Business Intelligence and Data Warehousing

The projects around Multi-channel Marketing must be proposed and rightly fleshed out. Without a dedicated team or a proper sponsorship, there are high chances that the project may not do well.

Step 2: Augment rather than re-build

It is always better to begin with the existing data rather than creating and exploring new data sets. The look-out for additional data sources is the next step in the process. Approvals would also be required in terms of the tools and techniques that would be applied.

Step 3: Make value to the customer a priority

It is important to gather and understand the requirements and the specifications of the customer. These need to be considered after the collection and prioritization of the data. Implementing strategies that do not suffice the needs of the customer would not be fruitful for the user as well as for the organization.

Step 4: Run an Agile shop and increment over time

Incremental releases and setting up of new data hubs is done once the project team and priorities are in place. It will aid in adjusting of the operations and would also help in understanding the ability of data to influence actions in the processes followed by the organizations. It is commonly seen that many of the projects do not pass because they attempt at covering everything in one go. It is suggested to take a slow start and then develop accordingly and adaptively.

Step 5: Link customer data to company process

Data driven decisions should then be implemented at the organizational level covering each and every single aspect that is involved.

Step 6: Create repeatable process and action paths

It is important to create a path and a planned set of actions which are necessary in the process. There are a few processes that repeat at a frequent interval and must be identified in the beginning itself. Also, a path to attain the same is also a significant step to take in the entire procedure.

Step 7: Test, measure, and learn

It is necessary to test the assumptions with each data set rather than suffering from the surprises at a later stage in the cycle.

Step 8: Map data to the customer’s life cycle

It is of utmost importance to map the progress with the requirements as listed down by the customer. It will help to track the faults and lags, if any and would also help in validation and verification of the progress made.

Alignment of Business Objectives as per the Business Strategy

The suggested business strategy would be applied to the following components to align the business initiatives and objectives in accordance to the same.

Data Mining

It is the process of analyzing and categorizing the data in a number of different categories. The data is studies from various angles and a correlation is formed. It is used in a number of financial, marketing, retailing and other industries to determine the relationship between internal factors such as price, cost, inputs and the external factors such as profits, losses, customer satisfactions and feedback.

Online Analytical Processing (OLAP)

Strategic monitoring is done by the Business Intelligence executives in order to sort and select aggregates of the data. The adjustments are done on the data which results in benefits to the entire business process and the organization as a whole (Villanova University, 2016)

Real time BI

It is done to make use of software applications in order to respond to the real-time events such as social networking trends or the trends in digital display. Special offers can be announced by the marketing team to take advantage of the choice and demands of the customers which in turn benefits the organizations in the long run.

Data Warehousing

It is the process that looks through structured and unstructured data and identified interrelated components in the same. It is an exceptionally significant procedure which has the ability to help the business. For instance, the process of data warehousing would help to track the time taken to complete one activity in various scenarios. The results would help to identify the best possible solution.

Business Performance Management

It is the application of tools and techniques to assess the performance of an organization with the help of analytics and performance management tools. It combines the data from various sources, applied queries to the same, analyses the results and draws conclusions. It also helps in the monitoring and measurement of the efficiencies (Olap.com, 2016).

Data Analytics

It is also a process with an aim to draw meaningful information from a set of data. It is different from data mining in terms of the scope and purpose. It can be done in two ways as exploratory and confirmatory data analytics (SearchDataManagement, 2016)

It will result in attaining the business objectives with the following added advantages.

Business Intelligence is based upon analysis and classification of data and creation of scenarios revolving around the same. Data mining, data analytics, predictive analysis and applied statistics are used for this purpose.

The base of the process is created on a large or huge set of data that needs to be correctly managed and looked in to. The data over here is not a single file or a folder but huge chunks which are not easy to manage. This is where the role of Big Data comes in to the picture.

The basic aim of business intelligence is to optimize the processes that are involved in handling the information and achieve best possible results with improved customer satisfaction. This is brought in to application with the aid of Big Data.

Big Data helps in figuring out the target customers and users. Predictive models are created and the inferences are made. Companies gather the data sets from the social media accounts, browser logs and other sources to gain an insight in to the general trends, practices and demands (Ap-institute.com, 2016).

Optimization of business processes is another expertise of Big Data. Analysis of performance of the employees, creation of reporting, online analytics and processing are done with the help of this amazing technology.

Other advantages of Big Data include:

Competitive Advantage

Big Data allows its users to access the data easily which was difficult or next t impossible to access earlier. With the aid of such a capability to access the data from a number of different sources such as social media accounts, search engines, media streams and several more, companies can predict the demand for a particular product and can also implement and design clear marketing strategies. With these advantages, businesses are able to gain an edge on their competitors and act more quickly and decisively when compared to what rival organizations do. Needless to say, a business that effectively utilizes big data analytics tools will be much better prepared for the future than one that doesn’t understand how important those tools are (Qubole, 2016).

New Business Opportunities

New business opportunities can easily be approached and deduced with the help of Big Data. Advertising and marketing are the two sectors that have largely benefitted with the help of these processes. Add-on services, new services and real-time services are provided to the customers on the basis of the analysis done through Big Data and the business expands with the help of the same. In addition to those benefits, big data analytics can pinpoint new or potential audiences that have yet to be tapped by the enterprise. Finding whole new customer segments can lead to tremendous new value.

Business Analytics

Methods and Technologies

The following techniques have been suggested for implementation of Big Data and its strategy in the structure of Multi-Channel Marketing.

Hadoop

It is an open source tool that is written in JAVA language and has the ability to handle the Big Data analytics in an excellent manner. It comes with the features of flexibility, scalability and supreme performance for handling huge chunks of data. These tools can run on different operating systems as well. Easy visualization, creative reporting and simpler indexing are the features that come handy with this tool (ITProPortal, 2013).

Hyperscale Storage Architecture

It is the storage architecture that is based upon Direct-Attached Storage (DAS) and can be accessed across varied environments. This is the technology which allows the ease of storage and management of different sets of data which may be properly structured, semi-structured and completely unstructured (computerweekly.com, 2016).

NoSQL

NoSQL is the database that performs the Big Data Analytics with extreme ease. It provides rich visualization for creation of reports and documents that support the analytics results. It allows the flexible mode of exploration that is on the basis of various parameters such as time, geographical locations, revenues, quantities and many more. Predictive analysis with powerful and advanced algorithms such as classification, regression and exploration is also possible with the help of NoSQL (Pentaho, 2016).

NoSQL data is the fabric that connects the more than 200,000 APIs (a number that continues to rise) in the world, each of which speaks the language of JSON or XML (Goes, J. 2016). It is an advanced database that promises consistency along with the scalability and supreme performance in the document and data storage and indexing (Big Data Made Simple - One source. Many perspectives., 2014).

Data Analytics and MDM to support DS and BI

Master Data Management (MDM) is a growing area of interest in the business world. MDM is categories in two different types as described below:

• Operational MDM (O-MDM) is focused on the distribution, synchronization or exchange of master data to ensure consistency in transactional operations.
• Analytic MDM (A-MDM) is concerned with the management of the master data items and associated hierarchies required for aggregation and analysis.

MDM and Data Analytics play an important role in DS and BS as they have the ability to:

• Easily synchronize all of the basic master data
• Reconcile the data with perfection in a quick turnaround time
• Maintain the consistency in transactional data
• Achieve improved business performance management (InformationWeek, 2016).
• Big Data considerations – Social, Legal, Global and Ethical

The two direction of the value creation are broken into three different levels viz.

• Market level
• Brand level
• Customer level (www2.hull.ac.uk, 2016).

The model for the value creation process is rightfully explained with the help of the following two models

• Five Force Analysis
• Value Chain Analysis

This analysis is based upon the activities which are categorized in primary and support activities. These are used to develop competitive strategy to establish a distinguished name in the market.

Conclusions

Big Data is a technology that is highly applicable in the field of Decision Support and Business Intelligence. Multi-Channel Marketing is the use case that was selecting for developing a Big data business strategy around the same. It offers a lot many advantages in the terms of cost-savings, analytics procedures and several others. NoSQL database can be used to support the Big Data activities and there are numeroud document storage and other databases that come under NoSQL. Master Data management (MDM) is also used to synchronize and recncile the Big Data and aids in the analytics and BI procedures. Big Data Value creation model is based upon two broad classifications as Five Force Analysis and Value Chain Analysis.

References

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