Best Practices to Ensure Data Quality, Accessibility, and Security as a Foundation to an AI-centric Data Architecture
Organizations continue to rush down the digital transformation path. Whether by modernizing their IT infrastructures, leveraging the cloud, or becoming data-centric and data-driven, organizations must become more agile in their business practices and within their IT infrastructure stack to effectively compete in today’s dynamic business environment. Between the speed and distributed nature of modern businesses, as well as the expectation of instantaneous access to data from everyday users, it’s not surprising that nearly one in three organizations are looking into ways to improve data analytics for real-time business intelligence and customer insight.
So how do organizations get there? Governance! It starts with creating a foundational inventory of data by discovering, cataloging, and integrating data when needed to help ensure data quality and protection, and leads to enabling improved data accessibility in a trusted and secure way. This is understandably a daunting task for many organizations, especially once factoring in the challenges that come with a globally distributed organization and workforce that relies on that data to gain as close to real-time insights as possible regardless of where it is being accessed from. And while this requires investment in personnel, infrastructure, and time, organizations recognize they must make the investment to remain competitive and keep their edge. As such, among respondents with purchase influence or authority for business intelligence and analytics solutions used to support big data initiatives, 56% of organizations said they would make the most significant investments in business intelligence.
It starts with creating a foundational inventory of data by discovering, cataloging, and integrating data in an ongoing way that ensures data quality and protection, and leads to enabling improved data accessibility in a trusted and secure way.
The Path to AI
While becoming more data-driven is clearly a priority for most organizations, it’s not just about having an agile data platform for analytics and BI that addresses the real-time needs of the business. It’s about applying automation, intelligence, and self-service across the entire organization to eliminate manual processes and accelerate business processes to empower not just one or two business units, but all of them such that data-driven becomes the norm throughout the firm. This transformation to an intelligent, data-centric culture requires a solid technological foundation where trusted and business ready Data Architecture is made available to the masses and leads to applying machine learning (ML) throughout the technology stack to automate the manual tasks. Once organizations master an agile and trusted data pipeline to serve ML, they can then deploy artificial intelligence initiatives that will accelerate their paths to transformation.
It is more apparent than ever that to achieve a market leading position within any industry, artificial intelligence and machine learning must become part of future plans, whether leveraging the technology as a feature within a product or deploying a separate infrastructure to support strategic AI and ML initiatives. In fact, according to ESG research, when asked how they viewed AI and ML as part of their organization’s digital transformation strategy, the majority of respondents (65%) said that AI and ML will be one of the forces driving their digital transformation efforts, with 22% citing it as the driving force behind digital transformation or Data Lake.
But it is important to note that organizations are not simply snapping their fingers and leveraging AI and ML—it’s an evolutionary process. ESG research shows that 17% of organizations are depending on AI/ ML to deliver significant measurable business outcomes immediately, 52% expect these outcomes to be achieved in the near-term, and another 25% expect business value eventually. While this alludes to the fact that organizations are at different stages of the AI journey, it is becoming clear that most of them understand that partnering with the right vendors is essential to success.
This transformation to an intelligent, data-centric culture requires a solid technological foundation where trusted data is made available to the masses and leads to applying machine learning (ML) throughout the technology stack to automate the manual tasks.