Data Analytics: What does the Future have in Store for You?

Data analytics is important, and thousands of pieces of content floating on the web justify it. However, what next? Where are data analytics going forward? How can we leverage the data analytics revolution?

In today's passage, we will deal with all these and more. We also recommend exploring Manipal University Jaipur to learn industry-ready data skills and knit a promising career in data analytics. Students can pursue a BTech in Computer Science & Engineering and specialise in Data Science to study all about data analytics.

Let us begin with defining data analytics.

What is Data Analytics?

According to Investopedia, data analytics is the science of analysing raw data and deriving conclusions from that information.

Example: An e-commerce organisation can use data analytics to track customer behaviour and employ insights to enhance the overall experience. Data analytics tools support the contemplation of previous trends and the prediction of data-backed future trends. They facilitate informed and better choices that are not based on random guesswork.

• Recognising the problem areas
• Aggregation of raw data sets that can help resolve the problems
• Cleaning the data for analysis, including duplicate records
• Analysing the data through data analytics tools
• Interpreting the results, finding patterns and improving on the results

Types of Data Analytics

• Descriptive
• Diagnostic
• Predictive
• Prescriptive

Data analysts require important tools for performing data analytics, such as Python, R. Tableau, Power BI, and SAS.

Core Sectors of Data Analytics Applications

Data analytics is helpful for almost all industries and sectors. Data-based information is crucial to making key decisions. As Tim O 'Reilly once said, "We are entering a new world in which data may be more important than software." The significance of data analytics is evident across the spectrum.

We have explored what data analytics is, its types and more. Now, let us examine its applications.

• Retail companies use data analytics to understand buying needs, patterns and areas of preference. Data crunching helps to forecast demand and manage inventory accordingly.
• From developing new medicines to analysing patient data for exact diagnosis and treatment, healthcare organisations and hospitals use data analytics for diverse purposes.
• Data analytics plays a crucial role in smartening up manufacturing. It is among the critical skills for Industry 4.0 and smart factories. Data crunching and deriving information can help solve complex supply chain problems, equipment breakdown and labour constraints.
• Banking, finance, and insurance sectors can leverage data analytics' exceptional advantages. These include fraud detection, customer segmentation, lifetime value prediction, personalised banking, customer relationship management, churn prediction, liquidity strategy, and more.
• Data analytics help to optimise logistics and minimise transportation costs. It can analyse traffic patterns, fuel costs, routes and other variables for time-efficient and cost-efficient transportation routes. Along with optimised transportation, it can also reduce delivery time significantly.

Learn BTech in Computer Science & Engineering with a Data Analytics specialisation at Manipal University Jaipur and learn about the diverse applications of data analytics across industries.

Future Trends in Data Analytics

Therefore, data analytics is a boon for informed business decisions, and industries need to incorporate this for efficient production, reduced wastage/losses, and improved human productivity. Here are key future trends in data analytics for your reference.

1) AI-Powered Data Analytics

Artificial intelligence in data analytics boosts data visualisation and analysis. It can also improve humans' data management skills. AI in Data Analytics speeds up business operations and helps with flexible decisions based on required changes. 

The reason behind its popularity is advanced and dynamic algorithms that analyse data at diverse scales. These algorithms can fuel several cases that are not possible with traditional methods.

2) Data-Centric AI

Data-centric artificial intelligence is another significant trend in data analytics. It refers to the systematic order of engineered data for generative AI processes. It primarily focuses on analysing, applying, and making decisions based on numbers and data. It uses machine learning and data analytics, not algorithms, for better data management.

3) Metadata-Driven Data Fabric

Data fabric is a method that separates information from metadata. It learns, analyses, and makes decisions accordingly. Metadata-driven data fabric also highlights issues or opportunities related to data and associated suggestions. It has improved data management efficiency by reducing deployment, design and other data management tasks.

4) Edge Computing

Edge computing is a series of networks and devices near users that help collect data from devices with low-latency connectivity, secure platforms, and high-performance processing. It is an emerging trend for data analytics in which data is processed around the network periphery. Edge computing enhances data travel time from a device to a nearby edge.

Best engineering colleges in India keep their curriculum flexible enough to incorporate new concepts like these and keep students updated with recent trends.

5) Augmented Analytics

AI and machine learning-operated augmented analytics is a sub-branch of data analytics. It boosts the human ability to interact with contextual-level data and information. The augmented analytics automate and process data through natural language processing and machine learning. According to a global market research firm, augmented analytics will attain the $ 66.54 billion mark by 2030 globally.

6) Natural Language Processing

NLP is a subfield of software engineering, semantics, and artificial consciousness. It helps seamless interaction between computers and human languages. It makes different languages understandable and interpretable to machines, enabling them to perform many functions, like chatbots, language translation, sentiment analysis, and more.

Career Options in Data Analytics

Here is the list of top ten career options after a degree in data analytics. We suggest Manipal University Jaipur for BTech Computer Science & Engineering with a specialisation in Data Analytics.

• Data Analyst
• Business Analyst
• Data Scientist
• Data Engineer
• Data Visualisation Specialist
• Machine Learning Engineer
• Quantitative Analyst
• Data Journalist
• Healthcare Analyst
• Data Architect

Conclusion

These are broad job roles available with a data analytics specialisation from the best engineering colleges in India. Refer to the future trends in data analytics and apply now for one of the best universities in India for BTech programs like Manipal University Jaipur.