While data preparation sounds perfect for ad-hoc queries and ad-hoc ideas, for datasets that are in constant use, it is too manual, time-intensive, and resource-intensive to proffer an effective solution.
And that’s where the data integration process meets data preparation. When different data sources are integrated, organizations can significantly cut down time-to-value and ensure predictable, economical scalability.
Let’s dive deeper to discover how a data integration platform can save business users time as well as money and streamline data preparation.
In the data preparation process, quality in data sources is often overlooked. But, when data analysts straight enter into the data cleansing process, without questioning the frequency or reliability of data sources, they create a lot of additional work.
Organizations will save time on a short-term basis, but it is hard for them to deal with issues in the future. Overall, poor data sourcing gives rise to quality, accessibility, and formatting issues, which can be frustrating. The role of data integration comes into play.
For companies, data integration is their greatest ally. By integrating data sources, users have a permanent way to process data loads and make them available 24X7. Instead of wasting 80% time on preparing the same datasets, companies can shift their focus on more important tasks such as driving innovation.
After finding a way to identify powerful data sources, the next step is to combine the data into a unified dataset with a data integration solution. This step is actually the bridge between data and analytics engine, so it plays a big role in shaping the final outcome.
Many organizations rely on ETL tools to perform this function, however, they won't provide performance and scalability they need. Complex, bidirectional data feeds call for more efficient profiling and must be evaluated for quality and formatting problems. In addition, these solutions entangle IT teams as it’s only them who could perform those operations. As a result, it is hard for IT teams to focus on governance and control.
This is where self-service based data integration pays rewards. It empowers users to maintain and reuse data transformation templates, so they need not invest weeks or even months to observe value without overreliance on IT. So, they are not starting from scratch for every connection. Since this platform is accessible to both developers and business analysts, all have the same trusted version of the data, which streamlines the process of data preparation further.
When an organization ingest large streams of data in batch or in real-time, there’s often little opportunity to cleanse and standardize them manually. Users must monitor data quality and fix errors before beginning transformation as this helps prevent dirty data from disturbing analytics. For this, companies need to form data validation rules that assess each new record as it is integrated into their analytics systems.
But, it is important to keep in mind that employing a lot of monitoring procedures generate complicated reports and inhibit decision-making. Conversely, employing too little monitoring leads to errors and discrepancies. The key is to strike a balance, but once users have the right combination rules, they can use them over and over again.
Organizations can taste success by handling data preparation correctly. Not only do data preparation tools save a lot of development time but also budget and effort.
To prepare data in an optimized way, the key is to embrace an efficient data integration strategy. Modern, automated integration tools can help companies streamline the data preparation process and use the cleansed data for quality decision-making. They empower “all business people” to prepare and integrate complex data feeds into a unified dataset for driving various operations in minutes- and not in days. As a result, the decision-making process is not only accurate but faster too. Meanwhile, IT can handle monitoring and control for achieving innovation initiatives.
Simply put, these solutions lend power to users as they strive to prepare and integrate data while IT can make efforts for delivering game-changing innovation.