Machine Learning: The Future Of Business Intelligence

Paul Gilbert

Business intelligence, a technology that was until recently considered largely unreliable, is now the fastest-growing branch of artificial intelligence (AI). While data warehousing has helped address many past business challenges, business intelligence has proven to be a superior approach to generating reports, as it is highly flexible and does not require complicated programming to stay updated.

Today’s business leaders are making decisions that are inspired by data and information, and automation is the next stage. Businesses that adopt AI and machine learning early are showing cost benefits, increases in customer satisfaction and revenue, and exponential growth in overall performance.

Intelligent advice

According to Zea Proukou, companies are now considering the notion of decision making inspired by data and information, which is showing some results in improving cost reduction, customer acquisition, overall business performance, and revenue management. Automation seems to be the next stage.

Getting things done

Two critical areas of business productivity—decision support and detecting anomalies—require careful strategy as well as the right tools and data. Having the ability to generate accurate static reports with minimal manual applications is the most effective way for forward-thinking companies to approach this challenge.

Moving beyond Hadoop

For the past decade, Apache Hadoop has been the dominant tool in the business intelligence industry. However, newer, cloud-compatible alternatives that offer dependable structure and framework are becoming increasingly available, offering companies more BI options to meet the business challenges they will face in 2017 and beyond.

Streaming analytics

Streaming is fast becoming the standard approach to data analysis, and enjoying its full benefits requires automation. As organizations increasingly depend on Internet of Things technology and real-time data analytics, we can expect to see greater adoption of streaming analytics in 2017 and beyond.

Data scientists are still needed

In 2017, we will continue to see strong demand for data scientists. As more data science graduates enter the field and address the labor shortage, hiring managers can shift their focus to specific applicant qualifications. Employers have set their standards high for both data scientists and data engineers because these fields will represent a hiring battlefield in the near future.

Cloud-based analytics

Data analytics is headed for the cloud in 2017. With cloud-based data storage becoming standard across most industries, the next move for businesses is to enable cloud-based data analytics. Stay tuned!

For more on how future technology is transforming business, see How To Take AI From Tech Dream To Mainstream IT Opportunity In 2017.



About Paul Gilbert

Paul Gilbert is a professional blogger, an enthusiast who loves to write on several niches.