Data management is a complex undertaking that businesses in every industry must tackle. It’s about proper data organization, compliance, access, security, quality, storage and usage—to name just a few of the moving pieces. Organizations must balance regulatory requirements against company policies to properly manage data. Businesses have more data than ever before at their disposal, and they need advanced data management to know how long to keep data, and how to evaluate its continued quality and fitness for a range of purposes. Yet the lifespan of data varies widely among companies. While many rely on regulatory requirements to determine how long their data is stored, others also establish customized data retention policies.
Whether an organization uses a large data center, securely stores their data in the cloud or leverages a hybrid approach, smart and efficient data management is critical to helping users quickly access data, to unlock relevant insights and make better business decisions.
Over the past few years, developing a data management strategy has taken on a heightened level of importance. However, many data management plans fail. Not from a lack of a data management strategy, but because the organization lacks a business-friendly enterprise data governance program.
In today’s data-driven world, data governance is the backbone of an organization. The ability to capitalize on the right data can make or break a business. When done correctly, data governance ensures that business users have both high-quality and easily understandable data to support complex decision-making to optimize operations, meet objectives and improve the customer experience.
Incorporating Divergent Lines of Business in Data Governance
Even with continued technological advancement, businesses still leverage IT-focused data management solutions and old-school tools like Excel spreadsheets or Wikis to document data and metadata. With the growing volumes of data and constant movement across environments, systems and processes within the data supply chain, outdated tools don’t hold up. These old technologies can be overly technical or unscalable, and most rely on manual methods that generate errors, and result in mismanaged, misunderstood data and a multitude of problems.
Data governance technologies today take a business-centric approach to data management, which eliminate the challenges users face leveraging legacy tools. Advanced data management takes a business-focused approach to data, fostering data understanding and empowering users of all skillsets to engage with and derive value from data. Integrated solutions, such as an all-inclusive data intelligence platform, can provide business users with self-service options and combine data governance, data quality and analytics to enhance business user control over data.
Modern data governance capabilities will deliver a complete view of an organization’s data landscape, from the data available, its owner/steward, lineage and usage, to correlating definitions, synonyms and business characteristics. Additionally, the platform will also enable users to define, track and manage all data assets easily, permitting simple collaboration across the enterprise. Most important, they increase users’ data literacy, ensuring the right data is used for the right purpose. Data understanding also breeds trust in data assets, which encourages utilization and leads to more data insights.
Data quality capabilities enable users to conduct data integrity checks, including data profiling, consistency, conformity, completeness, timeliness, reconciliation and transaction tracking. These checks establish trust in data among business users because data quality is validated across the entire data supply chain. Analytics capabilities also automate the continued monitoring and improvement of data quality rules by applying machine learning algorithms.
With business-friendly technologies and techniques, organizations can reap significant rewards.
Leveraging Modern Technologies for Proven Results
A great example of advanced data management in action comes from a major underwriter of property and casualty insurance with millions of customers. The agency was bringing new technologies to market in hopes of competing in the automobile insurance space. Their history, operational scale and growth were supported by legacy systems that grew in size to tens of millions of lines of source code.
As the company migrated to new open systems, the company needed to integrate with diverse systems spanning both old and new platforms. However, significant risks posed major obstacles in migration efforts, in areas such as data translation, synchronization, communication, complex business rules, data warehouses, transactions and regulatory reporting.
The company selected an updated, modern data intelligence platform that allowed them to scale and streamline performance in its highly information-intensive environment. The wide variety of analysis options enabled validation of content in various formats, from different platforms and databases. The solution enabled end-to-end transaction tracking across the insurer’s enterprise.
In addition, the data intelligence platform helped the insurer quickly codify data quality rules to align with an array of regulatory and business rules. As a result, the company increased efficiency and freed up operational, financial and IT analysts from tedious manual efforts, allowing these valuable resources to focus on higher value, growth-oriented business activities. The solution also gave management across diverging lines of business clear visibility into micro and macro-level business trends to inform better business decision-making.
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