The data analytics lifecycle is a plan for analyzing data that is based on business goals. This data cycle helps us figure out how well data analytics work and make it better.
This article talks about the stages of the data analytics lifecycle, goes into more detail about each step, and explains why it’s important to follow the steps.
Definition of the data analytics life cycle
In today’s digital world, data is very important. Throughout its life, data goes through several phases or stages as it is created, used, tested, processed, and reused. People who work in data science should follow these steps, according to a data analytics architecture. It is a cyclical structure that includes all data analytics lifecycle steps. Each step has its own meaning and set of traits.
Phases of the data analytics lifecycle
The six stages of the data analytics lifecycle are each influenced by what occurred in the stage before it. Because of this, most time is usually spent on the phases of the data analytics lifecycle. It makes sense to do each step in the order given so that data teams can decide how to move forward: whether to move on to the next step, redo the previous step, or throw out the whole process. By making teams follow these steps, the analytics lifecycle helps them through a process that could otherwise become confusing and aimless, with unclear results.
Discovery
This phase involves defining your problem and desired business outcomes.
Start by defining your business goal and project scope. Work out what data sources will be available and useful to you (for example, Google Analytics, Salesforce, your customer support ticketing system, or any marketing campaign information you might have), and perform a gap analysis of what data is needed to solve your business problem compared to what data you have, creating a plan to get any missing data.
Create a hypothesis after identifying your goal. Your analysis should decide whether to accept or reject this hypothesis. To ensure that your analysis is rigorous and follows the scientific method, determine the criteria for accepting or rejecting the hypothesis in advance.
Data preparation
In the next step, you must choose which data sources will be useful for analysis, collect the data from all these sources, and load it into a data analytics sandbox for prototyping.
Data must be transformed before loading into the sandbox. Preprocessing and analytics transformations are the main types of transformations.
- Preprocessing eliminates nulls, bad values, duplicates, and outliers.
- Analytics transformations can standardize or normalize data for use with certain machine learning algorithms.
Model planning
Data analytics models describe how two or more variables relate mathematically or programmatically. It lets us study how variables affect our data and make statistical assumptions about event probabilities.
SQL, statistical, and machine learning models are the main data analytics models.
- Business intelligence dashboards use SQL models, which can be as simple as SELECT statements.
- Statistical models show the relationship between one or more variables (a feature that some data warehouses incorporate into more advanced statistical functions in their SQL processing), and machine learning models use algorithms to recognize patterns in data and must be trained on other data.
- Machine learning models are used when the analyst doesn’t have enough data to solve a problem.
Building and executing the model
You can build models and draw inferences from modeled data once you know how they should look.
This phase of the data analytics lifecycle depends on your model. It can be one of the three models we discussed in step 3.
- SQL model
- Statistical model
- Machine learning model
After building your models and generating results, you can share them with stakeholders.
Communicating results
Data visualizations can be useful for effectively conveying your findings. Your analysis adds value to the company, so make sure to highlight that in any communications with stakeholders. To further demonstrate the reliability of your analysis, you should contrast the model’s findings with the original criteria you used to accept or reject your hypothesis.
Operationalizing
Once the stakeholders have approved your analysis, you can move on to using the same model on a production dataset outside of the analytics sandbox.
You need to keep an eye on the outcomes to see if they help you reach your business objectives. Deliver the final reports to your stakeholders and spread the word throughout the company if your goals are being met.
Also Read:
Data Analytics Made Easy: 10 Must-Have Tools for Every Business
Big Data Analytics Lifecycle
The stages of the Big Data Analytics life cycle are different from the stages of the traditional data analysis life cycle. This is mostly because big data is based on volume, variety, and speed.
The Big Data Analytics Life cycle has nine stages, which are called:
- Business Case/Definition of the Problem
- Identifying Information
- Acquiring and sorting data
- Extracting Data
- Processing (Verification and Cleaning of Data)
- Collecting and Storing Information
- Analysis of Uncertain Data
- Data visualization (measurement and analysis planning)
- Making use of findings from analyses.
Why the Data Analytics Life cycle is Important
The data analytics lifecycle shows how to make, gather, process, use, and analyze data to reach business goals. It gives a structured way to deal with data so that it can be turned into knowledge that can be used to reach project and organization goals. The process gives guidance and methods for getting information out of the data and moving forward to reach business goals.
Data professionals use the circular shape of the lifecycle to move forward or backward with data analytics. They can keep going with their current research or stop and do the whole analysis over again based on what they have learned. The data analytics lifecycle shows them how to do this.
Conclusion
In order to make better business decisions, it’s important to stick to the six stages of the data analytics lifecycle. Particularly helpful is making sure your data is clean and in an easily analyzed format, as well as having a firm grasp on your business goals from the get-go.
We hope you gained a basic understanding of the data analytics lifecycle and its six stages after reading this blog.
