The speed in which businesses gain insights and make decisions with their data is more important than ever. But it takes an effective means to make the data accessible and usable. If you don’t have an analytics solution that eases decision making, you are not getting the most value out of your data—and you’re probably leaving money on the table—or worse, throwing money away.Kevin Lobo, VP of Analytics at Analytics8, lays out the case for getting the most value out of your analytics solutions during our “Empower Your Data Users: Build a Superior Data Experience,” webinar. He discusses how to build a superior data experience based on three key concepts. He shares practical tips on how you can start helping your business users and customers take action with data.What Are the Key Drivers of a Superior Data Experience? Every data and analytics solution is at risk of being expensive shelf ware if the user experience isn’t prioritized. To drive adoption of your analytics investments, your solutions should be designed around making data accessible, actionable, and transparent.To create a superior data experience, your data needs to be accessible Making data accessible—not just available, but useable—is core to building a data-driven organization. There are many ways to make your data more accessible, but running cloud-based data workloads, embedding analytics in your BI tools, and adopting headless BI, are three concepts that will help any organization.Cloud-based data workloads: A superior data experience simply means having the ability to run data processes that allow for access to every data point when it’s needed. For example, we have a reporting app where users can review basic information on in-flight client work, but to see anything beyond that, they need wider access to underlying data. The cloud makes it incredibly easy to create a read-only, cloned version of the desired tables so that users access every data point without putting the source data at risk. A cloud-based platform—Snowflake, AWS, Azure, or GCP—enables business users to run processes without the concern of disrupting other business critical processes.Embedded analytics: Placing analytics within an operational workflow creates an opportunity for the user to take action on the insights they receive without switching applications or views. For example, if your sales team has to navigate through separate workstreams—a Sales dashboard, Salesforce, and SharePoint—to determine why there is a decrease in sales volume for one particular customer, they are likely to introduce errors while costing valuable time and resources. By merging separate work streams into one unified solution, you build a more cohesive and accessible user experience.The barrier to entry for embedded analytics has never been lower, especially with modern analytics solutions. Most modern analytics tools—such as Power BI, Looker, Tableau, Qlik, AWS QuickSight, and Sisense—allow for native embedding capabilities. Cost is the biggest consideration for embedded analytics. If not planned properly, embedded tier licensing can get extremely expensive, extremely fast. Perform capacity planning/cost estimation early on to avoid sticker shock.Headless business intelligence (BI): The most common application of headless BI is when you decouple the semantic or modeling layer entirely from your presentation layer. This allows your users to connect their own tool to the central governed dataset and run reports at their disposal. A well-built governed reporting layer, otherwise known as a data warehouse, serves this purpose. A BI tool inherits the logic and model structure from the data warehouse for the purposes of reporting—so, no transformations take place within the BI tool.For headless BI to be effective, you must invest the time, resources, and the energy in building a scalable data warehouse. When you leverage a cloud data warehouse like Snowflake, you can offload all the ingestion and transformation into the processes that feed into the data warehouse. Snowflake has a lot of utility with some of the modern analytics vendors out there, and tools such as Power BI, Looker, Qlik, and Tableau all can connect to Snowflake as a data source, inherit data logic from the data warehouse, and allow for applications to be developed.To create a superior data experience, your data needs to be actionable Your analytics solution should have mechanisms in place for users to act on the insights they derived from data. One way is through embedded analytics, and another is through the use of bots or low code apps.Embedded analytics for action: Embedded analytics provides easy access to various data sources, and it can be an excellent mechanism for users to initiate an action. Embedded analytics allows you to also place an operational workflow within your analytics solution—at the point of need—so that your users do not need to leave the application they’re working in. Using the sales report example from above, after reviewing all relevant data for a customer, the user can go on to take action—update customer information, schedule a follow-up meeting, or make a change to the order—all within the same workstream.Something to keep in mind is how technical your embed request may be. Simple use cases such as iframe embedding or writebacks don’t entail a high degree of technical sophistication. But things like custom web navigation or custom branding, themes, or design aesthetics necessitate an advanced skill set of web development and traditional analytics development. If you have a more advanced use case, make sure that you have the right resources on staff or the budget to hire help.Bots or low code apps: Bots or low code apps, such as Microsoft Power Automate (a low code utility within the Power BI platform) are another way to cut down the number of steps a user needs to take before being able to act on insights. For example, if you want to trigger a data refresh, you can build in a Power Automate visual within your Power BI reporting and all your users need to do is click a single button.Two tips:Consider the extent of and the permissions at which you’re giving your users to take action. Data governance is necessary in this process.Simple use cases for embedded and bots/low code apps are best—such as triggering alerts, kicking off workflows, or in simple writebacks. Don’t over-engineer a solution for the sake of action. A good rule of thumb is one or two mouse clicks to perform an action.To create a superior data experience, your data needs to be transparentUsers need to have the ability to see the context of their data. This means having things like a data glossary, data dictionary, and a data catalog for users to understand exactly what they’re looking at. This builds trust in the data—ultimately leading to better user engagement and adoption.Data glossary: Contains concepts and definitions of business terms that are frequently used within day-to-day activities in the organization. It’s meant to be a single authoritative source for commonly used terms for all your businesses. In order for a data glossary to be successful, you’ll need cross functional input, consensus and approval for agreed upon understanding of the key business concepts and terms, and a list of cross reference terms and their relationship to each other.Data dictionary: More of a technical documentation of data as well as its metadata. It’s going to consist of detailed definitions and dimensions, measure names, and things that you commonly find within databases and data tables. This information will be most beneficial to your technical users that work on the backend system, such as data engineers.Data catalog: An organized inventory of data assets that informs users on available datasets and really helps locate them quickly. Users have a clear view on what data the organization has, where it came from, where it’s located, who has access to it, and any risks or sensitivities that maybe involved—and it’s all housed within one central location.Making data transparent is tedious, but it is necessary in order to create a superior data experience for your users. And while it takes a lot of coordinated time and effort, it does have a high return on value.Start by exploring capabilities you have within your existing tools before looking to purchase any new technology.With a superior data experience, your data can be monetized If your organization has a valuable data asset that can be offered at a premium, you can create an analytics solution to make this data available as a paid product—usually this happens via a subscription service. Not only is this a revenue stream for your business, but it also allows for deeper penetration into different markets and opportunities for partnerships.Things to consider when monetizing your data:Strategic planning: A data monetization use case requires more strategic planning than an internal reporting use case. It will only succeed with a dataset that can command value—such as survey data, benchmarking data, or market research data.Design and prototyping: Development must be framed within the lens of building a product. There is opportunity cost everywhere in this process and spinning wheels on non-essential tasks is detrimental to development.Technology: New tech isn’t necessary. If you’ve already implemented an embedded analytics solution, you can likely use that. Just make sure you can create a custom portal or home screen in which users access the reporting/analytics and can implement a subscription or paywall element.Authentication and single-sign-on (SSO): Authentication and SSO strategy should be firmly established before embarking on the project. If you have a mixture of internal and external users accessing your product, the mechanisms, and methods in which they log in, authenticate, and access the reports must be accounted for to avoid exposing sensitive or confidential data from the solution.