Building high-performance data and AI organization

In this context, effective data management is one of the foundations of a data-driven organization. But managing data in a company is very complex. As new data technologies are introduced, the burden on old systems and data silos grows, unless they can be integrated or ringed.

Fragmentation of architecture is a headache for many data controllers (CDOs) due to the large number of tools based not only on silos, but also on cloud-based tools used by many organizations. Along with poor data quality, these problems are combined to eliminate the speed and scale required to achieve the desired business results that organizations ’data platforms — and the machine learning and analytics models that support them — achieve.

To understand how data management and based technologies are evolving among these challenges, the MIT Technology Review Insights examined 351 CDOs, key analysts, chief information officers (CIOs), chief technology officers (CTOs), and other technology leaders. We also conducted in-depth interviews with several other senior technology leaders. Here are the key findings:

• Only 13% of organizations are excellent at providing data strategy. This select group of “high achievers” delivers measurable business results across the company. They achieve success by paying attention to the basics of data management and a robust architecture that serve to “democratize” data and add value to machine learning.

• Technology-enabled partnerships are creating a work data culture. The CDOs interviewed for the research place great importance on democratizing analytics and ML skills. Pushing these to the brink with advanced data technology will help end users to capture more informed information about the business, which is characterized by a strong data culture.

• The impact of ML’s business is limited by difficulties in managing the extreme life cycle. Scaling ML use cases is very complex in many organizations. The most significant challenge, according to 55% of respondents, is not having a central place to store and find ML models.

• Companies are looking for native cloud platforms that support data management, analysis, and machine learning. The key data priorities for organizations over the next two years are divided into three areas, all supported by the wider adoption of cloud platforms: improving data management, improving data analytics and ML, and expanding the use of all types of enterprise data, including streaming and unstructured data. .

• Open standards are the key requirements for future data architecture strategies. If respondents could build a new data architecture for their business, the most serious advantage over existing architecture would be to embrace open source standards and open data formats.

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This content was created by Insights, a custom content from the MIT Technology Review. It was not written in the MIT Technology Review.