Posted by Data Literacy for Data-Driven Results
By 2025, smart workflows will be the norm for business operations, and interactions between human employees and smart machines will become standard. 84% of businesses see data literacy as a core competency over the next five years, and at that point, every employee in an enterprise will need to know how to interpret data to make a necessary decision. Data literacy for data-driven results is becoming everyone’s priority task. Businesses must open themselves up to automation, smart technologies, artificial intelligence (AI) and machine learning (ML) data analytics if they want to remain successful and relevant.
Who needs to be data literate?
Short answer: everyone. With AI and ML automated operations becoming the norm of day-to-day business processes, everyone at an enterprise must learn what to do with the vast amounts of data coming in. C-level executives must consider creating a data-centric culture in their organizations in order for employees to understand why analyzing data is relevant to their jobs. Shifting an organization’s culture to be data-driven is easier said than done. Based on NewVantage Partners’ annual survey, cultural change towards being data-driven is the most critical business strategy in 2022. One innovative way to implement data-driven practices is by incorporating AI and ML automation alongside the roles of employees. This way, employees can review large amounts of data and come together to make decisions for the betterment of the organization. Data is a business asset that permeates an organization, it is critical for business leaders to get their employees onboard with this.
Working with AI/ML
As the adoption of AI in the workspace continues to grow, employees from various industries must learn how to use AI-produced data to their advantage. Thankfully, ML can quickly collect and analyze Big Data so that a human employee can focus their energy on innovating and decision-making. Now, employees need to know how to work with the AI and what to do with the results that the AI has generated.
At Iterate.ai, we believe in the five forces of innovation, Big Data being one of the five. That is why we have our low-code platform, Interplay®, to support organizations’ ventures with AI and data analysis. For example, Interplay has a ML script that takes basic health information (e.g. age, sex, blood pressure) to predict risk for suffering a heart attack. At a macro-level, public health officials and public policymakers can use this data to make informed decisions that support the heart health of communities that they serve. Interplay also has an application that can use data from predictive analytics to determine whether an employee will stay with an organization long-term or not. With this data, HR managers or business executives can make informed decisions that will benefit the longevity and relationship of their team.
Looking ahead
Data literacy for data-driven results isn’t going anywhere; data is everywhere and exponentially increasing in volume. Let’s take a look at two components of data for businesses to watch out for.
1. Volume of data
Enterprises are having to confront huge amounts of data now– Big Data. With sensor data coming from IoT devices, AI/ML data, and increasing communication data (texts, pictures, customer feedback), it comes as no surprise that we need to seriously consider data literacy. Something to note, 80% of all new data is considered unstructured, meaning it is not easily quantifiable. Again, this is precisely why businesses need to move towards a data-centric culture so that new, incoming data can be processed and used by employees to make informed decisions.
2. DaaP
The concept of data as a product (DaaP) is at the forefront of the data hype cycle right now. Data products most notably use raw data and algorithms to automate business decisions, and we are seeing businesses commit to DaaP as the framework for how they collect, use, and manage data. DaaP truly shifts the paradigm for how data can be leveraged to benefit an enterprise’s outcomes. One advantage to DaaP is being able to provide data to stakeholders automatically. When company data is viewed as a product itself, the result is good decision making.
Need to consult with a team of experts on how to make your business data-centered? Want to use a low-code platform for fast data analysis? Please connect with us here so we can support your organization’s ventures with Big Data, ML, AI, IoT, and everything in between.