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  • J Beatrice

Why Companies Should Organize Big Data


A man pointing at Big Data graphics on a presenting screen.

For the top companies to keep up and compete in a crowded digital market, they rely on insights drawn from big data to make better decisions. The big data market is expected to reach $473.6 billion by 2030, owing to a continued rise in Internet and smartphone use. In fact, 58% of the world's large and small businesses are currently focusing on implementing big data technology. However, as its name suggests, big data is vast — and thus, much more useful when organized. Here are a few reasons why:


Provide clearer interpretation and analysis


Today, as the demand for big data analytics increases, there is a growing movement towards teaching data science to ensure future talent is equipped with the necessary skills and knowledge to work with data. Educators engage students by teaching them to analyze data across various topics, from crime to climate change. This pragmatic approach lets students learn how to interpret huge amounts of data in real-world scenarios.


This is ultimately why modern data science degrees focus on preparing individuals with the skills to interpret and translate large quantities of data into actionable insights. Students study tools and techniques to explore, analyze, monitor, manage, and visualize large data sets, allowing them to dig deeper and find richer insights. These organization processes help businesses get detailed interpretations of their collected data to make better and smarter decisions. In the real world, for example, data scientists face a world of opportunities where data science applies, such as forecasting risk for insurance companies or evaluating loan applications for banks. If data is too messy or unmanageable, you're unlikely to get any value out of it.


Speed up time of delivery


Metadata helps describe data for better categorization and organization. Most businesses today invest in digital tools such as cloud-based artificial intelligence (AI) and machine learning solutions to automate this process of capturing metadata, ensuring that large volumes of data are always categorized for faster and more efficient workflows. In our post discussing the importance of good metadata, we emphasize the use of taxonomies by way of metadata management. This allows AI engines to organize data at more incredible speeds than human manual adjustments can, creating more time to train the artificial intelligence needed for the process.


This doesn't mean that AI and tech can replace human efforts entirely. It just means the technology can offer knowledge workers more time and resources to perform other tasks and more valuable functions within the organization, as well as train AI for new processes.


Increase cost efficiency


Lastly, investing in organizing big data can lead to significant cost reductions and more cost-efficient means of working. AI and big data also have the potential to reduce costs while increasing profits for an organization. Taking the example of the supply chain and logistics industry, automating and organizing big data provides shipping and fulfillment departments with real-time insights amidst vast volumes of shipments moving throughout the world simultaneously. Managing big data, in this case, leaves less room for errors and mistakes that may otherwise cost companies and customers.


Real-time insights and tracking are also helpful in providing a better customer experience. Due to the Internet, customers and end users are closer in communications to brands than they used to, increasing their expectations for speed and constant contact. Organizing big data will ensure the correct information will be relayed to customers at all times, such as shipping information and tracking.




Submitted by J Beatrice

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