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Data Analytics Life Cycle Explained

Digital expansion across platforms endorses personalization and customization to add value to customer service while not recognizing the massive accumulation of big data in each interaction account. Consistent digital consumption leads to equally consistent data generation, which organizations acquire to bring value to their businesses. 

Although extending valuable information, imagine diving into a sea of data to find beneficial aspects without any unique strategy or tool to make it through? Not so easy, but this is where the data analytics lifecycle works. Just like the data science life cycle works to cleanse and model data, the data analytics lifecycle assists organizations in simplifying working through the big data process to extract meaningful insights from unstructured data sets. 

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The data analytics lifecycle is divided into six steps, but first, let’s find out more about data analytics!

What is data analytics?

Data analytics is an imperative weapon assisting data science in working through unstructured data using different steps. It uses systematic computational analysis to interpret data and find patterns and anomalies based on containing information. These patterns are further evaluated and cleansed to draw a structured database and reap insightful data values out of the structure. 

Data analytics is a necessary part of data science. It assists businesses and organizations in using generated data for their decision-making process, a tool to improve their working through reduced resources for efficient results. On the other hand, reliable data sets for efficiency also enable organizations to make statistics-based predictions to set future goals.

Using these six steps, let’s learn more about how data analytics works through unstructured data!

The Data Analytics Lifecycle

This step-by-step methodology is efficient for the data analytics lifecycle to manage real data analysis projects. It can help organize data features and their processing through a structured roadmap. Each stage is significant to give data a direction and use it to make valuable business decisions. With the correct analysis, analysts can assess whether to go ahead with the pre-existing plans or leverage a new strategic approach. Here are all the six stages of the data analytics life cycle. 

Stage 1: Discovery

As the starting point of the data analytics lifecycle, the stage serves as a base to obtain information, evaluate available resources, define the business domain and find flaws in the business strategies to target them in a curated hypothesis and test with the project. The most important aspect of this stage requires a significant amount of relevant information.

As the data analysis team works through the project and plan, they work on developing an in-depth understanding of the context to understand possible bugs. If working on a project to serve clients, analyze the demand and find missing points in the project through the framing process. Analyze these points further to create initial hypotheses to determine the failure or favorable outcome of the project. 

Stage 2: Data Preparation

The data preparation stage refers to the process of collecting data to prepare it for further steps that include evaluation, and conditioning, after which it can be extended to the model building process for the project. The data collection process uses diverse sources comprising manual entry, external sources, or the most prominent one, digital devices such as IoT-driven devices. Data preparation enables important information through restrictive time frames and resources so that analysts can identify model-building process tools. 

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Stage 3: Model Planning

The model planning step emphasizes analyzing data quality and using an analytics sandbox to store and assess large data sets and choose the suitable model for the project. An analytic sandbox is an environment containing part of the overall data lake architecture used to process large amounts of data. Big data, web data, and social media data can all be processed efficiently within a limited duration using the required tools under the analytics sandbox.

Model planning is processed under the presence of an analytics sandbox where the team executes data loading through three steps.

  • ETL (Extract Transform Load): Requires data transformation to comply with business rules before loading in the sandbox.
  • ELT (Extract Load Transform): Requires the data to load in the sandbox before transforming it according to the set rules.
  • ETLT( Extract Transform Load Transform): Combines the two mentioned processes and comprises two transformation levels.

The team further analyzes data to evaluate the variables to categorize data and find inconsistencies for improvements such as illogical values, duplicate values, or spelling errors. It cleanses data towards smoother data processing for the next steps in the model-building process.

Stage 4: Model Building

Datasets are built under this stage for analyzing, testing, and training to further create and execute models based on the planned structure and assessments. The developed model is run under a repetitive deployment process to evaluate whether the model satisfies planned business objectives in real-time projects or not. Although the process can be compiled in a single instance, consistent client changes often turn it into a repetitive process.

As the process requires thorough trials for optimum performance, statistical modeling methods such as decision trees, neural networks, regression techniques, and random forest modeling are also deployed on the models to compare the model quality and performance. Analysts often have to run multiple model variants simultaneously on different projects to compare the efficiency and extend the best results. 

Stage 5: Communication Result

The communication stage demands analysts to now actively get the clients and stakeholders involved in the project and analyze model intricacies, its requirements, and whether the established model delivers success or not. The stage requires analysts to maintain an informative communication extending all the possible details and findings of the analysis, business value upheld during the model creation, and an overall project summary.

The project might not end here and would require analysts to make several changes as directed by clients if found to contain any inconsistencies. However, issue-resolving is imperative in delivering to the client’s demands. Analysts must articulate the process as fluently and as detailed as possible to provide any possible information.

Stage 6: Operationalize 

The step refers to running one final analysis on the project and preparing a detailed report on the key findings, codes, briefing, and other documents to extend it to the authority stakeholders. Analysts further set up a pilot project to run under a regulated environment and evaluate its effectiveness in a nearly real-time environment. 

The project is monitored to see whether it complies with and delivers results per the suggested objectives. If errors occur, analysts turn back in the stages to make changes. If the model successfully delivers desired results, the project is extended to run in a live environment. 

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Conclusion

The data analytics lifecycle is a very detail-oriented process that uses six in-depth stages of assessing and preparing data to deploy well-structured models. Knowing project aspirations and business objectives can help analysts find a direction for their data analytics process. As an analyst, ensure the right idea of client demands to queue your available resources and use them to make the findings and fulfill required results.  

Why is the data analytics life cycle important?

Complex group of unstructured data can be tough to deal with. Therefore, to simplify the process for better comprehension and processing, it is divided into six phases, each assisting unstructured data in adding value to it by cleaning unusable data.

Which is the most crucial phase of the data science lifecycle?

Data science lifecycle is curated with the most critical phases, with each stage assisting data analysis tools in assessing data, so choosing the most vital step is inappropriate. But, if you are curious about the most significant aspect, the answer should be discovery, which is the first phase to kickstart the entire lifecycle.

Are data science and data analysis similar?

Both the terms are pretty relevant and related to each other but intrinsically mean a bit different. Data science is the entire process the data goes through to come out clean and evaluated, constituting six detailed steps. On the other hand, data analysis is a tiny part of the lifecycle, working as a tool to analyze the retrieved data.

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