How is Advanced Analytics Changing the Future of Business?

According to a survey, 90% of the world’s business data is unstructured, and sorting through it to find what is pertinent to your business is not as easy as a snap of the fingers. Whether it’s a production concern or a customer service issue, data analytics highlights critical areas of concern to help companies become more proficient at predicting obstacles and move forward with the best approach. Therefore, organizations worldwide are embracing advanced analytics to enable great functionality from their data assets, streamline operations, and improve processes.

Advanced analytics encompasses mature techniques such as process automation, machine learning, predictive modeling, etc. Data scientists often leverage advanced analytics tools to help an organization become more responsive in critical decision making. The tools provide a variety of options for improved visualization and predictive models, analyzing business information from various data sources, which helps organizations gain a competitive advantage.

Types of Advanced Analytics

Based on complexity, big data solutions can be divided into four categories.

Descriptive Analytics

From keeping tabs on operational performance to highlighting trends in historical and current data, descriptive analytics play a significant role in every kind of business. From finance to production and sales, social media engagement to surveys, descriptive analytics has a key role to play in the company’s growth. This type of analytics can be used to produce reports, KPIs, and business metrics to understand why something happened and predict potential future outcomes for determining possible actions. Metrics produced by descriptive analytics can be used in various ways like generating financial statements, displaying metrics in charts and other graphic representations, track progress and manage daily workloads. Companies use descriptive analytics to track their progress and compare competitors’ performance with other businesses. 

Diagnostic Analytics

Diagnostic analytics is performed to discover why things happened, dig deeper into the causes, and discover why they got better or worse. This type of analytics helps in problem-solving and adds significant value to a sought response. Diagnostic analytics comprises gathering data, provoke insights, create new information, and make correlations between variables. Knowing where you stand in the market can help provide you with a detailed picture of the business landscape and eliminate uncertainty in decision-making. Leveraging diagnostic analytics, the sales team can identify shared characteristics and behaviors of profitable customer segments. The marketing team can identify reasons for performance differences, and the finance team can compare the timing of key initiatives for Y-o-Y revenue growth. 

Predictive Analytics

Predictive analytics utilizes statistics and modeling techniques to make predictions about future outcomes. The analytics involves looking at current and historical data patterns to improve operational efficiencies and reduce risks. Predictive models are used for a variety of applications such as creating video games, weather forecasts, translating voice to text, customer services, investment portfolio development, and so on. 

Prescriptive Analytics

Prescriptive analytics help businesses make better decisions by analyzing raw data with the use of statistics and modeling. Prescriptive analytics recommends the future course of action, preventing fraud, limiting risks, increasing efficiency, meeting business goals, and creating loyal customers. Data-intensive businesses, financial services, health care sector, and government agencies leverage prescriptive analytics to reduce human errors. 

Advanced Analytics Techniques

Sentiment Analysis

Sentiment analysis or opinion mining is often performed on textual data to help businesses monitor brand and product sentiments and understand consumers’ needs. The analysis helps determine whether the data is positive, negative, or neutral using natural language processing, machine learning, and other data analysis techniques and derive objective quantitative results from raw text. Sentiment analysis involves examining social data available in the form of customer reviews, feedbacks, testimonials, opinions in survey responses, social media conversations, and other brand mentions. Since customers have become proactive in expressing how they feel about a product or service, sentiment analysis has become an essential metric for brands to monitor and understand that sentiment and meet evolving customers’ needs, design price plans, and enhance customer services.

Depending on how you want to interpret customer feedback and queries, you can tailor the categories into very positive, positive, neutral, negative, and very negative for fine-grained sentiment analysis. Emotion detection is another type of sentiment analysis that helps detect different emotions such as happiness, anger, sadness, and so on using lexicons or complex learning algorithms. Aspect-based sentiment analysis helps determine the opinion of customers about any specific product feature. Each type of sentiment analysis can be beneficial for sorting data at large scale, make real-time analysis, improve accuracy, and gain better insights. 

Data/Text Mining

Data mining is one of the best techniques for discovering correlations, patterns, and trends by analyzing large amounts of data stored in repositories. The technique is integral to business intelligence as it helps generate valuable insights with the help of different technologies such as natural language processing, artificial intelligence, machine learning, and other software tools that facilitate it. Some of the common data mining techniques utilized by statistical methods and pattern recognition technologies are as follows. 

• Pattern Detection is the very essence of the term data mining. Taking a set of data and applying statistical methods help researchers discover previously unknown patterns. Pattern detection can be represented with simple line graphs, bar graphs, or pie charts.
• Classification and clustering analysis are techniques used in data mining to identify groups and clusters within your datasets. While classification uses predefined classes to label data, clustering helps to identify similarities between objects.  
• Regression Analysis helps identify variables and their effect on different metrics that allow you to examine the relationship to gather detailed insights for improving products and services. Data collected from these feedback surveys measure the audience satisfaction levels for the offered product/service.  
• Predictive data mining or predictive analytics is the use of data to support forecast results, making use of algorithm-based tools. The analysis helps discover knowledge and find the best solutions.

Outlier Analysis Detection 

Outlier analysis or anomaly detection identifies data pointers, events, and observations that indicate critical incidents such as technical glitches or potential opportunities. Anomaly detection involves measuring the operational performance of application and infrastructure components as well as KPIs to explore the performance of their business. Successful outlier analysis hinges on the ability to analyze time-series data in real-time precisely. Time series data anomaly detection is used to measure valuable metrics such as web page views, daily active users, mobile apps installed, cost per head, cost per click, customer acquisition costs, bounce rates, churn rate, revenue per click, volume of transactions, average order value, and so on. 

Machine Learning

The use of machine learning (ML) is expanding rapidly in business with the rise of artificial intelligence solutions to help improve processes and streamline businesses. According to the International Data Corporation (IDC) estimates, spending on machine learning is estimated to reach approx. USD77 billion by 2022. Companies of all sizes are employing machine learning technologies to increase manufacturing and logistics efficiency, enhance sales, and create a better customer service experience. With the growing importance of machine learning, industries are developing more robust machine learning models that can analyze complex data faster. 

Visualization

Data visualization is the key to unlock big data and solve data inefficiencies. Enabling users to understand data rapidly, visualization can increase the speed of decision making and avert any losses. Analyzing data in a better way requires attention and helps analysts understand vital points needed for the business and help companies increase their profits and make better decisions. Besides, data visualization facilitates gaining insights into the vast amount of data while making sense of the patterns that require attention. Without visualization, it is difficult to spot errors and identify critical information from them. 

Pattern Matching

Some of the most successful companies have mastered the art of pattern recognition. Pattern recognition explains the use of computer algorithms to recognize data regularities and patterns. Pattern matching analysis can be made through various input types such as image recognition, facial recognition, and biometric recognition and is widely used in various fields such as healthcare, seismic analysis, and healthcare. 

Cluster Analysis 

Cluster analysis has extensive application in the business to break a large heterogeneous population into small homogenous groups. The exploratory data analysis tool aims to sort different objects into groups to form a maximum degree of association. For instance, a grocery retailer can group customers based on their purchasing behavior to build an effective marketing strategy. Marketers can come up with a different kind of group called ‘Fresh Food Lovers’ for customers who prefer purchasing a great amount of organic food, vegetables, salads, etc., and create a campaign the emphasizes the freshness of fruits and vegetables to appeal to this customer group. Clustering analysis can also be used to discover structure for interpretation or pattern detection. 

Multivariate Statistical Analysis

The majority of the real-world problems are multivariate, i.e., something that involves many dependent variables. Multivariate analysis is a statistical procedure to analyze a problem that involves more than one factor of independent variables, which influence the variability of dependent variables to make accurate predictions in real-life situations. 

Simulation

Businesses utilize simulated environments that allow you to test out new ideas before making complex business decisions by manipulating different parameters such as revenue, costs, etc. Simulation models provide a graphical display of information showing what might happen after taking specific actions. 

Complex Event Processing

Complex event processing (CEP) can be used to make high-level inferences about complex events within the business domain using models of causality and conceptual hierarchies. These tools help determine the cause-and-effect relationships, which provides organizations the ability to define and predict events at a rapid speed. CEP can be proactively used to observe business processes and other resources, analyze the most critical opportunities, track industrial facilities, etc.

Neural Networks 

Neural networks are a set of algorithms modeled after the brain to detect patterns and interpret data using a type of machine perception, clustering, or labeling raw input. The recognized patterns are contained in vectors or numerical, which can be interpreted into text, sound, time series, or images to understand real-world situations. The various advantages of neural networks for businesses include:

• Cost and time benefits 
• Faster than a human workforce
• High quality and accuracy in outputs
• Faster and error-free
• Improve business performance
• Better growth of the organization
• Better work performance 

Big Data Analytics 

Big data analytics involves examining big data to uncover hidden patterns, and customer preferences that can help organizations make informed decisions. Organizations can use extensive data analytics systems to make data-driven decisions, explore new opportunities, improve operational efficiency, and enhance customer personalization. Big data analytics finds applications in various operations such as:

• Customer acquisition and retention
• Price optimization 
• Product development
• Inventory management
• Risk management
• Pattern detection
• Improved decision making

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