Organizations often think they have to make a trade-off between broad data access and governance, particularly when it comes to regulations and policies around data privacy. But in reality, data governance can help users of that data—including customers and employees—more easily access the right data when they need it.
Demand for instant access to financial data from investors and traders around the world has shaken up the financial services industry, and Nasdaq, a pioneer in digitizing the trading process, continues to innovate for customers seeking mobile-first, real-time, mission-critical analytics. Its approach involves embracing the cloud, data and analytics, and an API-first mindset.
Cyberattacks continue to dominate the headlines. Attempts at digital fraud shot up during the first four months of 2021, especially in the financial services industry, where they ballooned 109% in the U.S. and 149% globally compared to 2020’s final four months. But thanks to behavioral analytics, machine learning, and the performance and scale of the cloud, the good guys are fighting back.
Legacy data infrastructures can no longer support the massive amounts of data that companies now create and collect. A modern data strategy, created from data lakes, purpose-built data stores, machine learning, and other cloud-based services, removes the restrictions of the traditional one-size-fits-all approach.
Years of exponential data growth, evolving business needs, and rising maintenance costs have put a strain on existing data infrastructure. The traditional data warehouse, with its inability to handle data from new sources or handle new innovations such as machine learning or predictive analytics, requires a makeover.
As more data migrates to the cloud, driven by the cloud’s near-infinite scale and horsepower, it’s imperative that enterprise data governance models evolve in step. IT and business leaders need up-to-date policies to protect data as it moves back and forth among different repositories and to accommodate changing privacy and data security regulations about where data can be stored.
While business intelligence (BI) was once reserved for the likes of highly skilled data scientists and IT professionals, advances in cloud and machine learning technologies are putting powerful BI capabilities into the hands of employees across the organization. Specifically, cloud analytics are democratizing data insights to improve decision-making and productivity enterprise-wide.
If your aging on-premises data systems are unable to meet current demands for business agility and are time-consuming and expensive to manage, why continue to invest in them? Organizations looking to become more data-driven should consider accelerating their timetables for migrating away from monolithic, siloed systems that inhibit innovation and agility to a modern data infrastructure.
For almost three decades, people typically built applications against a single database. In the 1980s, client-server was introduced and apps started to become more distributed in nature, but the underlying data model was predominately structured, and the database was often a monolith. In the ’90s, the internet and three-tier application architecture emerged, but again, the database was still monolithic.