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Top business intelligence software trends for 2020

(Image credit: Image source: Shutterstock/niroworld)

Despite becoming staples in the past few years, analytics and business intelligence are still a hot topic these days. Faster and more accurate whilst growing in complexity, BI software continues to highlight the fact that data is an asset – one that can easily be labelled as a top priority for companies across the board.

With 2020 now underway, things are bound to get more interesting. Through its multifaceted nature, business intelligence will once again underpin numerous developments aimed to make smarter and data-driven decisions at a reasonable cost. As organisations make great efforts to achieve success in this digital era, it’s paramount they keep afloat of the innovation and up-and-coming trends.

Here’s what’s in store for 2020.

Proliferation of voice technology

Digital assistants in the business environment have been around for some time, but what’s arguably been missing were voice capabilities. All that is about to change in 2020, as more people get used to voice as an interface. The shift in user behaviour will also shift the expectations to business applications, not only enhancing the user experience with conversational AI but also simplifying interactions and boosting productivity.

In fact, some companies like Oracle have already voicefied their assistants, offering an alternative and more intuitive way to interact and automate processes. As technology moves forward, a large number of analytical queries will be generated through search, natural language processing (NLP), and/or voice.

As a result, there will be a distinct need to analyse complex data sets and make subsequent insights accessible to everyone in the organisation in a more straightforward manner. Broader adoption of voice technology will facilitate BI software to be easily searched or interacted with via a conversation with a digital assistant. Bottom line: expect to hear more voice.

Augmented analytics

Lauded as the next wave of disruption in data and analytics, augmented analytics effectively combines machine learning and artificial intelligence to change how analytics are developed, used, and shared. In other words, this is the result of BI software’s capabilities maturing. For organisations looking to understand their operations more thoroughly, augmented analytics will become a key fixture in future business intelligence efforts and as such, will likely be the main driver of new purchases. It’s easy to see why. A feature that automates everything from data preparation and insight discovery to building the analytics models is more than appealing to a broad range of business users looking to simplify their analytical processes.

BI tools like Sisense, Tableau, Looker, and others radically reduce the time it takes to build, embed, and deploy intelligent analytics, whether it’s in the form of self-service analytics dashboards or white-labelled BI apps. Sisense’s Boto solution, for one, allows executives and line-of-business users to upload new data resources to their accounts using Slack, Skype or Messenger, and the software takes care of the rest, mapping out all the connections and getting everything ready for custom querying using NLP.

What’s more, these platforms will provide faster and less biased analyses, without requiring any special skills, training, or data expertise. Instead of the current manual approaches such as building different models or algorithms to uncover relevant findings, users will be in a position to enjoy more self-service and automation thanks to augmented analytics.

Augmented data management

Following in the same footsteps, augmented data management will rise in importance within organisations. Due to ML and AI developments, information management will be automated on a service level, leaving highly skilled technical users to focus on more important and higher-value matters. In addition, less technically skilled users will be more autonomous in how they use data.

Implementing augmented data management will assist companies in organising and maintaining data quality and integrity by making sure it’s accurate, complete, and consistent. Instead of spending most of their time in data wrangling, data scientists will enjoy a decrease in data cleaning activities and in turn, increase the productivity of their organisations.

As such, the promise of a significant reduction in manual data management tasks will entice companies to delve deeper into business intelligence.

Predictive analytics

It’s no secret that business agility is one of the most sought-after characteristics today. The demand for more fluid infrastructure puts analytics once more in the centre of attention, especially if the term “predictive” comes along with it.

In relation to the ongoing digital transformation and current operations and equipment, predictive analytics will provide businesses a competitive advantage. IBM’s SPSS Statistics and SAS Advanced Analytics are a couple of examples of software solutions that boost production and operational efficiency by extracting information and utilising it to predict various trends and customer behaviour patterns.

Another boon will be the ever-important cost. Due to the rising popularity and commercialisation of NLP and ML systems, predictive analytics will be fairly affordable for businesses of all sizes, making it a trend that will probably extend beyond 2020.

Machine learning as a service

Everything-as-a-Service (XaaS) will take off in 2020. The business model that has helped B2B landscape generate continuous revenue will gain even more momentum next year, particularly its machine learning subset.

The primary appeal of machine learning as a service (MLaaS) is the fact that native data centres handle the actual computation, meaning companies can start quickly without going through the hassle of in-house implementation. Machine learning algorithms have come to excel at data analysis and as value propositions go, MLaaS checks the box of all three major ones: speed, scale, and convenience.

This is arguably an alternative approach to ML deployment that provides companies an affordable way to create functioning models that will derive useful insights. In addition, the process can be performed without highly skilled ML staff, which makes it ideal for companies that either can’t do it on their own or don’t have the resources. While big players like Amazon, Google, and Microsoft are already offering MLaaS platforms, 2020 will be the year they go completely mainstream.

Conclusion

In a world where AI and ML are increasingly making decisions in place of humans, staying on top of these developments means having a 360⁰ view of the business. For those who recognise and understand the importance of business intelligence advancements, these will be exciting times. For those that don’t, this will be yet another missed opportunity to make a business not just more productive and efficient but even sustainable.

As the years pass, business intelligence software is becoming more and more refined when it comes to automating tasks, spotting patterns, and uncovering actionable insights. Hence, it’s safe to say that companies that don’t invest in BI in 2020 most likely won’t be around in the years to come.

 

Ralph Tkatchuk, freelance data security consultant and IT expert