The wave of Natural Language Processing in Business Intelligence
As this world witnesses the fastest progress in the automation industry, there’s only more exhilarating developments waiting for us to be discovered.
With a galore of technological advancements, Natural Language Processing brings in a great amount of potential to Business Intelligence. NLP, also known as computational linguistics, is a technology that uses a combination of multiple concepts like semantic search, Artificial Intelligence, machine learning and linguistics to let machines interact with humans in the human language.
NLP has already been a BI tool in various sectors. For instance, Chatbots that are capable of effective data abstraction from varied sources and are likely going to grow more significant.
As we look forward to the upcoming advancements, we will see NLP becoming more frequent in Data Storytelling and report generation, as it is able to translate data sets into common language. Essentially using machine learning, NLP tends to get smarter as the next phase of headway continues.
What’s in store for the users?
Ask Siri for directions and a twisted series of cutting-edge code is initiated that allows ‘her’ to understand your request, whether you wish to find any information or have any commands, Siri will respond to you in a language that you understand. How? Well, this has only become possible in the last couple of years. Until now, we had only been interacting with computers in a way that they can comprehend. We had learnt their language. What changed? Turns out now they’re learning ours.
Here’s what Natural Language Processing brings to the table:
1. Democratization of data
One of the most significant obstacles that NPL is expected to help overcome is diminishing, or absolute removal, of the difficulty of entry for BI and big data in general. As the largest companies prepare themselves for this trend and take steps to ensure data becomes more user-friendly and easily accessible, there is still a long way for it to turn into reality.
Let us break it down for you with a day-to-day instance.
You see how you can get answers to any important or complicated questions anytime, anywhere, at your convenience just after you have asked it. By turning Business Intelligence into a conversation with a chatbot, gathering information will be as easy as asking – ‘how do you determine what asset classes are good and bad value propositions?’ – instead of needing years of experience and any master, you just determine how to direct the question to receive the data you need.
2. Making BI more perceptive
As in today’s time, NLP is based on converting natural language into machine language. But as the technology develops – primarily the Artificial Intelligence segment – the computer is expected to become a lot more efficient at “understanding” the question and start to present customized answers rather than search results.
It is only one step away from asking the question in natural language. It’s about getting in a similar way too. Originally, the data chatbot will probably take a question such as ‘‘how do you determine what asset classes are good and bad value propositions?’ and deliver pages of data for you to examine.
For all you know, once it gets the semantic relations and reasonings of the question, it will be automatically performing the filtering and organize necessary data to provide a comprehensible answer, instead of simply giving you the search results.
All your answers will be made available for you in natural language.
3. Harnessing unorganized data
Natural Language Processing extends the scope of all the answer that may be extracted by making unorganized data understandable to a machine.
Initial attempts at analyzing sentiments go beyond recognizing when, for instance, a post is about your business, to examining the surrounding text and discovering whether that post is positive, negative or neutral. As speech recognition advances, audio and video also tend to become more accessible sources.
The development journey has just begun. Imagine today’s sentiment analysis as the sort of accuracy Google Translate brings to decode a French news article (well, it’s also a process that relies heavily on aspects of NLP) but there’s a lot more that is waiting to be discovered.
So, how ready are we for the future?
Since we are on the edge of significant advancement in Natural Language Processing that have the capabilities to revolutionize Business Intelligence in intellectual ways, we really have to caution you to have patience and learn the complexities associated with altering between machine language and natural language.
As you begin to understand the complexities of the structure of new branches and apps that rollout, you recognize the significance of the technology you’re utilizing, and also be sparing of initial glitches. Because with all these challenges and obstacles come the hope for future innovations.
One of the biggest challenges with big data is creating a sense of the large amounts of information available out there. It leaves us with a question of How do we find, secure, and examine only what we need out of an ever-growing sea of information? The future of Natural Language Processing carries a key that enables us to interact with our technology in a way that lets us tap the ostensibly immense potential held in the dataverse.
How do you imagine the future of Business Intelligence? Do you wish to add more to our analysis? Please share your insights in the comment section below.