In decades past, speaking to an assistant not physically present in the room to manage your calendar may have earned you a few funny looks. In 2021, the advent of the digital assistant has made it easier for business people to manage to-do lists, plan events, and search for data.
Applications such as Apple’s Siri, Amazon’s Alexa, and Google Assistant are becoming a norm in a digitising world which demands quick and ready access to collated business data whenever needed. In that regard, virtual assistants are as indispensable as administration tools.
However, these artificial intelligence (AI)-enabled data solutions would be useless without natural language processing features. The ability of digital systems to interpret and respond to human speech has changed how savvy business people search for information, and there can be no going back. Talk to a machine and natural language processing solutions can make it talk back.
What is natural language processing in data solutions?
Natural language processing helps digital devices, including smartphones, tablets and computers, to understand human communication and respond to us in our own language. This can be through text or speech, and is vital for any transaction between machine and operator.
Natural language processing is nothing new in tech development. We’ve been developing ways for computers to better understand how we communicate since programmers were required to use cards punched with coding language when interacting with early computers around 70 years ago.
However, in the modern world this capability has advanced to the point where you can state a claim such as “Alexa, I like this song and an AI-enabled system will be able to interpret your implicit expression – log this song to a personal library – and perform the command for you.
This natural language processing ability has significant applicability in data analytics and management. Imagine that you are trying to analyse a dataset. Speech recognition would allow an analytics tool to respond to a command like “Find sales reports from May 2021”, assessing and presenting all housed information within these simple parameters.
This is useful; but deep learning speech recognition enables you to go further. You could have the same command, but an advanced AI solution would be able to assess the information based on past searches, drawing out the trends you looked for before and matching it to similar data patterns for relevant insights. This is called ‘semantic searching’ and it stands apart from lexical searching, which solely pairs individual words to existing data, in its ability to consider context.
Machine learning in data analytics roles is only a small subsection of the tasks that natural language processing is capable of. However, nearly every application of deep learning speech recognition comes down to collating and presenting data for people to understand, from a simple search query on nearby coffee shops using Siri to analysing complex sets of sales data.
How can natural language processing help my business?
The benefits to natural language processing in data analytics are multifold. As mentioned prior, natural language processing helps to interpret implicit meaning in a statement and pull relevant information from data suites for analysts and business users to deploy. This helps to determine patterns in data more effectively, and empowers faster and more accurate decision making.
Using deep learning speech recognition as a tool for searching through data doesn’t just have applicability within a business. Chatbots are built on natural language processing technology, and are fast becoming a cornerstone of the customer service experience, across all industries.
By 2024, Business Insider predicts that consumer retail spend via chatbots will reach $142 billion globally. While this is no small figure, the benefits of chatbots go even further. They also help companies interact with customers in real-time, address problems at source and facilitate natural conversations online. This data can also be used as an additional analytics layer when searching for information.
These capabilities are central to deploying a more intuitive customer service experience that replicates elements of real-life communication and understanding in real time while pairing it with the convenience of digital communication. Chatbots and natural language processing, even at their most base level, are fast becoming a must-have for successful organisations of all sizes.
How can Cast Solutions help?
Rolling out a chatbot isn’t a one-stop-shop solution for all businesses, especially if you don’t have a significant digital footprint. Determining how you can get the best out of natural language processing solutions requires you to consider the balance between driving your business with machine intelligence and disseminating data analytics power democratically across operations.
Natural language processing when paired with the right data visualisation tools creates an ideal ‘augmented intelligence’ scenario in your organisation within which all employees, across different functions, are able to use natural language processing to search for data relevant to them and have it displayed in an easily communicable way. This can take time and resources to set up and then implement properly; working with a data analytics provider such as Cast Solutions can help.
There are multiple pathways businesses can choose when implementing a machine learning solution. The best option for your company depends on the complexity of your underlying datasets, the type of issue you would like to solve, and the scalability required for the solution.
One of our most popular tools is Qlik Sense, which offers a chabot feature called the Insights Advisor, that can be embedded within the framework of an analytics tool. This will help data analysts and employees to more easily call out information they are looking for in real-time. This solution is often best paired with PyTools, a programming language software that makes it easier to communicate with and adapt the deep learning speech recognition algorithms that will support operations.
For more advanced organisations, cloud machine learning services, such as those offered by AWS, can help analysts to perform more specific or sophisticated tasks. This includes training digital tools to interpret real-world images from data collections and analyse handwritten text for patterns.
Want to know more about what type of natural language processing AI may be right for your data operations? Cast Solutions can help you determine what you need with a product demo.
Working with Cast Solutions
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