data is being democratized, extending the audience for business analytics by sharing access with more enterprise users. In theory, that should mean more people can bring insights to the surface and uncover new opportunities to create customer value.
Advances like data orchestration and data lakes have made broader access easier – but many organizations still struggle to make widespread delivery of comprehensive, trusted data a practical reality.
Before analytics systems inherit it, data needs to meet regulatory, statutory, and governance requirements. It has to be correct, accurate, and properly catalogued with known lineage. All this should happen with minimal human intervention. Depending on a company’s existing data management infrastructure, that can present significant challenges.
The world’s store of data is set to hit 35 zettabytes this year(1), and that Everest of information increasingly lives in a blend of cloud and on-premises environments. It’s not uncommon for half of enterprise data to come from outside sources, from IoT to the vendors of vendors and customers of customers.
With data volumes skyrocketing and the number of places it’s stored in growing, it’s no wonder organizations find it hard to discover, understand, and trust what’s in their systems – much less be prepared to share it widely.
Legacy on-premises systems hobble things further. Too many of them lack the agility to deliver time-sensitive data insights quickly – an absolute requirement for staying competitive.
To overcome these barriers, organizations are investing in cloud data warehouses, cloud data lakes and, more recently – cloud data lakehouses, designed to store, update, and retrieve highly structured and curated data, primarily for business analytics and decision making.
But even the lakehouse model faces challenges. It needs enterprise-scale data integration, data quality, and metadata management to deliver on its promise. Without the capability to govern data by managing discovery, cleansing, integration, protection, and reporting across all environments, lakehouse initiatives are destined to fail.
As businesses look to move their data to the cloud, hand-coding often comes up as a straightforward way to build the data pipeline. But hand-coding can create bottlenecks. It’s also a manual process, and its cost can go up as complexity increases.
To deliver high-quality, actionable data to the business quickly, you need an AI-driven data management solution that offers a complete view of where all your critical data resides across different silos, cloud repositories, applications, and regions.
The stubborn resilience of manual processes is one of the biggest barriers to becoming a data-powered organization. Relying on them limits scalability and creates unnecessary bottlenecks in execution. Manual ingestion and transformation of data, for example, can be a complex multi-step process that creates inconsistent, non-repeatable results.
Getting rid of out-of-date processes can be a cultural as well as a technical challenge. Improving data literacy within the organization has to be part of the solution.
