HOW COMPANIES ARE ORGANIZING BIG DATA

How Companies are Organizing Big Data

Managing a million subsets of data has become crucial for any organization. But even with the mushrooming of data management systems, data officers, etc., most organizations are still lagging.

Studies have continued to demonstrate that organizations are still underutilizing their data. Deriving meaningful insights from these data is a priority for any organization. Extensive data collection and organization mostly involves structured, semi-structured, and unstructured data generated by people and computers.

The following are some of the strategies deployed by successful companies to organize their big data:

DATA MANAGEMENT OFFICERS

Having a team of data management officers is usually the first step in organizing big data. Some companies task these officers with a fairly focused view around support on traditional functions like marketing, pricing, and other specific areas.

This is usually done by going after a particular outcome and organizing a data set to accomplish that outcome. They are also tasked with pruning and making the company’s data to be of higher quality.

DATA ANALYTICS SOFTWARE

Companies are now setting up analytic departments for analyzing and evaluating their collected data. As data grows, it gets processed, stored, and analyzed.

A company can use an analytics platform for processing and analyzing data into meaningful information. In a nutshell, an analytics platform is a computer program that collects and processes structured and unstructured data. It then organizes the data into a form that can be analyzed. Finally, the data is presented to the business user so that he or she can understand.

Data is collected from various sources, such as traditional spreadsheets, finance systems, enterprise resource planning (ERP) systems and social media. Analysis platforms can integrate with all these systems and with a company’s website and mobile apps.

A company’s analytics platform should be able to handle big data and data of all volume, velocity, and variety. The platform should also provide a visual interface, so the business user can more easily find insights and make decisions.

There has been an emergence of AI training data programs such as Appen, specializing in collecting images, text, speech, audio, video, and sensor data to help you build, train, and continuously improve your organization’s most innovative artificial intelligence systems. Many companies offer an AI training data platform with a proven track record in speech and language technology, text analytics, and data management.

Training data can be defined as data used to teach AI models or machine learning algorithms to make proper decisions. Training data is crucial to the success of any AI model or project. Put simply, think of it as garbage in, garbage out (GIGO). If you train a model with poor-quality data, how can you expect it to perform?

CLOUD COMPUTING AND CLOUD-BASED SERVICES

Many companies are using cloud computing to manage big data. They use a cloud-based analytics platform to collect, store and analyze data. It is easier to use a cloud than invest in a data center. Cloud-based platforms have the following benefits:

  • Affordability: A cloud-based analytics platform is inexpensive to use. It is much cheaper than building and maintaining a data center.
  • Accessibility: Cloud-based analytics allow users to connect to their data from anywhere. They can access it from anywhere with a computer or smartphone.
  • Scalability: If more storage or computing power is needed, it’s easy for companies to scale up or down.
  • Security: Cloud-based analytics have better security measures. It’s easier to secure data on a server in the cloud than it is on a private network.
  • Data portability: Cloud-based analytics users can move their data to another cloud-based analytics provider.

FINAL THOUGHTS

The ability to manage large sets of data is more critical today than ever before due to the increase in the complexity of the data, the volume of data, the rate of data generation, and the challenges that come with the same. A tool that can simplify the complexities of dealing with large data sets is needed.

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