Business Intelligence Software Development Trends in 2020

Despite having become mainstream over the past few years, analytics and business intelligence continue to be a hot topic today. Today’s business intelligence software is faster, more accurate, and growing in complexity, highlighting the fact that data is an asset that can easily be labeled a top priority for businesses.

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Things are bound to get more interesting as we head into 2020. Through its multifaceted nature, business intelligence will once again support many developments aimed at making more informed and data-driven decisions at a reasonable cost. In this digital age, where businesses are struggling to succeed, it is vital to stay on top of innovation and future trends.

The proliferation of voice technology

Digital assistants in business settings have been around for some time, but voice capabilities may have been lacking. In 2020, this will all change as more people become accustomed to using voice as an interface. This shift in user behavior will also shift their expectations to business applications, which can not only enhance the user experience through conversational AI, but also streamline interactions and increase productivity.

In fact, companies like Oracle have already voiced their digital assistants, providing another more intuitive way to interact and automate processes. As technology develops, a large number of analytical queries will be generated through search, natural language processing (NLP), or voice.

There is a clear need to analyze complex data sets and deliver subsequent insights to everyone in the business in a more direct way. Wider adoption of voice technology will help make it easier to search or interact with business intelligence software through conversations with digital assistants. In short, I hope to hear more voices.

Enhanced analytics

As the next wave of disruption in data and analytics, augmented analytics is effectively a combination of machine learning and artificial intelligence, changing the way analytics are developed, used, and shared. In other words, it is the result of the increasing maturity of business intelligence software capabilities. For enterprises that want a more complete understanding of their operations, augmented analytics will become a key means of future business intelligence efforts, and therefore, they may be a major driver of acquisition activity. From data preparation and insight discovery to building analytical models, the ability to automate everything is more attractive to enterprises across a wide range of businesses that want to simplify their analytical processes.

Business intelligence tools such as Sisense, Tableau, Looker, etc. have significantly reduced the time required to build, embed and deploy intelligent analytics, whether in the form of self-service analytical dashboards or white-label business intelligence applications. For example, Sisense's Boto solution allows executives and industry 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 preparing everything for custom queries using natural language processing (NLP).

More importantly, these platforms will provide faster, less biased analysis without requiring any special skills, training, or data expertise. Unlike current manual methods (such as building different models or algorithms to reveal relevant findings), users will be able to enjoy more self-service and automation through enhanced analysis.

Enhanced data management

Following in the same footsteps, augmented data management will become increasingly important within the enterprise. Thanks to advances in artificial intelligence and machine learning, information management will be automated at the service level, allowing highly skilled technical users to focus on more important, higher-value matters. In addition, less skilled users will become more autonomous in how they use data.

Implementing enhanced data management will help businesses organize and maintain data quality and integrity, ensuring it is accurate, complete, and consistent. Instead of spending most of their time on data processing, data scientists will reduce data cleansing activities, thereby increasing the productivity of the business.

Therefore, the promise of significantly reducing manual data management tasks will entice enterprises to delve deeper into business intelligence.

Predictive Analytics

It’s no secret that business agility is one of the most sought-after attributes today. The need for more flexible infrastructure has once again put analytics in the spotlight, especially when predictiveness is paired with agility.

With regard to the ongoing digital transformation as well as current operations and equipment, predictive analytics will provide businesses with a competitive advantage. IBM’s SPSS Statistics and SAS Advanced Analytics are examples of several software solutions that improve production and operational efficiency by extracting information and using it to predict various trends and customer behavior patterns.

Another benefit will be lower costs. Thanks to the increasing popularity and commercialization of natural language processing (NLP) and machine learning systems, predictive analytics will become affordable to businesses of all sizes, a trend that is likely to continue into 2020.

Machine Learning as a Service

Everything as a Service (XaaS) will grow rapidly in 2020. Business models that help the B2B landscape generate continuous revenue will gain more momentum in 2020, especially its machine learning subset.

The main attraction of machine learning as a service (MLaaS) is that on-premises data centers can handle the actual computing work, which means that enterprises can start quickly without going through the hassles of internal implementation. Machine learning algorithms excel at data analysis, and as value propositions go, machine learning as a service (MLaaS) checks off three main factors: speed, scale, and convenience.

Arguably, this is an alternative method for machine learning deployment that provides businesses with an affordable way to create functional models that can generate useful insights. In addition, the process can be performed without the need for skilled machine learning staff, making it ideal for businesses that cannot do it themselves or do not have the resources. Although large companies like Amazon, Google, and Microsoft already offer machine learning as a service (MLaaS) platforms, 2020 will be the year they fully become mainstream.

in conclusion

In a world where artificial intelligence and machine learning are increasingly replacing human decision-making, staying on top of these developments means having a 360-degree view of your business. For those businesses that recognize and understand the importance of advances in business intelligence, these are exciting times. For those that don’t, they are missing out on opportunities that will enable them to not only become more productive and efficient, but also more sustainable.

As time goes by, business intelligence software becomes more sophisticated at automating tasks, discovering patterns, and uncovering actionable insights. Therefore, it’s safe to say that companies that don’t invest in business intelligence in 2020 are likely to struggle in the coming years.