The Big Data analytics software market has been on the rise. Such tools are increasingly used by companies and governments as they serve different purposes, such as fraud detection and risk management. According to MarketsandMarkets, the Big Data market size is projected to grow from $162.6 billion in 2021 to $273.4 billion in 2026, at a CAGR of 11.0% during the forecast period.
What Is Big Data?
Under Big Data, we imply information that can’t be processed or analyzed with traditional methods by humans and desktop computers. Its unique feature is that the array of information continues to grow exponentially over time. So, the computing power of special Big Data tools is required for effective analysis on such a scale that a human will never cope with.
Why It Outperforms Traditional Approach?
A company equipped with Big Data systems starts operating in a significantly different way. The roots of optimized operations can be traced in how data is analyzed.
| Traditional Analytics | Big Data Analytics |
| Incremental analysis of small volumes of information | Processing all available information at once |
| Editing and sorting out data before processing | Data are processed in their original form |
| Starting with a hypothesis and its testing | Identifying correlations before obtaining the requested information |
| Data are collected, processed, stored, and only then analyzed | Real-time analysis and processing upon receiving |
Why Businesses Implement Big Data?
Understand Clients’ Needs
Data collection and analysis build a detailed portrait of the potential consumer to anticipate audience expectations and optimize advertisements.
Personalized User Experience
Analysis of client behavior allows studying their preferences as well as best-selling items to customize services and increase customer loyalty.
More Accurate Decisions
Big Data analytics enables companies to capture market trends and, based on it, expand business in the corresponding directions (new regions, wider range of products, discounts).
Pricing
It uncovers the conditions which encourage clients to pay more and help companies choose a marketing strategy and increase profits.
New Product Launch
Big Data analysts analyze audience feedback, strategies of competitors, and moods in the market to offer the most appealing product.
Applications
Big Data analytics is used in a wide variety of industries. The greatest demand is observed in the following industries:
- It’s increasingly used in real estate to improve sales, analyze audience attitude, and spot target customers.
- Entertainment. Big Data platforms offer personalized recommendations and analyze viewers’ ratings to generate the most promising film to shoot next.
- Agriculture has been taking advantage of top-notch tools to analyze information on yield, weather, soil condition, air humidity, livestock, and optimize farming.
- E-commerce systems can study user behavior and help predict which product a particular buyer can pay attention to, which makes advertising more effective.
- In terms of security, it helps businesses combat cyber threats by identifying potentially dangerous, suspicious users and taking appropriate action.
- In healthcare, its algorithms collect, structure, and share information on research, diagnostics, treatment, and disease prevention to physicians from across the globe.
- Big Data analytics companies assist businesses in making more accurate data-driven decisions for various sectors (economy, crime, security, emergency).
Implementation Process
Many companies wonder how to implement Big Data systems in their businesses to reap benefits from it. Check out the process of Big Data analytics deployment in a business and the main steps you should take.
1. Preliminary Business Analysis
- Establish business goals
- Formulate questions and challenges that can be solved with Big Data tools
- Find resources that provide relevant data to help find solutions and reach the objectives of the project
2. Data Collection and Analysis
- Check data quality to see if it allows you to answer the questions you set
- Start automatic processing
- Configure a data pipeline for gauging new or regularly updated data
3. System learning
- Create data characteristics based on raw data to facilitate model learning
- Find the most accurate model by comparing success metrics from models
- Determine whether the model will be suitable for the working environment
4. Deployment
- Deploy the model in a working environment or similar conditions so that applications can access it
5. Final Check
- Make sure the deployed model meets the requirements
- Create value for your business with data-driven decisions
Takeaway
Data as a business asset is revolutionizing the global economy and has enormous potential to drive revenue streams for many market players. The earlier a business realizes its data-related challenges and perspectives, the more opportunities it’s bound to have after implementing Big Data analytics.