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    TRANSCRIPT

    I wanted to take a few minutes and talk about decision making based on data. It's really about how we can measure the results of decisions and be able to be intentional about decision making process. And this is also very valuable for making sure that we're getting the right ROI on our investments in technologies, that we're not breaking things when we make changes, and that we are not basing our updates and changes on opinion. But basing it on data.

    There's a couple of different things to think about. First of all, we want to come up with a baseline. You need to understand the processes that we're trying to impact. We need to measure them, we need to understand the current state, we then need to start thinking about changes what kind of design change, what kind of technology change, what kind of data change we're going to make, and essentially measure the impact of those changes. Once we measure the impact of those changes, we can determine whether they are going to bring us in the right direction, or whether we need to make a course correction. And then of course, we shampoo rinse and repeat. We use an approach that tries to connect to the top line value, whether it's increased revenue, reduced costs, whatever it might be, the motherhood and apple pie of every project that you ever talked about, right? We don't talk about projects that reduce our revenue and increase our costs. You don't hear about those.

    The executives want to say, Well, how is this going to increase our revenue. Many times data projects are very difficult to directly correlate with increases in revenue. Many times we're working on search applications or content or knowledge or product information, and things that are difficult to directly measure how they're going to move the needle. However, we can still look at many different measurements, we can look at data scorecards, we can look at data completeness, we can look at data quality, aligned with a specific process, attribute fill those types of things. We can measure our quality, we can measure the impact of any data or content or Knowledge Initiative, or for that matter, any technology mission. But the data or the content is in support of a business process. And those business processes are really the things that we're trying to measurably impact. Is it click through rates? Is it conversions? Is it for call center reduced time per incident? Is it reduced call abandonment. Is it first call resolution, whatever it might be. And of course, those processes are in support of a business objective or business outcome. And we can have outcome scorecards, such as improved likelihood to recommend or, or customer satisfaction scores or renewals. That business outcome is what's important.

    And that, of course, aligns with the organizational strategy. Maybe it's improved recurring revenue, increase the commerce volume, whatever it might be. And we need to maintain that linkage from the from those enterprise initiatives and strategy down through the business outcome scorecards down to the process measures and to ultimately the data.

    And establishing that linkage is what's so important. Looking at employee behaviors, or employee feedback, process improvements process, you know, cycle times, and, you know, whatever that might be system up, or whatever that might be product performance returns a quality, whatever those important KPIs are across those various domains. And what's important here is the fact that we start measuring the baselines. And then as we start to get more mature, we can begin to look at more detailed metrics, we can start to think about different reporting structures, whether we're looking at this across product groups, or business units, or product categories, or lines of business or divisions, and start looking at the accountabilities across the organization so that we can maintain the quality, we can benchmark, we can know whether we're the best of the worst, the worst of the best, whether we whether we can compare progress across departments, many times having these internal benchmarks are critical, because you start to build the competition amongst business units or lines of business or managers and they start to say, wait a minute, you know, what's going on? Why are we behind? Why are we ahead are great, we're ahead. What do we need to do catch up either to our industry peers, or to other parts of the organization. And so these things start to give you actual data that you can then extrapolate and start showing the impact of changes to your data quality and changes to your your data structures.

    So that's just a Quick overview of how we look at metrics and metrics driven governance. This can be used across every part of the organization lots of different ways. And ultimately, we're trying to build scorecards that tell us how we're doing in these different process areas looking at the relationship between data, knowledge, content, and customer journey and customer behaviors. Ultimately, that's the end game when you improve product when improved data will improve any part of the user experience. What is the impact on revenue, what is the impact on the customer experience and ultimately, the top line and the bottom line.

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    Earley Information Science Team
    Earley Information Science Team
    We're passionate about enterprise data and love discussing industry knowledge, best practices, and insights. We look forward to hearing from you! Comment below to join the conversation.

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