6 main steps on data projects

Purpose of Data Analytics projects is very diverse. From process optimization up to new revenue streams.

Today we are sharing which are main 6 project steps and key examples of each.

Remember that failing in 1 of the steps can led to a complete project failure. All are important and typically initial steps are the ones taking 80% of the effort (despite the last ones are more trendy nowadays)

1. Data collection

The first step in any data analytics project is to collect the relevant data. This can be done from a variety of sources, such as databases, surveys, social media, and customer relationship management (CRM) systems. The type of data collected will depend on the specific goals of the project. For example, if a company is trying to improve its customer experience, they might collect data on customer satisfaction, website usage, and product reviews.

2. Data cleaning

Once the data has been collected, it needs to be cleaned. This means identifying and correcting errors and inconsistencies in the data. Data cleaning is important because it ensures that the data is accurate and reliable, which is essential for producing accurate results.

3. Data preparation

Once the data has been cleaned, it needs to be prepared for analysis. This may involve transforming the data into a different format, such as from a text file to a CSV file. It may also involve aggregating the data or creating new variables. The goal of data preparation is to make the data as easy to analyze as possible.

4. Data analysis

Data analysis is the process of using statistical and mathematical methods to extract insights from data. There are many different data analysis techniques that can be used, depending on the specific goals of the project. For example, a company might use regression analysis to predict customer churn, or they might use clustering analysis to identify groups of customers with similar characteristics.

5. Data visualization

Data visualization is the process of creating charts and graphs to communicate data findings in a clear and concise way. Data visualization is important because it helps people to understand complex data sets and identify patterns and trends that might not be immediately obvious.

6. Advanced Analytics

• Data mining is the process of using sophisticated algorithms to discover patterns and trends in data that are not immediately obvious. Data mining can be used to identify new market opportunities, predict customer behavior, and detect fraud.

• Machine learning is the process of developing computer programs that can learn and improve on their own without being explicitly programmed. Machine learning can be used to build predictive models, automate tasks, and make recommendations.

When those 6 steps are done, the most challenging part begins:

ADOPTION & GOVERNANCE

Data analytics is a powerful tool that can be used to improve decision-making in any industry. By understanding and using data effectively, organizations can gain a competitive advantage and achieve their business goals.

If you are interested to better understand how data analytics can bring value to your organization, feel free to drop us a message