Data stewardship has become a critical component of modern data governance, especially with the growing use of artificial intelligence (AI). Despite its increasing importance, the concept of data stewardship remains ambiguous and varies in its application. This paper explores four distinct manifestations of data stewardship to clarify its emerging position in the data governance landscape. These manifestations include a) data stewardship as a set of competencies and skills, b) a function or role within organizations, c) an intermediary organization facilitating collaborations, and d) a set of guiding principles. The paper subsequently outlines the core competencies required for effective data stewardship, explains the distinction between data stewards and Chief Data Officers (CDOs), and details the intermediary role of stewards in bridging gaps between data holders and external stakeholders. It also explores key principles aligned with the FAIR framework (Findable, Accessible, Interoperable, Reusable) and introduces the emerging principle of AI readiness to ensure data meets the ethical and technical requirements of AI systems. The paper emphasizes the importance of data stewardship in enhancing data collaboration, fostering public value, and managing data reuse responsibly, particularly in the era of AI. It concludes by identifying challenges and opportunities for advancing data stewardship, including the need for standardized definitions, capacity building efforts, and the creation of a professional association for data stewardship.

The AIPPF, established in 2020 by the Bank of England and the Financial Conduct Authority (FCA), was set up to facilitate dialogue between the private sector, public sector, and academia regarding AI. Data comes first: The report describes data as foundational for AI, attributing most of AI's recent growth to a surge in the availability of data to contribute to models. Data is more connected to AI's pros and cons than other aspects, and "many of the benefits and risks can be traced back to the data, rather than the AI systems or algorithms themselves." Data is also among "the defining features of AI," which can process massive quantities of data and find patterns from it. The report honed in on key areas for banks' data operations for AI, such as: Data quality: This covers accuracy, timeliness, transparency, and completeness. Banks' quality challenges include needing to update their controls and processes for AI, and handling complex data sources.

SINGAPORE, February 3rd, 2022 – Businesses and public sector organizations will need to accelerate the modernization of their IT infrastructure and operations to be able to build a sustainable competitive advantage in the next 2 to 3 years. The ability to align to the digital paradigm is not only contingent upon investing in next-generation cloud-native IT infrastructure technologies, platforms, and solutions, but also how CIOs will help transform to autonomous IT operations using AI / ML technologies, delivering business resilience, agility, flexibility, and adaptability. The rapid proliferation of data-driven edge workloads, growing number of ransomware and malware attacks, and blistering growth in the volume of structured and unstructured data are creating significant challenges, as a result of which by 2023, most C-Suite will implement business-critical KPIs tied to data availability, recovery, and stewardship. IDC believes this will help to sustain data-driven innovation. "The CIO and IT decision-makers will need to do some serious thinking beyond modernizing the technology building blocks and platforms if they truly intend to align to digital business outcomes, SLAs, and KPIs. Cultural and mindset change is going to be one of the keystones of digital infrastructure paradigm, which goes far and beyond just embracing cloud as the defacto delivery platform or using OPEX based as-a-service IT consumption models. Digital Infrastructure represents the dawn of a new era for IT decision-makers to make an inedible mark in helping their organization lead into the future," says Rajnish Arora, Vice President Enterprise Infrastructure Research at IDC Asia/Pacific.

In our last post, we explored the importance of building a resilient data architecture to prepare your data for enterprise AI. The next step in achieving scale involves creating well-defined governance, including data management, data stewardship and change management practices. If you have poor-quality data, applying AI will only help you make bad decisions faster. Implementing foundational capabilities will help you put governance processes in place to identify bad data, capture it and make any changes as needed. Structuring data through validated business rules and putting appropriate access and controls around it will help this process go smoothly.

The ability to manipulate and understand data is increasingly critical to discovery and innovation. As a result, we see the emergence of a new field--data science--that focuses on the processes and systems that enable us to extract knowledge or insight from data in various forms and translate it into action. In practice, data science has evolved as an interdisciplinary field that integrates approaches from such data-analysis fields as statistics, data mining, and predictive analytics and incorporates advances in scalable computing and data management. But as a discipline, data science is only in its infancy. The challenge of developing data science in a way that achieves its full potential raises important questions for the research and education community: How can we evolve the field of data science so it supports the increasing role of data in all spheres? How do we train a workforce of professionals who can use data to its best advantage? What should we teach them? What can government agencies do to help maximize the potential of data science to drive discovery and address current and future needs for a workforce with data science expertise?

When it comes to business, machine learning is a powerful and disruptive technology, thanks to its ability to learn from and teach itself with vast amounts of "internet of things" data sourced from employee and manufacturing devices like laptops, equipment sensors and shipment trackers. But this type of cognitive computing couldn't happen without emerging tools from open-source efforts, like Hadoop's data management platform. "What we're finding is Hadoop and the big data space are uniquely positioned to solve these problems, both from quality control and process and management. You can get better uptime, better quality," said Gus Horn (pictured), global consulting engineer of big data analytics and chief technology officer ambassador at NetApp Inc. Horn spoke with John Furrier (@furrier) and Jim Kobielus (@jameskobielus), co-hosts of theCUBE, SiliconANGLE's mobile livestreaming studio, during the recent BigData NYC event in New York. Horn talked about data stewardship, machine learning and the role of cloud computing in these emerging tech trends.