Multidimensional Data Warehousing with NoSQL

This assignment delves into the implementation of multidimensional data warehouses using NoSQL databases. It requires a thorough understanding of NoSQL databases, their characteristics, and their suitability for handling complex multidimensional data structures. Students will explore various techniques for modeling and storing multidimensional data in NoSQL environments, considering factors like scalability, performance, and query efficiency. The assignment emphasizes practical aspects and may involve designing a conceptual schema or developing a proof-of-concept implementation using a specific NoSQL database system.

BUSINESS INTELLIGENCE USING BIG DATA
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Executive summary
The purpose of this report was to identify social networks for exploiting knowledge or creating
business intelligence strategies for 7-Eleven company. The developed big data strategy was
aimed at the determination of profitability of the business and its behavior. For the big data
business intelligence, four of the strategy elements that were identified were performance
management, data exploration, social analytics and decision science where each one of the
strategy elements played their specific roles. Emergence of big data has led to the creation and
development of technologies that assist in handling big data since they cannot be handled by
ordinary tools. Some of the identified technologies in the report include; Apache Hadoop,
Microsoft HDInsight, NoSQL and Big data in Excel. Master data management system (MDM)
are not used in business intelligence to support business transaction but rather to create positive
impact BI systems. Different NoSQL databases have been seen to be in four different categories
with various functions. Big data are important since among other things, they help in improving
the customers’ experience for 7-eleven value creation.
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Table of contents
Executive summary…………………………………………………………………………..……………ii
Introduction…………………………………………………………………………..……………………1
Organizational analysis………………………………………………………………………….1
Business strategies for big data use………………………………………………………………………3
Multidimensional Data analysis for decision support…………………………………………6
Required technology stack………………………………………………………………………………..7
Data analytics and MDM to Support DS and BI……………………..…………………………………9
NoSQL for big data analytics………………………………………..…………………………………..10
Different NoSQL databases and their uses……………………………………………………………..10
Role of social media in organization’s decision making process….………………………..…………12
Big data value creation in organizations……………………………………………………………..…13
Conclusion and recommendations…………………………………..……………………………..……13
References…………………………………………………………..…………………………………….14
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Introduction
Business organizations have been looking for ways to stay competitive in the market which
cannot be achieved without vast information of the business and market behavior Singhal et al
(2013). Big data has been acquired by both big and small business organizations to help them
excavate to the last hidden information and not leaving any information to chance. Big data
analytics experts are incorporated in business to help in extracting information from big data as
they are collected to help in betterment of the business organization. Though challenges have
been experienced with most of the businesses working with big data, the experts have been on
the rise to improve and provide correct information as they really are where later the information
is passed to the organizations’ administration and decision makers to come up with the right
decision that suits the desires of the customers and maintain or widen the market coverage of the
offered products Chen et al (2012). This report is aimed at developing the business strategies
using big data and how they are useful to fulfil the business objectives. Some of the strategies
that have been identified for the big data users are performance management, data exploration,
social analytics and decision science.
Organizational analysis
7-Eleven is American-Japanese international that operates the chain of convenience stores that is
having branches in almost 18 countries (e.g. Indonesia, Malaysia, Hong Kong, China, Japan,
Singapore etc.) with its headquarter in Irving, Texas in the United States. Until 1946 it was
known as Tote’m Stores where it was later renamed to the current name 7-Eleven. In the
countries like the United States, 7-Eleven supply Slurpee drinks (i.e. soft drinks that are partially
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frozen), fresh fruit, salads etc. 7-Eleven corporate is renowned for supplying big drink sizes such
as big gulp that is about 946ml. due to the need of continuous supply of fresh food on daily basis
to the stores all over their various locations, the management had incorporated technology to
keep the pace. The corporate has the mission of being close to their customers and convenient at
all times. This mission was set to ensure that the services offered by the corporate business meet
the requirements of the customers, fast, convenient and friendly. The vision on the other hand is
to ensure that they become the best retailer of convenience. In line with the vision and missions
set by the 7-eleven management team, there are some of the strengths, weaknesses, opportunities
and threats that the business face.
Some of the strengths as highlighted by the business management are; regarding franchising and
licensing, the company had earned a name to be the largest convenience store. It operates in over
39000 locations tha makes it ahead of its close competitor McDonalds. The company has a large
number of well trained employees of about 45,000 and the company as well has active services
and consistent performance which makes it part of franchise 500.
Some of the weaknesses a company has are as follows; the company lack touch and
communication with the customers due to least developed web based facilities and the internet,
the company as well lack that togetherness as a result of the diverse demographic locations since
the branches are spread to operate in different governmental policies and lastly, it has been
having ill management which can be proved by the turnover of employees more often.
Opportunities; the company signed a contract of 20 years with CITGO which has added an
advantage to it by placing it in the sustainable path. The purchasers’ trend is being shifted
towards privately labeled products. And lastly the threats that are experienced by the company
are as follows; the company faces stiff competition from the discounted stores like Wall Mart
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and others that are quickly adopting technology and enhancing their growth. Unavailability of
some of the products at the time of need by the customers as a result of the product being out of
stock is another major threat that would deteriorate the company operations.
To match the competitors in the market, 7-Eleven had to acquire and incorporate technology in
their daily operation. Their competitors like the Wall Mart have shown fast and rapid growth
with the increased use of technology like the websites and internet to reach their customers.
Social media provide the platforms for interaction between the company management and the
customers. In return, the company is able to collect data including the messages and chats
regarding the products supplied by the company and storing them in their databases. The amount
of data in the company is seen to be increasing and doubling for every two years due to
numerous branches and wide coverage the company has. Significant baggage are brought by big
data, since the problems business has do not end with having data, but use of data to extract
information in the daily operation of the business matters a lot. The decision making of the
business are based on the collected data as the data will be telling them what people are desiring
from them and how the market situation is at any moment and time. A process has to be engaged
in order to leverage and benefit from big data. The need for big data was then developed to help
the business know their customers who were digital and mobile.
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7-Eleven company strategies for big data use
Due to volume, speed and structure of big data, big data has become impossible to handle using
traditional tools. As a result therefore, business organizations that make use of big data like the
7-eleven Company there has been need to come up with big data strategies. Teradata were
collected by the company to create CRM systems with customer segmentation in the integrated
transnational data. The first strategic plan applied by 7-eleven comapany when using big data
was to have the performance management of big data. This strategy brought the sense of
understanding collected big data as they stream into the business databases Bharadwaj et al
(2013). Big data type that are used in this strategy are transactional data. They are analyzed to
answer some of the business questions and the information obtained from the analysis can be
used to make short term business decisions and also develop long term business plans from them.
Collecting Teradata and not exploring them would seem waste of time and company resources.
Big data are then collected and thorough data exploration done to them. Data exploration
strategy helps in applying statistical knowledge in the business experiment to help answer some
of the difficult questions that might not have been thought of by the manager or the decision
makers in the company. In this case, predictive model techniques are applied to forecast the
behavior of the business basing on the past business transactions Woerner and Wixom (2015).
To fully leverage the data and extract all the useful information, cluster analysis is applied where
customers are categorized into groups basing on their attributes and fully screening them.
Large non-transactional data are measured using social analytics. These types of data are
acquired by the business organization from the social media platforms i.e. twitter, facebook,
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Instagram etc. Various categories such as awareness, engagement and word of mouth are all
measured using social analytics McAfee et al (2012). All these categories are focused in
measuring the extent to which customers are reached through the social media platform and their
reactions towards the products offered by the business organization.
Lastly, decision science is applied to analyze non-transactional data to help the company draw
informed decisions from the available data from social media platform Woerner and Wixom
(2015). Each question is treated as a hypothesis where they are then tested by leveraging social
big data. As well, the questions from the company can be transmitted to the consumers about
their products through crowdsourcing. NoSQL is the widely known set of technology that is used
to handle big data and various NoSQL databases like the Hadoop adopted for the implementation
of the big data.
Multidimensional Data analysis for decision support
Data analytics is the process involving qualitative and quantitative techniques used to improve
the productivity of the business and its gains. Basing on the organizational requirements, data are
extracted from different categories to identify and analyze patterns, techniques and behavioral
data. Categorizing data into different groups by the time of carrying out data analysis results to
understanding information extracted from the data then improving the ideas whenever making
decisions. Information is always seen as a critical resource by the managers and also to exploit
competitive advantage, they require and incorporate systems for information exploitation.
Multidimensional data and online analytical processing give better way of using organizational
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data. Information from company databases are summarized and presented by OLAP and
MDDBs. Multidimensional analysis use the multidimensional structure that enable the managers
of the companies devise views concerning the company performance by fully exhausting and
drilling data to spotting the troubles Chevalier et al (2015). The only big challenge is to come up
with the most suitable system for online transaction processing (OLTP). Decision science
strategy is then applied on the data to interpret the data to derive meaning from the data that can
then be used by the managers in supporting the decision making process.
Required technology stack by 7-Eleven company
Dominance of big data since its emergence in the past few years and the inability of the
traditional tools to handle such data has led to the development of other technologies that help in
leveraging big data. Big data come in different forms i.e. they can be structured, unstructured or
even semi-structured. Traditional tools that are only structured into rows and columns find it
impossible to handle big data especially when they are semi-structured and unstructured. Big-
data analytics and decisions (B-DAD) comprised of big data analytics tools and methods that
need to be applied in the process of decision making are used Assunção et al (2015). Moreover,
big data as received, they as well need to be stored. In this case, the collected big data cannot be
stored using relational databases since they only deal with structured data. Being that
unstructured and semi-structured data are mostly part of the collected data in big data, Extract
Transform Load (ETL) is used to upload data from operational data stores to the storage.
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Apache Hadoop
Various technologies have been associated with storing, processing and analyzing big data in 7-
Eleven company. Apache Hadoop is one of the technologies that use java based free software
framework that is capable of storing vast clustered data effectively O’Driscoll et al (2013). It has
its storage system called Hadoop Distributed File System (HDFS) that is responsible in splitting
big data into clusters. Distribution of data into clusters gives way for high availability of data.
Microsoft HDInsight
This is set of another technology that provide solution of big data from Microsoft and it is
powered by Hadoop available and located in the cloud. The default file system used by
HDInsight is called Azure Blob storage that uses windows. This is seen important due to the fact
that it provides high availability data at a relatively low cost, this is according to Pokorny (2013).
This technology is associated in big data handling by 7-Eleven company because of its pocket
friendliness in the data analytics process.
NoSQL
This, apart from the traditional SQL that could handle large volume of structured data
effectively, the Not only SQL (NoSQL) is used to handle unstructured data Leavitt (2010). Since
all social media data obtained by 7-Eleven company are unstructured data of no particular plan,
they are stored in the NoSQL databases. Massive data performance enhancement is experienced
better with NoSQL. Many of the NoSQL databases are available in analysis of such big collected
data.
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Big data in excel
Most of the analyst seem composed and comfortable with analyzing data using excel. Excel 2013
has had an extra feature that is capable of connecting data in Hadoop. Power view feature in
excel can be used to obtain easy summary of big data stored in the Hadoop platform using Excel
2013.
Data analytics and MDM to Support DS and BI
Business intelligent system always has master data management represented as dimensions. Data
analytics is the process that is applied by the big data analysts to leverage big data. Master data
management system (MDM) are not used in business intelligence to support business transaction
but rather to create positive impact in BI systems Ellis et al (2012). MDM ensure that the names
and the definitions of data used in giving the description of master data entities are standard
names and definitions for a business enterprise. Shared business vocabulary (SBV) are contained
in the master data definitions. Same data definitions can be reused across all available
dimensional model in BI due to consistency drive across dimensional data. Adopting the use of
master data SBV is important in that it brings the sense of the in depth understanding of the
presented data in BI system report, scorecards, dashboards and OLAP analyses. BI can only be
trusted if it has the compliance with master data SBV. Data integration in a BI system can be
impacted on the arrival of an MDM system in the enterprise. Lack of MDM system will make
your BI system to depend on classical warehouse architecture where master data is divided into
multiple data stores and business operational system be on different lines. BI systems with MDM
makes the data easy to analyze using data analytics tools. Decision making process in the
company is supported by the analytical data through the analysis and identification of churn,
profitability and marketing groups Ding et al (2010).
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NoSQL for big data analytics
Problem in the storage of big data collected in business led to development of non-relational
databases where NoSQL is one of them. These databases are useful in managing unstructured
data as they focused in data model flexibility, large scaling and makes the deployment and
development of the applications simple Barlow (2013). Data storage and management are
separated by NoSQL databases since they are highly focused on the performance scalable data
storage that adds further advantage by allowing the data management tasks not to have been
written in database specific language but to have them written in the application layer, this makes
big data analytics more easy. One of the most popular NoSQL database used is Apache
Cassandra where many other are well available and used in analyzing unstructured data that are
stored in the cloud on multiple servers.
Different NoSQL databases and their uses
These are databases that were developed to help in handling non-relational large amount of data.
These databases are important in business enterprises especially when vast volumes of data are
involved and need to be analyzed. NoSQL databases are classified into four categories.
Key value stores
Data are stored in alphanumeric identifiers in these database management systems (DMS) where
values associated are transferred to hash tables Moniruzzaman and Hossain (2013). The referred
values in these DMS might be in form of simple texts or a much more complicated list and set.
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These key value stores are simple and suited for lightning fast and are useful in highly scalable
retrieval of the values that might be needed by tasks carried out by the application such as
managing user profiles and also retrieving the names of the products. Some of the existing
examples of key value stores are LinkedIn, Redis etc.
Document databases
These are the databases that were designed basically for managing and storing documents. The
value columns as found in the documents databases are more powerful as they handle semi-
structured data that are in pair of names where one column can handle hundreds of the same
attributes as the variation in number can be seen from row to row Moniruzzaman and Hossain
(2013). They are used in business in the management and storage of big data or collection of
literal documents. Example of referred documents are email messages and XML documents that
might be received from the customers in interaction with the products. CouchDB (JSON),
MongoDB etc. are database examples schema free and document oriented Abramova and
Bernardino (2013).
Wide column stores
This category of NoSQL databases are responsible and capable of holding multiple attributes per
key. Cassandra is one of the examples of the databases in this category that is suitable in
handling distributed data storage particularly the versioned data due to WC/CF time stamping
functions. As well, it is used to conduct exploratory and predictive analysis hence resulting to
informed decision making by the business management.
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Graph databases
They are the types of NoSQL that are concerned with relations and as a result therefore, they are
used in replacing relational tables using structured relational graphs thus making them human
friendly than other discussed categories. From the data collected in business, these graph
databases are important in examining the relationships that exist between data and itself.
Role of social media in 7-Eleven decision making process
People (customers) are nowadays in the constant use of the social media for communication and
many more. As a result, they are capable of sharing what they have including their experience
about products produced by a certain business organization (i.e. 7-Eleven). Decision makers in 7-
Eleven have to stay keen because once the customer gives a negative comment about the product
and manages to share their worst experience of the product with friends on social media, the
business will risk losing many customers. As a result therefore, 7-Eleven have created websites
and joined the social media such as Facebook, twitter etc. where they collect opinions of the
consumers of their products where by applying social media analysis are able to come up with
business decisions as per the findings from social media Aral et al (2013). Businesses are then
keen not to take for granted any information they obtained from the social media pages since
they count in making decisions. Social media provide a platform where producers are able to
meet with their customers and have a chat over the quality and the preference of the customers to
the already existing products in the market Zeng and Gerritsen (2014). When this is conducted
effectively, business organization can decide to innovate or bring a totally new product.
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Big data value creation in 7-Eleven company
Acquiring and storing big data has always been one story while making good use of big data and
turning it into something profitable for the business organization is another story altogether. Big
data incorporated in value creation should be concerned with improving the experience of the
customers Lavalle et al (2011). This will help in comprehending all the issues the customers raise
about the business organization and acting upon them to let stay in touch with the business. Big
data opens access to leveraging both internal and external sources of data across structured and
unstructured data concerning the customers towards building firmly on their journey and rich
experience of the business. Another value of big data in the value creation is to improve and raise
the precision of internal decision making of the 7-Eleven company. Insights are provided in the
process of decision making to ensure that quality decision are made. Decisions such as changing
the marketing plans, complain decisions or reducing or increasing some of the assignments in
business are precisely enhanced when big data are fully exhausted.
Conclusion and recommendations
In conclusion, big data are useful in the business organization in various aspects. When properly
analyzed, even the unimaginable information can be extracted that could help in the performance
of the business. Organizations’ decision makers are supposed to take into account all the
collected information as revealed from big data analytics and social media analytics. It is
therefore recommended that any of the information irrespective of where it is collected
concerning the business should be treated with seriousness and in decision making.
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