What is a Data Analytics Lifecycle?
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.
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.
One of the other main reasons why the Data Analytics lifecycle or business analytics cycle was created was to address the problems of Big Data and Data Science. The 6 phases of Data Analysis is a process that focuses on the specific demands that solving Big Data problems require. The meticulous step-by-step 6 phases of Data Analysis method help in mapping out all the different processes associated with the process of data analysis.
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So if we are to have a discussion about Big Data analytics life cycle, then these 6 stages will likely come up to present as a basic structure. The data analytics life cycle in big data constitutes the fundamental steps in ensuring that the data is being acquired, processed, analyzed and recycles properly. 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. The framework is simple and cyclical. This means that all these steps in the data analytics life cycle in big data will have to be followed one after the other.
It is also interesting to note that these steps can be followed both forward and backward as they are cyclical in nature. So here are the 6 phases of data analyst that are the most basic processes that need to be followed in data science projects.
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.
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 goal of this first phase is to make evaluations and assessments to come up with a basic hypothesis for resolving any problem and challenges in the business.
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. As a data analyst, you will have to study the business industry domain, research case studies that involve similar data analytics and, most importantly, scrutinize the current business trends.
Then you also have to assess all the in-house infrastructure and resources, time and technology requirements to match with the previously gathered data. After the evaluations are done, the team then concludes this stage with hypotheses that will be tested with data later. This is the preliminary stage in the big data analytics lifecycle and a very important one.
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. So you will need to develop an understanding and concept that will later come in handy while testing it with data.
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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.
The Data preparation stage in the big data analytics life cycle requires something known as an analytical sandbox. This is a scalable platform that data analysts and data scientists use to process data. The analytical sandbox is filled with data that was executed, loaded and transformed into the sandbox. This stage in the business analytical cycle does not have to happen in a predetermined sequence and can be repeated later if the need arises.
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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. This phase of the data analytics process is known as model planning.
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.
Data sets are developed by the team to test, train and produce the data. In the later phases, the team builds and executes the models that were created in the model planning stage.
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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.
It helps them determine whether the tools they have currently are going to sufficiently execute the model or if they need a more robust system for it to work properly.
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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.
If there are any performative constraints in the model, then the team goes back to make adjustments to the model before deploying it.
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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.
This is a small-scale example of how deploying the business analytics cycle can positively affect the profits of a business. But this model is used across huge business chains in the world.
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. Big data analytics technically means the process of analyzing and processing a huge amount of data to find trends and patterns. This makes them able to quickly find solutions to problems by making fast and adequate decisions based on the data.
- 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.
- Spotify is a platform that is using the data analytics lifecycle in big data to its fullest. They use this method to make sure that each user gets their favourite type of music handed to them.
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.
5. Optimize Cost
One of the greatest benefits of the big data analytics life cycle is the fact that it can help you cut down on business costs. It is a proven fact that the return cost of an item is much more than the shipping cost. By using big data, companies can calculate the chances of the products being returned and then take the necessary steps to make sure that they suffer minimum losses from product returns.
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.
upGrad’s Executive PG Programme in Data Science in association with IIIT-B and a certification in Business Analytics covers all these stages of data analytics architecture. The program offers detailed insight into the professional and industry practices and 1-on-1 mentorship with several case studies and examples. Hurry up and register now!