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Becoming More Data-Driven With Self-Service Analytics

Data Basics Joy Looney

The concept of self-service analytics isn’t exactly a new topic. However, there are many misconceptions around what self-service analytics involves. Many people think that self-service analytics is only about pulling data from dashboards. This is — spoiler alert — false!

This year at Everyday AI New York, Adrian Laus, Senior Data Specialist at Dataiku, gave us a refresher on what self-service analytics actually is, including his inside perspective on the ongoing practice of self-service analytics at Dataiku. Keep reading to catch the key takeaways from the session. 

Defining Self-Service Analytics

While the definition of self-service analytics is generally flexible, for the purposes of a foundation for the presentation, Adrian referenced this specific definition: “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 with minimal support.” Boiling that down even further, self-service analytics is primarily about removing the middle man and empowering end users to find valuable answers to their questions quickly. 

Dataiku takes the concept a step further by honing in on the “do” aspect of the self-service analytics process. What this means is taking a more holistic approach where analytics teams can focus on coaching people to lead their own analysis. Enabling users to find answers for ad hoc questions and utilize pre-curated content to answer recurring questions is the ultimate goal. With this approach to self-service analytics, it isn’t about simply pulling an answer from a dashboard the analytics team has handed over, it is guiding users to write their own data stories.  

Why Practice Self-Service Analytics in This Way?  

Employing self-service analytics as a “do” process offers advantages in time, value, collaboration, and creativity.

As mentioned above, the middle man is removed and end users can search for answers to their own specific inquiries, inherently saving time. This saved time is a double benefit, as insights can be applied faster to original questions. Where an analytics team would have previously been submerged in a project from start to finish, they can now guide users to traverse their own project and turn focus to more complex, high-priority projects for their organizations. 

Focusing on the “do” and empowering both technical and non-technical users to explore analytics turns the process from a transactional relationship to a collaborative one. Diverse talent across the organization can inform decision making daily — finally say sayonara to hindering siloes. Easy access and collaboration breeds the creativity that is needed for competitive innovation. With reliable analytics at their fingertips, users from all backgrounds are more willing to take questions a step further. This can look like introducing automations for efficiency, finding untapped value in an uncharted area, and more. 

Overcoming Challenges of Self-Service Analytics  

The advantages are obvious, but it’s also important to note that self-service analytics, like most worthwhile things, is easier said than done. Outcomes may not always turn out as anticipated which is why there are important factors to keep in mind while engaging in self-service analytics. There are areas that require special attention when communicating results from self-service analytics. 

Building the right culture around self-service and ensuring proper engagement is key. This means rallying efforts around three core pillars: the data, the tools, and the community. 

    • Data quality, access, and reliability are all needed for successful analytics projects.
    • Tools need to not only have the ability to craft dashboards and reports, but they also need to be approachable for all
      different kinds of stakeholders, for an array of needs. 
    • Community is crucial for enabling, consulting, and supporting projects. 

How Dataiku Reinforces the 3 Pillars

Built-in functionalities of Dataiku help users determine the access, quality, and reliability of their data. 

One of the easiest ways to look at quality is to quickly obtain the validity score that Dataiku creates. There are also predefined scenarios that are easily refreshed. Then, to continue to monitor quality over time, users can set up alerts for problem spots in data pipelines. It is easy for other creators to check in on the freshness of data as well. 

Not only does Dataiku make it easy to measure trust in datasets, but Dataiku functionalities also guarantee accessible data with seamless sharing. Taggable folders are easily searchable and the data catalog showcases every project. A details section let's data explorers capture key information at a quick glance. Plus, in Dataiku, it is easy to discover nifty features to incorporate into flows in the feature store

All projects in Dataiku are commentable, taggable, and describable. Flow zones create a clear organization while slicing and dicing is made simple with project wikis. Also note that filtering functions are within reach for all data explorers. And the best part, the option is open to code or not. Coders love the array of languages that can be incorporated and non-coders enjoy trouble-free visual recipes for data projects. 

Using Dataiku Self-service analytics isn’t going anywhere anytime soon, so investing in a platform like Dataiku that holds up the key pillars and addresses common challenges is a smart move for organizations looking to become more data driven. Looking into the “do” of self-service analytics is principal for empowering decision-makers and generating real value. 

Find step-by-step Dataiku tutorial videos and explore how these unique features work in the Academy. Users who have already jumped into Dataiku can hop over to the Community for support, consultation, and project inspiration. 

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