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Data has always been the key element for making decisions, even prior to the modern era; the only difference was how data was examined earlier, employing conventional rule-based procedures. Time has changed, and with the rapid onset of AI, business leaders are predicting disruption in the working practice of the workforce. 

AI is transforming the workforce. Great! That’s news but what’s more important for any organization that is presuming to draw tangible benefits from this tech is to know it better— Know the technology and data. As data is fueling this AI technology era, understanding data is getting data literate. 

Without getting into the details about how to learn more about the technology, let’s move on to data literacy straight.

What is data literacy :

Data literacy empowers anyone within a workforce, especially those with a non-technical background, to understand how to harness emerging technology, like AI, to get the intended outcomes. It’s the capacity to interpret, organize/create, and deliver value in terms of data. With literacy, one can assess how best to leverage data for any use case. Exploring the context within data can help in choosing an AI application. Experts with domain-specific knowledge can leverage data to the best of their interests and be the drivers of AI projects with data literacy.  

So typically, with data literacy, one can comprehend:

  1. What are the sources for different data types?
  2. Who can have access to what data?
  3. What are techniques and tools applied?

What is its significance to your business?

Data literacy will impact all aspects of the business, from the performance of employees to meeting business targets. Almost every business has a volume of data unless they will reap benefits. At least they know what actual fortune they are beholding. A few of the values one can decipher are:

  1. It helps you decipher the relevance of data and communicate better with different stakeholders.
  2. With data literacy, an organization sets a base for effective AI governance, helping AI systems to generate reliable predictions.
  3. Reliable predictions aid in fostering trust in AI systems, hence making data literacy a fundamental component of ethical AI
  4. Data ecosystems are different for different organizations and may overlap in a few instances. Being data literate paves the way to a better understanding of your organization’s ecosystem.

A roadmap from data literacy to embracing data-driven culture.

Measuring the level of data literacy maturity within a company is the first step toward a data-driven culture. Only then can you begin instilling values. The first step in fostering a culture of data awareness among employees is encouraging them to “think data” to achieve strategic objectives and deliver exceptional customer service. Being data-aware empowers any role with the responsibility of asking the right questions. If not met, this milestone becomes the biggest roadblock to adopting AI.

Establishing a data-driven culture includes practices that should not be confined to the walls of the CDOs’ and CIOs’ offices. There should be well-framed programs and training regarding data orientation and how it can benefit one’s vision. Top leaders should communicate the democratization of data where employees can be data heroes contributing unique perspectives to business decisions—henceforth empowering everyone across the table to experiment with data.      

Keep on refining!

It’s crucial to continuously evaluate your progress, gauge minor improvements, spot gaps, and adjust your data literacy program as necessary. Successful programs won’t be a “set it and forget it.”Effective programming constantly adapts to the organization’s changing needs and technology developments.

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