What 2017 has in store for enterprise analytics – and what you need to do about it
The last year has seen big changes in how people adopt and use analytics. Many analytics tools today require users to leave the apps they use every day to gain any benefit. This is inefficient and outdated, which is one reason why we have seen adoption of self-service tools decline 20% since 2014.
Today’s users are desperate for something that is better, easier, and more efficient for them to work with in their daily workflows and app usage patterns. In other words, there’s a big opportunity for product managers to improve or update the way analytics are presented in their products and applications. Here’s what we predict will be in store for the data analytics industry.
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 24% 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 recognise 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.
Your software will need to offer more than just visualisations
Visualisations are being commoditised. Amazon and Google have both released near-free offerings that offer their users basic visualisations. We believe this trend will continue and it will drive down the perceived value of visualisations in general, including those embedded within software.
Instead, software vendors must consider which sophisticated 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.
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.
Advanced analytics don’t need to be part of your short term product requirements
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 prioritising 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 visualisations and reports.
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.
UK businesses must work harder to address the STEM skills gap
In 2017 it’s expected that we’ll start to see renewed confidence in the future of the technology industry in the UK, following the promise of increased investment in the industry by the UK government. This investment will empower fresh innovation in the sector as well, enabling the UK to continue its position as one of the most innovative technology hubs in the world, especially when it comes to software and mobile apps.
However whilst this investment will be good for the longer term, it’s making no attempt to address the biggest concern in the sector - the lack of talent. In 2017, this issue isn’t going to go away and I expect that we’ll continue to hear about organisations struggling to find individuals with the relevant skills. As a result, next year organisations will need to be even more vocal about addressing the skills gap, looking at how they can do their part to help, if they want to continue developing innovative products which can be viable in an increasingly competitive marketplace.
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