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End to End Process on How a Data Analytics Project is Managed

In a world where data has become as valuable as fuel, data analytics plays a vital role in helping companies use the data they generate more efficiently. It works as a tool that enables decision-makers to transform unstructured data into structured business insights and make prudent and informed decisions.

Of course, while the objective is always clear and pretty straightforward, what confuses many is the process that governs a data analytics project. With so much to do, it is pretty obvious for people to wonder what are the stages of a data analytics project.

So, let’s get straight to the point to understand the various phases of a data analytics project or, in other words. the end-to-end process of a data analytics project.

Explained: End-to-End Data Analytics Project Management

Following this organized data analytics project management process can help you achieve the desired results. Let’s see how to manage a data analytics project.

  1. Setting Goals

Data analytics incurs efforts and time. Going halfway and realizing you’ve come a long way on the wrong track can let all your actions down the drain. So, the first step is to set the goals and objectives that your business wants to achieve. It involves examining the overall scope of the project, stakeholders’ requirements, the kind of analysis you want to leverage, and the deliverables involved. Setting the expectations right can help you create the data model that delivers the required insights. Datafortune with offices in Atlanta, US and India can assist you in identifying and setting the goals for your project.

  1. Data Collection and Comprehension

The next step is to identify various data sources such as social media, web servers, online data repositories, etc. Relevant data gathered through diverse sources can add better value to the project. The next part is to explore data, which involves analyzing data and summarizing it. The latter can help identify client behavior and transactions and increase the decision-maker’s familiarity with these aspects to comprehend information better. Essentially, this process lets you know what to do with the data.

  1. Data Cleaning

Data cleaning refers to structuring the data. It involves delving into the existing data and identifying links to connect the data with your objectives. It also comprises looking into the data to identify errors like data duplication, omission, illogical data, etc. Rectifying these errors can help you hone your data to achieve the project’s goals.

  1. Data Modeling

This forms the most significant step in a data analytics project that demands writing, running, and refining programs to analyze and get valuable insights from the data at hand. It involves building models to test the data and get answers concerning your goals. Some standard models include random forest modeling and linear regression. You can use statistical modeling methods to decide on the best-suited model.

  1. Validation and Interpretation of Data

Now, the question is whether you have the correct data at hand? You can know this through data validation. However, data validation and interpretation require utmost attention. You must check if the model you’ve created has worked as expected, do you have to clean the data further, has the data delivered the expectations of your stakeholders, etc. You will have to go through the entire process again if the responses aren’t adequate or negative.

Data interpretation refers to deriving meaningful insights from the numerical data you’ve gathered and analyzed. You must establish a uniform fundamental method to provide a well-laid base. Individual, dissimilar approaches can lead to inconsistent results. You wouldn’t derive any comprehensive or practical value from the data you’ve worked on.

  1. Deploying the Model and Visualizing it

You are a data scientist who can encode and decode every aspect of data. But your clients wouldn’t necessarily be as tech-savvy. The data you’ve analyzed should tell a story that generates valuable insights. Large datasets should be shown in a format the stakeholders understand what the information means and what your conclusions are. Accordingly, you can use formats like graphs, charts, etc., to present your findings more straightforwardly to the clients or stakeholders.

  1. Training and Retraining

Train and retrain your model regularly using new data, as otherwise, it could turn redundant over a while. The way you train your model with your data while creating it for the first time, you must constantly reevaluate, retrain, and upgrade it. It will help maintain the model’s analytical efficiency.

Unleash the True Potential of Data through Datafortune’s Data Analytics Services

Datafortune can be your end-to-end data analytics partner, helping you manage every aspect of the project just like we have done for many of our successful customers. We enable you to derive structured insights vital for your business. Our qualified and experienced data scientists handle complex data analyst requirements and work with your in-house teams and verticals to efficiently and effectively complete the data analytics project.

To know more, connect with us through our Contact Us section or write to us at Info@datafortune.com.

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