The concept of big data has been with us for quite some time now. While it has no strict definition, it enforces the idea of a huge dataset whose true value can not be unlocked by assessing it in parts. Analyzing or even reviewing such a dataset would require advanced data processing tools, which brings us to artificial intelligence (AI).
Data is the building block of AI systems because it is labeled and then used to train and improve AI algorithms. On the other hand, a well-trained AI system can help make sense of large datasets with ease. This mutual relationship is extremely beneficial for businesses across the globe as they can utilize both of these technologies to strengthen their enterprise data ecosystem.
Here’s a quick introduction to how AI and big data work together to revolutionize data processing. We’ll also cover the role of data annotation (and that of working with a data annotation company) in this context to make sure that you can make the most of your enterprise data.
How AI Is Transforming Big Data Management?
In recent years, artificial intelligence has become an increasingly important tool in big data analysis. Not only is AI able to process and interpret large amounts of data much more quickly than human beings, but it can also identify patterns and trends that would otherwise be difficult or impossible to detect.
Here are the four significant ways in which AI has helped transform the way businesses manage big data.
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Contextualizing Data
Artificial intelligence (AI) can help contextualize data by providing a more comprehensive view of the data. AI can also help identify patterns in the data, which can assist in making better predictions. Thus, businesses can develop more effective marketing campaigns and create tailored customer experiences.
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Making Data More Accessible
Instead of having to go through each piece of data (across multiple systems) to find valuable insights, augmented analytics automates this process. This makes it much easier for users to find what they’re looking for.
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Improving the Process of Data Recording
Usually, collecting data that can be used for analysis is a time-consuming and tedious process. To make sense of all the data that’s being collected, analysts first need to enter that data into a central repository. Then, they use various data analytics tools to make sense of it. While data entry still remains a major manual process (aided by AI tools in parts), there are certain AI-based systems that can help expedite it.
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Ensuring Higher-quality Data
Big data presents the challenge of maintaining data accuracy and quality across possibly hundreds or thousands of datasets while preserving time. Reasonably, companies have turned to automation for assistance. Many AI-based systems can be used for classifying data, identifying errors, validating datasets, etc. Although most such tools require human intervention at one or the other point, the semi-automated ecosystem thus created is very helpful in maintaining enterprise datasets quickly and effectively. Additionally, it empowers businesses to make better decisions based on empirical evidence rather than intuition or guesswork.
How Can AI and Big Data Help Boost Business Profits?
Businesses of all sizes are looking for ways to boost profits. AI and big data are two powerful technologies that can help businesses do that while reducing their overhead expenses.
Big data provides a more detailed account of information on customer behavior and trends. AI can help capitalize on that data by improving the accuracy and speed of analysis (and hence, decision-making). Combined, these two technologies can give businesses a 360-degree view of their operations, increase their understanding of consumers and the market, and help them grow towards their desired objectives with data-backed decisions.
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Intra-company Connectivity
Internal communications can be improved by using artificial intelligence (AI) and big data. With big data, organizations can collect and analyze a wider range of employee data to better understand their needs, motivations, workflows, and obstacles. This information can then be used to create strategies that can boost employee productivity. Additionally, AI-based systems can help organizations identify trends and patterns that they would have otherwise missed. In all, these technologies come together to improve the connectivity between the employees and the management.
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Customer Satisfaction and Fulfillment
Using AI and big data can raise efficiency standards while also helping businesses improve customer experience. For instance, an eCommerce seller can use an AI-based system to manage customer orders and collect the data generated using this process to study consumer behavior. They can analyze that data to predict repeat purchase possibilities or identify causes or returns and minimize return possibilities. By automating certain other tasks (determined by analyzing past data), businesses can ensure faster delivery times and improved satisfaction rates for their customers. Furthermore, big data can help identify patterns in sales reports and indicate which products are selling well. This information can then be used to make better decisions about inventory and product selection.
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Capital Acquisition & Investment Decision-making
The ability to access and utilize capital quickly and efficiently is critical for businesses of all sizes. One way that AI and big data can help here is through their ability to identify potential investors and partners quickly. Through predictive analytics, AI can identify patterns in large data sets that would not be apparent to humans. This information can then be used to identify potential investors and partners. Additionally, by understanding customer needs and preferences, AI can help businesses target specific investors and partners.
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Targeted Marketing Strategies
Digital marketing is a very data-dependent field. That’s because any marketing campaign driven by comprehensive data-backed insights is more likely to succeed, as opposed to one that’s based simply on intuition or meager competitor analysis. AI can help companies with marketing decision-making by improving their understanding of customer behavior patterns, market ups and downs, changing consumer preferences, regional user behavior, and such other critical parameters. This information can be analyzed and the outcomes utilized to create targeted marketing campaigns that will reach more consumers and deliver better results.
But, Where Does Data Annotation Come into This Picture?
As already mentioned, data annotation is at the core of a functionally efficient AI system. It is the process of labeling elements in a dataset (for instance, adding a label to every vehicle in an image). An AI algorithm feeds on this data, learns how to recognize those elements, and then tries to identify them in a separate dataset. Eventually, with many practice cycles, the AI learns to identify that element independently.
Data annotation is important because it allows AI models to work properly. Without labels, big data would be completely unstructured and useless.
Therefore, if you want to make the most of the big data your business is collecting, the first and foremost step you must take is to build or buy an AI tool that is capable of processing said database. In case you choose to buy one, you will find many choices in the market. In case you are more inclined to use a proprietary AI system for your organization, it will be more cost-effective to outsource data annotation services. That’s because you will incur a lot of expense in the process of building an AI system; a data labeling company can help minimize one major aspect of that while also ensuring good outcomes.
Take Away
Managing a large amount of data can be a very time-consuming process, making it difficult to achieve the desired outcomes internally. At the same time, processing big data sets requires specific expertise, tools, and support across multiple avenues. For any business, there is no better tool than an AI-based system to achieve those goals organically.
Hopefully, this article helped you understand why AI and big data are critical for your enterprise.
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