BI Trends

How Analytics will Impact Software Product Managers in 2017

By Josh Martin
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It’s that time of year again – as we quickly approach the New Year, we start to look ahead at what businesses might expect in 2017. As we are all increasingly judged on how we use data to more effectively do our jobs, there’s a big opportunity for product managers to improve or update the way analytics are presented in their products and applications.

So here are a few ways we think analytics will impact product managers next year, as well as some considerations to help you deliver on time, on budget and get ahead of the competition.

1. Decline in adoption and use of standalone tools will create opportunity for you

Investment in standalone data discovery tools by IT departments has not yielded the increased usage they expected. According to the 2017 State of Analytics Adoption, there has been a 20% decline in adoption of self-service analytic tools over the past two years. Supporting these tools has inhibited spend in other areas. And as IT departments begin to recognize the situation, it will free up budget to support spend on analytics embedded within the software and applications users are already relying on. This is the perfect opportunity for software vendors seeking to create premium embedded analytics offerings. And by getting to market quickly, they can win some of this uniquely available budget.

2. Your software will need to offer more than just visualizations

Visualizations are being commoditized. Amazon and Google have both released near-free offerings that offer their users basic visualizations. We believe this trend will continue and it will drive down the perceived value of visualizations in general, including those embedded within software. Instead, software vendors must consider which sophisticated business intelligence requirements will be most appealing to their users and prospects, and focus on bringing them to market to drive value. Advanced features include: white-labeling embedded analytics so they look and feel like your app, enabling direct data connection to allow for immediate database write-back, or providing in-app self-service tools that allow for data exploration by end users.

3. You’ll need to consider more than just the end user when designing your roadmap

In 2017, we will start to see the analytics value chain expand beyond the developer and the end user. We believe that availability of simple to use authoring tools will allow professional services teams at OEMs and Subject Matter Experts in enterprises to serve as application extenders. These individuals will help transform a general application and its analytics into a custom app for their company, their teams and even individual employees. The burden of data discovery, querying and picking the right visual will be shifted to those better suited for those responsibilities, while end-users can focus on using the data to perform better in their jobs. However to successfully make this pivot, OEMs must consider this broader ecosystem when designing their product, their messaging and their go-to-market strategy.

4. Advanced Analytics don’t need to be part of your short term PRD

Advanced analytics has been the “next hot thing” for a while now, yet it is still primarily the domain of analysts who are mining data, creating models, and testing their hypotheses.

Over the next five years, we expect these capabilities to themselves become a component of analytics platforms and widely available to and used by all users throughout the analytics value chain.  However, we don’t believe 2017 is the year these offerings go mainstream or even cross Geoffrey Moore’s proverbial chasm into the broader market. So, when prioritizing your plans for 2017, Product Managers would be wise to avoid including advanced analytics and instead should focus on other sophisticated requirements that move their analytics beyond just visualizations and reports.

5. You will need to reconsider your data connectivity strategy

The advent of mobile phones, fitness trackers and the internet of things have made streaming data in the consumer world a reality. The same is true for the industrial/commercial space, where more devices providing real-time data amplify the need for reconsidering your data strategy. Many analytic tools require data to go through a transformation process before analysis, which can add latency. While this strategy has its benefits it does make accessing data from the internet of things – which often demands real-time access to allow for rapid situation assessment – more difficult. Thus, we believe one way software vendors will differentiate their analytics in 2017 will be in how quickly users can gain access to the data from streaming devices, how rapidly they can act on that data and how quickly they can react to that data.


Originally published December 2, 2016; updated on June 15th, 2018

About the Author

Josh Martin is the Director of Product Marketing at Logi Analytics. Prior to joining Logi he was an industry analyst covering bleeding edge distribution channels and their impact on the consumer market. In this role he was a thought leader and advised clients on how to successfully benefit from market shifts while positioning products and services for long-term success. Josh holds a Bachelor degree in Business from Babson College.