What is Data Analytics, and What Are Its Types?

Business applications involving data analytics serve distinct long-term objectives. Sometimes, insightful discoveries depict how a company has performed in the preceding financial year. Otherwise, a data analytics company might specialize in predicting revenue growth next fiscal. This post will describe all these types of data analytics.

What is Data Analytics?

Data analytics involves implementing computing logic to extend statistical modeling capabilities for scalable insight extraction across corporate databases. So, enterprises get help from a data analytics company

• to improve competitive strategies and pattern identification in business intelligence (BI).

It eliminates inefficient number-crunching, allowing analysts to concentrate on optimization and data quality management (DQM). Besides, you can enhance its ease of use through flexible data visualizations by leveraging reporting dashboards in the BI platforms.

Part I – Types of Data Analytics Based on Business Modeling

1| Descriptive Analytics

Entrepreneurs want to understand their historical performance. So, modern data aggregation solutions

• tap into company databases, media coverage sources, and user-generated content (UGC) to help businesses explore how their brand perception and sales evolved in the last few decades.

Finding insights into historical data is the primary objective of a descriptive analytics company. It also offers multiple "data views" and facilitates DQM to identify irrelevant records or perform automated in-place corrections.

2| Diagnostic Analytics

Diagnostic analytics assesses the dataset created for descriptive analysis to highlight the company’s past performance challenges. Moreover, it can suggest improvement ideas to mitigate the related risks in your ongoing projects.

For example, a diagnostic analytics company will offer your insights that can solve problems like supply chain inefficiencies observed in the last business quarter. Otherwise, you can utilize these insights to improve the precision of other types of data analytics.

3| Predictive Analytics

Innovations in data engineering services have realized the human ambitions of estimating what will happen tomorrow. Therefore, predictive analytics assist organizations in calculating revenue growth for the next fiscal quarter.

Likewise, it is essential to risk assessments associated with how a change in corporate policies or administrative laws impacts an industry. This analytical modeling discovers patterns in the future, allowing enterprises to gain a realistic outlook on their financial resilience and operational sustainability.

4| Prescriptive Analytics

If you know an accident will happen in seven possible ways, you will develop a protection mechanism that avoids or reduces the risks. This concept also demonstrates the significance of prescriptive analytics.

It takes predictive analytical models and adds value by offering risk mitigation recommendations. Furthermore, a company providing prescriptive data analytics can suggest modifications in risk response strategies when the underlying predictive model evolves upon receiving new data.

Part II – Types of Data Analytics Inspired by Business Use Cases

1| Marketing Analytics

Organizations utilize data aggregation solutions to analyze market trends. After all, they require intelligence on customer segmentation and product performance. A marketing analytics company will leverage data management and insight discovery to help businesses modify their marketing strategies for sales growth.

2| Competitor Analytics

Competition helps encourage innovation, keeps rewards proportional to risks, and provides consumers with multiple purchasing options. However, you require competitor analytics to ensure the company’s market share keeps increasing. Collecting BI that encompasses the business rivals’ press announcements and sales tactics lets you develop counterstrategies to surpass them.

3| Supply Chain Analytics

Manufacturing output depends on the timely delivery of suppliers’ offerings. So, data engineering services will develop programs and application programming interfaces (APIs) that help analytics professionals evaluate a company’s supply chain strengths and weaknesses. Later, managers can collaborate with vendors to identify shipment scheduling and price negotiation insights.

4| Customer Analytics

Customer analytics applies modeling and insight discovery solutions to consumer data to highlight marketing personalization opportunities. Additionally, it allows you to calculate customer lifetime value (CLV) and net promoter score (NPS). These metrics help you estimate consumer satisfaction and brand loyalty.

5| Human Resource (HR) Analytics

Diagnostic analytics will leverage the employee performance data from aggregation solutions to list productivity enhancement insights. Simultaneously, a company can employ productive data analytics to forecast employee attrition risks. HR analytics also discusses the workplace and office culture, emphasizing employees’ sociability and team spirit.

6| Financial Analytics

Cash flows bear risks due to debt liabilities, corruption, and operational inefficiencies. So, financial analysts use data aggregation solutions to find accounting inconsistencies. Besides, analytics optimized for the finance and sales departments improve reporting quality for regulatory disclosure in investor relations (IR).

7| Production Analytics

Machines need servicing, while computers require new firmware. Yet, factory management must specify a good maintenance routine that does not threaten continuous production. Therefore, organizations employ data analytics to find extensive insights into production strategies. Later, the acquired insights can encourage equipment upgrades and IT modernization.

Conclusion

The corporate world has used advanced data engineering services to unlock the potential of statistical modeling in improving commercial activities. So, descriptive and diagnostic analytics will increase your understanding of a company’s past. Moreover, predictive and descriptive analytics can conduct scenario simulations to modernize risk management.

You can also distinguish the types of data analytics based on how their applications solve a particular business problem. Consider analytics’ business use cases across human resource management, finance, production, marketing, customer relations, supply chain, and competitor intelligence.

SG Analytics, a leading data analytics company, has served global organizations in finding actionable insights into multiple business operations using the latest technologies. Contact us today to acquire data-driven performance enhancement strategies powered by deep learning models and domain expertise.

SG Analytics is a leading global insight and data analytics research company facilitating tailor-made data-centric solutions to enterprises across the globe.