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Is the pressure to achieve clean, quality data creating a bottleneck in your business? Data preparation is an essential step in the overall success and deployment of analytical applications and initiatives in your enterprise. Whether the goal is operational success for your day-to-day challenges or streamlining and scaling your business long-term, the responsibility for data cleansing should fall on software and processes—not your data scientists. Don’t “boil the ocean,” though—prioritize which data is most important for meeting insights needs.

No Data Preparation? Here’s What’s at Stake

In fact, a 2021 study found that 39 percent of data scientists’ time was spent on data preparation and data cleansing, which was more than the total time spent on model training, model selection, and deploying models combined. Over one-third of your data scientist and analysts’ time is spent on ensuring data is viable rather than actually interpreting the data to provide insight and information to your enterprise. Without data preparation, your team will be allocating their time, resources, and money to a process that should be automated to help your business scale. 

6 Tips to Achieve Data Preparation

As much as we’d like to snap our fingers and ensure your data management solutions are offering your organization the best information and insights possible, we can’t. You will have to make conscious decisions to implement self-service business intelligence and standardization processes. The following six tips will provide you with the right approach to data preparation in your organization: 

1. Prepare for Preparation 

Before getting started, it’s important to determine what data needs to be prioritized, who will complete which preparation tasks, on what timeline, and for what specific business purpose. In addition to laying a strong foundation for subsequent steps in the data preparation process, this approach also ensures that no time or resources are wasted. 

2. Don’t Pretend the Data is Perfect 

The process of data preparation provides a closer look at the data itself, and as a general rule, there will be gaps in this information. Some of those gaps may be addressed by using analytics tools themselves to assess the data. If there are still important gaps, share these findings with relevant stakeholders so that expectations can be adjusted accordingly early in the process—rather than when the data is said to be ready for analysis. 

3. Don’t Overlook the Importance of People

Automation and other technologies can certainly help with data preparation, but human involvement is essential to ensure data quality and also to provide a context for the data. 

4. Do Hypothesis Testing to Understand your Data’s Distribution 

Hypothesis testing can help determine the right distribution of your data and, as a result, uncover outliers and missing values. 

5. Prioritize Data According to Your Use Case 

Another reason human involvement is critical to the data preparation process is that judgment is needed to prioritize data sources for specific models and use cases. Ranking which data sources are most likely to be useful to the model for each project can significantly streamline the data preparation process and also help companies keep costs under control. 

6. Take Data Storage Seriously 

Planning ahead for storage and standardizing data formats when data is being ingested can alleviate many data preparation challenges.

Implementing Data Preparation

If you’re looking to easily cleanse your data, scale your analytics, and automate your enterprise, head over to this TIBCO whitepaper on how to accelerate data preparation at your company.  For more information on critical data preparation tips, visit TechTarget or learn more about how TIBCO prefers to wrangle and prepare data with TIBCO Spotfire software.