Advanced Analytics vs. Business Intelligence 

What is analysis?

Complex systems need to be broken down into smaller modules to make more sense out of them. The analysis involved focusing on smaller modules to understand the intricacies.

What is analytics?

The study of data sets, facts, and figures to deduce meaningful patterns that may help make better predictions and models. Enterprises use analytics to increase their return on investment using business analytics and prediction modeling.

What is advanced analytics?

This is a technique to extract meaning and infer beneficial deductions out of data. The main idea is to find forecasts and trends which help to make the future of enterprises more progressive. Techniques such as; data mining, machine learning, data science, image processing, pattern recognition, market basket analysis, sentimental analysis, statistical modeling, and computational intelligence, make it possible to carry out advanced analysis. Data mining is the main branch that drives advanced analytics because it works with big data processing, which later makes it possible for predictive analysis to deduce results. Business improving insights is the key product of advanced analytics.

Nowadays, it is being used to make critical business decisions, strategies, and to plan the future.

What is the traditional BI?

Advanced analytics is an improved version of traditional business intelligence. Traditional business intelligence focuses on business and marketing techniques only, whereas advanced analytics makes use of computer science techniques to generate valuable business insights. Business Intelligence(BI) and advanced analytics differ by purpose, approach, data used, and techniques.

Traditional BI vs. Advanced Analytics

Advanced analytics can dig deeper, be more strategic, and provide workable insights from the data as compared to traditional BI reporting.

Advanced Analytics Methods & Techniques

The advanced word refers to multiple quantitative methods of operations such as mathematical modeling, statistics, algorithms, and stochastic operations. All techniques are future-oriented and support data-driven models. It can be broken down into four main functions; descriptive analysis, data science, predictive analysis, and simulation/optimization.

• Descriptive Analysis: Answers queries to describe the underlying phenomenon.
• Data Science: Finds useful patterns in the data.
• Predictive Analysis: Finds underlying relationships between elements for better predictions.
• Simulation/optimization: Replicates the environment and observes the transitions.

To understand why something is occurring the way it is and what could happen if the same pattern gets repeated over and over again, there are advanced analytics methods.

• Data Mining: Identifies sequences, anomalies in raw data.
• Machine Learning: Finds patterns and creates statistical models to replace manual efforts.
• Cohort Analysis: Examines behaviors of people such as customers in fixed periods.
• Cluster Analysis: Recognizes similarities (or differences) and provides visualizations.
• Retention Analysis: Recognizes what makes customers stay (or leave) with changing strategies.
• Event Analysis: Showcases the effects after a change such as changing prices.

Benefits of Advanced Analytics

Businesses need advanced analytics to fine-tune their business operations. The data-driven approach is the most modern way to manage the processes and use the data to the best of its abilities. Advanced analytics and business analytics drive the decisions to the best and highest expected potential. Mostly with detailed analysis, personalized strategies can be made such as marketing, expansion, product differentiations, or any other business strategy. Good strategies make the customers more satisfied and result in lesser churn rates. On top of that, future predictions can be done to carry out better decision-making. For example, a superstore figuring out the best time to put on bumper sale. Another advantage of using detailed analysis is that higher confidence levels can be attained. Data can be utilized to the best of its potential and better ROI eventually.

Overall, every decision is backed up by logic and facts, which helps the management to make efficient decisions.  

With help of a safe and reliable analysis tool, companies can reliev this time-consuming task .
Check out BiG EVAL Data Quality or Data Testautomation.

Challenges of Advance Analytics

Even though advanced analytics is an advanced form of business intelligence, it does have a higher failure rate than BI. Traditional BI may show simpler reports and the dependency is that the data is structured and correct, it does also imply that advanced analytics may need to work with haphazard data and eventually fail to produce any insight from the raw and unstructured data. Advanced analytics is also fairly new so it involves a lot of experimentation and an expert skill-set. Only a highly skilled person can carry out advanced analysis, unlike traditional BI professionals.  Also, to understand the product of advanced analysis, an expert understanding is required. No layman can interpret the results and why a certain algorithm or a model is best suited. High computational power is a requirement and results can’t be produced using small processors.

Best Practices of Advanced Analytics

Data is the centre point of organizations now a days. For good analysis-based culture, keep the following points in mind.

• Use the latest tools and technologies.
• Make use of automation tools.
• Hire analytical professionals and train them.
• Develop POCs (proof of concept) before starting any development.
• Cleanse the data set.

Future of Advanced Analytics

Advanced data analytics will gain more importance in the future. It is going to become the highest-paid skill in the coming years. Organizations will realize the potential of all sorts of data and use that in their favor. In the future, it is going to be a necessity rather than a bonus as company data is going to get much more complex in the future. Multinational companies are already having dedicated teams for advanced data analysis and this practice is going to gain more momentum shortly.

BiG EVAL understands the importance of big data management and analysis and provides suitable solutions to all of its customers.

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