' behaviour on the
website, Zinio app, social media, e-mail, and survey.
TASKS:
1. Track & monitor operational tasks
2. Collect transactional & non-transactional data
3. Analyse collected data to identify reader interests & book performance
4. Review operational processes
DATA SOURCES:
1. Website
2. Social Media
3. Zinio App
4. Operational data
5. Marketing data
6

Bautista, Gizzle
6. Customer data
Technology Stack
The technology stack refers to the big data technology requirements of an
organization. This discusses the steps in order to make the big data business
strategy into action.
For Wavesound, the below proposed KPI was created to be able to
measure the progress and success of each business initiative which then
identify the supporting business intelligence.
In identifying advanced analytics, business questions and business
decisions should be established to support the business initiatives. While
identifying the analytic algorithms and modeling requirements support each key
tasks. Finally, in identifying data warehouse requirements the supporting
data should also be clearly identified.
7

Bautista, Gizzle
Data Analytics and MDM
In the big data strategy presented previously in this report, the data
sources cited are website, social media, zinio app, operational data, marketing
data and customer data. Identifying these sources of data is the first step in
master data management (Maginfo 2015). Also, according to the video, data
governance should be implemented wherein the identification of how data are
stored, updated, and maintained will be handled.
With the correct data management and analysis, reader analytics can then
be pursued. As per Rhomberg (2014), “Reader analytics can highlight that a
book that didn’t sell actually engages readers very strongly, but that the cover
was poorly designed (the most common error), that a stronger book launched at
the same time and overshadowed the book”. Another main point raised by the
author is that data analytics do not make the decision for the organization but it
helps the organization to allocate precious marketing budget on the right books.
NoSQL
It is essential for businesses nowadays to invest in a database because
everything is moving online and organizations are most likely dependent on their
website and/or mobile application (Nusca 2015). According to Davenport (2014),
traditional publishers will be left out in the cold if they don’t find a way to have a
direct contact with their customers. Also, the author pointed out that “social
media has to be mined for sentiment along with clickstream data”. Additionally,
the article enunciated that the goal is not to sell content but to extract
information and build customer loyalty.
Furthermore, according to Stephan (2013), businesses now are
transferring to NoSQL from relational databases due to the need for scalability
and flexibility. The article mentioned content management as one of the use
cases that NoSQL can best address. Images, comments, videos and other user-
generated content can easily be incorporated to generate new content by using
NoSQL (Stephan 2013).
8

Bautista, Gizzle
Role of Social Media in Wavesound’s Decision Making
Process
Teradata Perspectives (2015) cites how correlating social media strategies
with KPIs could help in determining probable ROIs. Additionally, the article states
“Search activity contains information about the success of a new product. Search
drives revenue.” Thus, tracking search and social data, organizations could
measure consumers’ interest and use it for market or performance strategy
(Teradata Perspective 2015).
Another insight comes from Hung’s (2016) article, wherein the author
cited that “content is information and so are views, likes, shares, follows,
retweets, comments, and downloads”. Additionally, the blog post states that past
performances are no longer the only gauge of probable product success. Thus,
the author concluded that ignoring big data is a threat to the business.
Reflections and Conclusions
Based on the literatures discussed in this report, a big data strategy
for Wavesound could be generated, to follow and implement, in order to
gain profit and reduce costs. First, Wavesound need to clearly identify its
value streams. The need for streamlining is extremely high as it is usually
the cause of delays which might cause problems in retaining relevancy in
the publishing business.
Secondly, to streamline the processes, the warehouse department
of Wavesound should create use case diagrams which could expose
problem areas or opportunities where the business could engage more. It
is highly possible that duplication of tasks can be found that leads to
customer dissatisfaction. With the analysis of data sources (inputs,
outputs, etc.), the specifics of where big data technology can be applied
will be achieved. Lastly, the proposed big data strategy can then be
turned into action by identifying relevant business questions and
analytics.
It is also important not to overlook the data sources included in the
big data strategy as they are as important in implementing the big data
technology. Data governance is then recommended to make sure that
relevant data is updated, stored and handled correctly. Otherwise, it could
lead to a more damaging business decision caused by a wrong marketing
forecast due to incorrect pull of data. Thus data analytics, specifically
readers analytics, come into play for traditional publishers.
Another area where Wavesound could benefit from big data is with
its Zinio app and company website. The sample use case presented in this
report is highly useful and relevant. With big data technology, a powerful
search engine, reader behavioral analytics and customer data analysis
could provide not only high customer satisfaction but also astounding
profit.
9

Bautista, Gizzle
Going in depth with the reader behavioral analytics, it could also
determine which magazines are checked out but left unread. This will help
the company identify later on if certain magazines are worth including in
their subscription. Also, since libraries are the main clients of Wavesound,
being able to predict first the upcoming fiction best-sellers (which most-
likely can be determined by author performance analysis or by book
performance through social media) than its competitors will definitely
benefit the company. All of which, NoSQL can be of help.
With the benefits cited, cons could also be expected. Investing in big
data technology for a small company, with financial constraints and
overseas stakeholders, it might be near to impossible. Additionally,
privacy issues regarding reader behavioral analysis could raise a bigger
problem for the company. However, with the right strategy, analysis, and
determination, Wavesound (and other traditional publishers facing the
challenge of digital publishing) could easily find ways to make it possible.
10

Bautista, Gizzle
References
Blackman, A 2016, What is business intelligence, viewed 29 May 2016,
http://business.tutsplus.com/tutorials/what-is-business-intelligence--cms-23412
Davenport, T 2014, Book publishing’s big data future, viewed 30 May 2016,
https://hbr.org/2014/03/book-publishings-big-data-future/
Harmon, P (ed) 2014, Business process change: a business process management
guide for managers and process professionals, 3rd edn, Elsevier, USA.
Hung, D 2016, The impact of big data on social media marketing strategies,
viewed 30 May 2016, http://tech.co/impact-big-data-social-media-marketing-
strategies-2016-01
ICC/ESOMAR 2008, ICC/ESOMAR on market and social research, viewed 29 May
2016, https://www.esomar.org/uploads/public/knowledge-and-standards/codes-
and-guidelines/ICCESOMAR_Code_English_.pdf
Le, ThienSi 2015, Relationship of BI & DSS, viewed 29 May 2016,
https://www.linkedin.com/pulse/relationship-bi-dss-thiensi-le
Maginfo 2015, What is master data management?, viewed 30 May 2016,
https://www.youtube.com/watch?v=fH03Rj4O0PU
Nelson, P n.d., How big data helps online publishers boost revenue and retention,
viewed 7 May 2016, http://www.searchtechnologies.com/big-data-online-
publishing
Nusca, A 2015, Why your company should care about its database, viewed 30
May 2016, http://fortune.com/2015/01/29/couchbase-nosql-database/
OS Financial Trading System 2011, The value chain and Amazon.com, viewed 31
March 2016, http://www.ftsmodules.com/public/texts/valuationtutor/VTchp3/
topic4/topic4.htm
Oracle 2013, Overview: Oracle master data management, viewed 29 May 2016,
http://www.oracle.com/us/products/applications/master-data-management/mdm-
overview-1954202.pdf
Press, G 2014, 12 Big Data Definitions: What’s Yours?, viewed 11 April 2016,
http://www.forbes.com/sites/gilpress/2014/09/03/12-big-data-definitions-whats-
yours/2/#58cc49291fd7.
Rhomberg, A 2016, Data vs. instinct – the publishers dilemma, viewed 31 May
2016, http://www.digitalbookworld.com/2016/data-vs-instinct-the-publishers-
dilemma/
SAS Institute Inc n.d., Big data analytics, viewed 29 May 2016,
http://www.sas.com/en_us/insights/analytics/big-data-analytics.html#modal1
Sherman, R n.d., Business use cases can determine the right BI analytics tool,
viewed 29 May 2016,
11

Bautista, Gizzle
http://searchbusinessanalytics.techtarget.com/feature/Which-BI-analytics-tool-
does-my-company-need
Stephan, T 2013, 10 use cases where NoSQL will outperform SQL, viewed 29 May
2016, http://www.networkworld.com/article/2999856/big-data-business-
intelligence/10-use-cases-where-nosql-will-outperform-sql.html
SourceMaking n.d., Use case diagrams, viewed 29 May 2016,
https://sourcemaking.com/uml/modeling-business-systems/external-view/use-
case-diagrams
Tay, M 2013, Business process architecture - from value chain to process, viewed
4 April 2016, http://blog.maxconsilium.com/2013/08/business-process-
architecture-from.html
Teradata Perspectives 2015, Capitalize on social media with big data analytics,
viewed 30 May 2016,
http://www.forbes.com/sites/teradata/2015/05/27/capitalize-on-social-media-
with-big-data-analytics/#329167c9ca3e
Vaughan, J 2013, The buzz: what are NoSQL databases?, viewed 30 May 2016,
http://searchdatamanagement.techtarget.com/news/2240181822/The-buzz-
What-are-NoSQL-databases
12