January 4, 2023
NaN minute read
Data-driven companies all have accessible, trustworthy data to explore and work from. For data teams to scale the impact of their work, they can set up accessible charts, tables, and reports for business teams to trust and use to make decisions from. At the center of a delicate but increasingly important matrix of cross-organization decisions is self-service analytics.
Data teams unlock the ROI on the modern data stack they’ve built by creating the opportunity for every user to become an analyst with self-serve analytics.
Self-service analytics is an ongoing process where people throughout an organization can access verified and trustworthy metrics to make decisions from.
Self-serve analytics has become an incredibly important part of data strategy and is the most successful when the data team sets up the metrics and reports for stakeholders. When business teams have access to trustworthy self-serve reports, the organization can increase its performance, efficiency, and overall data literacy.
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Business teams can’t always wait on data teams for trustworthy data. People need data every day to monitor the performance of their functions and to inform their broader strategies.
When there is no centralized, trustworthy data to work from, the company risks many decisions across the organization being made from siloed and minimal data that tells an incomplete picture or no data at all. The impact can add up over time and result in a business running on inefficiencies.
When data teams set up the foundation of self-serve analytics, people in an organization are able to unblock themselves. As Forrester reinforces for us, an insight-driven business is 78% more likely to grow revenue and have nearly 137% more competitive advantage over others.
Self-service looks a little different depending on the organization, but you can think about getting started with three high-level steps.
Step 1: Get a modern business intelligence tool, like Mode, that can act as a central hub for all of your company's data. In these tools, you can bring together data from all platforms and analyze it in the same place. The best BI tools are user-friendly for non-analysts and just as friendly and powerful for data teams.
Step 2: Work with business team leaders and stakeholders on creating consistent definitions of metrics. This is so that everyone is working from reports with the same definition of what a lead is.
Step 3: Have the data team start building reports for the most critical functions of the business. Centralized KPI reports that display revenue and user growth are good places to start and help create alignment across an org.
Step 4: Get ongoing feedback and continue collaboration between data and business teams often to ensure reports are useful.
Self-serve analytics is successful when your organization is using a modern BI tool and adopts a collaborative data culture. Successful self-serve analytics will help the data team deliver insights without resorting to manual, repetitive analysis or playing constant catch-up as the business evolves. Here are three components that make self-serve analytics successful.
1. Centralized analytics
With a single, centralized hub for all your analysis, you can source everyone’s shared knowledge in the same place. At this “watering hole” for data, all teams can collaborate more deeply and share more context allowing them to move faster, together. You can turn organizational knowledge into a competitive advantage by putting all of your analytics in one place that lets you easily find and build upon the work that’s already been done.
2. Collaborative analytics
Building self-serve analytics should be a collaborative process between business teams and data teams. Business teams should be able to fluently work in the same tool as data teams. This allows for the data to be informed by everyone’s expertise and helps maintain that the highest priority questions are getting answered.
For example, product decisions are made collaboratively. To get the most-informed answers to product questions, data scientists, product management, growth teams, and other domain experts should be able weigh-in—easily viewing or editing work—in the same tool.
A template for data teams and stakeholders to define metrics together.
3. Distributed analytics
Successful self-serve also means that folks across teams are getting the most up-to-date data in the places they visit the most. This could look like data teams setting reports to automatically be refreshed every morning, setting up reports to be embedded in other tools or white-labeled, or if it's delivered to their inbox every week.
3. Context to inform ad hoc analysis
Ad hoc analysis should drive self-serve reporting and self-serve reporting can and should lead to deeper questions that inspire even better ad hoc analysis. This process should be complementary and easy to from in a modern BI tool, like Mode, providing more and more relevant context to everyone over time.
Learn how to unlock a trusted data experience across your org.
1) Business teams need easy entry points into data.
It can be overwhelming for business stakeholders to work in an analytics tool for the first time. Even when SQL isn’t required, users may log in, look around, and might not be sure what to do next. A good self-serve analytics tool is easy to navigate and provides very clear direction on where, when, and how to get started.
Non-analysts can log in to Mode and start exploring Collections, where data teams can easily organize Reports by team, by function, or in any way that works for their organization.
Mode’s Reports allow for descriptive text where report creators can explain what’s being measured and clarify any nuances in the dashboard. The Explorations feature enables business users to explore an analyst-built report with familiar drag-and-drop tools, creating a new slice of the data that is most relevant to them and explore data on their own, in chart types that they know.
2) Business teams need to be able to explore data-team vetted analysis
When companies equate self-service with dashboards, they’re missing out on a key aspect of self-service—data exploration and drilldown. Looking at dashboards is not enough to explain a sudden spike or decline in metrics. Data consumers should be able to slice and dice charts different ways to get a closer look at what's going on.
Data teams should create the data assets that others can then explore on top of. Data consumers can explore these reports with Mode's Report Exploration feature. Additionally, analysts can always see the underlying data powering a report in order to ensure accuracy.
Stakeholders need to be able to run new analysis on their own and not just view and filter existing charts.
3) Data teams can't be limited with advanced analysis capabilities Tools for self-serve analytics should allow for flexible, iterative, and powerful analytical processes and visualization capabilities. This requires flexibility in visualizations and a high technical ceiling, that lets analysts bring in raw data if they need to.
For a data team to run fast, they need a tool that enables them to work in languages they’re familiar with like Python, R, and SQL, and then be able to visualize the data in endless ways. With Mode, analysts can start in these languages, use endless visualizations to cut the data, build reports, and share them out with permisssions all in one tool. This all-in-one workflow lets data teams do powerful analysis, fast—and allows business teams to jump in where they need to.
The need for self-serve is not going to go away. As businesses become more and more data-driven, the demand for data across organizations is only going to get higher.
Data teams want to make it easy for business users to do self-serve analytics with guard rails and want to lower the barriers to entry for creating reports and analysis, thereby making data culture pervasive. Our customers have been using Mode to do just that, accelerating their intelligence.
Even if stakeholders can't access every single data point they need, using reports that the data team has built tells a much more accurate story than, for example, a siloed lead count in Marketo. Most of the time, even a little bit of data team reports are enough for folks to choose better paths forward, until the data team can work them directly. Request a demo.
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