Phase 1 - Discovery
Overview: Discovery marks the beginning of the data analytics lifecycle, focusing on understanding the purpose of the data analysis and how to achieve the desired outcomes by the end of the process. This phase involves identifying key objectives and mapping out the data to uncover valuable insights for the business.
Key Processes:
-
Learning the Business Domain:
- The team immerses themselves in understanding the specific industry or domain in which the business operates.
- This involves gaining knowledge about the business processes, challenges, and goals relevant to the project.
-
Evaluating Resources:
- Assessing the available technology, human resources, data availability, and time constraints.
- Understanding the tools and resources needed to conduct the analysis effectively.
-
Framing the Problem:
- Defining the problem statement or the question that the data analysis aims to address.
- This step involves clarifying the scope of the project and identifying the specific objectives to be achieved.
-
Identifying Key Stakeholders:
- Recognizing the individuals or groups within the organization who have a vested interest in the outcomes of the analysis.
- Stakeholders could include decision-makers, subject matter experts, and end-users of the insights generated.
-
Interviewing the Analytics Sponsor:
- Engaging in discussions with the individual or team sponsoring the analytics initiative.
- Gathering insights into their expectations, priorities, and requirements for the project.
-
Developing Initial Hypotheses:
- Formulating preliminary assumptions or hypotheses based on the initial understanding of the problem and available data.
- These hypotheses serve as starting points for further investigation and analysis.
-
Identifying Potential Data Sources:
- Exploring the various sources of data that could provide valuable insights for the analysis.
- This includes internal databases, external repositories, third-party data sources, and any other relevant sources of information.
In Simple Terms: Imagine you're setting out on a journey to explore a new territory. In the discovery phase, you're like an explorer mapping out the terrain before you begin your adventure.
First, you learn about the land you're venturing into, understanding its features and challenges. Then, you gather your tools and resources, making sure you have everything you need for the journey ahead.
Next, you define your goal – what you want to discover or achieve by the end of your expedition. You also identify the people who will join you on this journey and seek their insights.
As you prepare, you develop initial theories or ideas about what you might find along the way. Finally, you identify the potential sources of information that will guide your exploration.
By completing these steps, you set a clear path for your journey, ensuring that you're well-prepared to uncover valuable insights and navigate the challenges that lie ahead.
Summary
MM - Phase 1 - DiscoveryMM - Phase 1 - DiscoveryHere's an improved and structured version of the notes for the Discovery phase of the Data Analytics Lifecycle, incorporating elements from the reference materials mentioned previously. Below that, you'll find keywords and short sentences to create mind maps for efficient recall. Discovery Phase of Data Analytics Lifecycle: Detailed Notes Overview The Discovery phase is the initial and critical step in the data analytics lifecycle, where the foundation for the analytics project is laid. It in
Backlinks
- Explain Data Analytics life cycle with the help of diagram.Explain Data Analytics life cycle with the help of diagram.Data Analytics Lifecycle: Overview The data analytics lifecycle is a structured approach to conducting data analytics projects from inception to completion. It encompasses various phases that involve understanding the business problem, preparing data, conducting analyses, and delivering actionable insights. This process helps ensure that the data analytics work is thorough, relevant, and aligned with business needs. The lifecycle concept is well articulated in texts like "Data Science & Big Dat
- List different phases in data analytics life cycle and explain Model Building phase in detail.List different phases in data analytics life cycle and explain Model Building phase in detail.Data Analytics Lifecycle Phases The Data Analytics Lifecycle is a structured framework that guides the steps needed to transform raw data into actionable insights. The framework can vary slightly depending on the source, but generally includes the following key phases: 1. Problem Definition: This phase involves identifying the business or research question that the data analytics project aims to address. It includes defining the scope of the project, the objectives, and the potential impact.
- Data Analytics Life CycleData Analytics Life CyclePasted image 20240512180604.png Overview : The data analytics lifecycle is a structured approach designed for tackling big data problems and data science projects. It consists of six phases, each of which can occur simultaneously and iteratively. This cyclical nature allows for flexibility, as work can return to earlier phases based on new information uncovered during the process. Mnemonic for Memorization We'll use the first letter of each phase to create a memorable sentence: D**iscovery