Big Data Strategy Development: Woolworths Supermarkets

This report develops a big data strategy for Woolworths Limited Supermarkets in Australia, aiming to improve decision-making and business profitability. It explores strategies like performance management, data exploration, social analytics, and decision science, considering Woolworths' strengths, weaknesses, opportunities, and threats (SWOT). The report highlights the importance of NoSQL databases for handling unstructured data, discusses business objectives, and recommends the adoption of big data strategies for businesses of all sizes. It covers required technology stacks, data analytics, and the role of social media in decision-making, emphasizing the value creation potential of big data. The report also includes an introduction, strategies for big data use, business objectives, SWOT analysis, required technology stack, data analytics, and MDM, NoSQL, social media's role, big data value creation, conclusion, and recommendations, along with references.

Cover page
Developing a big data strategy for Woolworths Limited Supermarkets, Australia
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Executive summary
The purpose of this report is to develop big data strategy for Woolworths limited. Strategies
identified for development and used were performance management, data exploration, social
analytics and decision science strategies. They have been developed to meet the business set
objectives including; determining how to improve decision making process in business and to
determine the profitability of the business. SWOT analysis of Woolworths business limited has
also been brought to light to help combat the possible challenges. Big data emergence resulted to
development of various technologies to make its storage and analysis simple. NoSQL is a big
data analysis technology that is capable to handle unstructured data and other data of any kind
through data analytics. From the variety of benefits of big data, it was recommended that all
businesses irrespective of their sizes adopt big data use.
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Table of contents
Introduction………………………………………………………………………………………1
Strategies for big data use in business………………………………………………………….2
Business objectives……………………………………………………………………………….4
SWOT analysis………………………………………………………………………………...…6
Required technology stack………………………………………………………………………6
Data analytics and MDM to Support DS and BI………………………………………………8
NoSQL for big data analytics……………………………………………………….…………..8
Different NoSQL databases and their uses……………………………………………….…….9
Role of social media in organization’s decision making process…………………………….10
Big data value creation in organizations…………………………………..…………………..11
Conclusion and recommendations……………………………………………….……………11
References …………………………………………………………………………………...….13
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Introduction
Woolworths is one of the Australian grocery suppliers with branches in various parts of Australia
including Sydney, Victoria, Tasmania, New Zealand, Queensland and New South Wales. The
purpose of this report is to come up with the strategy for using big data in Woolworths limited
supermarkets. Some of the big data strategies that will be discussed in aiding to conquer the
market against the competitors are social analytics strategy, performance management strategy,
data exploration and decision science. The aforementioned strategies will be looked at into
details in this report how they can be incorporated for the informed and sound decision making
and betterment of the business.
Sturdy stay of the business in the market is in most of the cases ensured if the business
management is aware of their strengths, weaknesses, opportunities and the possible threats that
could be on attack of the business operation Bohari et al (2013). These will be possible to be
looked at exhaustively through carrying out SWOT analysis of the business organization. SWOT
analysis is important to be carried out since it will help to create awareness on some of the key
areas that would require more efforts on Xingang et al (2013). Proper application of big data
have been a major challenge to most of the big data users. Full exploration of big data as
collected will lead to ease business decision making process that would help in improving the
business Wu et al (2014).
Leveraging big data and adopting big data strategies will lead to meet some of the business
objectives such as determining how to improve decision making process in business. Second and
last, big data strategies aids in determining the profitability of the business. All the information
as they really exist on the ground in the market are easily unearthed through carrying out
thorough big data analytics and treating each information with equal value Idreos et al (2015).
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Data analytics experts are therefore expected stay put to overcome the challenges associated with
using big data handling process. This will be useful to ensure that all the information are
exhausted to the bottom line with the aim of providing relevant and informative message that can
be relied on when making decisions.
A business organization supplied with the correct information as obtained from the collected big
data is most likely to make informed business decision that will better the business operation and
all its activities only if the information provided by the analysts are considered LaValle et al
(2011). Social media play a fundamental role nowadays in business since it gives the platform
where consumers can be able to interact with the producers and the business to do crowdsourcing
to have response to some of the critical questions from the consumers about their products.
Social analytics will hold importance to help bear fruits from social media collected data. Since
these data are non-relational, they are perfectly handled using the NoSQL databases. The
databases are capable of handling large volume of unstructured and semi-structured big data as
opposed to traditional tools Stonebraker (2010).
Strategies for big data use in business
Business strategies are documents that structure the business organization how they are to
achieve their set objectives. The guidelines of how the company will score the set goals and the
principles of the business are all shown in the business strategies. Woolworths is one of the
biggest grocery suppliers in Australia with branches in almost over five towns including major
cities in the country. In all the branches, the business organization has over two hundred
thousand employees including all the staffs and casual workers across the country. The company
have covered a larger proportion if the Australian market of up to over 80 percent as compared to
their competitors such as Coles and others.
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Customer in any business is the center to which every activity in business is to suit the desire and
needs of the customers. On the same, Woolworths is one of the business organizations that
prioritize the needs of customers and putting them in the frontline and constructing big data
business strategies that focus and put customers first. Performance management strategy
comprises the conceptualization of the true meaning of big data in the business database MCAfee
et al (2012). Multidimensional analysis and predetermined queries are used to bring better
understanding of big data. Customers’ activities such as purchasing and their inventory levels are
determined by applying transactional analysis. From the dashboard of the business analysis tools,
the analysts are offered with variety of choices for which query to run. The will and trust of the
customers is maintained by the Woolworths business organization through introducing and
reducing pricing range and implementing it. Engaging in price measure will act as a tools to fight
their competitors in the market since it will be aimed at lowering the prices and improving the
range of brands they offer.
Social analytics is another big data business strategy that is applied by business organizations in
the analysis of non-transactional data that is found today in the databases. This strategy is mostly
concerned with data that is obtained in the social media platforms that range from conversations
and reviews of the social media users Sharda et al (2013). Reach is a category of the social
analytics that measures the extent to which contents in the social media platforms are shared and
reach other users from the other end. The number of retweets on twitter and links shared on
facebook are the variables that can be used by reach, among other categories are awareness and
engagement. Profound understanding is required by the analysts on the category of measure they
are analyzing in order to extract dependable information that can be considered in decision
making.
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Data leveraging require deep data exploration that constitutes of immense statistics incorporation
to experiment and arrive at solutions to some of the insidious problems that would not have been
detected by the managers. Users’ behaviors based on their past transactions and preferences,
predictive modeling techniques are applied. Customers can be divided into groups by virtue of
their attributes using cluster analysis. Upon discovering the groups of customers, target actions
are then performed by the managers including upgrading services, marketing messages and
selling to each unique groups. Extensive statistical techniques leads to fast and direct results for
the data exploration business organizations. Being that big data has come to light in the recent
past and joined the mix of data, there is still a challenge of lack of expertise with vast and
profound knowledge of business analytical techniques Newton (2013). Many organizations are
overcoming this challenge by training the available analysts and also inciting the young
generations to join data analytics and acquire expertise knowledge and skills.
Collection, storage, analysis and interpretation of big data is always for the purpose of acquiring
the information that would be useful in answering critical questions that would be arising in
business operation process Chen et al (2012). As a result therefore, decision science strategy
adds value in the decision making from the information drawn from big data since it is
comprised of experiments and the analysis of non-transactional data that include the consumer
generated product ideas and their reviews in regards to the products. Most of the questions that
are asked are all hypothesized by the decision scientists in exploring social big data. Data
scientists enable the business organizations to obtain feedback from the community through
crowdsourcing where they are capable of determining the value, feasibility and validity of the
collected ideas Russom (2011). Intense and proper assessment of these ideas are not taken for
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granted but are considered in case any action is to be taken by the business management
including decision making.
Business objectives
Some of the set objectives for this business report that need to be achieved from the strategies
are; to determine how to improve decision making process in business. In order to achieve this
objective, decision science strategy plays a fundamental role for through crowd sourcing, the
ideas of the customers concerning the products are collected that are then considered in case any
action is to be taken. Non-transactional data are analyzed help in improving decision making
process considering consumer generated products ideas. The effectiveness in the decision
making process applied by the decision scientists consists of listening tools that is responsible in
text and sentiment analysis.
The second big data business objective aimed at in this report is to determine the profitability of
the business. Through better understanding of the collected big data in the business, transactional
data are then used for analysis to determine the purchasing activities and inventory levels and
screening all the customer segments to determine the most profitable one. Real time answers are
then obtained that are applied in making short-term business decisions as well as coming up with
long term business plans. Decisions made in business are focused to maintaining the current
number of customers and also looking for ways of reaching for more customers to increase the
sales thus maximizing the profits.
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SWOT analysis
Strengths of Woolworths is that it is one of the oldest and renowned retail brands in Australia.
On top of that, it as well has a huge number of supermarket and market share accompanied with
great number of goods and services.
Some of the business weaknesses include the following; it has been experiencing low
international presence. Due to changes and acquiring the use of social media, the business lately
joined online retail and finally, the business has lost competitive advantage to the competitors.
Opportunities include; promising better promotion to win back the trust of customers and also
growth in return, engage in large retail market in the emerging economies and also ensuring the
engagement of customers through social media.
Threat faced are; discounting wars with the competitors, slow growth as a result to loss of
customers.
Required technology stack
Since the coming of big data and its dominance in business organizations have led to coming and
development of various technologies in response to the big data development. It is obvious that
traditional tools cannot be used to handle big data since big data is unstructured and semi-
structured Barlow (2013).
Pig
It is a platform that is available and capable of analyzing and processing big datasets. The stored
data in the clusters are interacted with by the Apache pig access engines. Apache Hadoop users
are allowed to use Pig Latin, a simple scripting language to write complex map reduce
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transformation O’Driscoll (2013). Translation of pig Latin by pig makes simple to be executed
within YARN in order to access single dataset that is stored in HDFS.
Hbase
This is a non-relational database that runs over the HDFS. The real time read in the large datasets
is provided by Apache Hbase since it is an open source NoSQL database. It is capable of
handling large volumes of data with numerous number of rows and numerous number of
columns since its scaled linearly to handle big data by easily combining data sources that use a
wide variety of schemas and structures by introducing SPOUT and BOLD concept Chen and
Zhang (2014).
Apache Hadoop
This is one of the technologies that use java based free software framework that has the
capability of storing enormous clustered data. It storage system is called Hadoop Distributed File
System (HDFS) that carries out the function of splitting data into clusters making data highly
available.
NoSQL
Different from SQL, NoSQL is specifically to handle unstructured data where they are as well
stored. Better experience of large data performance is enhanced by NoSQL databases. This
makes the analysis of big data easier and simpler since the performance is enhanced.
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Data analytics and MDM to Support DS and BI
The purpose of master data management (MDM) in business is not to support the business
transaction, but to create positive impact in the business BI systems Mertens et al (2013).
Incorporation of MDM ensures that the definition and names of the used data in the master data
entities are standard for a particular business organization. One of the constituent of master data
definitions is shared business vocabulary (SBV). Consistency portrayed in the data definitions
makes available dimensional model reused in BI. Deeper understanding of presented data in BI
system reports is improved by adopting master data SBV. Lack of MDM will make the data
difficult to analyze using data analytics tools. The results obtained in the process after the data
have been analyzed provide decision support (DS) in organization. Analysis of the analytical
data supports the decision making process in the company through identification of churn,
profitability and marketing.
NoSQL for big data analytics
Non-relational databases such as NoSQL were developed to carb the problem of big data
handling and analysis. Unstructured data are comfortably managed by NoSQL databases where
they are specifically concerned with model flexibility and large scaling that makes development
and deployment of applications easier Barlow (2013). In the operation of big data, the
management and storage are alienated by the NoSQL databases since they are directed towards
the performance scalable data storage that boosts advantages by permitting the data management
tasks not to be written in specific language for database but in application layer thus making big
data analytics process simpler and easier. Unstructured and semi-structured data stored in
multiple servers in the cloud are analyzed using various types of NoSQL databases such as
Cassandra.
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Different NoSQL databases and their uses
NoSQL databases that were developed to particularly handle non-relational big data are
categorized into four categories.
Document databases
They are databases that were developed mainly for document storage and management. They
include Couchbase, CouchDB, MongoDB and Riak Abramova and Bernardino (2013). Powerful
value columns are found in document databases that are capable of handling semi-structured data
in pairs where a column handles hundreds of same attributes while difference is seen from row to
row Moniruzzaman and Hossain (2013). They are used in storage and management of big data
literal documents e.g. email messages and XML documents.
In-memory databases
Moniruzzaman and Hossain (2013), values associated are transferred to hash tables and data is
stored in alphanumeric identifiers in the database management system. Values in the DMS can
either be simple texts or complicated inform of lists and sets. They are used in business for the
retrieval of values that might have been called for in the worked task by the applications such as
managing user profiles and also retrieving the product names. They include; Memcached, Redis,
Riak and VoltDB
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Graph databases
They are human friendlier more than other discussed categories that help in looking into the
relations existing in the big data. Structured relational graphs are used to replace relational tables.
The graphs give pictorial representation of data as it is in the dataset which help the business
management to view the phenomenon in business operational activities. They include
InifiniteGraph, Neo4J and OrientDB.
Column store databases
This category of NoSQL databases include Apache Hbase, Cassandra and Google Bid Table.
They are capable of handling numerous attributes per key. Distributed data are easily handled
and stored in specific version data as a result of WC/CF time stamping functions. Predictive and
exploratory analysis are carried out by these databases that are thus used by the management to
make informed business decisions.
Role of social media in organization’s decision making process
Social media provide a platform for easy advertisement that is widely used in business
organizations to reach their intended customers Aral et al (2013). This particular type of
advertisement is kinda accurate since they capture the targeted groups at the right time of need,
this can be helpful in deciding what type of product of how much products to produce as per the
demand. Crowdsourcing done by the producers is always with the intention of collecting the
views and experience of the consumers to the products. Social analytics is then later carried out
by the decision scientists to extract useful information that could be used and considered in
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decision making. Business organizations benefit from social media due to its wider coverage
across the globe, thus making it important for businesses of all sizes.
Big data value creation in organizations
Information extracted from big data are made to the success of the business since it enables and
gives room for new thinking. Big data therefore adds success value to business, data analytics
specialists leverage the data to draw information that can be used in decision making of the
business and result to success if implemented Dean (2014). Old habits in the business
organization are aborted since big data will resulting to the development of new ideas as they are
captured and reported by the analysts. People in business will be transforming from manual ways
of doing things and adopting new digital ways of handling issues affecting business. Security of
the collected data mostly in the business organizations need to be upheld and big data bring the
value in offering security to the data. Setting big data strategy is one of the things the companies
and business organizations are working to implement. Employees are in most of the
organizations trained to acquire the knowledge of how they can handle big data to enjoy all its
due benefits.
Conclusion and recommendations
To conclude, big data is essential in the modern business organization sector in pushing for better
and constructive ideas in business that cannot be neglected when making decisions. Social media
plays an important role for business organizations in meeting and collecting the opinions of a
wide range of the customers. It is therefore recommended that all the business organizations to
acquire the use of big data for their betterment and growth. Also, the business that have already
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acquired and are using big data are supposed to ensure that employees are trained and
knowledgeable about big data.
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