All Posts

[Video] How does information governance help make data driven decisions?




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.


Ready to discover where your data can take you? Contact us for a consultation.


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.

Recent Posts

Conversation with ChatGPT on Enterprise Knowledge Management

In another article, I discussed my research into ChatGPT and the interesting results that it produced depending on the order in which I entered queries. In some cases, the technology seemed to learn from a prior query, in others it did not. In many cases, the results were not factually correct.

The Future of Bots and Digital Transformation – Is ChatGPT a Game Changer?

Digital assistants are taking a larger role in digital transformations. They can improve customer service, providing more convenient and efficient ways for customers to interact with the organization. They can also free up human customer service agents by providing quick and accurate responses to customer inquiries and automating routine tasks, which reduces call center volume. They are available 24/7 and can personalize recommendations and content by taking into consideration role, preferences, interests and behaviors. All of these contribute to improved productivity and efficiency. Right now, bots are only valuable in very narrow use cases and are unable to handle complex tasks. However, the field is rapidly changing and advances in algorithms are having a very significant impact.

[February 15] Demystifying Knowledge Graphs – Applications in Discovery, Compliance and Governance

A knowledge graph is a type of data representation that utilizes a network of interconnected nodes to represent real-world entities and the relationships between them. This makes it an ideal tool for data discovery, compliance, and governance tasks, as it allows users to easily navigate and understand complex data sets. In this webinar, we will demystify knowledge graphs and explore their various applications in data discovery, compliance, and governance. We will begin by discussing the basics of knowledge graphs and how they differ from other data representation methods. Next, we will delve into specific use cases for knowledge graphs in data discovery, such as for exploring and understanding large and complex datasets or for identifying hidden patterns and relationships in data. We will also discuss how knowledge graphs can be used in compliance and governance tasks, such as for tracking changes to data over time or for auditing data to ensure compliance with regulations. Throughout the webinar, we will provide practical examples and case studies to illustrate the benefits of using knowledge graphs in these contexts. Finally, we will cover best practices for implementing and maintaining a knowledge graph, including tips for choosing the right technology and data sources, and strategies for ensuring the accuracy and reliability of the data within the graph. Overall, this webinar will provide an executive level overview of knowledge graphs and their applications in data discovery, compliance, and governance, and will equip attendees with the tools and knowledge they need to successfully implement and utilize knowledge graphs in their own organizations. *Thanks to ChatGPT for help writing this abstract.