Business Intelligence using big data

This report discusses the importance of big data in business development, with a case study of Spotify. It covers business strategy, technology stack, data analytics, MDM, NoSQL databases, and the role of social media in decision making.

Running head: BUSINESS INTELLIGENCE USING BIG DATA
Business Intelligence using big data
Name of the student
Name of the University
Author’s Note

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Table of Contents
Introduction......................................................................................................................................3
Business Strategy for Big Data Use Case........................................................................................3
Business initiatives & objectives.....................................................................................................4
Technology Stack............................................................................................................................5
Data Analytics & MDM to support DS&BI....................................................................................7
Support of NoSQL for Big Data Analytics......................................................................................8
Different NoSQL databases & use in Big Data...............................................................................9
Role of Social media & human elements in organizations decision making process...................10
Conclusion.....................................................................................................................................11
References......................................................................................................................................13

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Introduction
Business Intelligence & Analytics has been emerging as an important topic for
researchers & practitioners. This is related to the Big Data analytics that has been becoming
strong in both the academic & business perspective. Various researches have been done on the
Business Intelligence & analytics that have been helpful in maintaining a proper analysis of big
data in business organization. It also helps in affiliate marketing of the services provided by the
company to the users in the market. The user can look more songs over the social media
platform a share their feelings related to the applications. Social media strategy has been one of
the part of the business strategy for the company in the market. The business model of the
company has been enhanced by the implementation of the social media in the business structure.
This report has been focusing in the importance of the big data aim the business development of
a company. This report is based on how Spotify has been extending its business strategy in the
market.
This report has discussed about the data analytics & MDM to support DS & BI. There
have been discussion in different NoSQL databases & utilization in Big Data use case. Tis report
has discussed about the importance of social media & human elements in Spotify in its decision
making process. This report have outlined Big Data value creation process.
Business Strategy for Big Data Use Case
Big Data has been started in the late 20th Century in the market. In recent years, it has
been capturing market in its pace. Several organizations have been implementing big Data
analytics in their business operations for gaining advantages. As commented by (Aljawarneh,
Alawneh, & Jaradat (2017), the use of the big data has been creating opportunities for the

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business organization. There are various use case of the big data analytics including streaming
analysis, advanced analytics, data warehouse, 360 degree view on customer & Operational
efficiency. This report has focused on the streaming analysis used in the Spotify for streaming
music & songs to the users.
Figure 1: Big Data Use Case Framework
(Source: Raghupathi & Raghupathi 2014)
As commented by (Raghupathi & Raghupathi (2014), The commercial music streaming
service Spotify has been launched in 2008 & have already 24 million active users. There are 3.7
million Facebook fans of Spotify. The company has a huge data base having 20 million songs
online & everyday 20,000 new songs are added on to the database. Therefore, it can observe that
Spotify deals huge amount of data at a time in their database. As mentioned by Stimmel (2016),
the implementation of the big data analytics in the company has been a great step by the

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company. The big data has been helping in managing & monitoring huge amount of songs &
data. Spotify is a data-driven company which depends on data & information.
As commented by Kambatla, Kollias, Kumar & Grama (2014), Spotify users create 600
Gigabyte of data per day & 150 Gigabyte of data per day through different services. Every day 4
Terabyte of data has been generated in Hadoop. Therefore, it becomes dofficult for the company
to store such a huge amount of data & information in the market. As mentioned by
(Govindaraju, Raghavan & Rao (2015), the use of the big data analytics in the company have
been helping in maintaining this huge amount of data & information in the company. The
company started as a python services provided to the customers in the market. The business
strategy of the company has been a simple one. They have developed a workflow manager, Luigi
by opening sources. Luigi is a python framework for data flow definition & execution.
Business initiatives & objectives
Business strategy has been an important part of the business organization. IT deals with
the business models implemented in the company. The use of business models help in
maintaining a keen approach to the development of the company in the market. As mentioned
by Buyya et al. (2015), the Spotify has been able to maintain its business strategy & model for
the prioritizing its business in the market. Big data has been a great approach to the maintenance
of data & information in the organization. The scope of the project is depended on providing
numerous number of songs to the user online. The business strategy has been targeting all type
of users online. The company used to provide premium business services to premium customers.
As commented by Zakir, Seymour & Berg (2015), the premium services include removal of
advertisements & download limit of sings from the Spotify. The bitrate of every song is changed
to 320kbps. The company also helps in providing student subscription services to the students.

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These services have been increasing business strategy of the company in the market.
However, there are three different subscription in the business including Spotify free version,
Spotify premium & Spotify family. The business models & strategy has been focusing in the
branding & revenue on advertisements. As mentioned by Najafabadi et al. (2015), there are
various branding moments in the company that has been maintaining a keen approach to market.
The use of various sponsors in the market has been helping in providing a different approach to
maintain different subjects in the market. The use of several audio advertisements in the Spotify
helps in generating revenues for maintaining a different approach to the market. The use of
display advertisements helps in maintaining a peculiar approach to create revenue for the
company over the application. The company has been able to launch an application that can be
installed in android & iOS system.
Technology Stack
As suggested by Riggins & Wamba (2015), technology stack refers to the use of the
combination of programing & analyzing tools for creating a product in the market. The business
of the Spotify is totally depended on its marketing strategy. Therefore, there is a need of
implementation of technological stack for solving business problems & minimize the total cost
of ownership (TCO) & risks in technology during maximizing performances. Data h&ling has
been one of the biggest challenges in this industry. The common data sources have been from an
internet analytics tool. As commented by Hu Wen, Chua & Li (2014), data feed contains 300-
400 data fields per record. Therefore, row sizes range from a thousand characters to 3-4 thousand
characters per row. Sheer size of data has not been always only the driver of big data problems. It
has been a contributing factor with huge no. of rows & columns in the database. Spotify is a
data-driven company which depends on data & information. As mentioned by Wang,

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Gunasekaran, Ngai & Papadopoulos (2016), Spotify users create 600 Gigabyte of data per day &
150 Gigabyte of data per day through different services. Every day 4 Terabyte of data has been
generated in Hadoop. Therefore, it becomes difficult for the company to store such a huge
amount of data & information in the market. The huge number of IT organizations have been
facing these challenges in the operational department of the company. The traditional response
of huge quantities of data has been a straightforward & effective aggregation. The technology
used in the company has been the creating a variety of chances & opportunity in the market. The
big data analytics has been helping in maintaining the change in the traditional way of marketing
& data string in the database. It has helped in in using advanced technology in the database
capacity. As mentioned by Singh & Reddy (2015), the relational database has been changed by
NoSQL database. The use of big data analytics has been imagining a keen approach to the
maintenance of the technological aspect of the data storage. The traditional method of data
storage is not able to store huge number of data & information in the database. The maintenance
of the data & information has not been maintained in the traditional database. The big data
analytics has been helping the Spotify in maintaining & storing databases.
Data Analytics & MDM to support DS&BI
As stated by Saa, Moscoso-Zea, Costales, & Luján-Mora, (2017), the poor quality of data
storage in the database has been a major problem for company. The use of the Master Data
Management (MDM). MDM is a modern technique that helps in maintaining & minimizing poor
quality of data storage. Oracle‟s MDM employs powerful prebuilt data models that support
operational workloads & service oriented architectures (SOA). It provides tools such as fast &
secure parameterized search engines; duplicate identification, elimination & prevention; data
attribute survivorship; data quality rules engines; hierarchy management; data standardization;

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real time change management; & data synchronization. As mentioned by Loebbecke & Picot
(2015), it employs interfaces to third party data augmentation & address standardization
providers. The company has been focusing on the standardization in the centralized database.
The company has been focusing on the Customer Relationship Management. Quality product
data has been available for the Product LifeCycle management & ERP applications. MDM helps
in holding authoritative governed dimension data. The use of the MDM techniques has been
reporting various benefits in the maintaining the quality of data & information in the company.
Figure 2: Big data Analytics Architecture
(Source: Salehan & Kim 2016)
As mentioned by Salehan & Kim (2016), MDM has been holding corporate cross
reference for key direction including product & customer. MDM helps in maintaining the id of
connected system with the source system. Therefore, it creates & maintain a regular approach to
the data & information included in the database. The implementation of the big data analytics in
the company has been a great step by the company. The big data has been helping in managing

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& monitoring huge amount of songs & data. Spotify is a data-driven company which depends on
data & information. The use of the big data analytics in the company have been helping in
maintaining this huge amount of data & information in the company. The company started as a
python services provided to the customers in the market. As commented by Kwon, Lee & Shin
(2014), it helps in maintaining a cross reference of the data & information stored in the database.
This technique helps in maintaining an accurate reporting & analyzing in the database.
Support of NoSQL for Big Data Analytics
As mentioned by (), Big data has been focusing on huge volume of data & information in
the database. The traditional database has been using relational database. This type of database
has been creating problems in the analyzing huge number of data & information in the database.
Various data sources are used in the data collection technique. This creates a lot of problems for
the data base to manage data & information. Therefore, NoSQL helps in maintaining proper data
base. . Various structured & unstructured are included in these huge volumes of data. Therefore,
cloud servers are used to store such a large volume of data & information. The use of big data in
the organization has been helping in maintaining & managing large volume of data &
information. As mentioned by Salehan & Kim (2016), the efficiency of this work has been
maintained by the big data analytics. The concept of big data analytics has helped in maintaining
the databases of the companies in order to provide a proper maintenance of data & information.
NoSQL provides different type of framework of databases that helps in providing agile process
& high-level performance in data processing at huge scales. This help in increasing performance
of database & high data processing ability. There is a varied set of difference between NoSQL &
other traditional relational databases. The traditional databases are more structured in nature
whereas the NoSQL databases trade off stringent consistency features in order to gain more

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speed & agility in data handling (Moniruzzaman & Hossain, 2013). The NoSQL distributed
database infrastructure has been the solution to handling some of the biggest data warehouses on
the planet – i.e. the likes of Google, Amazon, & the CIA.
Different NoSQL databases & use in Big Data
There are four types of NoSQL including Key-Value Stores Database, Document
Oriented Database, Column Store Database, & Graph Store Database. As mentioned by Reyes-
Ortiz, Oneto & Anguita (2015), the key-value database focuses on the data storage in the form of
a pair. Therefore, this pair consist of a key & value chain. As commented by (), the data types
has been lacking in providing an efficient & powerful model in order to maintain various
standardized form of pair as attribute is the KEY & data has been related to the VALUE.
However, the structure of the company needs to be combated with various other programmable
languages for checking the accessibility criteria. However, the social database has been
maintaining a superior adaptable type of information display in which each line in the
information design can be unique.
In document oriented database, report needs to be made by storing points of interest in
the form of database. Therefore, the use of information needs to be kept away from the record
that can be utilized for making different inquiries in the database. This comes helpful while
utilizing blog type of use & putting away machine or sensor-produced information. As
commented by Chen, Preston & Swink (2015), in Graph Database, the information is put away
with a connection to the non-exclusive charts. The hubs help in the investigation & research of
the relations & the association. The information composes makes utilization of the edges & the
hubs to make speak to & portrayal of the information & closes by the store it. The hubs are

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utilized for the portrayal of articles & the edges are utilized for the generation of the relations.
Segment Store Database can be said to be the customary type of database (Takkar 2017).
Role of Social media & human elements in organizations decision making
process
Decision making process has been an important point in the organizational structure. The
use of the decisions making system in the organization has been maintaining the growth of the
company in the market. The decision making policy has been an important factor in the market.
Implementation of the social media in the decision making system has been helping in
maintaining the growth of the company in the market. As mentioned by Lee, Ardakani, Yang &
Bagheri (2015), most of the youngsters & teenagers are available in the social media sites
including Facebook, Twitter & Google++. Therefore, these platforms helps in online marketing
of the services & products offered by any company. In this case, Spotify might provide their
advertisements over the internet through social media platforms. Social media platforms helps in
providing a huge number of customers & users over the internet. Therefore, the databases of the
company might be filled with a huge number of data & information about the users of the
applications over the internet.
The feedback of the customers & users over the internet might help in taking decision in
the system. The changes & upgrades in the application might get easier for the developer in the
company. As mentioned by Xu, Frankwick & Ramirez (2016), social media helps in maintaining
a keen approach in the development of the customer base over the internet. The use of the social
media in the company might help in accessing various profiles of the customers in order to
understanding their taste of music. It also helps in affiliate marketing of the services provided by

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the company to the users in the market. The user can look more songs over the social media
platform a share their feelings related to the applications. Social media strategy has been one of
the part of the business strategy for the company in the market. The business model of the
company has been enhanced by the implementation of the social media in the business structure.
The quality of the marketing & advertising of the company has been increased with the help of
the social media platform the decision taken by the management of the company helps in
providing path form the developers of the application in order to growth in the market.
The communication between the customers & company officials can be increased with
the implementation of the social media. As stated by Saa, Moscoso-Zea, Costales, & Luján-
Mora, (2017), both way communication between company & users helps in creating transparent
channel for communication in order to increase the sales of the services provided to the users in
the marmite. The internet has been considered as one of the most important feature in might of
marketing & advertising services of the company. Social media platforms are made for sharing
feelings, emotions & opinions of users over the internet. This have helped in exploring the world
related to the online communities of people.
Conclusion
It can be concluded that the Spotify has been able to gain the competitive advantages in
the market. The company has been successfully adopted the social media marketing platform for
growth in the business. The information of the big data has been helping in maintaining the
database of the company. The big data has been able to change the traditional relational database
to advanced NoSQL database. The use of NoSQL has helped in maintaining & increasing the
performance of the database. NoSQL has helped in increasing the performance of the data
processing & analyzing in the market. The business strategy of the Spotify has been discussed in

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the report. A proper discussion in the data analytics & MDM has been provided in the report.
This helps n maintaining appropriate approach to the development of the company in the market.
The use of the big data analytics in the company has been explained in the report. The
importance of the social media in the development of the decision making system has been
explained in the report.

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