Six Considerations for Choosing the Right Analytics Solution

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Six Considerations for Choosing the Right Analytics Solution

We examine key factors to keep in mind when choosing an analytics solution that will help provide fast time to market and drive high adoption.

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Incorporate a Modern, Intuitive User Experience

Depending on your needs, specific functionalities such as end-user data mash-ups, advanced data visualizations, and an open client interface that enables users to plug Excel worksheets into your new analytic offering can increase the value of your application. To get started, determine your user experience (UX) needs and how you can cater to all of your users, whether they are C-level executives or data scientists. Look for analytic interfaces that can serve different user personas and be wrapped in white labeling and single sign-on frameworks to deliver the look and feel you desire.

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Opt for a Single Platform

It's important to have built-in data management so you can easily turn your information assets into analytics and insights for your customers. An embedded analytics solution should be able to extract data from multiple sources—application data, customer data stores and even public sources of data. Then it must bring this data together into a unified view so users can analyze it within the application rather than extracting the data, importing it into Excel and working offline. After all, isn't the point of adding analytics to your application to drive stickiness and adoption?

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Find Flexibility

Look for a solution that can be deployed in the cloud or on premise. With a hybrid model, you can pick the approach that best fits your customer needs when the time comes. As you bring new customers and users on board, you don't have to worry about their requirements. Some may choose for a cloud option while others might desire an on-premise version. Look for a solution that can be deployed in a public cloud, inside your own private cloud or on the premises of each of your customers. Additionally, embedding analytic software should easily integrate with your customers' data sources and other applications to avoid creating silos and enable an easy exchange of information between systems.

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Look for Multi-tenancy

Single-tenant solutions require you to set up a separate business intelligence stack for each additional customer. If you have a lot of customers, this is not sustainable. You need a solution with built-in multi-tenancy that does not require a lot of overhead for on-boarding each new customer. If you have to re-create your data transformations, business logic, data processing and loads, metrics, definitions and reports every time you add a new customer, you had better be prepared to increase your administrative staff exponentially. Additionally, you should be able to customize your analytics for some customers, either to support new pricing strategies and higher-end packages, or to respond to special requests.

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Think About Product Use in Strategic Ways

Usage analytics can be the key to new insights. Understanding which parts of your products are the most liked and used can help you realize and set the strategic direction of your roadmap. Collecting usage statistics can also benefit customers. By creating benchmarks, you can offer customers an easy way to compare their performance against industry averages, and see their strengths and weaknesses versus companies that are in the same regions and geographical distributions, or have the same size and operational model.

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Prioritize Time to Market

The complexities associated with an enterprise analytics architecture often translate to a slow product rollout. A delayed time to market depletes resources, while also increasing customer frustration and slowing user adoption. Consider using solution providers that can help get you up and running quickly and technology that is easy to deploy so that you can build, deliver and support an engaging product that customers use.

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Putting IBM's Watson Analytics to Work—From Law to Universities

In a report published last year, Gartner predicted that by 2017, most business users and analysts will have access to self-service tools to prepare data for analysis. In a second report, Gartner also predicted that the number of citizen data scientists will grow five times faster than the number of highly skilled data scientists. To capture that market, IBM launched Watson Analytics in December 2014 as a new kind of analytics solution designed for non-traditional data scientists or statisticians. The service extends cognitive computing to line-of-business users by linking natural language processing with guided data discovery, and in doing so, helps users remove bias from their analysis by providing suggestions on starting points for interrogating their data. Users don't need specialized knowledge of programming languages to explore patterns in their data. And they don't need advanced training...
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