– Article in partnership with Talend –
Data are assuming increasingly prominent roles in business flows and activities: supporting business objectives, facilitating decision-making processes; and improving production strategies. Let’s now look at how data literacy and data fabric add the missing pieces to enhance our digital future.
The value of data: how it improves decision making
The “Dunning-Kruger effect” describes a tendency to overestimate our abilities, even when we have limited skills or information about a given topic. Within a corporate setting, this phenomenon is particularly dangerous. I say this because it implies that the less familiar someone is with data, the more convinced they will be that they are doing well and that they do not need to know how to process information to make strategic, far-reaching, or long-term decisions.
This concern took hold while I was attending the Gartner Data Analytics Summit. As Talend’s experts were delivering a keynote entitled “Building trusted insights to achieve business outcomes”, they highlighted how to strategically use data to improve products, services, and support value-based decision making. “Data health”, they explained, is measured by how well a company uses data to support its business objectives.
I have already spoken at length about data health in a past article, which you can read here. Today, I want to draw your attention to its cousin concepts, data literacy and data fabric.
Those who are proficient in the former have the knowledge and skills to interpret and strategically use data, while the latter refers to an emerging design strategy that is characterized by a unified architecture and harmonious data management.
Data literacy
With the major upheavals of the pandemic, rising inflation, and supply chain shortages, businesses are becoming increasingly motivated to digitize their processes, streamline operations, sustain themselves, and learn how to navigate an uncertain future. More than the simple act of generating reports or anticipating and capitalizing on emerging trends, data is at the heart of many classes of algorithms.
For example, machine learning — particularly cognitive automation — enables businesses to drastically expand operations and improve their efficiencies. This subset of artificial intelligence-based technologies mimics human behaviors to improve compliance and overall business quality, scale operations, reduce turnaround time, and lower error rates.
This is precisely where data literacy comes in. The term refers to a broad and valuable range of skills that enable teams to identify, organize, understand, use, and share data. Of course, the goal of these activities is to harness data so that it benefits a business, organization, or a company.
Bearing that in mind, organizations that lack data literacy will struggle to maintain a competitive advantage in our quickly evolving global business landscape. Simply put, data literacy is critical for long-term success and sustainability.
Data health expresses a company's ability to use data to support its business goals and is complemented by data literacy and the fabric of data: the weave that ensures an empowered digital future. Click To Tweet
Data fabric
In a literal sense, data fabric is a way to design a data management process so that it is flexible, can be reused, integrated, organized, and, ultimately, achieve better semantics through the use of active metadata. The objective is for it to perform semantic inferences, as opposed to logical inferences — i.e., to add new data to a dataset that was created from existing data. I’ve created a related infographic for a visual depiction of this process.
According to the philosopher, Robert Brandom, the concepts that arise from a piece or a sequence of information are derived. Grasping content does not require both knowing how a word or concept behaves during an inference and how to apply it. For Brandom, the application and inference are one and the same.
Allow me to elaborate. The traditional view maintains that having the concept of a giraffe requires: (1) being able to (and being inclined to) infer that it is an animal, and (2) applying that knowledge in context — e.g., during an African safari. In a clear departure from this view, Brandom emphasizes the human and social character of content. Paraphrasing him, there’s nothing more to having a concept than knowing how to use it.
This philosophy is being increasingly applied to cloud-based models. After all, hybrid cloud, multi-cloud, and edge computing all enable us to scale operations with agility. At the same time, they can create data redundancies which, if left unmanaged, can result in inefficiencies — especially for new AI-based algorithms and cognitive automation.
Conclusion
To face the challenges of our digital future, where data is no longer just computer evidence, we need to increase awareness about how to interpret and use data, and create designs that draw on the best practices of data fabric. Only then will it be possible for companies to not only compete, but thrive.
I would like to sincerely thank Talend for giving me the opportunity to research these fascinating topics and write this article. Their enduring commitment to promoting data health is crucial to each company’s long-term success. To learn more about data fabric, you can have a look here.
- Original article previously published here
