What’s Next for Business Intelligence Software? 2019 Predictions
According to the Business Application Research Center’s (BARC) 2018 Business Intelligence Survey, the BI market is awash in a sea of IT meta-trends. These meta-trends lately have included data digitalization and its security/privacy, solution agility and cloud deployment (SaaS), mobile platforms and artificial intelligence.
Taking the initiative, we asked several BI software experts to predict what awaits the market in 2019 and beyond. Their replies corroborate much of BARC’s findings, but also uncover some outlier trends.
This is what they said:
In 2019, BI goes AI
The confluence of factors pointing to the emergence of AI in business is undeniable. Machine learning is changing the world, and business finally has the right tools to amass the training data that algorithms need. 2019 will see headlines that show a broadening of the niche ML solutions we’ve seen in business to date. Automated expertise won’t just be the realm of boutique airline pricing or insurance risk prediction. Instead, digital agents will begin to collaborate with business analysts to automate decisions like where and how to invest in innovation or new markets, marketing spend, or competitive forecasts.
Of course, the effect of this new collaboration (which Gartner calls “Augmented Analytics”) will not be shorter workdays and more time on the beach. Instead, competition will drive business to use AI agents as a means to increase the pace of decision-making and feedback cycles. In other words, AI will boost the efficiency of operations by letting businesses optimize resource allocation more frequently. This will be the start of a cycle that will disrupt many industries, just as many technological advances have in the past. Smart companies will start small and get quick wins in order to learn how AI works before scaling it up.
Chief Data Scientist
AI will make data accessible (Finally!)
In 2019, I predict we’ll see artificial intelligence start to move from science project to production throughout the analytics ecosystem and really start to deliver on the promise of democratizing access to insights. AI will fundamentally change who has access to data, broadening the proverbial aperture and allowing anyone to interact, analyze, and utilize data.
While I predict this will alleviate much of the access issue in analytics, it’s not some silver bullet that will automatically turn organizations into data driven machines. All this access means enterprises need to teach their teams what data is available, what it actually means, and how to effectively use it. Enterprises next year must not only grapple with implementing AI, but simultaneously instill a culture of data literacy throughout the organization. It’s not going to be an easy transition, but for those that can do it successfully, the opportunities are endless.
Chief Data Evangelist
Insights embedded everywhere
“Software,” it is said, “is eating the world,” but analytics are eating software. And AI is eating analytics.
In other words, as software automates our existence, it creates a level of data size and complexity that only analytics can solve. As more people demand data-driven insights faster, those analytics will be streamlined, and eventually augmented by machine learning and AI.
We see 2019 as the year of “insights embedded everywhere” – analytics embedded not into graphs and dashboards, but into the very fabric of the workplace: products, processes, and places.
The objective is for business decision-makers to get the data they need, when they need it, where they need it – and to eventually eliminate dashboards and even standalone analytics themselves.
ETL capability will be further integrated into BI platforms
With the growing demand for real-world machine learning, all modern data analytic applications will start to offer serious end-user driven data preparation and ETL capabilities alongside classic analytic functionality, because it’s the only way to make machine learning work properly.
Today, it is common for ETL tools used to Extract, Transform, and Load data into the target database to be separate from BI platforms. This limits the effectiveness of machine learning algorithms because they are applied to data that has already been prepared and aggregated. By incorporating ETL capabilities into the analytics platform, it becomes possible to apply machine learning to the raw data, leading to more relevant correlations and insights.