Efficient data analytics needs structured and accessible data. Enterprise can leverage data transformation to structure raw data as required to gain valuable insights.
Large enterprises are generating a massive amount of unstructured data that has valuable business insights that they can bank on. As most of the data is migrated to cloud and hybrid cloud environments, distributed amongst multiple servers and systems, CIOs should consider developing effective data flow to manage, store and process accurate data for data analytics. It is crucial to design a robust data transformation approach to optimize business outcomes.
There are many data transformation tools available in the market that businesses can leverage to resolve compatibility challenges and enhance data quality. Enterprises need to set efficient practices to aggregate, segment, and clean data to get actionable business insights to make strategic data-driven changes. Here are a few ways to overcome the data transformation challenges:
Lack of standardization in the cloud partners
Modernized data management requires a comprehensive technology stack to gather, manage and process data. It needs a seamless integration between data ingestion tools, data transformation platforms, data cataloging applications, and business process automation tools to streamline information flow. Furthermore, all these tools should comply with the data governance policies to ensure data compliance. But it is a challenging part for businesses to manage and maintain consistent data governance policies to achieve data compliance. If the enterprise partners with various data and analytics vendors, it will increase the challenge of data policies’ standardization.
Organizations need to set vigilant vendor evaluation processes while embarking on their journey of data migration. CIOs should consider exploring, evaluating, and choosing the best cloud data warehouse that would suffice the organization’s data requirements. Moreover, they should also thoroughly analyze the other tools like extract, transform, and load (ETL), data preparation, and data visualization, which will be required. There is a surge in the number of vendors and other service providers, increasing the challenges of consistent data governance and workflows. It is crucial for businesses to set the best data integration and transformation practices to enable seamless data governance and management.
CIOs should consider exploring data transformation solutions that seamlessly integrate with cloud data warehouses to reduce friction. Data lineage, audit logs, and auto-documentation will help to promote data governance standardization. Seamless integration between all the vendors like data catalogs, data preparation, ETL, and governance platform is necessary for end-to-end integrations.
Challenging to manage data quality
Ingesting data into the warehouse does not imply that it’s ready for data analytics. Data ingestion is an easy task, but many businesses find it a time-consuming and challenging process to prepare data for analytics. Furthermore, industry 4.0 has enabled enterprises with new data sources like IoT and other channels. Many legacy ETL platforms have become obsolete because they are unable to process manual data. As a result, businesses are not able to generate real-time insights and data. It is a significant challenge for organizations that depend on real-time data monitoring to make strategic decisions.
Enterprises need to provide accurate real-time data for analytics for valuable insights. To achieve this, businesses need to set robust and quick data flow without compromising their governance policies and quality. Advanced data transformation tools will help enterprises automate data preparation to gather, process, and structure data for data analysis.