7 Steps To Execute A Successful Data Analytics Project

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Building a successful data analytics project
January 07, 2021 | 6 min read

Often, when we talk about data analytics projects, it looks pretty vague to know where to start once you’ve decided that you want to deep dive into the fascinating world of data and AI (Artificial Intelligence). It doesn’t seem very easy to figure out how the entire process from gathering, analyzing, and presenting the results of the data comes about. Just looking at all the technologies and tools you have to understand and use is enough to scratch your head.

The workflow of a data analytics project should not merely focus on the process but should emphasize data products. Solid planning and process is an essential step to kick-start your project. 

In this post, we will break down the entire data analytics process. We will take you through each step of the project lifecycle –

– Understanding Your Business Objective and Requirements
– Data Acquisition
– Data Cleaning and Exploration
– Data Modeling
– Data Validation and Interpretation
– Data Deployment and Visualization
– Deploy Machine Learning

These steps will help organizations unlock business value from data, make effective strategic decisions, and mitigate the risk of error.

1. Understanding Business Objective & Requirements

There is no point in building a great data model only to find out later that what it predicts doesn’t match the current business needs.

The first step is to understand your business problem and the questions being asked. You should identify the key objectives that the business is trying to achieve. You should examine the overall scope of the work, information the stakeholders are seeking, the type of analysis they want you to use, and the deliverables they want. To provide the best results, you need to have all these elements clearly defined before beginning your data analysis project. This ensures that a complete understanding is gained to confirm what the model is going to predict. 

2. Data Acquisition, Understanding & Exploration

Once you have your objectives figured out, it’s time to start looking for your data – the second phase of a data analytics project. Data analytics project begins with identifying various data sources, which could be logs from web servers, social media data, data from online repositories, data streamed from online sources via APIs, web scraping, or data present in an excel or any other source. What makes a great data project is mixing and merging data from as many data sources as possible.

For analyzing data to summarize its main characteristics, exploratory data analysis forms an integral part. Summarizing the clean data can help identify customer behavior and transactions and become more familiar with it to understand the information better. This step helps data scientists answer the question as to what they want to do with this data.

3. Data Cleaning & Preparation

Once you have your data, it’s time to start working on it in the third data analytics project phase. To perform any analytical activity on any data, it needs to be in a structured format. This step is known as Data Cleaning or Data Wrangling. Start digging to see what you’ve got and how you can link everything together to achieve your goal.

When going through the data sets, look for errors in the data. These can be anything from omitted data, duplicate data, data that doesn’t logically make sense, or even spelling errors. These missing variables need to be modified so you can adequately polish your data. 

4. Data Modeling

Data modeling is the core activity in a data analytics project that requires writing, running, and refining the programs to analyze and derive meaningful business insights from data. In this step, you will begin building models to test your data and seek out answers to the objectives. Using different statistical modeling methods, you can determine which is more suitable for your data. Common models include linear regressions, decision trees, and random forest modeling.

5. Data Validation & Interpretation

Once you have crafted your models, you need to assess the data and determine if you have the correct information for your deliverables. Always ensure that data is validated and interpreted correctly. Did the models work properly? Does the data need more cleaning? Did you find the outcome the client is looking to answer? If not, you may need to go over the previous steps again.

The interpretation of data is designed to help people make sense of numerical data that has been collected, analyzed, and presented. A baseline method for interpreting data will provide your analyst teams with a structure and consistent foundation. Some mismatched objectives can result if several departments have different approaches to analyze the same data while sharing the same goals. Disparate methods will lead to duplicated efforts, inconsistent solutions, wasted energy, and inevitably time and money.

6. Model Deployment & Visualization

Model deployment and visualization is the most crucial step of your data analytics project. This step examines how well the model can withstand the external environment. After setting up a model that performs well, you can deploy the model for different applications. 

Being able to tell a story with your data is essential. When dealing with large volumes of data, visualization is the best way to explore and communicate your findings. Not all clients are data-savvy, and interactive visualization tools like Tableau are tremendously valuable for illustrating your conclusions to clients. Using visual elements like charts, graphs, and maps, data visualization tools provide a quick and effective way to communicate and present your findings.

7. Iterate, Train & Deploy

This is the final and the most crucial step of your data analytics project and is critical to the entire data life cycle. To complete your first data analytics project, ensure that you regularly train your model using new data. If you don’t train it regularly, your model might not perform great over time.

When you start making your model selection, you first train your particular model using your training set and evaluate its performance using your performance measure. You need to reevaluate, retrain it, and develop new features constantly. Whatever model you use, you follow the same steps. You train your model and evaluate it.

Wrapping Up

From small businesses to global enterprises, the amount of data being generated by companies today is simply staggering, and that is why the term big data has become a buzzword. Without data analysis, this mountain of data will clog up the cloud storage and databases. To uncover various insights that sit within your systems, consider what data analytics can help you achieve and the above steps that come with it.

Having a standard workflow for data analytics projects ensures that the various teams within an organization are in sync to avoid any further delays. Once a model is implemented, it generally requires little upkeep as it continues to grind out actionable insights for many years. However, the lifecycle of a data analytics project mentioned above is not definitive. It can be altered accordingly to improve the efficiency of the project as per the business requirements.

If you want to know more about data analytics projects or if you’d like a bit of advice, don’t hesitate to get in touch.

About the Author

A Digital Marketer at Scalex, Nikita is devoted to her motto - to write simply and clearly about complicated things. Backed up with 6+ years of experience in marketing, she creates informative but exciting articles on technologies.

One Comment:

  1. Well, that’s a fair or standard explanation of the data analytics project lifecycle but I wonder if this process suits all kinds of projects!

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