Conventional Data Integration is No Longer Sufficient
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The original premise of data integration was focused on the correct goal of unifying data, but its execution was fundamentally flawed in that it sought to consolidate all enterprise data into one physical place. After fifty years of data warehouses and other ETL-based solutions, it’s clear that their promise for fast, complete analysis has fallen short.
Data is distributed over hundreds of different places with incompatible schemas, maintained by disjointed departments. Today, the average company uses over 400 different data sources for analysis, and for many, their real needs span over 1,000 data sources. Attempting to conform all data to the same structure is not only a time-intensive process but also offers no flexibility for analysis. Unstructured data doesn’t lend itself to a rigid tabular format and is readily left out of analysis. Further, with the adoption of cloud storage, data is now spread across on-premise and cloud environments, complicating physical consolidation due to security protocols.
Internal business practices have also hindered data integration projects. Many times, information is fragmented across internal departments without any standardized definitions or naming conventions. While this allows individual departments to perform their day-to-day tasks, it becomes extremely hard for an organization-wide initiative to reach across all departments to get a consistent, global view.
These challenges can have great costs. On average, data scientists spend 80% of their time cleaning and preparing data for analysis. It’s not just technical teams that are trying to access data. All employees are constantly searching for information throughout their day. An IDC survey found that organizations with 1,000 workers lose almost $6 million dollars a year with employees spending 36% of each day searching for information. Half the time, employees can’t even find it.
Poor data management causes more than just increased operational costs. The tragic Space Shuttle Columbia disaster in 2003 revealed that poor data management contributed to the accident. The Columbia Accident Board Report notes that “the Space Shuttle Program has a wealth of data tucked away in multiple databases without a convenient way to integrate and use the data for management, engineering, or safety decisions.” Since then, NASA has revolutionized their data management practices and can now understand the impact of changes throughout the mission from the earliest flight modeling stages to launch and beyond.
An inability to easily access your data hinders decision making. But it’s not just about having access to the the right information to make the initial decision. The need to adapt and change direction quickly is a core need for any business. We operate in a world that is constantly changing - new regulations are introduced, new business opportunities explored, unforeseen supply chain issues arise, new mergers and acquisitions are undertaken. Data management tools must enable flexibility.
Key characteristics of flexible data management tools:
Data fabric is the enterprise data management solution that meets the demands of the modern enterprise. Data fabrics weave together data from internal and external sources and create a network of information to power business applications, AI, and analytics.
Want to learn more about how a data fabric can resolve your data integration problems? Start by reading our Data Fabric whitepaper.
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