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The Future Of Enterprise Management With Big Data And Some Essentials Of Data Streaming

2021-08-27 by Jiggy Clark

Big data is the future as data is produced in a huge volume in every human and machine interaction. When it comes to the future of data analysis using big data, we will discuss some of the major predictions in this field, which is taking over the data world by storm.

 

Machine learning for autonomous processes

Machine learning is one of the major applications of big data. ML is not only helping out businesses to automate and streamline their processes but also has a positive impact on the daily life of humans too. As per a study conducted by Ovum, machine learning is predicted to the forefront of big data in the future.

 

Chief Data Officer as a key player in enterprises

When it comes to corporate management, we know the role of CEO, CTO, COO, CMO, etc., and the new chief officer position we are going to be used to is of a CDO, i.e., chief data officer. As per a report by Forrester study, chief data officer will be a key position in modern-day organizations as data will be considered one of the most valuable assets of the organization. The position of a CDO will become a norm in organizations to ensure adequate and secured practices in enterprise data management.

 

Role of data scientists

As we discussed, the volume of data is increasing data by day, so the demand for data management specialists and data scientists can manipulate this data to gain some actionable business insight. There is a significant skill gap in this sector now, and skilled data scientists may be one of the most sought-after careers in the future.

 

Algorithms instead of business software

While building enterprise software or ERPs for automating or supporting business processes, we can see big data may significantly change this. Businesses have started looking for appropriate algorithms for their industry to custom build their enterprise management solutions. Rather than stubborn ERPs, algorithms are more flexible to customize based on your goals and needs. By building applications based on this algorithm, they can customize it for their business goals and load the data into the same for analytics.

 

Considering all these predictions and the fact that many of those have already started rolling, we may assume that the big data platforms will take care of end-to-end business management needs shortly itself. Almost all business intelligence apps are now following these trends. There are plenty of next-gen business applications out there with optimum analytical abilities and data-driven business decision-making capabilities.

 

Data Streaming

The major advantage of big data when it comes to analytical processing and decision making is data streaming in real-time. Rather than storing data in databases to be used for historical data analysis, live streaming data from sensors and servers can be used for live processing. Providers like RemoteDBA.com are already offering real-time data streaming servers and solutions to various industries.

 

From small businesses to larger corporates, data streaming offers many advantages. As the volume of data is increasing day by day, there is also a need for quick and easy processing of the same to facilitate big data management. To leverage this, users also need to know many basic concepts of big data management. Let us further look into data streaming in big data in a bit more detail.

 

When it comes to real-time processing industries like banking or financial services, there are many real-time calculations to be done, like EMI calculations of the loans or the current market performance of a mutual fund. For the users, these need to happen at nearly lively. A website providing such insights to the customers should take data from various sources and combine these in real-time to derive some actionable insights from the same. Data streaming enables this real-time loading and analysis of data, which is a major goal of big data applications.

 

So, data streaming is the process of handling a huge chunk of live streaming data simultaneously to derive timely and accurate results from it in a fraction of a second. With efficient data streaming and accurate analytical procedures, the users get real-time data and data-driven insights on what they search for.

 

You can see a real-time use case of data streaming in the video streaming services of Netflix or Amazon Prime etc. When you sign up for the service, you will gain instant access to the portal. While signing up, you will be able to find many flickers in various languages shown on your feeds, which feature the latest movies and TV series you can live stream instantly.

 

Data is constantly in motion, and in big data, these data sets are processed in various server clusters and stored on to a big data DB. Analytical processing is run simultaneously to this data filtering, and data aggregation is done in real-time. In big data, the major element which decides the effectiveness of the performance is speed. Many modern-day applications like online gaming, mobile apps, social media feeds, e-com stores, banking applications, geospatial tools, healthcare, biotech, and educational applications now leverage data streaming and AI and machine learning capabilities to deliver better services to the customers.

 

Data streaming covering up the drawbacks of batch processing

As we saw above, the major element of data streaming is speed, which was the major thing lacking in the old batch data processing model. Data streaming will consider only a fragment of data at a time called a 'micro-set to deliver more efficient results in nearly real-time.

 

On the other hand, batch processing may be ideal when humans try to assess the stats like attrition rate in a company against employee satisfaction. Further, let us see some of the major differences between data streaming and batch processing.

 

Batch processing

Data streaming

Uses queries from various datasets to provide results

Rely more on individual records and recent data sets for live processing.

The latency can be from a few minutes to many hours based on the data process it handles.

Latency may only be a few milliseconds.

There are a lot of complex algorithms to be run

Involving only simple operations like rolling out the metrics, aggregation, response functions, etc.

 

So, big data comes with many advantages like data streaming, which will revolutionize the future of business applications and functions. You may analyze your changing organizational needs to seek the most appropriate big data solutions to adopt.

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