Why Self-Service Analytics is the IKEA of Data Exploration

Furnishing a home can be a daunting task, especially if you’re living in a place with a few funny angles and oddly shaped nooks. IKEA, the furniture retailer known for their DIY kits, can provide you with some great easy-to-assemble pieces to fill your space, but for unique layouts, prefab furniture isn’t always going to be a perfect fit. These are the times when bringing in a custom or niche-focused solution delivers the perfect fit. When you finally have all your furniture, the result will be a blend of unique and off-the-shelf pieces that all work beautifully together. Similarly, every organization functions better when they have the right mix of IKEA-like DIY data analytics tools, such as self-service BI software, and custom solutions like AI-guided analytics that are capable of exploring complex data and discovering insight hiding in unusual places.

Data exploration requires more than one tool

The applications used every day to run businesses create and capture thousands of data points every second. As a result, there is a deep treasure trove of information buried in these systems…but not a lot of resources or skills to analyze it all.

Fortunately, there has been a ton of innovation in the BI technology space, making it easier for data consumers to now create their own reports and dashboards. This means they can get answers to some of their recurring questions without waiting for an inundated data scientist or analyst to find space in their project queue. In other words, they’ve now got their very own IKEA of business analytics at their fingertips.

What’s also great about self-serve analytics is that it allows consumers to create their own reports within the boundaries set by experts. When data analysts are freed from creating and maintaining BI dashboards and spreadsheets for data consumers, they’re able to use their time and skills towards putting the correct guardrails in the self-serve software. This will minimize problems that come from using the wrong data, but it does limit the scope of inquiry…and that means some insights go unseen.

This leads us to the custom solution that complements self-serve data work: AI-guided analytics. Platforms like Virtualitics give analysts the ability to dive deeper into data and find insights that will set your business apart. Deep exploration of complex data does require advanced analytic skills, but by leveraging AI-powered Intelligent Exploration solutions, data analysts can become stronger strategic advisors.

3 benefits of AI-guided data exploration

Some influencers believe the data analyst role will be made extinct by self-service analytics…but it’s not. A house decked out entirely in IKEA furniture may function, but it will still have those odd nooks and crannies that require a different solution to reach the home’s full potential.

This is why organizations that reduce their analyst teams in the sole pursuit of analytic solutions that are using AI to facilitate self-service reports and dashboards risk going backward. Not just because the consequences get real when consumers use the wrong data for business decisions, but also because you’ll miss strategic opportunities if your analysts aren’t empowered to go searching for big business-changing insight.

Opportunities like improved reporting, strategic decisions, and accurate root cause analyses.

1. Improved reports

Sorting through all the data to find important signals requires skills and tools that are beyond the reach of the typical data analyst. This leads to reports that are lacking in valuable information, and without the right solution, an analyst doesn’t know to look for the missing insight. With Intelligent Exploration platforms, AI does a lot of the heavy lifting of sorting through wide and complex datasets. Virtualitics uses machine learning to instantly pull out the features from your data that are driving results and impacting success.

This can be a game-changing capability for organizations that rely on equipment to stay operational. Reports that help identify weakening machinery before it breaks or fails, while also keeping users aware of resource constraints and inventory, are key to minimizing downtime.

2. Strategic decisions

Multidimensional data often goes untapped because analysts can’t explore it and data science teams don’t have the bandwidth to use code to find the information and attempt to apply a visualization that would adequately communicate it. Virtualitics makes it easy to visualize and compare all this complex data and also automatically generates insights from it for analysts.

Imagine a luggage retailer wants to improve its target marketing in the APAC region. It can be difficult to know where to begin to make sense of the data they have, but Virtualitics can guide a data analyst to an insight that shows a relationship between the time of day and the size of orders in this region. This leads to strategic timing to send out email offers and potentially triggers bigger orders as a result.

3. Accurate root cause analyses

Knowing where to prioritize resources can be incredibly difficult, especially when your retail operations, for example, are spread out across many different locations. Virtualitics’s exploratory environment enables analysts to do deep analysis on complex, interrelated data sets like this and use natural language to ask questions that will guide them closer to the answer. 

For example, instead of constructing a series of customer segmentation analyses trying to get to the key factors that drive sales (Is it staffing? How about inventory volume? Does store square footage make a difference?) analysts can simply ask “What’s driving sales?” Virtualitics will evaluate the entire width of the dataset and rank each feature’s importance in driving sales and generate a visualization that illustrates the top three. This enables analysts to not only see which features are truly behind sales but also which ones work in concert.  

Blended data analysis is better

Differentiation is key in home design and in business. Sometimes it means doing something drastically different and sometimes it means a more nuanced take on an old problem. An analyst empowered with both self-service analytics and an Intelligent Exploration platform will gain the bandwidth and capabilities to deck your organization out with insights that will propel your business forward. 

Related Articles

Federal Tech Podcast Ep. 131 Exploring & Understanding Data

The Role Of AI In Supporting Business Analysts

Startup pitches AI tool to prevent Pentagon procurement blunders

The U.S. military is exploring a range of artificial intelligence capabilities, from predictive maintenance of ICBMs to analyzing reams of satellite data.

Virtualitics Makes Built In’s 2024 Best Places to Work List for Third Consecutive Year

Annual Built In Award Recognizes Virtualitics as One of the 50 Best Startups to Work for in Los Angeles PASADENA, Calif, February 6, 2024 — Virtualitics, a

Caltech Spinoff Gives Back to the Place Where It All Began

Support for Caltech's Computing, Data, and Society Program Fellows will boost research in artificial intelligence (AI). In the early 2010s, data scientist and entrepreneur Michael Amori

AI-Guided Integrated Resource Optimization for Mission Readiness

Predictive maintenance has steadily been adopted across maintenance operations teams because it helps leaders figure out “what’s about to happen.” While those predictive maintenance systems