In the past, preparing data for analysis was a complex and time-consuming process, a task that was relegated to the IT squad and encompassed multiple steps such as data extraction, transformation, loading (ETL), data warehousing, and more. It required more skilled manpower, more time, and more effort. With technological improvement and introduction of a comprehensive suite of self-service data preparation tools, the business landscape is now ready to dispel complexity and rigorous effort by empowering business users.
Self-service data preparation tools empower users to disintegrate the barriers of operational data silos and make information more accessible than ever. By restoring data quality and improving its access, users can extract value and make faster decisions.
For better understanding, let us delve into challenges posed by conventional data preparation approaches and how self-service tools can help companies overcome them.
Traditional data preparation tool leads to compromised outcomes that:
Take a lot of effort and time: Conventional data preparation solutions take a lot of time to make data fit for discovery and analysis. Big data collected from a host of sources is difficult to curate and these tools fail to address the nuances.
Put a lot of pressure on workforce: In traditional ETL approaches, two sources prepare data. IT professionals, who construct multidimensional tables of data that end users explore and analyze from many different perspectives using BI tools, and individual coders spread across the organization. Besides, both of these sources need to be well-versed in data integration, data quality, and information governance to set the corporate standard for business-critical information. This puts a lot of pressure on a particular segment of organizations, essentially IT, plummeting productivity to a minimum.
Limit data access: In conventional tools, IT and data analysts have access to cleansed data. Due to their limited and disproportionate data access approach, organizations become less flexible and ultimately difficult to do business with.
Result in poor data quality and impact decision-making: Conventional data preparation tools encompass several manual processes and hand-coding steps that make the end result, i.e. data, prone to errors and discrepancies. To add, these processes do not scale, and lack of coordination between processes becomes a huge problem as the big data tsunami hits. Lack of quality and precision in the prepared data act as a roadblock for decision-makers and analysts to respond optimally to new business opportunities.
Self-service data preparation tools help organizations overcome these challenges without a hitch. The architecture of these modern self-management solutions gives them an edge over the legacy ones.
Data management products have a monolithic architecture. On the other hand, next-gen self-service data preparation and management solutions have a flexible architecture that arranges an application as a compilation of loosely coupled services.
While the former is slow and complex, the latter offers a lot of speed and flexibility from a development perspective. This means developers can incorporate changes more quickly than they could have with a monolithic or legacy architecture.
In a monolithic architecture, services are tightly coupled meaning that changing a feature can have severe ramifications that include cost and effort. While in the microservices architecture, developers can easily make changes without making an impact on the “contract” with other services. Moreover, microservices architecture can be scaled to meet ever-changing customer demands.
Modern self-service based data preparation solutions provide high-class back-end services with unique features and capabilities to empower business users to prepare data for analysis without IT support.
Ease of integration is another main advantage of these solutions. Users can not only use these tools when they integrate disparate data sources and applications but also when they bring siloed data systems together for the first time in a data warehouse or repository. As a result, robust communication is established.
Next-gen tools streamline data preparation and management and make life easier for businesses by offering numerous benefits such as:
1. Better Architectural Flexibility: Modern data preparation tools have microservices architecture that offers flexibility by simplifying deployment and supporting the latest data processing frameworks.
2. Reduced Time and Effort: It provides data access to business users with the aid of a centrally managed system that features a user-friendly dashboard and a library of pre-built connectors. With data access, business users become equipped to execute data preparation tasks without any difficulty or support. This makes the process quick and independent. In addition, using a self-service tool reduces a lot burden on IT, making them available for more strategic tasks.
3. Accelerated Data Quality: Self-service data preparation tools harness the value of data through automation. The automated data preparation processes allow decision-makers take the right action.
4. Improved Security: Rules and authorized access is required to use the solution that makes it secure and efficient.
5. Easier to Scale: These tools are easier to scale to match customers’ expectations. Plus, they are compatible with all major cloud service providers.
6. Lower Total Cost of Ownership: Features like automated reporting and dashboards in real-time allow marketers use the tool without excessive reliance on resources and infrastructure.
Modern data preparation tools leverage self-service capabilities to extract maximum value from data for informed decision-making. By empowering business users to execute data preparation tasks without IT support, organizations can easily transform data and use it for growth and innovation.