Think about your favorite frozen yogurt shop for a moment.
The frozen yogurt business model involves each customer coming in and selecting a container to hold their tasty treat, and then moving one by one through the flavors and toppings to build and customize the most Instagram-worthy assembly of complementary flavors and colors in their cup.
Now imagine a slightly less flavorful world where all of your organization’s core data systems are the flavors. End-users select their preferred form of consumption (Excel, Tableau, Power BI, etc.), they fill up their container with complementary flavors of data (ERP, CRM, third-party APIs, etc.), and finally, they make the finishing touches to ensure whoever is looking at their masterpiece agrees it couldn’t have been any better if they had done it themselves.
In a perfect world, enabling self-service analytics should look a lot like a frozen yogurt shop. While this example feels pretty straightforward, doing this successfully in the real world doesn’t come without its challenges.
In this post, we’ll go beyond the frozen yogurt example to define what a successful self-service analytics strategy looks like and how to get started from any phase of your analytics journey.
What is Self-Service Analytics?
Self-service analytics is the enablement of users across an organization to access data and generate insights without the intervention of a deep technical expert or relying on IT. The key delineator between traditional business intelligence and self-service analytics is that rather than IT delivering BI to end-users, IT’s role is instead to enable BI for those same users.
In a traditional BI setting, an end-user might file a request to IT to query data to generate reports or Excel spreadsheets with the data they requested. This method allows IT to maintain more control over data quality but throttles the creativity and insights the business is able to generate quickly.
Alternatively, self-service analytics removes IT as the middleman and allows end-users to drive their own analysis without giving up control of how data is ingested and governed. Removing the IT bottleneck from data analysis activities is a key first step in enabling self-service, but it doesn’t mean taking a hands-off approach.
Instead, IT’s role should be to continuously educate business users to understand and interact with the data using tools or procedures already in place, help maintain a single source of truth, and enable collaboration between the two parties.
Why is Self-Service Analytics Important?
According to a study done back in 2018, insights-driven businesses are 137% more likely to differentiate their customer experience with data and analytics. However, in 2019, less than 10% of companies were able to say that they were insights-driven.
While the primary driver for enabling self-service analytics is often to gain insights more quickly, the benefits go beyond just improving the efficiency in the process of data consumption in your organization. Self-service can improve the relationship between business users and IT as a whole, creating a symbiosis where both rely on the other more than ever before.
When data is presented in an understandable way, it encourages business users to dive into the analysis. Rather than spending hours pencil-whipping data together through spreadsheets, business users are freed up to spend more time analyzing and finding key insights in the data that may lead to process improvements, automation of manual tasks, or other actionable insights.
Keys to Adopting Self-Service Analytics
Adopting a self-service culture is a marathon, not a sprint, and the value realization continues to increase with the maturity and capability of users. Organizations that successfully enable self-service do so through a few key principles:
- IT prioritizes clean and accurate data for analysis
- Business users are empowered and educated to perform data analysis
- Collaboration is enabled and fostered between users and IT
- Companies adopt a formal federated analytics model
In the frozen yogurt analogy, customers don’t go straight to the back of the store and start throwing raw ingredients in their bowl. The same holds true in self-service analytics, and it’s the responsibility of IT to ensure the ingredients are already in place before business users get ready to consume the data.
This is often achieved through the creation of a semantic layer or data catalog, which quite simply maps complex data from multiple sources into familiar buckets or categories that can be easily identified by a business user who has never seen the raw source data. A data catalog enables IT to demystify data complexity while also maintaining the needed governance and control over what gets reported on.
A successful data catalog should offer the ability to search and filter multiple sets of data to quickly find what they’re looking for.
In addition to creating a data catalog, business users should know it exists and have access to training on how to consume, analyze, and report on the data. While this key seems easier than the first, it’s important to foster a culture of data literacy to make your self-service rollout successful.
For more information on improving data literacy, check out our 5 Steps to Analytics Modernization.
Lastly, creating and managing an open channel of communication between IT and business users is crucial for success in the self-service analytics model. This can be as simple as adopting the Apple Genius Bar approach for fielding data-related questions, hosting weekly or monthly “community” calls with your analytics users, creating a simple ticketing system to field data availability requests or a combination of all three.
Another advantage of simplified communication between IT and the business is that it provides key insight into what the actionable goals of the business are. If IT is responsible for enabling BI across the business units, increased communication and transparency between the parties will help inform IT of what to focus on next.
Regardless of which approach you choose, nailing these keys to success will help ensure you foster a self-service analytics culture.
By now we should be thinking about all of the amazing frozen treats our business users are going to be creating from increased access to high-value ingredients, but it’s important to know that it takes time to get to peak self-service efficiency.
If your self-service analytics framework already exists today, check out our post on understanding analytics maturity models. For others, your journey may start with developing an actionable data strategy.
At phData, we know this work is foundational to your success and we are here to help! Drawing from years of experience, learning, iterating, and doing this for customers at every stage of their life cycle, we’ve built a set of tools, processes, reference architectures, and a team that is ready to help you get on the right path towards utilizing self-service analytics within your organization(s).
While a semantic model isn’t required to get started in self-service analytics, it’s highly recommended to keep in mind with your long-term goals. Without a semantic approach, you will likely end up with several models and queries custom-fit for individual end-user goals. These models will have limited modularity and reuse as your data culture grows, and inevitably will be unable to stay evergreen through technology and personnel changes. Think of the semantic layer as putting the labels on the flavors at the frozen yogurt shop, or even introducing some predefined combinations of ingredients. The semantic layer increases consistency of usage between shared data sources and definitions across all of your models and use cases.
The best way to get buy-in is by first finding leaders who are interested in partnering with your teams, developing their use cases, and elevating their successes. Second, your entire organization needs to understand the basics of working with data: how it is structured, how to do simple analyses, and how many of the manual processes they are completing could be automated. They don’t need to know how to do it yet, they just need to be aware of how that could happen, and how valuable these skills can be to your organization as it potentially increases the capacity of each employee. For a detailed step-by-step process to achieving this, check out our 5 Steps to Analytics Modernization.