Business intelligence (BI) can be described as the strategies and technologies used by businesses to collect, interpret and utilize data. BI plays a significant role in formulating strategies, functions and efficiency within a business. But, regardless of the advantages presented by BI, many businesses fail to take advantage of the tools that can help to improve their business processes.
Combining BI with machine learning can create a significant impact in the way a business derives insights from its available data, making BI a proven game-changer in enabling businesses to improve productivity, quality, customer service etc.
Let’s look at 10 keyway businesses can leverage machine learning to improve BI:
1. Improve Operational Processes
Machine learning possesses the ability to improve several operational processes including customer service, finance, marketing etc. Machine learning applications can gather and use data from these operational processes and aid businesses to accordingly automate processes to increase productivity.
2. Personalize Customer Funnels
BI experts are hesitant to realize that ML is a serious factor capable of affecting both; the top and bottom of a business customer funnel. By increasing the implementation of personalization using ML on websites, email campaigns and even social media ads, businesses can make their prospective customers feel more valued and important.
3. Give Customer Experiences A Human Touch
Machine learning provides business leaders with the ability to process massive amounts of data and extract actionable insights and patterns in an instant. This can be leveraged for customer service to better understand customer sentiment, detect dissatisfaction and amend any damaged relationships. Today, what this means is for all business, improved customer service is just one algorithm away.
4. Learn More About Each Prospect
Machine learning allows BI experts to learn more about each prospect. BI experts and their marketing counterparts can then choose to activate the insight for each unique prospect and tailor their route through the marketing funnel to generate more revenue. ML can help detect patterns to enable BI and marketing experts to create an experience customized to each prospect, one that was not possible before.
5. Analyze Large Sets Of Data
BI processes deal with analyzing massive sets of data and if it is done manually, it is quite time-consuming. Machine learning can automate the process which allows BI experts to switch their attention to higher-level trend analysis and behavior patterns that has the potential to quickly bring increased value to the business.
6. Improve Data Quality Checks
Predicting and automating business decisions using AI is quite common but AI can also be deployed by BI teams to improve the way they manage data quality checks in both; extraction and transformation. A good example of this is in anomaly detection in data, outlier identification and triaging, metadata checks and cataloging data better for the use of analytic users and the business, which can all help BI improve data governance standards.
7. Provide Actual Forecasting Answers
Besides creating models that predict market trends, revenue levels etc, ML can also generate actual answers. As many businesses are now learning, ML facilitates them a generation of highly accurate estimates of future behavior – i.e., answers – based on the massive volume of historic data. Technological advancements in the field of neural networks have also benefited ML applications, making BI and forecasting more intelligent and reliable.
8. Achieve Real-Time Data Analysis
Using machine learning, anomalies can be detected in real-time, according to which immediate action can be taken. For example, incidents of fraud can be detected immediately or customers can be kept on your website rather than learning about them through their purchases or experiences elsewhere. Systems can be set up immediately that can avoid anomalies in the future, thereby increasing operational efficiency.
9. Identify Patterns Among Employees
BI experts are always looking for new ways to use product data from growth, but not enough of it deals with the creation and development of the product. The same ML algorithms that can detect patterns and anomalies within a market can also be used in internal processes, to analyze metrics such as time spent on tasks, automate this analysis and then use the resulting data to become more efficient.
10. Build Optimum Data Pipelines
ML can be leveraged for BI to analyze a business’ source data as well as the underlying metadata in its native state, with the resulting data being used to recommend and create the most optimum data pipelines and storage locations. It can even recommend correlations between data elements for creating suggestions on how to categorize and document the data.