Four Tips to Design an Analytics Product that Democratizes Data

Customers want instant data insights, but the ever-growing pile of data makes it feel increasingly difficult to sift through. To balance these demands, organizations are shifting their focus toward data analytics tools that democratize access to data and insights.

However, creating an analytics product that provides easier access to data is a journey. Those creating analytics products must define their user bases, personas and features among a myriad of other considerations. Here are four tips for designing an analytics product that democratizes access to analytics.

Persona-Building is Crucial to Successful Product Design

This is the drawing-board stage. The goal of persona creation is to paint a picture of what types of people will benefit most from democratization of data analytics. Establish that understanding and the product development will follow more easily. Consult internal teams — sales, marketing, engineering, and customer success — to figure out which persona likely would experience the greatest data-related difficulty.

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Don’t be afraid to start small before building a more extensive persona bench. Here are a few persona titles to consider:

– Content or RFP managers
– GM and strategic executives
– Tactical and day-to-day operations employees
– Marketing and product managers

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Define a Minimum Viable Product (MVP) for Analytics to Add Value

First, speak with prospective customers to understand pain points and use this feedback to outline a minimum viable product (MVP). It’s inefficient to build every single feature customers want from day one. Instead, utilize one of these strategies for defining, developing and releasing your product:

– Identify areas where direct financial value exists as a corollary to a problem many customers have. Develop an MVP to address that specific, financially viable, pain point and build from there.
– Examine issues prospective customers might have internally. For example, one company focused its analytics platform on human relations (HR) processes. The product integrated data from different systems to enable HR to better understand where recruits were coming from and gain improved visibility over the entire recruiting process.

 Stick with the 80/20 Rule

It is important to realize that analytics is not a cookie-cutter solution. Rather, it requires a certain level of customization.

Although you can develop your solution in a way that meets 80 percent of your customer demand, there will be that 20 percent that you need to tailor. Create a standard offering that applies to about 80 to 90 percent of your users or situations. For the rest, you can customize offerings and sell at a premium price.

However, to make a standard offering, ensure that you are using a multi-tenant cloud analytics architecture that can instantiate new environments for new customers rapidly. This multi-tenancy should enable you to reuse your data model and reports across 80 percent of your client base quickly, without having to replicate the work.

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 Consider How You Can Bring Business and IT Together

On one hand, you have end users who need information for their day-to-day decisions, but they don’t know the ins and outs of data analytics. On the other hand, you have IT or data analysts, who know how to work with the data to deliver insights to business users, but are a step removed from making business decisions.

Consider how your analytics offering can bring these two groups together. The goal is to provide tools for the data people and visualizations for the business people – all stemming from the same semantic layer.

This is especially important in heavily regulated environments, such as insurance, healthcare, financial services and government, where IT is in charge of making sure data meets data privacy and security requirements. In these types of industries, removing those barriers and bringing IT and business users together is critical.

Do Not Forget Where the Product Came From

Democratization of data analytics is the near future of big data and business intelligence, but don’t forget the process that led to the final product. Remembering the steps required to develop the product will go a long way toward improving the longevity of the product and your company.

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