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Building a high-performance data and AI organization
Organizations’ top data priorities over the next two years fall into three areas, all supported by wider adoption of cloud platforms:
improve data management, enhance data analytics and ML, and expand the use of all types of enterprise data.
CxOs and boards recognize that their organizations’ ability to generate actionable insights from data, often in real time, is of the highest strategic importance.
To become data-driven, companies are deploying increasingly advanced cloud-based technologies. What these tools deliver, however, will be of limited value without abundant, high-quality and easily accessible data. See what CIOs, CDOs and other data and analytics leaders have to say in the recent MIT Tech Review Insights report.
The key findings include the following:
Just 13% of organizations excel at delivering on their data strategy
They are succeeding thanks to their focus on the foundations of data management and architecture, which enable them to “democratize” data and derive value from ML.
Technology-enabled collaboration is creating a working data culture
Pushing analytics and ML capabilities to the edge with advanced data technologies will help end users make more informed business decisions — the hallmark of a strong data culture.
ML’s business impact is limited by difficulties managing its end-to-end lifecycle
The most significant challenge, according to 55% of respondents, is the lack of a central place to store and discover ML models.
Enterprises seek cloud-native platforms that support data management, analytics and machine learning
Organizations’ top data priorities over the next two years fall into three areas, all supported by wider adoption of cloud platforms: improve data management, enhance data analytics and ML, and expand the use of all types of enterprise data.
Open standards are the top requirement of future data architecture strategies
If respondents could build a new data architecture for their business, the most critical advantage over the existing architecture would be a greater embrace of open source standards and open data formats.
Effective data management is one of the foundations of a data-driven organization. But managing data in an enterprise is highly complex. As new data technologies come onstream, the burden of legacy systems and data silos grows, unless they can be integrated or ring-fenced. Fragmentation of architecture is a headache for many CIOs and CDOs, due not just to silos but also to the variety of on-premises and cloud-based tools organizations use. Along with poor data quality, these issues combine to deprive organizations’ data platforms — and the machine learning and analytics models they support — of the speed and scale needed to deliver business results.
“The issue of multiple deployments and error-prone handoffs between data science and production is a huge issue. There’s often a gap between the data science output and the results we get after operationalizing it.”
VICE PRESIDENT — DATA, CRM, ANALYTICS
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