Last week we explored the woes data teams experience when managing their service desk. Handling stakeholder requests is often the most hated part of a data team’s job, taking the team’s time away from building impactful projects, and locking them into transactional relationships with stakeholders.
We talked about how our loathing stems from a long history of awful service experiences, and how our chosen model of service desk reflects deep power dynamics within our organizations. Finally, we took a look at the competing centers of gravity that data teams face, and how the failures of service desk and agile frameworks undermine teams’ ability to navigate service versus product work.
Is there a solution to this existential crisis? After talking with countless teams, we believe that there is a better way for analytics teams to handle this part of their workflow: implementing a data concierge. Implementing a data concierge can take some of the pain out of the service desk nightmare, and begin to create an environment where everyone’s experiences with data are collaborative, productive, and delightful.
I think it's our responsibility to facilitate the conversations to get people talking about these learnings. And then it kind of spurs and inspires new ideas and new questions, and just kind of gets this cycle going. I like to think of it as one of our responsibilities, to create that shared knowledge and shared understanding.
– Michelle Ballen-Griffin, Head of Data Analytics, Future
A data concierge provides stakeholders with information about their data directly in context, dramatically reducing the number of stakeholder questions, freeing up valuable time for your data team, and increasing the adoption of your data assets.
Having this kind of solution in place is crucial, because ultimately, the success of teams should not be measured by traditional metrics such as the speed by which we deliver requests, or deliver feature stories – but by our ability to create a shared consciousness about the data that pushes our organization forward. Without a shared consciousness, data teams will remain mired in the hated service desk dynamics of the past.
As explained by General Stanley McChrystle in his book Team of Teams (Portfolio/Penguin, 2015), the modern world is “complex,” where the speed and interdependence of activity makes it “impossible to tell which events might lead to what kind of results.” Traditional systems and teams are – to put it simply – linear and modular. Systems are designed to transform inputs into outputs, be they ore into bars, vehicle parts into cars, or customer issues into resolutions. Personnel can be divided into modular sub-teams performing specific tasks, where relatively little context about the prior or next step in the process is required to fulfill one’s role.
In contrast, complex systems are multi-dimensional and interdependent. Inputs come from multiple sources simultaneously, and outputs follow any vector. To handle this world, team members need two things:
This is the world that data-driven teams inhabit: a complex one. It is the space between data people and the others, where every person needs access to the shared consciousness needed to execute at any moment. It is a world that traditional management systems were not designed for, nor can even fathom.
A data concierge imbues these teams – data people, and all of the others – with a shared consciousness about their data, and it empowers them to execute independently. It provides everyone with knowledge about their data, whenever and wherever they need it, on demand and in context. It imparts the understanding of one person to another. It orients the user to exactly what they are reading, and it validates and contextualizes its source. It helps them understand what questions others have asked, and not only what the answer was, but what was then done about it.
It treats every piece of data, and every output, like a product that people need to be onboarded to, and it teaches them how to use it correctly.
Who might build a data concierge, and make it a universally adopted and loved component of the data stack?
Salesforce is the silverback gorilla in the room. Not only does Benioff own a thousand-foot skyscraper that blights the skyline of San Francisco, but he recently acquired Tableau for $15.7B, and Slack for $27.7B.
Modern data innovators often malign Tableau as antiquated (which is largely deserved), but it continues to be the leader in the Gartner magic quadrant alongside PowerBI. Tableau largely owns the market, and, if we are being honest, no competitor offers as many features around data visualization; in that regard, everyone is largely still trying to catch up.
While Tableau’s native collaboration (basic commenting) functionality is lacking, Salesforce has publicly talked about their intent to make Tableau “Slack first analytics”, and already sells a Service Cloud (their customer support offering that competes with the likes of Zendesk). Each of these products has features necessary to a data concierge, yet there is no vision or drive to create it and put it in the hands of their sales army.
Which so far is a good thing. The idea of Salesforce owning this is, quite frankly, horrifying.
Might the latest crop of data visualization tools build this into their now-for-everyone offering?
While theoretically any independent analytics solution might pursue a data concierge – from modern data stack OGs like Mode or Looker to more recent entrants – there is, of course, no hotter product right now than Hex.
Hex seems as likely to build this as any company, given some of their early investments in Figma-style real-time collaboration and knowledge management.
So extending that to create a data concierge isn’t out of the realm of consideration. And after all, analytics tools already own the surface area of the core analytics work, making it a natural place to expose a data concierge.
However, the data concierge does not belong to Hex, or another analytics solution, because there are so many different places that people consume data today.
It’s quickly becoming the norm for even smaller teams to deploy multiple analytics tools within the same environment.
This most often is BI for dashboards & reporting, and notebooks for exploratory data analysis. But spreadsheets for (hopefully) one-off work, and documents, or slides for longer-form presentations are still very common. As are tools like Heap or Amplitude for product analytics, and more recently, reverse ETL solutions that push data into operational systems like Salesforce and Zendesk, where business users can easily consume it.
The same team might deploy all of these solutions for various analytical jobs to be done, meaning that the data concierge needs to be available across all of them. This is increasingly important given the Cambrian explosion of new vendors building analytics tools for different jobs. A heterogenous, rather than a homogeneous, data consumption layer is the future.
In the next ten years, we believe every team will be using multiple tools to consume and analyze data. And we see clearly that it’s already started: customers in the Workstream beta already use an average of 2.6 tools across their various use cases. This is why we believe that a data concierge to rule them all is not only invaluable, but inevitable.
Data teams will never reach their full potential if we continue to be bound by the constraints of tickets, prioritized backlogs, and power struggles. All of us – data teams, and others – need to rise above the misperception of us-versus-them, and invest in developing the shared consciousness about our data which truly sets the best organizations apart.
We believe that the service desk, one of our most collectively maligned tools and activities, is destined to be replaced by something delightful, and which helps us all do more together: the Data Concierge. It will free us all from the collective angst of our past service experiences, and open a world in which we are all empowered, wherever and whenever we need to be, by the collective knowledge of our most important asset.
Download our free e-book to learn how to kill your service desk today.
In this article, we discuss how data-driven teams can tame entropy and empower stakeholders with a single access plane for analytics.
Data asset entropy has been an issue for as long as data has been used in decision making. But in the modern organization, where analytics tools have exploded in number and type, entropy has become a huge stumbling block to effective data-driven operations.
The goal of your workflow is not just to uncover valuable wisdom from your data. It is crucial to communicate those actionable data observations to the organization which can use them to make decisions.
Receive regular updates about Workstream, and on our research into the past, present and future of how teams make decisions.
