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The amount of data companies collect is staggering. Even a mid-sized business can quickly generate millions of raw data points about its customers, business, and technology performance. As a company’s analytics multiply, proper data management can become insurmountable for even the most seasoned data prep expert — not to mention companies without a specialist on hand. Data prep tools are designed to rummage through this pile of data and aggregate relevant insights for users. These tools are increasingly valuable and necessary for businesses with an endless influx of large data sets. These tools help draw valuable conclusions about important data points through the noise of excess information.
A popular term for this process is called data wrangling. Data wrangling evokes the full capabilities of these tools. They can mine useful, relevant analytics from an overwhelming stream of different data sources. Modern businesses must make timely, critical decisions in response to the diverse insights generated by these data wrangling tools. These tools compile real-time analytics about product users, sales numbers, system performance, and more. The tools in this emerging space help streamline the data preparation process, gleaning precise information from large data sets. As a business’s data piles up, data prep tools enable users to find important data points with the push of a button. This way, companies can leverage actionable insights immediately without sorting through hours of data.
In the early days of analytics, a small team would be responsible for manually preparing data — managing quality assurance for an entire company’s database, and pulling together actionable insights. This is still the case for thousands of organizations across multiple industries. As technology advances, the volume of unstructured data has grown immensely. People generate more data than businesses know what to do with, creating a unique and unprecedented challenge for data science experts and executives trying to make sense of the analytics. Data prep technology was created out of this growing necessity, with the ability to pick through massive amounts of unstructured data and present only the data points that matter for a given scenario. This relieves IT specialists of this strenuous task and makes an impossible amount of data more digestible.
In addition to finding, profiling, and combining data based on user specifications, certain solutions in this category assist with data transformation or converting data types into different forms or structures for analysis purposes. This creates a unified view of the most relevant analytics for convenient analysis and eventual exporting into external systems. Just as the amount of data has increased in recent years, so has the variety of data types, formats, and sources. Data preparation platforms work to identify or profile the most valuable data across these various types and deliver it in the most useful way for each new scenario. These advanced tools can save employees time while creating opportunities with previously unattainable data, especially if a business has an extensive portfolio of data sources.
The solutions in this category benefit companies with a substantial pool of data and a complex network of data sources. For smaller companies in certain industries, data prep may still be a manual process that does not require new technology. However, since many organizations utilize various types of software and third-party partnerships, they generate mountains of data on a daily basis. As a result, more and more businesses are eligible for these tools.
The following teams or individuals will most likely use these solutions in a given organization.
IT specialists — If a company has an IT department, these employees are the most logical choice for general data and test data preparation. IT specialists already have a comprehensive view of the computer systems and software platforms used across an organization. They may already be the primary owners of analytics tasks such as data enrichment and data cleaning. The analytics platforms featured in this category empower IT specialists to expedite the quality assurance process and create clean data sets for internal use or to be shared across their organization.
Data analysts and engineers — As the data realm has swelled in size, tech-forward companies have started to seek designated employees to collect and draw conclusions from company analytics. These data analyst roles are typical in organizational structures and third-party agency settings, such as data governance services providers. Whether employed with one of these firms or on a company’s full-time staff, data specialists benefit from one of the tools in this space. In some cases, data prep will be a daily responsibility in this line of work. Pulling various data sets for additional analysis or tests and using the results to influence business outcomes emphasizes the impact this technology can have on a given organization. The correct data prep solution can be an indispensable asset for data engineers, analytics executives, and others with a strong focus on data work.
The robust tools in this software category offer a diverse range of functionalities related to the process of data preparation. The following are some prominent features of these unique offerings.
Workflow scheduling and monitoring — Depending on the intended use of these tools, employees may want to map out an automated query to prepare certain groupings of data regularly. This might involve a custom data flow builder or a similar user interface for customization. Using these tools, administrators can adjust the specific details of each workflow, including analytics filters, which sources to pull from, and the schedule for executing the query. A company may be able to adjust other components of the process, such as validation details and the destination for exporting finished data sets. Dashboards on some tools can help display analytics related to data prep workflows, including general efficiency and results summaries.
As a company creates data prep queries, whether for one-off events or routine workflows, a company may be able to configure the data blending and joining process as it relates to each function. Data blending is another common term used to describe the merging of analytics from separate sets into a cohesive group to draw conclusions and continued analysis. When configuring the intelligent algorithms on these platforms, companies can specify how they want the data joined together and presented, for instance, which data type they prefer and how the data should be ordered. Whether called data preparation, data wrangling, or data blending, the solutions in this category can assist with this increasingly popular business strategy to help bring divergent analytics together for a unified purpose.
Data profiling — Once the intended analytics are pulled and organized using these tools, certain platforms can assess the data and help determine the additional purposes it can be used for. This is also known as data profiling. Some tools in this category offer more powerful profiling features than others, allowing for rich analytics and summaries about prepared data sets as they are constructed. If data profiling features are not present, a company might assign certain data analysts or other specialists to profile the finished data sets and determine the best course of action to take as the results are delivered.
When selecting a data preparation tool, consider a few key factors to ensure it aligns with your unique data needs and organizational resources.
First, assess your data's complexity and your team's technical skill level. Some tools are better suited for advanced technical users with programming knowledge, while others are designed for ease of use, making them accessible to non-technical team members. Look for a tool that strikes the right balance between functionality and usability for your team.
Next, think about performance and scalability. As your data grows, your tool should be able to handle increased volumes without a dip in efficiency. Make sure the tool integrates smoothly with your existing infrastructure, such as cloud storage, data lakes, or on-premises systems, to avoid compatibility issues down the line.
Don’t overlook the specific needs of your data workflows. Consider how often your data is updated and whether you need real-time processing capabilities. Advanced features like data profiling, which helps uncover patterns and quality issues, or specialized data transformation options might be essential for more complex datasets. Evaluate these aspects carefully to ensure the tool meets your immediate and long-term data preparation needs.
By evaluating these factors, you’ll be well on your way to choosing a data preparation tool that meets your current requirements and can scale as your organization grows.