The Fabric of Success: A Roadmap to AI

In today's rapidly evolving technological landscape, artificial intelligence stands out as a beacon of innovation, offering immense potential to transform industries and drive success. However, beneath the surface of AI's promise lies a crucial foundation that determines its effectiveness: data quality and management. As highlighted in a recent webinar on Inside Analysis, the concept of a "data fabric" is emerging as a vital framework for organizations aiming to harness the power of AI effectively.?

Eric Kavanagh , Host of Inside Analysis, welcomed Alla Zaykin of Athena Solutions and Rob Schoenfeld of Qlik onto the show.?

Data Quality Counts?

"On average, organizations are losing 15 million dollars due to poor data quality," noted Schoenfeld. This staggering statistic not only shows how critical high-quality data is to businesses, but also makes it clear that businesses are not suffering in a vacuum — the effects of bad data quality are felt across our collective ecosystem.?

While data science may be trending online, a statistic like this shows that it is not entirely understood as a concept. And “if data is not understood, it can become a liability, not an asset," emphasized Kavanagh. Data, in its raw form, can be overwhelming and potentially detrimental if not properly managed and interpreted. Significant changes are necessary to ensure that the innovations businesses invest in are leading to net positive outcomes. The erroneous insights and flawed decision-making that result from bad data ultimately yield financial losses. Fortunately, the solutions discussed on Inside Analysis are already overturning negative outcomes in sustainable ways.?

The Role of Data Fabric

A concept introduced on this episode of Inside Analysis is that of a data fabric. A data fabric is an integrated layer of data that connects underlying processes through continuous analytics. Deploying a data fabric allows for the utilization of integrated and reusable data across all environments, including hybrid and multi-cloud platforms. Kavanagh defined a data fabric as “the construct through which you can organize your data my and get your data warehouse in order.” Users can leverage this approach to achieve multiple objectives at the same time, not only improving data quality but also enhancing analytic capabilities.?

As Schoenfeld pointed out, Qlik’s data fabric stack gives organizations the ability to both consume APIs and create their own APIs for greater monetization opportunities. This flexibility allows businesses to leverage their data assets more effectively, creating new revenue streams and enhancing operational efficiency.

The Evolution of Data Governance

In pondering the changes needed for our future as data practitioners, it can be encouraging to see how far we have evolved from the past. "Ten years ago, data governance was wishful thinking because you had to just hope that people would actually read the rules," noted Kavanagh. "A data fabric weaves together processes and policies to govern data at scale."?

The evolution of data governance from a theoretical concept to a practical, scalable framework is a testament to the advancements in data management technologies. A robust data fabric can build on existing governance mechanisms to ensure data integrity, security, and compliance, enabling organizations to manage their data assets responsibly and efficiently.

Enhancing Collaboration and Efficiency

According to Zaykin, collaboration is essential when inserting data fabrics into business systems. “What happens with the lack of collaboration is that resources are being wasted,” she said. “Time is the most important resource.”?

Implementing a data fabric correctly fosters collaboration by providing a unified platform for data access and sharing. This increased collaboration between teams previously operating in silos leads to faster decision-making and more efficient use of resources. As Schoenfeld highlighted, “engaging with data makes individuals more collaborative and accelerates their actions, driving organizational agility and innovation.”?

In some cases, getting employees to embrace data and AI within their daily work is challenging. "Most people want to be involved in a process and they don’t because they are afraid of appearing ignorant," Kavanagh remarked, bringing to light a significant barrier to effective data utilization: the fear of looking stupid in front of one’s peers. A data fabric can mitigate this fear by providing user-friendly tools and interfaces that empower individuals to engage with data confidently. By democratizing data access and simplifying its interpretation, organizations can foster a culture of data-driven decision-making.

Navigating Project Realities

Even with collaboration, businesses will only be successful provided they can be introspective about their progress. "Where you are in a project isn’t always where you imagine you are," cautioned Zaykin. Failing to revise initial assumptions and plans saves time and money down the road. And though changing course can seem daunting, a data fabric provides the flexibility to adapt and pivot as needed, ensuring that projects stay on track and deliver value.

The strategy of working in smaller increments can also make adjusting to feedback more manageable. As Schoenfeld advised, "You can’t approach a large project all at once. You approach it at a bite-size level and get a quick win from starting at the source.”?

The Maturation of Data Strategies

"All in all, data fabric is a maturation of everything we’ve learned from ETL/ELT and other strategies we’ve applied over the years," concluded Kavanagh. The maturation process he described has led to the development of a robust, flexible, and scalable framework that supports the diverse data needs of modern organizations.

Most strategies are fine-tuned by the use and examination of metadata. “Metadata is a huge part of data fabric and how to align and manage systems," stated Kavanagh. Schoenfeld added that “socializing” with metadata to identify potential compliance issues is essential.

?The ability to understand our data through the information we collect about it is at the heart of data fabric. Communicating with others to build a comprehensive data fabric is essential to business success. Yet, humans aren’t the only active players in this environment.? As data continuously updates, it grows a dynamic voice capable of interacting with users in a manner that seems sentient. This two-way conversation between users and their data can lead to increased innovation and also a lessening of the chance that valuable pieces of a project’s puzzle will be lost in translation.