As data becomes increasingly important to modern businesses, so does the practice of data management. After all, to get the most value out of data, organizations need to ensure that their data is clean, curated, and enriched.
Because of the myriad of components and capabilities that make up a data management stack, organizations may be hesitant to invest the time, resources, and budget needed to make it truly successful across all capabilities. But we promise you, if you dedicate the time and effort, your organization will become more efficient and reap the rewards that your data – and data management – have to offer.
6 Ways Data Management Drives Efficiency
It improves data quality: if you have dirty data, you’ll make bad decisions. It’s simple, but true. Think about it. When you surface dirty, junky, source system data for use in BI, analytics, and decision making, the business will inevitably make bad decisions.
Data mastering, a key component of data management, helps you overcome your dirty data challenges by using machine learning to consolidate, clean, and categorize internal and external data. And while data mastering is machine learning-driven, it also keeps humans in the loop, providing users who know the data with the opportunity to provide feedback that makes the algorithms even better over time.
It fosters trust: when your data is dirty, business users are skeptical about using it to make decisions. But when you employ data management, not only do you fix the dirty data problem, but you also raise confidence in the data.
In order for business users to use data to make decisions, they must feel confident that the data surfaced through BI and self-service analytics tools is correct. They need all the data to be reliable so that they can trust the insights and use them to make data-driven decisions.
It allows for scalability: it’s no secret that data volume is growing exponentially. And mastering that growing volume of data across a myriad of disparate systems and departments is a challenge.
But when you have strong data management, scalability becomes easier. Using machine learning-driven data mastering with humans in the loop helps your organization manage the deluge of data while ensuring the quality remains high.
It sparks collaboration
Collaborating around data is a hallmark of data-driven organizations. And good data management, specifically in the areas of data sharing and privacy, help facilitate these partnerships.
Companies that have data management in place speak the same language because they are all working off of the same version of the truth. Business users, across divisions and departments, can look at data and easily know and understand what it means, making collaboration a whole lot easier.
Embracing data management is key to preventing data engineer – and data scientist – burnout. Instead of spending their days correcting data quality issues or putting out data fires, data management enables them to shift their focus to higher-value work like improving the data pipeline.
It increases data’s value
Companies today are looking to gain greater business value and ROI from their data. But in order to do so, it’s imperative that they manage data as an asset. That’s where data management comes in.
Instead of experimenting with various tools and technologies, data management enables organizations to curate and clean, secure and store, and enrich and maintain their data so the business can use it to drive better decisions.
To sum it up, data management is a key practice that helps organizations improve data quality. And better quality fosters greater trust. It promotes scalability and more secure data sharing, which enables better collaboration. And it prevents burnout of critical, data-related resources, all leading to more efficient management of your data.