Data is crucial in today’s digital world. As it gets created, consumed, tested, processed, and reused, data goes through several phases/ stages during its entire life. A data analytics architecture maps out such steps for data science professionals. It is a cyclic structure that encompasses all the data life cycle phases, where each stage has its significance and characteristics.
Table of Contents
- Phases of Data Analytics Lifecycle
- Phase 1: Data Discovery and Formation
- Phase 2: Data Preparation and Processing
- Phase 3: Design a Model
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- Phase 4: Model Building
- Phase 5: Result Communication and Publication
- Phase 6: Measuring of Effectiveness
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- Importance of Data Analytics Lifecycle
- Big Data Analytics Lifecycle example
- Who uses Big data and analytics?
- Benefits of Big data and analytics
- 1. Customer Loyalty and Retention
- 2. Targeted and Specific Promotions
- 3. Identification of Potential Risks
- 4. Boost Performance
The lifecycle’s circular form guides data professionals to proceed with data analytics in one direction, either forward or backward. Based on the newly received information, professionals can scrap the entire research and move back to the initial step to redo the complete analysis as per the lifecycle diagram for the data analytics life cycle.
However, while there are talks of the data analytics lifecycle among the experts, there is still no defined structure of the mentioned stages. You’re unlikely to find a concrete data analytics architecture that is uniformly followed by every data analysis expert. Such ambiguity gives rise to the probability of adding extra phases (when necessary) and removing the basic steps. There is also the possibility of working for different stages at once or skipping a phase entirely.
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Yet, suppose, there is ever a discussion about the stages of the data lifecycle. In that case, the below-listed phases are likely to be present, as they represent the fundamentals of almost every data analysis process. upGrad follows these basic steps to determine a data professional’s overall work and the data analysis results.
Phases of Data Analytics Lifecycle
A scientific method that helps give the data analytics life cycle a structured framework is divided into six phases of data analytics architecture.
Phase 1: Data Discovery and Formation
Everything begins with a defined goal. In this phase, you’ll define your data’s purpose and how to achieve it by the time you reach the end of the data analytics lifecycle.
The initial stage consists of mapping out the potential use and requirement of data, such as where the information is coming from, what story you want your data to convey, and how your organization benefits from the incoming data. Basically, as a data analysis expert, you’ll need to focus on enterprise requirements related to data, rather than data itself. Additionally, your work also includes assessing the tools and systems that are necessary to read, organize, and process all the incoming data.
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Essential activities in this phase include structuring the business problem in the form of an analytics challenge and formulating the initial hypotheses (IHs) to test and start learning the data. The subsequent phases are then based on achieving the goal that is drawn in this stage.
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Phase 2: Data Preparation and Processing
This stage consists of everything that has anything to do with data. In phase 2, the attention of experts moves from business requirements to information requirements.
The data preparation and processing step involve collecting, processing, and cleansing the accumulated data. One of the essential parts of this phase is to make sure that the data you need is actually available to you for processing. The earliest step of the data preparation phase is to collect valuable information and proceed with the data analytics lifecycle in a business ecosystem. Data is collected using the below methods:
- Data Acquisition: Accumulating information from external sources.
- Data Entry: Formulating recent data points using digital systems or manual data entry techniques within the enterprise.
- Signal Reception: Capturing information from digital devices, such as control systems and the Internet of Things.
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Phase 3: Design a Model
After mapping out your business goals and collecting a glut of data (structured, unstructured, or semi-structured), it is time to build a model that utilizes the data to achieve the goal.
There are several techniques available to load data into the system and start studying it:
- ETL (Extract, Transform, and Load) transforms the data first using a set of business rules, before loading it into a sandbox.
- ELT (Extract, Load, and Transform) first loads raw data into the sandbox and then transform it.
- ETLT (Extract, Transform, Load, Transform) is a mixture; it has two transformation levels.
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This step also includes the teamwork to determine the methods, techniques, and workflow to build the model in the subsequent phase. The model’s building initiates with identifying the relation between data points to select the key variables and eventually find a suitable model.
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Phase 4: Model Building
This step of data analytics architecture comprises developing data sets for testing, training, and production purposes. The data analytics experts meticulously build and operate the model that they had designed in the previous step. They rely on tools and several techniques like decision trees, regression techniques (logistic regression), and neural networks for building and executing the model. The experts also perform a trial run of the model to observe if the model corresponds to the datasets.
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Phase 5: Result Communication and Publication
Remember the goal you had set for your business in phase 1? Now is the time to check if those criteria are met by the tests you have run in the previous phase.
The communication step starts with a collaboration with major stakeholders to determine if the project results are a success or failure. The project team is required to identify the key findings of the analysis, measure the business value associated with the result, and produce a narrative to summarise and convey the results to the stakeholders.
Phase 6: Measuring of Effectiveness
As your data analytics lifecycle draws to a conclusion, the final step is to provide a detailed report with key findings, coding, briefings, technical papers/ documents to the stakeholders.
Additionally, to measure the analysis’s effectiveness, the data is moved to a live environment from the sandbox and monitored to observe if the results match the expected business goal. If the findings are as per the objective, the reports and the results are finalized. However, suppose the outcome deviates from the intent set out in phase 1then. You can move backward in the data analytics lifecycle to any of the previous phases to change your input and get a different output.
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Importance of Data Analytics Lifecycle
The Data Analytics Lifecycle outlines how data is created, gathered, processed, used, and analyzed to meet corporate objectives. It provides a structured method of handling data so that it may be transformed into knowledge that can be applied to achieve organizational and project objectives. The process offers the guidance and techniques needed to extract information from the data and move forward to achieve corporate objectives.
Data analysts use the circular nature of the lifecycle to go ahead or backward with data analytics. They can choose whether to continue with their current research or abandon it and conduct a fresh analysis in light of the recently acquired insights. Their progress is guided by the Data Analytics lifecycle.
Big Data Analytics Lifecycle example
Take a chain of retail stores as an example, which seeks to maximize the prices of its products in order to increase sales. It is an extremely difficult situation because the retail chain has thousands of products spread over hundreds of sites. After determining the goal of the chain of stores, you locate the data you require, prepare it, and follow the big data analytics lifecycle.
You see many types of clients, including regular clients and clients who make large purchases, such as contractors. You believe that finding a solution lies in how you handle different types of consumers. However, you must consult the customer team about this if you lack adequate knowledge
To determine whether different client categories impact the model findings and obtain the desired output, you must first obtain a definition, locate data, and conduct hypothesis testing. As soon as you are satisfied with the model’s output, you may put it into use, integrate it into your operations, and then set the prices you believe to be the best ones for all of the store’s outlets.
Who uses Big data and analytics?
Huge Data and analytics are being used by medium to large-scale businesses throughout the world to achieve great success.
- The king of online retail, Amazon, accesses consumer names, addresses, payments, and search history through its vast data bank and uses them in advertising algorithms and to enhance customer relations.
- The American Express Company uses big data to study consumer behavior.
- Capital One, a market leader, uses big data analysis to guarantee the success of its consumer offers.
- Netflix leverages big data to understand the viewing preferences of users from around the world.
Big data is routinely used by companies like Marriott Hotels, Uber Eats, McDonald’s, and Starbucks as part of their fundamental operations.
Benefits of Big data and analytics
Learning the life cycle of data analytics gives you a competitive advantage. Businesses, be it large or small, can benefit a lot from big data effectively. Here are some of the benefits of Big data and analytics lifecycle.
1. Customer Loyalty and Retention
Customers’ digital footprints contain a wealth of information regarding their requirements, preferences, buying habits, etc. Businesses utilize big data to track consumer trends and customize their goods and services to meet unique client requirements. This significantly increases consumer satisfaction, brand loyalty, and eventually, sales.
Amazon has used this big data and analytics lifecycle to its advantage by providing the most customized buying experience, in which recommendations are made based on past purchases and items that other customers have purchased, browsing habits, and other characteristics.
2. Targeted and Specific Promotions
With the use of big data, firms may provide specialized goods to their target market without spending a fortune on ineffective advertising campaigns. Businesses can use big data to study consumer trends by keeping an eye on point-of-sale and online purchase activity. Using these insights, targeted and specific marketing strategies are created to assist businesses in meeting customer expectations and promoting brand loyalty.
3. Identification of Potential Risks
Businesses operate in high-risk settings and thus need efficient risk management solutions to deal with problems. Creating efficient risk management procedures and strategies depends heavily on big data.
Big data analytics life cycle and tools quickly minimize risks by optimizing complicated decisions for unforeseen occurrences and prospective threats.
4. Boost Performance
The use of big data solutions can increase operational effectiveness. Your interactions with consumers and the important feedback they provide enable you to gather a wealth of relevant customer data. Analytics can then uncover significant trends in the data to produce products that are unique to the customer. In order to provide employees more time to work on activities demanding cognitive skills, the tools can automate repetitive processes and tasks.
Conclusion
The data analytics lifecycle is a circular process that consists of six basic stages that define how information is created, gathered, processed, used, and analyzed for business goals. However, the ambiguity in having a standard set of phases for data analytics architecture does plague data experts in working with the information. But the first step of mapping out a business objective and working toward achieving them helps in drawing out the rest of the stages.
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Yes, Data Analyst is one of the most in-demand job roles in 2022-23. If you’re thinking of pursuing Data Analytics as a career, now is probably the best time. According to research, more than 2.5 quintillion bytes of data are created every day, and this number keeps increasing at a fast pace. To make good use of this data for the company’s growth, a Data Analyst is required. India is the second most important hub of jobs for Data Analysts. Considering this fact, it is an excellent career option for those who want to learn the life cycle of data analytics.
The top skills required to become a Data Analyst are: 1. SQL is one of the most essential skills for a Data Analyst. It is the industry-standard database language which is used to handle large databases. 2. Solid programming skills in R, Python, Java, C++, etc. 3. A Data Analyst needs to have good critical thinking. He/she needs to understand the data beyond numbers. Identifying patterns in the data and extracting hidden insights from the data are some of the applications of critical thinking. 4. A Data Analyst needs to have mathematical skills. Two specific topics over which a Data Analyst needs to have command are Linear Algebra and Calculus. 5. Soft skills, like networking and communicating, are a cherry on the top.
According to Glassdoor, the average salary of a Data Analyst in India is around ₹6L/annum. However, the salary of a Data Analyst depends on several factors, including company size, the company’s reputation, location of the job, educational qualifications, work experience, and most importantly, your skills. An entry-level Data Analyst can easily make around ₹3L/annum, a mid-level Data Analyst with work experience of 5 to 9 years can make around ₹6L/annum, and a Senior Data Analyst who knows a life cycle of data analytics with work experience of 10 to 15 years can make up to ₹13L/annum. Data Analyst indeed is a high-paying job role, and if you’re interested in the field, it is totally worth it to pursue it.
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