Modern enterprises have become obsessed with data-driven decision-making. Of course, it’s the only way to stay competitive in today’s digitized, fast-paced business landscape. A pre-requisite for a data-driven approach is an effective data management system that would allow analysts to derive actionable insights for decision-makers. So, the questions arise: how to prepare for data analytics? The answer lies in the question itself. 

You need to prepare data for analytics. Data preparation serves as a foundation for analytics and business intelligence (BI). Let’s find out more! 

Why is Data Preparation Important?

Data preparation allows organizations to convert raw data into quality data that allows for accurate and efficient analysis to gather actionable insights. The process involves cleansing, formatting, and transforming data to make it suitable for BI tools. 

During the data preparation process, analysts comb meticulously through raw data, performing a wide range of steps, such as changing formats, deleting unnecessary fields, and consolidating relevant datasets before loading data to a warehouse for analytics and BI.

Done properly, data preparation results in the following benefits:

Improve Accessibility:

Business users can quickly retrieve relevant data for analytics, which saves time and speed up business processes.

Better Data Quality:

Removing and modifying irrelevant and inaccurate information leads to increased data accuracy.

Higher Return on Investment (ROI):

Higher data accessibility and improved data quality mean organizations can get a higher ROI from their analytics and BI initiatives. 

5 Steps to Prepare Data for Analysis

Data preparation is a time-consuming and labor-intensive task. Data scientists spend most — as much as 60 percent —of their time cleaning and organizing data. It is performed in several steps, which varies for different data professionals, but these are typically the steps to prepare data for analysis: 

Data Collection 

Data preparation begins with collecting data from relevant sources, such as business operational systems, data lakes, and warehouses. During this stage, data professionals carefully evaluate datasets to determine their relevance to the planned analytics objectives. 

Data Cleaning

The next step involves data cleansing, which is typically the most cumbersome and time-consuming process. It generally involves:

  • Removing irrelevant or extraneous data
  • Adding missing information in the datasets
  • Deduplicating data
  • Fixing structural errors, such as incongruent naming conventions and typos
  • Validating data to confirm consistency and quality

Data Structuring

Data structuring involves organizing the cleansed data into a specific format to make it usable for analytics and BI tools. Its main objective is to make data more accessible and manage large volumes more efficiently. For example, structured data is stored in comma-separated values (CSV) format in rows and columns. 

The structures are used by programmers in the analysis stage to create algorithms and purpose-driven automated programs to streamline and automate data. 

Data Transformation

Data transformation involves merging, splitting, or joining the datasets to make them more suitable for analytics. For example, merging two data sets of sales figures in different file formats into a single uniform layout. Data transformation may be: 

  • aesthetic (example: standardizing address formats)
  • constructive (example: adding the last name)
  • destructive (example: removing postal code)
  • structural (example: merging multiple columns in a database).

Data Validation

Data validation is the culmination of the entire data preparation process for analytics. It’s the final step that involves validating data accuracy, completeness, and consistency. Data professionals check data for inconsistencies, errors, and gaps in information. After passing data validation checks, it is moved to a data repository for analytics and BI. 

When does the Data Analysis Process Begin?

Technically, the data analysis begins with the first step of the data preparation process, where data professionals determine the data requirements and objectives. Once the prepared data is moved to a warehouse, it is then ready to be used for BI. Business users can use several data analysis techniques to interpret data and derive meaningful insights for decision-makers.

For instance, statistical data models like correlation analysis can be used to identify the relationship between different data variables. For example, sales managers can determine how spending more money on marketing a product to a specific region translate to sales revenue. Higher sales would show there’s a positive correlation, showing that it’s the right decision.  

Data analysis allows organizations to derive valuable insights by connecting patterns and relationships. These insights can be used to solve critical business problems.

Data Preparation Best Practices

The objective of data preparation is to convert the raw data into quality data to ensure accurate analysis. The data preparation process is critical to translating and interpreting the information to produce viable results to meet business goals. Here are some of the best practices to follow during the data preparation process:

Define Outcomes for Optimum Results 

The success of data analytics depends on outlining precise outcomes and outlining clear-cut targets. For example, the information required to perform an analysis for decreasing sales will be different from that of data collected to restructure supply chain operations. It’s important for analysts to understand business purposes to ensure optimum results. 

Perform Extensive Data Cleaning

While data cleaning can be a time-consuming activity, it’s essential to guarantee the accuracy and quality of the information used in analytics. Users must check for duplicate fields, errors, and outliers. Dot the i’s and cross the t’s to ensure reliable datasets. It’s critical to have the highest quality information for effective decision-making.

Leverage Modern Solutions

The process of getting data ready for analytics can be simplified by using modern solutions. AI and machine learning technologies help expedite data preparation steps to facilitate faster analytics and BI. For instance, using a no-code solution can significantly cut down the time it takes to extract, cleanse, and transform data.

Final Words

Data preparation tools enable enterprises to convert raw data into meaningful insights to power-charge their analytics. A consolidated repository of accurate and reliable data serves as a solid foundation for business decisions to allow organizations to meet their business goals and drive profits with impactful decision-making.

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