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Combining the Big Data with Small Data to Produce Different Types of Deliverables for the Business in Enabling Revenue and Profitability

Adding the Big Data to Small Data Provides a Comprehensive Picture of How Best A Company Can Benefit from Strategic Data

There are lots of attention being given to big data. Navigatinganalytics.com believes very strongly that the growth in the volume of big data will continue to increase the complexities, sizes and dimensions of data. The access to big data allows for a company to tap into the universe of the former, current and potential customers.

Also with big data, among others, a company can have:

  • Tremendous insights available for the strategic planners given the fact that there are comprehensive information embedded in the big data on customers' behavior in terms of psychographics, motivation, ability, values, personality, lifestyles and location changes which can be strategically turned into business art of customers' connection, customers' retention, higher sales and maximum revenue through building social buzz so as to influence the customers decision making processes.
  • The breadth and depth of understanding in terms of segmenting the big data to know where the next generations of sales will be coming from and where there are up sell and cross sell opportunities
  • Superior assessment of the market, competitors and emerging trends (including for examples - changes in technologies, economies, social etc.) as the precursor for understanding how best to leverage the real and lurking opportunities in marketing and sales in filling the gaps that are obvious in customers’ comments, feedbacks, chats and even dissatisfaction as can be captured when a company looks at the various customer reviews on Amazon.com, Facebook.com, Twitter.com, Yelp.com and TripAdvisor.com

Even with the benefits of the big data, there is also the need for the company to bring in the small data.

With small data, a company can leverage its data about the current customers, employees, clients, products and services. Also where ever the laws permit, an indepth analysis of small data will show how the previous customers were lost customers. A real-time imformation from small and big data will show when the customers are going to be lost. Big data will show where the previous customers are sitting today and where and how a company can get them back including some new customers.

For a successful company, there is the need to improve the skillsets of its sales people if the company plans to gain from the better insights that are coming from the benefits of the big data. Also, a company must ensure that its infrastructures are ready and the productivity of the employees are optimized if the company wants to gain from expanding its customers' base.

New customers attempting to reach the customer service team expect prompt response from the company's employees. 

Here Comes the Small Data Which Is Huge And Can Drive Tremendous Value
Process Tables That Show Combined Small and Big Data Mapped To Value-Added Output

Navigatinganalytics.com presents different deliverables that can derived from combination of both big and small data as shown in the Process Tables below:

  1. Generating Analytics Outputs that include categories within Analytics and BI Reporting, Workforce and Performance Management

  2. Generating Analytics Outputs that include Devices that Show Interfaces for Business Users

  3. Generating Analytics Outputs to Support Monthly and Quarterly Business Review etc.

  4. Generating Analytics outputs that are Delivered Based on Machine Learning

  5. Generating Analytics outputs that are Delivered based on Analytics Methodologies and Services for Decision Supports

  6. Linking Analytics outputs with Business Rules Engine Execution

  7. Leveraging the Streaming Power from Storm in Hadoop Ecosystem to support Real Time Streaming Needs

  8. Streaming Power from Storm in Hadoop Ecosystem to support Real Time Streaming Needs

  9. Leveraging Mapreduce (Hadoop Ecosystem) in generating Analytics Outputs

  10. Interaction of Mahout (Open Source Analytical Software, Digital Analytics Software and Traditional Mathematical and Statistical Software) with the conclusion that all can still work together

  11. Raising the bar to the New Height with SAP HANA

  12. Commercial Analytics Products and Services: Raising the bar to the New Height with SAP HANA, Digital Analytics Software and Traditional Mathematical and Statistical Software

  13. Bringing All Together: Leveraging Data, Analytics and BI as Enablers for A Profitable Company

  14. Next Frontier: Leveraging Fast Processing Power For Machine Leaning of the Data, Analytics and BI + In-Memory Processing To Provide Additional Value-added From Analytics

Please Note:
  • All the Process Tables are at very high level of presentation.
  • Each of the Process Tables have 5 - 8 levels below them.
  • Process Tables (11 and 12) with SAP HANA Platform have even more levels.
  • The Deliverables in Process Tables 2, 3, 6, 7, 8, 9 and 12 have additional independent process levels.
Assumptions:
For all the Process Tables below, the following are assumed: 
  • Project Management Governance
  • Design, Architecture and Application Development
  • Data and/or Information Stewardship as applicable

Process Table 1: Generating Analytics Outputs that include Categories within Analytics and BI Reporting, Workforce and Perfomance Management

Process Table 1: Generating Analytics Outputs that include categories within Analytics and BI Reporting

  • Analytics and BI Reporting

  • Workforce and Workforce Management

  • Performance Metrics

Process Table 2: Generating Analytics Outputs that include Devices that Show Interfaces for Business Users

Process Table 2: Generating Analytics Outputs that include Devices that Show Interfaces for Business Users

  • Self Service Reporting

  • Ad hoc and Scheduled Analytics

  • Strategic, Executive, Operational and Productivity Dashboards

  • Web Based Analytics and Embedded Reports

  • Basic Operations Command Center

Process Table 3: Generating Analytics Outputs to Support Monthly and Quarterly Business Reviews etc

Process Table 3: Generating Analytics Outputs to Support Monthly and Quarterly Business Reviews etc 

  • Monthly and Quarterly Business Reviews

  • Providing Input to Improve Training and Development for example in a Call Center environment

Process Table 4: Generating Analytics Outputs that are Delivered Based on Machine Learning -  For Example Using Pattern Discovery or Data Mining Tools and Methodologies

Process Table 4: Generating Analytics Outputs that are Delivered Based on Machine Learning

Process Table 5: Generating Analytics Outputs that are Delivered Based on Analytics Methodologies and Services for Decision Supports

Process Table 5: Generating Analytics Outputs that are Delivered Based on Analytics Methodologies and Services for Decision Supports

Process Table 6: Linking Analytics Outputs with Business Rules Engine Execution

Process Table 6: Linking Analytics Outputs with Business Rules Engine Execution

Process Table 7: Leveraging the Streaming Power from Storm in Hadoop Ecosystem to Support Real Time Streaming Needs

Process Table 7: Leveraging the Streaming Power from Storm in Hadoop Ecosystem to Support Real Time Streaming Needs in:

  • Command Center Strategic Planning Room

  • Command Center War Room for Tactical Responses

Process Table 8: Streaming Power from Storm in Hadoop Ecosystem to support Real Time Streaming Needs

Process Table 8: Streaming Power from Storm in Hadoop Ecosystem to support Real Time Streaming Needs in:

  • Command Center Strategic Planning Room

  • Command Center War Room for Tactical Responses

  • Corporate Atrium where the employees can see what are happening with performance in real time on the Operations Fronts

  • Smart Mobile Devices that include Smart Phones and Smart Watches

Process Table 9: Leveraging Mapreduce (Hadoop Ecosystem) in Generating Analytics Outputs

Process Table 9: This is essentially like Process Table 1 except that there is a zoom in on how Mapreduce (Hadoop Ecosystem) works in  generating Analytics Outputs that include categories within:

  • Analytics and BI Reporting

  • Workforce and Workforce Management

  • Performance Metrics

Process Table 10: Interaction of Mahout (Open Source Analytical Software, Digital Analytics Software and Traditional Mathematical and Statistical Software) with the Conclusion that All Can Still Work Together

Process Table 10: This is essentially like Process Table 1 except that there is an increased focus on interactions of Mahout (Open Source Analytical Software), Digital Analytics Software and Traditional Mathematical and Statistical Software with the conclusion that all can still work together. What tools to use will be dependent on the sources of the data, types of problems and type of output that needs to be generated. Combining all these as part of Analytics Tools and Services ensures that all tools are at a go to solve the problems – No matter what the problems are!

Process Table 11: Raising the bar to the New Height with SAP HANA with Digital Analytics Software and Traditional Mathematical and Statistical Software

Process Table 11: This raised the bar to the New Height with SAP HANA because of the sophisticated and easy to use interfaces and the emphasis on the in-memory technologies that ensure robust streaming for real time analytics tools and services

Process Table 12: Commercial Analytics Products and Services: Raising the bar to the New Height with SAP HANA Digital Analytics Software and Traditional Mathematical and Statistical Software

Process Table 12: Process Table 11 all over again but put together with a view towards looking at what can happen in  Commercial Analytics Products and Services Market.

Process Table 13: Bringing All Together: Leveraging Data, Analytics and BI as Enablers for A Profitable Company

Process Table 13: This Table leverages the data through advanced analytics to generate BI Output that can be used as Enablers to:

  • Run Profitable Operations

  • Drive Excellence in Customer Service

  • Ensure Retention of Good Employees

  • Drive Smart Growth

  • Ensure Effective Enterprise Risk Management

Process Table 14: Next Frontier: Leveraging Fast Processing Power For Machine Leaning of the Data, Analytics and BI + In-Memory Processing To Provide Additional Value-added From Analytics

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