Understanding Machine Learning in Business Intelligence

November 30, 2017

Machine learning is playing an increasingly important role in business intelligence. Businesses across the spectrum have realized the potential of data is a critical aspect of any business strategy. Investments in Big Data related technologies and business intelligence have increased as an enhanced ROI is now possible.

What Does Business Intelligence Mean?

The term business intelligence was first used 150 years ago at the end of the civil war. It refers to data that helps businesses to deal with the ever-changing requirements of the industry. However, the technology used today has become more advanced than back then. Business intelligence integrates applications into data warehousing, online analytical processing, ETL tools- extract, transform and load, and the open source framework Apache Hadoop for distributed data storage and processing.

Micro-Segmentation Analytics – With this, analysts can divide any population like customer data into entities. This will help analysts understand the behavior of these groups more closely. Simply put, data specialists can convert incomprehensible data into meaningful targeting profiles.

Direct Visualizations from Cloud-based Storage – The process of decision making becomes better and faster with the insights that businesses can develop through data visualization. The format of presented data is vital; if the analyzed information is incomprehensible to laypeople, then firms cannot make use of this data.

Running visualizations inside cloud-based storage systems minimize time and resources required for making a source data copy and storing it on the premise. This means that people with less technical knowledge and small organizations with small budgets will also have access to the same data as large institutions.

BI Dashboard – Businesses with a well-designed dashboard can integrate crucial big analytics tools into a single interface that will allow product managers to review operations at a glance.

Now, rich visualizations are easy to build, filter, and iterate with a web-based UI and data scientists can embed and share them securely with anyone; on-site or off-site.

What Does Business Analytics Mean?

BA refers to all the approaches and technologies that businesses use to access and explore data to draw out useful insights. These insights can improve business planning and derive growth in profits.

The use of statistical analysis and predictive modeling is important in business analytics to establish trends, correlations (and causations), and predictions. Data science unifies statistics and data analysis and all their related methods to analyze and understand a phenomenon with data.

The use of big data is different from business intelligence in many aspects:

A distributed file system stores all information in a big data environment, but not a central server.  It has more flexible space and is much safer.

As the analysis is information-centric, it becomes easy to handle the bulk of information in a more agile way.

Whether data is structured and unstructured, Big Data can analyze data in different formats. Unstructured data that is not stored in traditional databases is growing as compared to structured data. The analysis of unstructured data possesses many challenges. Big Data solves these challenges efficiently as it allows analysis of various sources of information globally.

Big Data solutions process data that can be derived in real-time or historically. Therefore, businesses can make decisions that bring agility to work effectively.

The use of parallel mass processing (MPP) concepts in big data technology speeds up analysis. Various jobs are divided into several parallel execution parts, and instructions are executed simultaneously with MMP, which leads to faster processing of the overall results. Thanks to this, it is now possible to process huge amounts of data in a relatively short period.

Predictive vs. Descriptive

BI describes the current situation of your business to you. An efficient BI platform relays real-time and granular information to you as and when you need it.

BI deals with historical data and leads right up to the present. With this information, you can learn from past mistakes and implement these insights into your decision-making strategy, thus ensuring that you replicate what works and change what does not work. Fundamentally, your expertise and judgment are also essential.

Machine learning has an important role to play in Business Intelligence.  Any futuristic activity that consists of the term “Predictive” involves Machine Learning in it. Activities, like predicting future orders or finding a fraudulent insurance claim are examples of Machine Learning. To learn more, you can take an online Machine Learning Course and gain a deeper understanding of the important algorithms that help fuel this field.

BA also helps you understand how business strategies work. By combining predictive analytics with statistical analysis, you can anticipate developments and make changes in your company’s strategies to get better results.

Both BA and BI are valuable but in different ways. To decide what is more relevant to you, analyze if predictive analysis or descriptive analysis is more important to you and then invest accordingly.

For instance, a way to generate predictions is only beneficial for you if you can understand the basis of these predictions. If this ability is beyond your expertise, then using predictive analysis could be redundant when making business decisions.

The Human Role in Machine Learning Business Intelligence

To gain the benefits of machine learning in business intelligence, data streaming through the algorithms need to be in a structured format. While many times, BI software can draw from raw data in data storage systems or data warehouses, there are edge cases that need human intervention. For example, extracting data from a receipt. While computers can extract a lot of it, humans are still needed to verify the information as receipts vary in formats.

This data is then used to create relevant reports, graphs, and charts that decision-makers can use identify trends in their industry. You can use business intelligence analytics for any amount of data.

The fact that BI is scalable (both large and small) is what makes it extremely viable for businesses of any size. Often terms like Big Data may make you feel like you are punching above your weight, but this is rarely the case.  Educate yourself on the options available, and you will realize that there is a BI solution that can economically and efficiently add value to your organization.