Data Preparation Best Practices for Effective Analytics

December 8, 2021

What is Data Preparation?

Data preparation includes finding, combining, cleaning, transforming and sharing curated datasets for various data and analytics use cases. Data preparation is a must-have capability for organizations that are looking to accelerate time-to-insight from data through decentralized, self-service analytics. Increasingly diverse data, and the pressure to provide the right data to the right people at the right time, is driving this demand for data preparation capabilities. This is why data and analytics leaders must integrate data preparation into their overall data management strategy.

Data preparation is usually a manual, time-consuming process but using advanced analytics tools with effective data preparation capabilities can significantly automate the process, freeing up time for analysis of the prepared data. Data preparation tools deliver faster time to insight by allowing business users including analysts, citizen integrators, data engineers and citizen data scientists to integrate internal and external datasets for their use cases. Additionally, they enable users to identify anomalies and patterns faster and improve and review the data quality of their findings by automating certain repeatable data preparation tasks.

Data Preparation Best Practices

Data preparation is a critical aspect of data management and by allowing business users to prepare their own data for analysis, organizations can reduce reliance on IT, accelerate time-to-insight, and enhance decision-making. Here are a few best practices to effectively prepare your data for actionable analysis.

1. Build a Robust Data Governance Strategy

Data governance is essential because it helps you define the standards that your data preparation efforts must meet. Data must be seen as a strategic asset for your business and stakeholders must own and be accountable for data governance efforts. Data governance should focus on business value and outcomes, and must be aligned with achieving specific business priorities. The scope and accountability must be assigned by key stakeholders in the organization, and must be documented and routinely reviewed. This helps you clearly understand where your data and analytics assets are created, consumed and controlled, and will better assure business stakeholders that the right governance processes and controls are in place.

2. Ensure Good Data Quality

Resolving data quality issues requires a collective approach that involves people, governance, processes and technologies as key factors. Data and analytics leaders must collaborate with business stakeholders to build a data quality operating model that enables you to allocate the appropriate resources and improve the skills, technology, and processes required to implement your data quality program.

3. Verify the Accuracy of Your Data

Get to know your data before you prepare it for analysis. Examine, visualize, detect outliers, and find inaccurate or junk data in your data set. This can help you decide if the data source is worth including in your project. Disqualifying a data source early on in your project can help you save significant effort later on in trying to clean up inaccurate data. If your data source is good enough and you want to include it, this exercise can help you understand the ETL effort needed to make this data suitable for analysis.

4. Identify Outliers and Missing Values

Identify the data points that are out of sync with the rest of your data. These are called outliers and are either very small or very large values when compared with the rest of the dataset. Outliers or missing values can compromise your analytics processes because they can have a huge impact on your mean values. When faced with outlets, you can run your analytics twice – once with outliers included and once with them excluded and evaluate which method suits your needs better.

5. Start with Small Data Sets

Working with large data sets can be cumbersome and tedious. You should always start with a sample of your data for initial analysis and data preparation. Creating data preparation rules based on a smaller data set greatly simplifies the process and accelerates your time-to-insight.

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