Looking back at 2019, we saw many trends go mainstream, like cloud and artificial intelligence (AI), and new trends emerge to deal with the data challenges many companies are facing today. Below are three of our top data management predictions for 2020.
As the adoption of AI and machine learning (ML) continued to grow in 2019, we saw it bleed into the data catalog space. Companies today are dealing with rapid growth in the volume and variety of data which leads to data sprawl, with their data being stored across functional silos and in cloud and on-prem environments.
Companies are turning to AI and ML to improve the accuracy and time it takes to inventory, profile, and classify decentralized data. According to Gartner, “By 2022, over 60% of traditional IT-led data catalog projects that do not use ML to assist in finding and inventorying data distributed across a hybrid/multi-cloud ecosystem will fail to be delivered on time, leading to derailed data management, analytics, and data science projects.” (Source: Gartner)
In contrast to traditional or “passive” data catalogs that are used purely for inventorying, augmented data catalogs leverage active metadata that tracks all activity and actions taken, resulting in a rich and detailed history that makes it easy for users to find relevant data and understand the lineage of that data. Augmented data catalogs reduce the time it takes to get large volumes of high-quality data into the hands of analysts and data scientists for improved decision making.
Data sprawl also causes challenges with regulatory compliance. With data privacy and security in the spotlight, current regulations are becoming stricter, and new regulations are being passed, making it imperative that companies have thorough, compliance-ready data governance in place.
Active metadata is at the core of a modern data governance approach, providing detailed data lineage that tracks what’s happened to the data and who has accessed it over time. An advanced data catalog, such as an augmented data catalog, automates many manual tasks that improve data quality, ensure data privacy with tokenization and masking capabilities, and can provide role-based access control to provide data security.
Robust data governance is the catalyst for self-service data access, making data democratization a reality. By being able to control data throughout its lifecycle from source to end-user, companies can open up data access with confidence.
Responsible investing continued to grow in popularity during 2019, with more investors wanting to invest their money in companies that are responsible when it comes to environmental, social, and governance (ESG) issues. As asset managers develop responsible investing options for their customers, they run into challenges integrating and managing the new 3rd party, ESG, and alternative data sources that are required to evaluate and rate their portfolio companies.
These new 3rd party data sets are expensive to source and challenging to integrate with traditional financial data sources. We saw this first hand with our customer, Nuveen, who was facing this exact problem. They wanted to quickly onboard and integrate their ESG data into a centralized repository, provide data quality checks, and provision the data into a consolidated responsible investing dashboard used by asset managers.
Zaloni’s Arena DataOps platform provided the end-to-end capabilities needed to build a cloud data lake on AWS that could onboard, transform, and provision analytics-ready data to the dashboard for immediate insights.
Nuveen is seen as an innovator in the responsible investing space and won the CIO 100 award for their related project. Additionally, Zaloni won the 20th Banking Technology Award for our work unifying Nuveen’s ESG data.
If any of these trends resonate with you, reach out to us! We’d love to discuss your use case and how Zaloni can help you make them a reality.
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