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You are required to do market research and write a report on how Big Data can be used in Decision Support and Business Intelligence (DS&BI). You are required to select/ research a use case for Big Data Strategy. You are required to identify and create business strategy for Big Data use case. Business strategy should be mapped clearly to business initiatives, objectives and tasks. You should able to define required technology stack and required data and analytics architecture for Big data for DS&BI including the Master Data Management (MDM).

You should able to address advanced analytics requirements necessary to support the business strategy they have selected. And the role social media plays in organisations decision making process. You are required to discuss Big Data Value creation process. The report should address the followings:

  1. Identify, create and discuss Business Strategy for a Big Data use case
  2. Identify and align business initiatives, objectives and Tasks with the developed Business Strategy.
  3. Identify and discuss the required Technology Stack4. Discussion on Data Analytics and MDM to support DS&BI
  4. Discuss support of NoSQL for Big Data Analytics.
  5. Discussion on different NoSQL Databases and its use in Big Data use case you have chosen.
  6. Role of Social media in organisations decision making process
  7. Discussion on Big Data Value creation process.

Big Data Use Case: Multi-Channel Marketing

Organizations and internet is filled up with huge clusters of information and data this exchanged and transferred in massive volumes every day. It is a primary concern for the organizations to effectively manage the information.

Big Data is the buzzword in the present era in the field of technology and innovation. Big Data is a concept that refers to huge clusters of information that may comprise of data in several structures and types. Big Data and its tools are therefore being used and implemented in the organizations to efficiently use the data and to store and manage the same without any errors (Venturebeat, 2016).

Business Intelligence, abbreviated as BI is an umbrella that includes a variety of technologies under it. It is a concept that deals with the information to study and analyze the same to draw out some meaningful information from the same (Google, 2016).

Multimedia and multi-channel marketing is something that has become a necessity in the present times. There are various forms of media that are now present such as tele-media, print media, social media and a lot more. Marketing strategy that is carried out involving various media channels and devices is termed as multi-channel marketing. Big Data plays an important role in this particular form of marketing. The users make use of several different devices such as Smartphones, tablets, laptops and many others to access a particular site or an application. Acquiring data from all of these sources and then analyzing the same through manual processes and obsolete tools is quite troublesome. Big Data tools and applications can be used to discover important information regarding user choices and preferences to strengthen the multi-channel marketing strategy for the organizations (Qubole, 2016).

The following is the strategy that shall be used and applied in case of the collaboration of Big Data and multi-channel marketing.

Step 1: Acquire sponsorship at executive-level

The project that is based upon the concept of multi-channel marketing shall be analyzed in terms of objectives and goals to gain proper sponsorship. Lack of the same will enhance the chances of failure.  

Step 2: Augment instead of re-build

There is a lot of information that is present with the organization in terms of the past project documentations, information from employees, material with the sponsors and likewise. It would therefore be required to make use of the information and data that is already present instead of looking out for starting the whole process from scratch.

Big Data Strategy

Step 3: Place customers at top priority

In case of multi-channel marketing, the major aim is to attract and engage the customers to a particular product or application and therefore the requirements and expectations of the customers shall be well understood.

Step 4: Run an Agile shop

Agile processes allow the procedures and methods to gain the qualities such as flexibility and scalability as compared to their traditional counterparts. In case of multi-channel marketing, there will be a lot of data and huge number of requirements that will be associated. It would be necessary to adapt and proceed through agile processes as these are ad-hoc in nature and the change in the requirements will be easily incorporated.

Step 5: Link customer data to organization culture

The policies of the organization along with the strategies and concepts that are followed shall be linked with the customer expectations.

Step 6: Form repeatable process and Steps of action

In case of execution of multi-channel marketing using Big Data, there shall be repeatable processes that shall be created.

Step 7: Test and Learn

It is often observed that testing activities are not paid due attention which result in the failure of the project at advanced stages. Testing activities shall be carried out to make sure that the defects and loopholes are not present.

Step 8: Mapping of information to the customer’s life cycle

As stated earlier, customer shall be placed on the top priority and steps shall be taken to make sure that adequate mapping of the results and the customer oriented activities are done.

The business strategy that has been illustrated above would be applied to the components as listed below for the alignment of business initiatives.

Data Mining

Data Mining is a process that comes under the umbrella of Business Intelligence (BI) which is used for data analysis and data categorization to place the data in various categories on the basis of several factors. Co-relation between the sets of the data is found through this technology and it would be highly applicable in case of multi-channel marketing to understand the relationship between internal and external attributes such as between cost and profits, input and customer feedback and likewise.

Online Analytical Processing (OLAP)

Online Analytical Processing which is abbreviated as OLAP is a technology that comes under the umbrella of Business Intelligence. It is a technology that includes strategic monitoring for sorting and selection of aggregates related to the data. Adjustments on the data is done to make sure that there are considerable benefits provided to business processes (Olap, 2016)

Alignment of Business Objectives in accordance with Business Strategy

Real time BI

Multi-channel marketing along with several other activities that are carried out in the organizations of different sectors demand real-time execution and real-time view of the information. These real-time views allow the organization to learn about the trends and patterns. Real time BI can provide the users with the required details.

Data Warehousing

Data warehousing refers to one of the concepts and technologies under Business Intelligence (BI) to highlight the inter-related information sets. It allows the business analyst and experts to develop the statistics associated with the business. For instance, in case of multi-channel marketing, there may be different timeframes required for media exchange on the basis of network performance and media type. These patterns and the relationship of the components on each other can be easily studied.

Business Performance Management

Performance of the business activities are required to be studied to understand the areas of improvements along with the strengths and weaknesses. Business Performance Management tools are used to assess the performance of the organization. Data and information from a number of different sources is studied to highlight the performance of the business operations and the level of efficiencies can also be easily monitored (Villanova University, 2016).

Data Analytics

Analytics is a concept under BI which is used to analyze the data sets that may have any structure to find out the details such as patterns, behavior, statistics and likewise (SearchDataManagement, 2016)

Reporting

Enhanced reporting is a necessity in the present times to understand the status and progress that is made at every step.

Data Sources

There are several data sources that are associated with Big Data and multi-channel marketing that are required to be studied to collect important information from the data sources.

  • Business Intelligence (BI) would allow the analysis and warehousing activities to be carried out on the Big Data to understand the patterns and behavior. Predictive analysis can also be done easily.  
  • Use and application of Big Data would make sure that the base is set correctly. It is not a single file or a particular document that is required to be managed; however, there is an extensive monitoring and management required which will be made easy.
  • Customer satisfaction will also improve as the business strategy that will be created will be based upon the studied user patterns and statistics.
  • There are several data sources that are present which may be internal or external to the organization. Big Data would allow the identification of the target audience for each of the business objective and business strategy (Ap-institute, 2016).
  • It would also allow the optimization of the business processes and activities. There might be a few unnecessary activities that may exist which will also be removed.  

There are some additional benefits of Big Data that will result which are as listed below.

Cost Savings

There are several tools that are available in the market for the implementation of Big Data activities and technologies. One such tool is Hadoop. This is the tool that would allow the storage of Big Data at extremely low costs. This is the tool that has the capability to perform the activities that come under data warehousing and the costs associated with a dedicated tool for data warehousing are also avoided. Once the storage of the data is done with success, the data is moved to the regular database to enhance the data analysis. These tools are versatile in nature that will provide a lot of operational benefits to the users and the organization.

Competitive Advantage

Accessing massive data sets along with their storage, organization and management has been made easy with the use and implementation of Big Data. The data can also be accessed from numerous data sources to ensure that all the relevant data associated with an application is collected. With the addition of all of these capabilities, it becomes easier for the organizations to efficiently perform all the tasks and activities. The same will allow the generation of newer values and will provide the organizations will competitive edge (Qubole, 2016).

New Business Opportunities

The business opportunities that are associated with a particular sector or an organization are many in number and newer opportunities are being created with each passing day. Many sectors have benefitted with the use and application of Big Data tools and two of such industries that have seen massive improvements are marketing and advertising. Real-time services, analysis of the data to highlight customer preferences, enhanced reporting etc. has led to the creation of new and improved opportunities to allow the organizations to perform better and expand themselves.

There are several techniques that may be used and applied in the decision making and decision support for multi-channel marketing in collaboration with Big Data.

  • Hadoop

Hadoop is a Big Data tool that is open source and offers enhanced inter-operatibility as it is written in JAVA. There are several tools that are available in the market for the implementation of Big Data activities and technologies. One such tool is Hadoop. This is the tool that would allow the storage of Big Data at extremely low costs. This is the tool that has the capability to perform the activities that come under data warehousing and the costs associated with a dedicated tool for data warehousing are also avoided. Once the storage of the data is done with success, the data is moved to the regular database to enhance the data analysis. These tools are versatile in nature that will provide a lot of operational benefits to the users and the organization (ITProPortal, 2013).

  • Hyperscale Storage Architecture

This is the technology that makes use of Direct-Attached Storage (DAS) to carry out the activities that are associated with the analysis of Big Data. If there is an occurrence of failure in one of the components then it is often seen that the entire work cycle suffers. However, in case of Hyperscale storage architecture, data is stored across several channels and failure in one area does not impact the other (Computerweekly, 2016).

NoSQL

NoSQL is the category of the databases that have the ability to perform analysis on Big Data with a lot of convenience. The database provides the users with enhanced visual appeal and also has the capability to create customized reports which can be easily extracted. The mode of operation that is used in case of NoSQL databases is also flexible in nature and is based upon several attributes such as revenues, location, time and likewise. These databases also allow the predictive analysis to be carried out with the aid of powerful and efficient algorithms (Pentaho, 2016).

NoSQL databases have expanded rapidly in the recent years and have connected more than 210,000 APIs so far (Goes, 2016).

Master Data Management (MDM) is a concept that is used for the management of enterprise data. MDM is categorized in to two categories on the basis of its structure and operations.

  • Operational MDM (O-MDM) is the type of MDM which is associated with the distribution along with the synchronization of the master data to reflect the attributes associated with consistency in all the transactional operations.
  • Analytic MDM (A-MDM) is the type of MDM in which the master data is managed with the aid of hierarchies that are necessary for analysis and aggregation purpose.

Data analytics along with MDM have an important role to play in terms of DS and BI.

  • Synchronization of the entire basic master data is made extremely easy
  • Turnaround time that is spent in the reconciliation of the data is extremely quick  
  • Consistency is reflected and maintained in terms of the transactional data
  • Business performance management is improved (InformationWeek, 2016).

MongoDB

It is a NoSQL database that is open source in nature and therefore can be acquired for free of cost. It is based on agile framework and therefore includes the system qualities such as flexibility and scalability.

Elasticsearch

RESTful web interface is demanded in most of the applications in the present scenario. This particular NoSQL database offers the same to the clients.

CouchDB

This is the NoSQL database that has been primarily designed for the storage of documents. It allows the users to query the indexes with the aid of web browsers.  

AmazonSimpleDB

Amazon SimpleDB is a NoSQL database which is all-time available and extremely flexible in nature. There is a very little administration and human intervention necessary in this one.

MarkLogicServer

This NoSQL database can be used at the enterprise level and targets the analytics requirements for the applications.

Terrastore

This is the database in which consistency of the operations along with enhanced performance is provided to the users (Bigdata-madesimple, 2014).

Social media and its use have expanded in the recent years and have brought a transformation in the field of media and marketing. It has a significant role to play in the areas of Decision Support (DS) and Business Intelligence (BI) as well. Some of the popular social media channels include Twitter, Instagram, Facebook, Snapchat and many more.

These social media accounts are used by the users to put their details and for a social network. These are also used to post about the likes, dislikes along with the preferences. The data and information from the social media and networking accounts can be acquired to understand the user behavior and preferences. These can also be used for purpose of marketing on the social media channels.

There are several complexities that are associated with the business operations and business activities. Value creation for an organization in terms of Big Data can be explained and can be understood from two aspects.  

  • Value for the company
  • Value for the customer

The role that data analytics has in terms of the data along with the data sources is also important. The data can have any type, structure, content and specifications over here which may be acquired from internal or external sources.

The two directions that are associated with the value creation have been classified in three levels which are as listed below.

  • Market level for value creation
  • Brand level for value creation
  • Customer level for value creation (Hull, 2016).

The two models have been used to illustrate the concept of value creation.

Five Force Analysis

                              

                             

Value Chain Analysis

There is a specific list of values that is used for the creation and generation of values associated with Big Data. Primary and secondary activities are includes in association of the same.

                                   

Conclusion:

Big Data is the buzzword in the present era in the field of technology and innovation. Big Data is a concept that refers to huge clusters of information that may comprise of data in several structures and types.  Big Data and its tools are therefore being used and implemented in the organizations to efficiently use the data and to store and manage the same without any errors. Multimedia and multi-channel marketing is something that has become a necessity in the present times. There are various forms of media that are now present such as tele-media, print media, social media and a lot more. Marketing strategy that is carried out involving various media channels and devices is termed as multi-channel marketing. Big Data plays an important role in this particular form of marketing. Big Data tools and applications can be used to discover important information regarding user choices and preferences to strengthen the multi-channel marketing strategy for the organizations. There are several tools that are available in the market for the implementation of Big Data activities and technologies. One such tool is Hadoop. This is the tool that would allow the storage of Big Data at extremely low costs. Accessing massive data sets along with their storage, organization and management has been made easy with the use and implementation of Big Data. Many sectors have benefitted with the use and application of Big Data tools and two of such industries that have seen massive improvements are marketing and advertising. Real-time services, analysis of the data to highlight customer preferences, enhanced reporting etc. has led to the creation of new and improved opportunities to allow the organizations to perform better and expand themselves. NoSQL is the category of the databases that have the ability to perform analysis on Big Data with a lot of convenience. The database provides the users with enhanced visual appeal and also has the capability to create customized reports which can be easily extracted. Social media and its use have expanded in the recent years and have brought a transformation in the field of media and marketing. It has a significant role to play in the areas of Decision Support (DS) and Business Intelligence (BI) as well. Some of the popular social media channels include Twitter, Instagram, Facebook, Snapchat and many more. There are several complexities that are associated with the business operations and business activities. Value creation for an organization in terms of Big Data can be explained and can be understood from two aspects as value for company and the customer.

References:

Ap-institute. (2016). How is Big Data Used in Practice? 10 Use Cases Everyone Must Read. [online] Available at: https://www.ap-institute.com/big-data-articles/how-is-big-data-used-in-practice-10-use-cases-everyone-should-read.aspx [Accessed 4 May 2017].

Bigdata-madesimple. (2014). A deep dive into NoSQL: A complete list of NoSQL databases. [online] Available at: https://bigdata-madesimple.com/a-deep-dive-into-nosql-a-complete-list-of-nosql-databases/ [Accessed 4 May 2017].

Computerweekly. (2016). [online] Available at: https://://www.computerweekly.com/feature/Big-data-storage-choices [Accessed 4 May 2017].

Goes, J. (2016). How to choose a NoSQL analytics system. [online] InfoWorld. Available at: https://www.infoworld.com/article/2983953/nosql/how-to-choose-a-nosql-analytics-system.html [Accessed 4 May 2017].

Google. (2016). Business Intelligence | Information Builders. [online] Informationbuilders.com. Available at: https://www.informationbuilders.com/business-intelligence [Accessed 4 May 2017].

Hull, (2016). [online] Available at: https://www2.hull.ac.uk/hubs/pdf/NEMODE%20big%20data%20scientist%20report%20final.pdf [Accessed 4 May 2017].

InformationWeek. (2016). MDM for Operations and Analytics - InformationWeek. [online] Available at: https://www.informationweek.com/software/information-management/mdm-for-operations-and-analytics/d/d-id/1042903? [Accessed 4 May 2017].

ITProPortal. (2013). Big data: 5 major advantages of Hadoop | ITProPortal.com. [online] Available at: https://www.itproportal.com/2013/12/20/big-data-5-major-advantages-of-hadoop/ [Accessed 4 May 2017].

Olap. (2016). What is Business Performance Management? BPM Definition. [online] Available at: https://olap.com/learn-bi-olap/olap-bi-definitions/business-performance-management/ [Accessed 4 May 2017].

Pentaho. (2016). Pentaho and NoSQL Databases. [online] Available at: https://www.pentaho.com/big-data-analytics/nosql-databases [Accessed 4 May 2017].

Qubole. (2016). Big Data Use Cases | Qubole. [online] Available at: https://www.qubole.com/resources/solution/best-use-cases-for-big-data-analytics/?nabe=5695374637924352:0&utm_referrer=https%3A%2F%2Fwww.google.co.in%2F [Accessed 4 May 2017].

SearchDataManagement. (2016). What is data analytics (DA)? - Definition from WhatIs.com. [online] Available at: https://searchdatamanagement.techtarget.com/definition/data-analytics [Accessed 4 May 2017].

Venturebeat. (2016). Big Data implementation mistakes to avoid. [online] Available at: https://venturebeat.com/2014/11/25/5-big-data-implementation-mistakes-to-avoid/ [Accessed 4 May 2017].

Villanova University. (2016). Business Intelligence (BI) Overview of Major Components. [online] Available at: https://www.villanovau.com/resources/bi/overview-of-business-intelligence-bi-components/#.VzordTB97IU [Accessed 4 May 2017].

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