Advanced Analytics – Part 3

Data Analytics Project Journey - A 5-step guide

This article provides an introductory guide for organizations who are wanting to start implementing a data & analytics solution.

May 30, 2022

The main focus is on planning, preparation, developing, and setting adequate goals for the migration or adoption of analytics technologies in your organization, detailed in a 5-step guideline for your own Data & Analytics journey:​​​​​

1. Structure Your Data Analytics project

A successful project starts with a strong foundation. This step defines the structure and direction of your project based on the following key elements:

  • What is the purpose of supporting the business objective?
    ​​​​​​​For example, using customer data to analyze the needs of key customer groups for products and services to improve the quality of products and services
  • Determine the stakeholders/users and the persons responsible for establishing a working group under the project. Such sales department, production department, customer service department, technology department, etc.
  • Set a time frame and budget for the implementation.

2. Identify the Requirements for your data analytics scope

  •  Analyze and define needs using three concepts of stakeholder/user inquiry methods.
    This focuses on addressing problems encountered in current working processes and on how the stakeholder is currently working to better understand the root of the problem and the desired outcome.
    • Concepts of problems (Pain Method) focuses on pinpointing problems or difficulties encountered by users in their working processes to better understand the root of the problem and what the desired outcome is by conducting interviews. Examples of such pain points can include:
      • Problems from insufficient or unavailable data used to support work processes
      • The problem of manual steps that must be performed to prepare or collect data before the workflow can be performed.
      • Inability to analyze the data or the inaccuracies in the results of analysis.
    • Concepts of need (Need Method) identifies and discovers the needs of users. This focuses on things that can solve the problems mentioned previously and confirms the need by analyzing if this is not implemented, how it will make an impact on the expected outcome of the solution? For example:  
      • Users need a system that can collect and transform data for making management reports comparing production and sales processes to solve the difficulty in the time taken for report development. Including reducing the resources that are used in data preparation and report development from both departments
      • They need a system to retrieve data and display the machines working status and situation in near real-time to monitor disruptions on the production line and analyze machine performance for maintenance planning.
    • The ideal needs concept (Dream Method) identifies and discovers additional needs benefits beyond solutions that can improve productivity. It focuses on identifying the potential benefits if developments are made according to such requirements to assess the cost-effectiveness of prioritized actions.

Note: In many cases, when it comes to requirements scope, it is often difficult to differentiate between what is essential and what is ideal since, from the point of view of the process owner, they want to optimize or minimize operational problems as much as possible. But from the perspective of the project scope, this should be prioritized by the essential need first, then consider the benefits of the ideal weighted against what is the best value for the organization.

  • Identify desired outcomes and set goals, aligning these with key deliverables based on the information collected in the previous steps. This can determine the direction of the design and choice of tools or technologies to prevent future development constraints and, based on priorities can result in multiple project phases to deliver quickly, measure project success, and allows for flexibility to adapt to change.
  • Determine project implementation guidelines by surveying and defining what is available or lacking in terms of people, processes, technology/tools, and resources, to provide a course of action that aligns with the goals.

3. Project preparation

  • Selection of the scope of demand at different phases and let's set up a project and operate according to the priorities and readiness of the organization.
    Noted: The project could be done separately for each project or run in parallel as appropriate.
  • Select and procure tools, technology, or platforms for the project.
  • Based on any lack of available internal expertise, there may be a need to bring in experts from external vendors to support the project. Sourcing hires suitable team/persons according to project objectives.
  • Determine the roles and responsibilities of all stakeholders which also provides the levels of accountability.
  • Determine and list the initial activities that need to be prepared before starting the project.
  • Assessment of project potential risks and preparing risk control and risk management guidelines.​​​​

4.      Implement your Data Analytics solution

We can use any project development model depending on the suitability of the scope requirements of the project.

Sample development models that are commonly used as follows detailed below:

  • Waterfall development model

Ideal project development for projects where the project objective and the outcomes of each step had very clearly defined from the beginning of the project and need to operate hierarchically. However, changing requirements during the development are difficult to accept because of the impact on downstream activities in the project.

Approach: Stakeholders must have clearly defined goals, methods, and outcomes at the beginning of the project.

Restriction: The project must be completed, in order, to be able to proceed to the next step.

  • Agile development model

This model was developed to address the inflexibility of the Waterfall model. It is an iterative approach to project management and software development that helps teams deliver value to their customers faster and with fewer issues. Instead of risking the entire project on a single large deliverable, an agile team delivers work in small, but manageable, increments. Requirements, plans, and results are evaluated continuously so teams have a natural mechanism for responding to change quickly.

Approach: Requires frequent involvement of stakeholders to review and determine project outcomes during each development cycle (Sprint).

Restriction: Requires high-level engagement from stakeholders and frequent requirement changes. ​​​​​





Fixed timeline

Flexible on-demand

Client Involvement

Established and the end of the project

At every step







5. Measure the Success of Your Data Analytics project

Because the benefits of a Data analytics project may not be concrete, they can be difficult to measure. However, we can measure the change in efficiency from the business processes that use the platform and the value to the business, for example:

  • Work efficiency – Increased work efficiency from the data analysis process.
  • Data quality – The quality of information is practical and impactful for the business.
  • Time – saving time from certain activities from analytics systems.
  • Adoption – Number of process owners or staff using the analytics system.

In this article, we want those who are interested in developing a data & analytics solution to be able to apply these guidelines and adapt as appropriate for your organization.

Please feel free to contact us if you are looking for a data analytics partner or have questions about how to properly develop them in terms of readiness, processes, or technology tools, we are happy to help at zero cost. Contact us on the channels below or directly at +66 (0)2 117 4344.