Sure, data knowledge sounds fascinating, but what the hell is it? What does data knowledge actually mean for your teams and business outcomes?
We know that the question you’re likely asking yourself, right now, somewhat skeptically, is “Is data knowledge just another buzzword for data insights?” Or “Is this another re-branding of data cataloging?” The BS-free answer to both questions is no, it’s not. Instead, we compel you to consider more important questions like, “Have we created a culture of data knowledge at our company?” And, “How does data knowledge relate to the goal of creating a data-driven culture for our organization?” Or “Do people actually trust and want to use the data provided to them?”
If you believe that simply providing data insights to your organization is not a panacea, you should read on. And if you believe that despite all of your efforts to make data available, the impact of data on business outcomes is still lacking, you’re in the right place.
To start, here is the most straightforward definition of data knowledge:
Data Knowledge is the combination of data insights with all of the contextual information required for a person on any team to take the next best action with the data at the speed of business. An individual in your organization has data knowledge when they can easily find, trust and correctly apply data to their job function.
When you think about what a data team does and what it controls, ultimately it’s the data and the insights it provides to the business. These outputs manifest themselves in many different ways, such as reporting in dashboards to tell business people what happened, or insights that could recommend a future action.
But whether people use the provided data, and incorporate it into decision-making, that's their choice. And they will make that choice based on really important context — such as, can they find the data in the first place, do they understand how to use it, and can they trust that data at that moment in time?
To further simplify, data knowledge is the practice of data plus impactful action. This means that an individual in your organization has data knowledge when they can easily find and apply data to their job. At its sum, data knowledge becomes the bedrock of an organizational data-driven culture that actually delivers on the promise of data access and self-service.Let’s dive deeper into creating a culture of data knowledge; you can keep reading, and/or watch this free, un-gated 15-minute explainer video.
It’s no secret that today’s workplace has created more users of your data than ever before. But the industry spends so much time speaking about the increased importance and availability of data that we lose sight of its impact on your organization’s most valuable resource – your people.
Modern workplace technologies, including the data stack, have brought data into every system and corner of the enterprise. With more data assets living across dashboards, reports, spreadsheets, or even data objects living in operational systems like Salesforce, people find themselves navigating a sea of chaos and are dependent on broken service desk dynamics to find the answers they’re looking for. Investing in proper data knowledge practices solves your people problem!
Everyone today is expected to be somewhat data literate, but we all know this pinnacle has not been truly reached in most places — this is because data knowledge is relatively immature in most organizations.
With the proliferation and increased usage of data comes an inconvenient truth: when left unchecked, simply pushing more data on the business will actually create more confusion than clarity. This is the phenomenon of entropy that most data teams will experience, where over time their analytics environment descends into a state of chaos.
This is a natural and inevitable descent that happens when, despite a team’s best efforts, too much data is shared without sufficient context.
The epitome of this is unregulated self-service environments, where thousands of data assets have been created by users all over your organization. These assets clutter up your environment, and nobody knows what to use, how to use it, or what data should be trusted.
Tragically, in this world of massive data proliferation, not only do end users lose the trust and confidence to apply data, but data teams find themselves sucked into even more busy work answering annoying questions and tactically supporting business users. This is because, simply put, there are just too many data assets and too many data users for a team to effectively support.
What this all means is that despite our best efforts in building data teams, and our massive investments in standing-up technologies to increase data availability, data tragically fails to reach its potential in impacting the business for the better and unlocking value.
While we have lacked much of the language to communicate these dynamics, data teams intuitively know all of this already. They know that to influence everyone to apply data, they must deliver not just data insights but data knowledge.
And so teams have historically invested in a number of data knowledge practices to empower action across the lifecycle of their customers. These include training to onboard new users, ongoing technical support to answer people’s questions, and critical work to understand and drive data usage and evolve the data platform and surrounding knowledge base.
Example practice #1: user onboarding
Companies are constantly growing, and the data platform is ever-evolving. And so data teams are routinely faced with new users to onboard, and new features to train people on.
Typical knowledge practices for these situations include:
Example practice #2: technical support
Every data team gets questions from end users, across a number of different vectors. This could include basic inquiries like how to use a filter, or serious concerns about whether the data looks right. Handling technical support is often pointed to as the most frustrating part of data work, and teams implement a number of methodologies including:
While these are important practices for teams to invest in building, they historically feel like unappealing grunt work, and typically leave everyone, especially data teams, feeling frustrated.
Frustrations can boil over at any size, but reach the inevitable tipping point with entropy: where confusion abounds, and there is simply too much data and too many data users to support effectively. At scale, it quickly becomes too manual and cumbersome for data teams to build and maintain knowledge, and there is too much effort required for everyone else to access it — so you end up with data knowledge that is ephemeral, not available when needed, or at worst, misused.
Data insights are a prerequisite for data knowledge. The reality is that data teams have a relatively small sphere of control. As far as headcount goes, data teams are small (~3% of total headcount when compared to the rest of your organization).
The challenge is that while this group is highly capable, they typically do not deliver business results. Instead, data teams only control the data and insights they provide to influence the results of the business. Within this context, data knowledge practices become critical enablers for small, influential teams supporting the larger organization.
Okay, okay: here’s how you do the damn thing well within the chaos of today’s stack. Forward-thinking teams are embracing data knowledge systems to supercharge their data knowledge practices.
They do so by institutionalizing, unifying, and automating the creation of knowledge in a trusted repository. These teams then leverage that repository to enable everyone with data knowledge, and uncover it whenever people need it – not via broken Service Desk dynamics, but via a Concierge.
Historically, teams have recognized the need to build repositories of their most important data and knowledge. We use the word “repository” loosely here, as typically this ends up as a set of cobbled-together systems across which data assets and knowledge sprawl haphazardly.
The old way:
When teams implement a purpose-built data knowledge system, they replace this sprawl with a unified repository.
The new way:
Once teams have unified information, they leverage their data knowledge repository to enable the end users of their data. Again the traditional approaches towards enablement have historically suffered from manual overhead required to train end users, and discoverability challenges given the sprawl of assets and knowledge across systems.
The old way:
These drawbacks of traditional approaches most often result in a lack of adoption – as end users often find it easier just to ask rather than search for answers. This leads data teams to feel that their efforts in maintaining knowledge and enablement are wasted.
In contrast, a purpose-built repository brings all of this critical information in context with your live data and surfaces it wherever your people already consume or collaborate on data already.
The new way:
Where traditional data knowledge practices most clearly show their flaws is the way in which teams are still sucked into handling the bottom of the pyramid, reactive support.
Despite all the work done to proactively enable users with knowledge, there will always be folks who cannot find it, or just naturally have questions that need attention (squeaky wheels, or otherwise). The inbound questions from users in Slack are just inevitable.
The old way:
So if your data team wants to be free of these constant interruptions – without attempting unrealistic, draconian measures like pushing everyone to always open up a ticket in a transactional system, try (for free) a dedicated data concierge.
A Data Concierge works proactively to support both your data teams and end-users, based on all the knowledge living in your dedicated data knowledge system. It provides users answers on demand, wherever they already work.
Empowering organizations with data knowledge is what we do at Workstream.io. We provide the most innovative teams with a trusted repository to enable everyone with data knowledge. And then we uncover that knowledge proactively, wherever and whenever people need it, via a data concierge.
For companies, this approach unlocks the business value of data by enabling everyone to apply it and build a culture of data knowledge. It creates a shared consciousness around your organization’s collective understanding of data and what it has done with that data.
In quick time, this practice will become institutionalized, and it will empower everyone to leverage data knowledge independently, without waiting for others to get their job done.
For data teams, it allows them to spend 90% of their time doing what they are great at and love doing — coding, building the data platform, and providing data insights. It does that by automating the busy work typically required to enable the business with data knowledge.
And for business operators, data knowledge is available on demand. It is available to them wherever they already work, instantly, at the moment they ask for it. It becomes easy to apply data the right way because knowledge is simple to find and understand.
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