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Data junkies rejoice, this is the episode for you.
On this week’s episode of Dev Interrupted, Einat Orr, co-founder and CEO of Treeverse, sits down with us to talk about the state of data… where it’s been, where it’s going and why having bad data might be worse than having no data at all.
With data being valued more than oil in recent years, many organizations feel the pressure to become innovative and cost-effective when it comes to consolidating, storing, and using data. Although most enterprises are aware of big data opportunities, their existing infrastructure isn’t always capable of handling massive amounts of data.
By migrating to modern cloud data warehouses, organizations can benefit from improved scalability, better price elasticity, and enhanced security. But even with all these benefits, many businesses are still reluctant to make the move.
Are you spending too much time tracking down bugs for your C-level dashboards? Are different teams struggling to align on what data is needed throughout the organization? Or are you struggling with getting a handle on what the impact of a potential migration could be?
Data lineage could be the answer you need for data quality issues. By improving data traceability and visibility, a data lineage system can improve data quality across your whole data stack and simplify the task of communicating about the data that your organization depends on.
Consumers interact with a brand through hundreds of touchpoints across devices, platforms, and channels. During the buyer’s journey, consumers use 3-4 internet-connected devices. And by 2021, the number is expected to increase to 13 devices. This exponential increase in device usage indicates a sudden surge in data as well. This data influx is demanding organizations to have proper data cleansing strategies in place so that their organizational data is always kept up-to-date, accurate, and consistent.
Companies gather this data from various consumer touchpoints and use it to design better, personalized experiences for them. And if data is being gathered using multiple disparate systems – which nowadays, it normally is – it becomes crucial to perform identity or entity resolution.
IBM is reporting that data quality challenges are a top reason why organizations are reassessing (or ending) artificial-intelligence (AI) and business intelligence (BI) projects.
Arvind Krishna, IBM’s senior vice president of cloud and cognitive software, stated in a recent interview with the Wall Street Journal, “about 80% of the work with an AI project is collecting and preparing data. Some companies are not prepared for the cost and work associated with that going in. And you say: ‘Hey, wait a moment, where’s the AI? I’m not getting the benefit.’ And you kind of bail on it.” [1]
The impulse to cut project costs is often strong, especially in the final delivery phase of data integration and data migration projects. At this late phase of the project, a common mistake is to delegate testing responsibilities to resources with limited business and data testing skills.
Data integrations are at the core of data warehousing, data migration, data synchronization, and data consolidation projects.
With data today in constant motion, automated data management strategies are critical to meet operational objectives and build a competitive advantage. In this two-part series, I explain how active metadata and data governance are transforming how organizations manage and leverage data.
Success in any organization today depends on understanding, harnessing, and deploying data resources to support enterprise departments. Effectively leveraging metadata is essential to these efforts. It represents a powerful tool to help organizations classify, manage, and organize massive amounts of data and select the right data for advanced analytics to drive actionable insights.