Business Analytics Software Development Process and Resulting Product Evaluation Model

The power of any business analytical software, lies in the capability maturing level of the software development process and product. Business analytical software are created to identify, separate, quantify, and learn more about the business. Usually, such software products perform quantitative data manipulation on historical and live data sources. Ensuring the optimal performance of analytic software products, requires that the software development process (Methodology) and product must show some level of maturity (trust). When testing the capability maturity level of analytical software development process and resulting product quality, five factors are considered. This article reviews the five factors considered in the conduct of capability maturity test on analytical software development process and the resulting product quality.

Below are the factors considered when conducting capability maturity test on analytical software development process and product for success:

1. Skill and Experiences of Team Members: Data Analytics software development without a prescribed process where each developer users his or her own tools or methods can lead to anarchy and sub-standard product. Trusted data analytics software development process and product quality is only achievable with the help of skilled and experienced team of software developers and analysts. It is said that “success or failure is a function of the skill and experience of the project team”. Looking at data analytics roadmap, as it affects the management of the process of developing data analytics tools for the conduct of descriptive, diagnostic, predictive, and prescriptive analytics, data analytics tool developers and analysts are expected to have commendable skill and experience in the following associated practices:

Descriptive Analytics

1. Comprehension of past events, patterns, trends, and sta statistical measures
2. Data modeling
3. Data visualization and
4. Basic dashboard design

Diagnostic Analytics

1. Comprehension of past events, patterns, trends, and sta statistical measures
2. Data modeling
3. Data visualization skill
4. Basic dashboard design
5. Learning how to learn (The ability to persist in learning and to aticulate one’s own learning)
6. Research and
7. Computer programming

Predictive Analytics

1. Comprehension of past events, patterns, trends, and sta statistical measures
2. Data modeling
3. Data visualization skill
4. Basic dashboard design
5. Learning how to learn
6. Research and
7. Computer programming
8. Problem identification
9. Specific action planning (setting of SMART objectives) and
10. Goal setting

Prescriptive Analytics

Prescriptive analytics has to do with the act of calling for a clear corrective action that is based on firmly established results from descriptive, diagnostics, andpredictiveanalytics. Therefore, analytics software developers and analysts are expected to build:

1. Strong Comprehension of past events, patterns, trends, and sta statistical measures
2. Advanced Data modeling skill
3. Advanced Data visualization skill
4. Advanced Basic dashboard design skill
5. Advanced Learning how to learn skill
6. Advanced Research skill and
7. Advanced Computer programming skill (Including Machine Learning) s
8. Advanced Problem identification skill
9. Specific action planning (setting of SMART objectives) skill and
10. Goal setting
11. Project Management: Competency in data analytics software project management is also a factor used to evaluate for capability maturity. The project management process should be able to put measures in place to track project costs, schedules, and functionalities. Effective project management frameworks lay a foundation for standardized processes.
12. Compliance with Standards: Compliance with approved standard for analytics software development process (Also known as methodology) can also be evaluated using the capability maturity model. This is to ensure consistency and high-quality documentation and deliverables.
13. Management for productivity: management for production which has to do with the evaluation a project’s teams productivity achieved through effective management of team members and resources. Detailed measures of the standard data analytics software development process and the product’s quality are expected to be routinely collected, stared in a database, and used to guide improvement strategies. In this way, management seeks to become more proactive than reactive to system development problems such as cost overruns, scope creep, and schedule delays. In the event of unexpected problem or issues, the process can be adjusted based on predictable and measurable impacts.
14. Improvement Achieved: The assessment improvement achieved in data analytics software development process is also a vital practice that cannot be undermined as it adds up to the creation of a reliable data analytics tool. Therefore, there is need to use well defined standards to continuously monitor, and make further  improvements as may be necessary. This can include changing the technology and adopting best practices used to perform activities. The continuous assessment of improvements that are achieved aims at eliminating inefficiencies in the systems development process while sustaining quality. So it can never be taken for granted.  

It is evident that every advanced analytics process, demands for improved skill set and experience. So, when forming a data analytics software development team, the type of data analytics to be conducted should be used as a guided. The project management model should focus on data analytics software development project management, not the software development, as the software development process may vary from project to project. This understanding, gives room for reuse of successful methodologies. Effective project management practices further lay the foundation for standardized processes and enable management for higher productivity and ease of optimization processes. Thereby increasing the methodology’s competitive capability. You would not want to engage inexperienced people or people with inadequate skill set otherwise, you will end up with a methodology with high risk result in poor quality software product.

Business Analytics Software Development Process and Resulting Product Evaluation Model

The power of any business analytical software, lies in the capability maturing level of the software development process and product. Business analytical software are created to identify, separate, quantify, and learn more about the business. Usually, such software products perform quantitative data manipulation on historical and live data sources. Ensuring the optimal performance of analytic software products, requires that the software development process (Methodology) and product must show some level of maturity (trust). When testing the capability maturity level of analytical software development process and resulting product quality, five factors are considered. This article reviews the five factors considered in the conduct of capability maturity test on analytical software development process and the resulting product quality.

Below are the factors considered when conducting capability maturity test on analytical software development process and product for success:

1. Skill and Experiences of Team Members: Data Analytics software development without a prescribed process where each developer users his or her own tools or methods can lead to anarchy and sub-standard product. Trusted data analytics software development process and product quality is only achievable with the help of skilled and experienced team of software developers and analysts. It is said that “success or failure is a function of the skill and experience of the project team”. Looking at data analytics roadmap, as it affects the management of the process of developing data analytics tools for the conduct of descriptive, diagnostic, predictive, and prescriptive analytics, data analytics tool developers and analysts are expected to have commendable skill and experience in the following associated practices:

Descriptive Analytics

1. Comprehension of past events, patterns, trends, and sta statistical measures
2. Data modeling
3. Data visualization and
4. Basic dashboard design

Diagnostic Analytics

1. Comprehension of past events, patterns, trends, and sta statistical measures
2. Data modeling
3. Data visualization skill
4. Basic dashboard design
5. Learning how to learn (The ability to persist in learning and to aticulate one’s own learning)
6. Research and
7. Computer programming

Predictive Analytics

1. Comprehension of past events, patterns, trends, and sta statistical measures
2. Data modeling
3. Data visualization skill
4. Basic dashboard design
5. Learning how to learn
6. Research and
7. Computer programming
8. Problem identification
9. Specific action planning (setting of SMART objectives) and
10. Goal setting

Prescriptive Analytics

Prescriptive analytics has to do with the act of calling for a clear corrective action that is based on firmly established results from descriptive, diagnostics, andpredictiveanalytics. Therefore, analytics software developers and analysts are expected to build:

1. Strong Comprehension of past events, patterns, trends, and sta statistical measures
2. Advanced Data modeling skill
3. Advanced Data visualization skill
4. Advanced Basic dashboard design skill
5. Advanced Learning how to learn skill
6. Advanced Research skill and
7. Advanced Computer programming skill (Including Machine Learning) s
8. Advanced Problem identification skill
9. Specific action planning (setting of SMART objectives) skill and
10. Goal setting
11. Project Management: Competency in data analytics software project management is also a factor used to evaluate for capability maturity. The project management process should be able to put measures in place to track project costs, schedules, and functionalities. Effective project management frameworks lay a foundation for standardized processes.
12. Compliance with Standards: Compliance with approved standard for analytics software development process (Also known as methodology) can also be evaluated using the capability maturity model. This is to ensure consistency and high-quality documentation and deliverables.
13. Management for productivity: management for production which has to do with the evaluation a project’s teams productivity achieved through effective management of team members and resources. Detailed measures of the standard data analytics software development process and the product’s quality are expected to be routinely collected, stared in a database, and used to guide improvement strategies. In this way, management seeks to become more proactive than reactive to system development problems such as cost overruns, scope creep, and schedule delays. In the event of unexpected problem or issues, the process can be adjusted based on predictable and measurable impacts.
14. Improvement Achieved: The assessment improvement achieved in data analytics software development process is also a vital practice that cannot be undermined as it adds up to the creation of a reliable data analytics tool. Therefore, there is need to use well defined standards to continuously monitor, and make further  improvements as may be necessary. This can include changing the technology and adopting best practices used to perform activities. The continuous assessment of improvements that are achieved aims at eliminating inefficiencies in the systems development process while sustaining quality. So it can never be taken for granted.  

It is evident that every advanced analytics process, demands for improved skill set and experience. So, when forming a data analytics software development team, the type of data analytics to be conducted should be used as a guided. The project management model should focus on data analytics software development project management, not the software development, as the software development process may vary from project to project. This understanding, gives room for reuse of successful methodologies. Effective project management practices further lay the foundation for standardized processes and enable management for higher productivity and ease of optimization processes. Thereby increasing the methodology’s competitive capability. You would not want to engage inexperienced people or people with inadequate skill set otherwise, you will end up with a methodology with high risk result in poor quality software product.