How many times have you been in a room where a decision is made because someone likes something done a certain way or it's always been done that way with no data to support it? I'm not saying instincts aren't helpful, but wouldn't it be nice if it was second nature to make decisions based on data? This requires data literacy. But what does a data literate culture look like for users?
And when I say users, there's really two groups that we're thinking about. So we've got business users. and data scientists. Business users need to understand the data that's relevant to their role within the context of their day-to-day work.
Data scientists need to understand the business context around data to help create solutions with the data that ultimately drive business value for the organization. Data literacy allows teams to make smarter, better, more informed decisions. And there's four foundations of data literacy.
Data access, organization, training, and empowerment. Now the first foundation is all about simplifying or democratizing data access. This simplified access requires the right data architecture so that as you organize different data sets, you can put the right sharing permissions in place so that employees...
have access to the data they need, but only the data they need to do the work relevant to their role. Now the second foundation is focused on integrating and organizing information in a clear and transparent manner. People need to be able to understand data's value, origin, quality, And these three things together empower people to really trust whatever data-driven solutions you come up with. So whether it's a dashboard or a virtual assistant or anything else, those tools are meaningless unless someone understands the value, origin, and quality of the data, and they ultimately trust what's in there.
This goes beyond data architecture to include data lineage, observability, governance tools, and algorithms and AI transparency. Now with the right technology to get the right data to users at the right time, we can go ahead and shift our focus to people taking advantage of the data at their fingertips. So the third foundation is data literacy training. And this is for both data scientists and data users.
And when I say data users, think of business users. Both groups need to be able to confidently find relevant insights within data sets. And they need to be within the context of their business problem.
And this is key because the fourth foundation, which is empowerment, is all about empowering people to actually act upon the insights they see. And this should drive business goals forward. So data literate employees will model the data-driven decision-making in their day-to-day work and ultimately inspire others to do the same. To bring it all together, What connects the data literate business users and data scientists to the technology and create that data-driven organization we're all looking for?
It's your overall data strategy and the process in place to support it. And a great example of that is a retail bank. There's a lot of client information that could be used to comply with KYC regulations that impact other workflows.
But for this conversation, let's focus on... fairly and equitably processing mortgage applications. Making this decision without the right data can result in a wrong call.
But with any loan application, there's lots of internal and external data that impacts the applicant's eligibility. So when the data is organized for a 360 degree view of that client, the big data scientists and business users are better positioned to make unbiased decisions. Plus, those decisions are more likely to be in the bank's best interest because the people making the decisions understand data's value, origin, and quality. This creates a trust in those organized data sets that allows users to see errors in data based on trends, pull in additional relevant sources related to the current market, and provide the necessary explainability to back up their decisions.
To best associate the risks with an application, a loans officer needs the training so they can easily access and use internal and external data responsibly. And, you know, as we think about this, I think credit lines are pretty key here and credit history. But they also need to be able to convey their decision to others through data storytelling.
Because as people within the mortgage teams listen to these stories and act upon the data and the data literacy training that they have, it actually inspires others to work similarly. And then you create that data-driven culture that you're looking for. That culture goes ahead and reinforces your data strategy across the board.
To learn more about how data literacy and data strategy are connected, head over to the Data Differentiator, IBM's guide for data leaders. Thanks so much for your time and don't forget to like and subscribe.