Our experience while using apps in our daily lives was revolutionized forever when search technology evolved allowing us to ask questions in simple syntax, and powerful algorithms returned the most apt and precise answers. All you have to do while using your favorite ecommerce app for example, is search for what you need.

Embedded analytics works similarly - to provide insights within a current platform/program without having to break your workflow by going somewhere else to find an answer. But historically, the questions users could find answers to with these analytics were limited. Embedded analytics as we know it relied on static, inflexible dashboards and predefined answers. This severely limited its ability to provide customized, personalized responses that users have come to expect in this new age of experience-driven products and solutions. Imagine being able to only browse the pre-defined product catalog of your ecommerce app without the ability to search.

Let's take another example - banks have been leveraging analytics for years to understand their customer risk profile, investment appetite, and cross reference this data with demographics to provide suitable product recommendations to account holders. This might include a retirement
planning offering that has analytics embedded in it’s retirement planning guide. Users could ask, “How much do I need to save every month to retire with a specific amount of money?” and a pre-built chart would help them understand their options.

But what about the inevitable follow up question – this time more specific to the customer “How will this change if I have an investment account?” That might trigger a more specific question, “What if I have three investment accounts with different rates of return?” Eventually, customers won’t get the answer they are seeing because embedded analytics solutions come with defined dashboards that provide generic insights at best.

D is for Data. D is for Dynamic
A related major change is the realization that we are well past the age of boring, static reports and dashboards for all analytical needs. Savvy leaders will be the first to point out that the future is about making analytics accessible such that employees across the organization can ask questions and get answers in a dynamic way with an easy user experience.

According to a Harvard Business Review (HBR) Research Report, 'Meet the New Decision Makers', a staggering 84% of frontline workers report a poor experience with current analytics solutions and difficulty accessing data and insights. It's time analytics worked for everyone and not just the data analysts - enter the need for a differentiated, intuitive and consumer-grade analytics experience. In its Top 10 Data and Analytics Trends Report of 2019, Gartner predicted that by 2021, natural language processing (NLP) and conversational analytics will boost analytics and business intelligence adoption from 35% of employees to over 50%, including new classes of users, particularly front-office workers.

It's time to monetize analytics with a new breed of data apps
This is all about to change now, propelled by two main factors - companies are sitting on stacks of data today compared to twenty years ago. This gives them a very real opportunity of mining this data for rich market insights that create real value for customers. Also, the entire analytics technology has changed – thanks to cloud platforms and APIs, it's become easier than ever before to connect data together. This would have been almost impossible and cost prohibitive to do just five to 10 years ago.

Enabling users of all skill levels to freely question and seek answers using search, rather than be constrained by inherently inflexible dashboards, creates the empowered organizations of tomorrow where frontline workers are equipped to take decisions that boost business productivity. In the same HBR study, 72% of the survey respondents said that productivity had increased through empowering frontline workers with data-based insights.

Customers are demanding better insights too and coming up short. So if an enterprising business were to develop a new data app backed by the richness of data it has accumulated over the years and front-end it with an easy to use search driven interface, most customers would be willing to pay for this service. Let's go back to the bank example - an Invest Right app that provides answers to all the ad-hoc questions a customer may have presents a very real opportunity to monetize data for a business and open a new revenue stream.

To accomplish this successfully, companies need to move their focus from the analytics layer to the product/app experience. The analytics need to be accessible, searchable, so anyone can use it. This will ensure an engaging data app that can be used by many. It needs to be scalable and connect natively with the company's cloud data warehouse. Last but not the least, it needs to be simple to integrate and maintain, so it is economically viable, allowing companies to unleash the power of analytics everywhere.