A Closer Look at the Data, Information, Knowledge, and Wisdom Hierarchy (DIKW)
The Data, Information, Knowledge, and Wisdom hierarchy (DIKW) has been a staple of business intelligence for a good amount of time now. It’s generally understood or at least falls into the category of relatively familiar terms for most business owners, CDOs, CFOs, and the like. But what is the hierarchy? What purpose does it serve? And how can the healthcare industry benefit from utilizing it? In this article, we’ll delve deeper into the DIKW hierarchy (usually represented by a pyramid) and study some of the perspectives from which the pyramid can be viewed. We’ll wrap up by discussing how an understanding of DIKW as a process can influence and assist healthcare facilitators and practitioners in the modern age of big data.
The DIKW Hierarchy at a Glance
Overall, the DIKW hierarchy is a system of translating raw data into a usable form. It’s taking raw data and an accumulation of disassociated facts and arriving at a comprehensive understanding of not just the what but the why and how behind it.
What Is the Hierarchy?
The hierarchy is a systematic approach to the interpretation of data. In other words, it’s a way to take the results of a survey and translate that raw data into actionable wisdom that can be used to create better solutions and more efficient processes, among other things. The pyramid is used in different ways depending on who’s using it and what they’re trying to attain. For instance, a technology firm seeking to understand how they can better serve their IT department is going to utilize the DIKW process differently than someone developing healthcare software that helps hospital staff more accurately track the prescription needs of their patients. However, the beauty of DIKW is that no matter what your goal is or how you use it, it offers a rather systematic approach for achieving that end goal.
What Does the DIKW Hierarchy Aim to Achieve?
The end goal of the DIKW hierarchy will be different for each organization. However, the general goal of the pyramid structure is to help those who need to take raw data and arrive at actionable wisdom. In this sense, it aims to achieve the most comprehensive understanding of a given set of data possible. It also provides a philosophical framework within which healthcare practitioners and professionals in other fields can create tangible solutions.
Why Should Healthcare Practitioners and Facilitators Care?
The DIKW pyramid is often used in business intelligence and information technology sectors. However, given that big data informs so much of the healthcare industry now, this same pyramid can help researchers and technology developers take raw data from a variety of sources and arrive at comprehensive solutions to challenging problems. Given that the pyramid offers a structure and system that has been generally proven effective, efficient and comprehensive understanding can be attained without having to reinvent the wheel, as it were. The implementation of hierarchical ideas has already provided benefits to specific healthcare areas like nursing informatics and can be used across the system in order to effectuate improvements and change.
Defining the Terms of the Hierarchy
For some, terms like information and knowledge might appear to be interchangeable. But when we’re talking about the DIKW hierarchy, they’re two very distinct ideas. Understanding the way DIKW defines its terms can help those studying it arrive at more accurate conclusions and utilize their research in more efficient ways.
Data is exactly what you might think it is: the raw data. Charts, graphs, raw survey data, cost data — it’s all wrapped up in the “data” portion of the hierarchy. If data hasn’t been touched, analyzed, or processed, it’s still in its “raw” form and thus belongs in this section of the hierarchy.
Information is the next step in the hierarchy. Here, raw data is slightly more defined. Many DIKW charts refer to this section as the “who, what, and when” section of the pyramid. Information exists in standalone facts that aren’t necessarily related to the whole. For instance, a chart illustrating the increasing prevalence of a disease would be raw data. The statement “This disease (what) affects children (who) more than adults.” You don’t know how or why and there aren’t any clear conclusions as to how you should proceed in order to mitigate the problem, but you know the problem itself exists.
Knowledge is often considered the “how” section of the DIKW hierarchy. This is where connections start to emerge between pieces of information derived from multiple data sources. In other words, if you start to make the connection that the children in our previous example contract this disease during their first year at school, you might be able to conclude that the disease spreads communally, or is contagious. That would be the “how”.
Finally, wisdom is being able to take something you know and understand why it’s true. Continuing with our example, if you’re able to conclude that this disease is most common among school-aged children entering their first year in public school, and you realize that it’s spreading from child to child, you might be able to look further into the issue and realize why this disease is spreading. Once you know why, you can create data-based strategies to help combat the problem.
DIKW from the Perspective of Context
The previous example is obviously a simplified explanation of how the DIKW hierarchy is utilized, but it provides a starting point for the broader conversation. In practice, the DIKW hierarchy can be quite sophisticated, especially when it’s understood from a variety of perspectives. One of the perspectives from which the hierarchy can be viewed is that of context.
Gathering the Parts
When seen from the contextual perspective, the “data” segment of the hierarchy has to do with gathering the parts. In other words, this is where you accumulate your data sources and organize them in a structural system that will help you analyze and translate them. It’s accumulation and organization and not much more than that. No analysis or conjectures take place here, you’re just gathering the sources you’ll be using to continue up the hierarchy. If you were working on a puzzle, this would be the stage where you pour the pieces out, group them, and flip them all over to make sure they’re image side up.
Connecting the Parts
Moving on with the contextual perspective, “Information” would be the stage in which you start connecting the parts you’ve accumulated. This is when you start studying the data you have and making connections, discovering patterns, and finding trends. You haven’t quite formulated a new “whole” out of the pieces, but you’re starting to see how they go together. If you were putting together a puzzle, this is where you’d start to realize that certain groups pieces have the same coloration. You’re making generalized links between pieces, but no two pieces of the puzzle have been connected.
Forming the Wholes
Moving on, the contextual perspective would next lead you to the “Knowledge” section, which in a contextual sense would be forming wholes from the pieces you saw similarities in or links between. If you were working on a puzzle that had trees, buildings, and an ocean, this is when you’d start putting the pieces of each of those areas together. The whole picture hasn’t come together yet, but meaningful wholes are emerging from the related fragments.
Linking the Wholes
Finally, the “Wisdom” section, from a contextual perspective, would be the stage during which you can finally connect all the wholes into a single, comprehensive picture. Now that you know what the shape of things is, you can create the necessary strategies and solutions to address the challenges you’ve learned about. Actionable, meaningful answers to known, well-understood problems or challenges can now be created.
Why the DIKW Hierarchy Matters in Healthcare
There are other perspectives from which to view the DIKW hierarchy, such as the temporal and understanding perspectives. However, no matter how you look at the hierarchy, the idea is the same. You start with pieces, you find connections, you create portions of the overall picture, and then you can link everything together and arrive at actionable wisdom. This wisdom can be utilized to create a number of solutions from pharmaceutical developments to administrative strategies, all of which work together to facilitate a higher quality of patient care and satisfaction. From enterprise case studies to specific applications, the healthcare industry has already found a wealth of uses for the hierarchy and its framework. The DIKW hierarchy matters in healthcare for multiple reasons, but two of those reasons are as follows.
Data Alone is Not a Solution
Having data is pointless without a system of analyzing, interpreting, and utilizing it. All the charts and graphs in the world can’t tell you what the cumulative message of the data is, and without a systematic approach to translating that data, validation becomes nearly impossible. For the same reason that the scientific method exists, the DIKW hierarchy, while not as exact, does provide a similar structure for the analyzing and understanding of data that is universal. Working within existing frameworks helps to create redundancies that work to ensure the integrity of the methodologies used and the validity of the resulting conclusions.
Paved Roads to Understanding Can Save Time
Apart from helping ensure integrity, existing systems of analysis and understanding are more efficient. Instead of having to reinvent the wheel every time you want to analyze data, you can turn to an existing and well-respected process. This limits the amount of time you’re required to spend on methodology and helps facilitate a more efficient path to analysis.
Big data is a big topic, and no single article — indeed, no single book — could ever fully discuss the complexity and nuance. Even within the DIKW hierarchy, there are layers of complexity that cannot be fully explored in a single sitting. That being said, a cursory look at some of the perspectives from which the hierarchy is already being utilized and the development of a base understanding of the terms used within the hierarchy can set the stage for growth and efficiency. Healthcare organizations, facilitators, researchers, and administrators can all benefit from the products and processes that result from the hierarchical process. What’s more, a structure for analyzing big data will become increasingly necessary as the volume, velocity, and variability of healthcare data grows, increases, and expands. As such, adopting the hierarchy and its multiple perspectives now can help improve the quality of healthcare and set the stage for a new era of data-driven care.