7 Steps To Execute A Successful Data Analytics Project

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

Often, when we talk about data analytics projects, it seems very vague as 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 seems hard to figure out how the entire process from gathering the data, 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 lay more emphasis on data products. The creation of strong 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 is predicting doesn’t match with 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. You need to have all these elements clearly defined before beginning your data analysis project to provide the best results. This ensures that a full understanding is gained to confirm exactly 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 that could be present in an excel or can come from 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 their main characteristics, exploratory data analysis forms an integral part at this stage as summarization of the clean data can help identify customer behavior and transactions, and become more familiar with it to obtain a deeper understanding of the information gathered. This is the step that helps data scientists answer the question as to what they want to do with this data.

3. Data Cleaning & Preparation

Once you have got 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 properly polish your data. 

4. Data Modeling

This 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, among others.

5. Data Validation & Interpretation

Once you have crafted your models, you will need to assess the data and determine if you have the correct information for your deliverables. Always ensure that data is properly validated and interpreted. 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. Having a baseline method for interpreting data will provide your analyst teams a structure and consistent foundation. Indeed, if several departments have different approaches to interpret the same data while sharing the same goals, some mismatched objectives can result. Disparate methods will lead to duplicated efforts, inconsistent solutions, wasted energy, and inevitably time and money.

6. Model Deployment & Visualization

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

Being able to tell a story with your data is essential. When you’re 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 useful in illustrating your conclusions to clients. By using visual elements like charts, graphs, and maps, data visualization tools provide a quick and effective way to communicate and illustrate your conclusions.

7. Iterate, Train & Deploy

This is the final and the most crucial step of completing your data analytics project and one that is critical to the entire data life cycle. In order to complete your first data project, you want to make sure that you are training your model regularly using fresh data. If you don’t train it regularly, your model might not perform great over time.

When you start doing your model selection, you first train your particular model using your training set and evaluate it on how it’s performing using your performance measure. You need to constantly reevaluate, retrain it, and develop new features. 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 businesses 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 a variety of 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 so that any further delays can be avoided. Once a model is put into action, 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 and can be altered accordingly to improve the efficiency of a specific 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

Nikita has extensive experience in developing data-driven marketing campaigns and strategies. She has worked with SaaS, IT, Travel, Software, eCommerce, and digital marketing companies in the past. She leads marketing teams and has been successful in attracting prospects through content marketing strategies and social media channels.

Leave a Reply

Your email address will not be published. Required fields are marked *