Most business intelligence and analytics applications place a lot of emphasis on their visualization capabilities. They will be the first to tell you about their advanced charting abilities. And they will be eager to show you gallery after gallery of what will undoubtedly be beautiful visuals. But while visualizations make for great sales demos and marketing materials, they are in fact, only about 20% of what a full-featured data platform should be able to provide. If you’re curious about the 80% you’re missing, keep reading.
Every great visualization begins and ends with the data. If your data is incomplete, disorganized, living in multiple places or in simpler terms, “is a mess,” then so too will be the charts and visuals you build from them, if you can build them at all. That’s why it’s imperative your BI and analytics tools also include the data tools you need to ensure that your visuals not only look great, but are build on a solid foundation.
The first step in the data process is to collect, organize and transform your existing data sources into what we call an analytics-ready state. Put frankly, if your data isn’t ready for analysis, it really has no value to your organization. Being analytics ready means your data platform can ingest any type of data, no matter where it may reside and it must include a powerful transformation engine to ensure every field and column has a complete, consistent format.
Next, your embedded analytics software needs to offer data management tools. Data profiling allows you to visualize your data the moment it is loaded and is essential for identifying many common data problems, like gaps of missing or incomplete data. It’s also useful for spotting outliers and other erroneous data that will hamper your analysis efforts.
Once all of your data has been fully loaded, you’ll probably want to deal with those gaps of missing or incomplete data. That’s why your data platform also needs mechanisms for completing, enriching and augmenting data. Data linking can help by bringing multiple datasets together. Data uploads can augment data to add additional context. And tools like web forms can be used to collect even more data for the most complete picture possible.
Further adding to that complete picture is the next category of functionality, which includes advanced analytics like machine learning capabilities. Traditional BI and analytic tools can only work with the data provided. But a true data platform can use machine learning to automatically identify, group and sort things like objects, places, phrases and sentiment. It can analyze text, images or video to spot trends and make correlations that humans simply aren’t capable of.
The final category of functionality your embedded analytics platform must have is the ability to make your data actionable. Your company’s data isn’t standing still; things are constantly in motion. That’s why tools like automation workflows are the best way to keep your data up to date and in sync. Workflows can use elements like metrics, alerts and notifications to automatically alert users to when the data changes or when thresholds are met or exceeded. They can also ensuring that everything in your new analytics-ready data powerhouse is always ready for analysis.
When you add up all of these data collection, transformation, profiling, advanced analytics and automation features, it’s easy to see how your current visualization tools are missing a full 80% of the functionality you really need. That’s what makes all-in-one data platforms, like Qrvey, so unique and valuable.