12 Impactful Ways To Incorporate Machine Learning Into Business Intelligence

• Read original article here

Business intelligence—the strategies and tech companies use to collect, interpret and utilize data—plays a primary role in informing the strategies, functions and efficiency of a company. However, as essential to a company’s success as BI is, many businesses don’t take advantage of the tools that can improve their BI efforts.

Combining machine learning with BI can have a far-reaching impact on the insights a business gets from its available data, making BI a true game-changer in helping companies improve productivity, quality, customer service and more. Below, 12 members ofForbes Technology Council explore the ways businesses can use machine learning to improve BI.

Machine learning has the ability to improve many operational processes, such as customer service, finance, marketing and much more. Machine learning can collect and use data from all of these aspects of a business and help you automate processes to increase productivity. -Thomas Griffin,OptinMonster

Business intelligence professionals don’t seem to realize that machine learning can seriously affect both the top and bottom of your customer funnel. As the importance of personalization on websites, email campaigns and even Facebook ads increases, it’s so important to be using ML to make your prospective customers feel important. Personalize your message for them at both the top and bottom of the funnel. -Ben Lee,Rootstrap

Machine learning gives business leaders the capability to crunch massive amounts of data and extract actionable insights in an instant. For customer service, this can be leveraged to decipher customer sentiment, detect dissatisfaction and fix any damaged relationships. For all businesses today, better customer experiences are just one algorithm away. - Marc Fischer,Dogtown Media LLC

4. Learn More About Each Prospect

Machine learning helps BI professionals learn more about each prospect. BI professionals and their marketing counterparts can then activate that insight for each unique prospect to tailor their journey through the marketing funnel and deliver more revenue. ML can find patterns and help BI and marketing leaders activate experiences at a level of granularity that was not possible before. -Guy Yalif,Intellimize

BI processes involve analyzing large sets of data—if done manually, this is time-consuming. Machine learning can automate the process so that BI professionals can focus on higher-level trend analysis and behavior patterns that can bring greater value more quickly to the organization. - Sanjoy Malik,Urjanet

While it is common to use AI to predict and automate business decisions, AI can be used by BI teams to improve the way they do data quality checks—both extraction and transformation. For example, anomaly detection in data, outlier identification and triaging, metadata checks and cataloging data better for use by business and analytics users—all these help BI improve data governance standards. - Swetha Ganeg Basavaraj,Datavisor

Beyond demand models that only predict market trends, revenue levels and so on, ML can generate actual answers. As some businesses are now discovering, ML lets them produce highly accurate estimates of future behavior—i.e., answers—based on large volumes of historic data. Ongoing advances in neural networks also continue to aid the ML push, making BI and forecasting more intelligent and concrete. -John McDonald,ClearObject

Through machine learning, anomalies can be spotted in real time and immediate action can be taken. For instance, fraud can be spotted right away—not a week later—or customers can be kept on your website rather than learning about them after they have bought something elsewhere. Systems can be created immediately to avoid the anomalies in the future, increasing operational efficiency. - Ankit Sharma,Inventive Byte

There is a lot of discussion regarding how product data can be used for growth, but not enough of it is related to the creation and development of the product. The same ML algorithms that detect patterns and anomalies within the market can be applied to your internal process—analyze metrics like time spent on tasks, automate this analysis then use the data to become more efficient. -Artem Petrov,Reinvently

Machine learning can be used for BI to analyze your source data and the underlying metadata in its native state, with the resulting data used to recommend and build the most optimum data pipelines and storage locations (based on data types, size, projected uses, etc.). It can suggest correlations between data elements for building recommendations on how to categorize and document the data. -Heine Krog Iversen,TimeXtender

In cybersecurity, automated protection to reduce risk windows or revenue loss is critical. This creates an enormous amount of data processing that must be analyzed quickly. Mature machine learning systems can autonomously collect, analyze and classify threats. This ability to scale with machines is vital to fight cybercrime and helps reduce operating expenses and improve accuracy with continuous learning. - Michael Xie,Fortinet

ML can translate data concepts into business language and use that information to establish a data warehouse cloud. This can act as a semantic layer that maps BI concepts within a data architecture, creating a credible and good-quality point of reference for the business. This will build a digital environment that understands your business and is better suited to answer specific questions. -Nacho De Marco,BairesDev

Business intelligence—the strategies and tech companies use to collect, interpret and utilize data—plays a primary role in informing the strategies, functions and efficiency of a company. However, as essential to a company’s success as BI is, many businesses don’t take advantage of the tools that can improve their BI efforts.

Combining machine learning with BI can have a far-reaching impact on the insights a business gets from its available data, making BI a true game-changer in helping companies improve productivity, quality, customer service and more. Below, 12 members ofForbes Technology Council explore the ways businesses can use machine learning to improve BI.

Machine learning has the ability to improve many operational processes, such as customer service, finance, marketing and much more. Machine learning can collect and use data from all of these aspects of a business and help you automate processes to increase productivity. -Thomas Griffin,OptinMonster

Business intelligence professionals don’t seem to realize that machine learning can seriously affect both the top and bottom of your customer funnel. As the importance of personalization on websites, email campaigns and even Facebook ads increases, it’s so important to be using ML to make your prospective customers feel important. Personalize your message for them at both the top and bottom of the funnel. -Ben Lee,Rootstrap

Machine learning gives business leaders the capability to crunch massive amounts of data and extract actionable insights in an instant. For customer service, this can be leveraged to decipher customer sentiment, detect dissatisfaction and fix any damaged relationships. For all businesses today, better customer experiences are just one algorithm away. - Marc Fischer,Dogtown Media LLC

4. Learn More About Each Prospect

Machine learning helps BI professionals learn more about each prospect. BI professionals and their marketing counterparts can then activate that insight for each unique prospect to tailor their journey through the marketing funnel and deliver more revenue. ML can find patterns and help BI and marketing leaders activate experiences at a level of granularity that was not possible before. -Guy Yalif,Intellimize

BI processes involve analyzing large sets of data—if done manually, this is time-consuming. Machine learning can automate the process so that BI professionals can focus on higher-level trend analysis and behavior patterns that can bring greater value more quickly to the organization. - Sanjoy Malik,Urjanet

While it is common to use AI to predict and automate business decisions, AI can be used by BI teams to improve the way they do data quality checks—both extraction and transformation. For example, anomaly detection in data, outlier identification and triaging, metadata checks and cataloging data better for use by business and analytics users—all these help BI improve data governance standards. - Swetha Ganeg Basavaraj,Datavisor

Beyond demand models that only predict market trends, revenue levels and so on, ML can generate actual answers. As some businesses are now discovering, ML lets them produce highly accurate estimates of future behavior—i.e., answers—based on large volumes of historic data. Ongoing advances in neural networks also continue to aid the ML push, making BI and forecasting more intelligent and concrete. -John McDonald,ClearObject

Through machine learning, anomalies can be spotted in real time and immediate action can be taken. For instance, fraud can be spotted right away—not a week later—or customers can be kept on your website rather than learning about them after they have bought something elsewhere. Systems can be created immediately to avoid the anomalies in the future, increasing operational efficiency. - Ankit Sharma,Inventive Byte

There is a lot of discussion regarding how product data can be used for growth, but not enough of it is related to the creation and development of the product. The same ML algorithms that detect patterns and anomalies within the market can be applied to your internal process—analyze metrics like time spent on tasks, automate this analysis then use the data to become more efficient. -Artem Petrov,Reinvently

Machine learning can be used for BI to analyze your source data and the underlying metadata in its native state, with the resulting data used to recommend and build the most optimum data pipelines and storage locations (based on data types, size, projected uses, etc.). It can suggest correlations between data elements for building recommendations on how to categorize and document the data. -Heine Krog Iversen,TimeXtender

In cybersecurity, automated protection to reduce risk windows or revenue loss is critical. This creates an enormous amount of data processing that must be analyzed quickly. Mature machine learning systems can autonomously collect, analyze and classify threats. This ability to scale with machines is vital to fight cybercrime and helps reduce operating expenses and improve accuracy with continuous learning. - Michael Xie,Fortinet

ML can translate data concepts into business language and use that information to establish a data warehouse cloud. This can act as a semantic layer that maps BI concepts within a data architecture, creating a credible and good-quality point of reference for the business. This will build a digital environment that understands your business and is better suited to answer specific questions. -Nacho De Marco,BairesDev