Unlocking a treasure trove of data with chatbots
Let’s face it. Data is scattered all over the place in silos in an enterprise. Starting from email server, intranet, countless documents, reports, presentations, charts/graphs in various document repositories, ERP, internal systems, e.g. HRIS, databases, source code control system, release repositories, wikis, ticketing system etc., we can keep going and find how data is distributed in the enterprise.
Now let’s take a familiar situation. Say the CEO of a company wants to find everything about an employee. Someone, who recently presented a path breaking paper at an internal conference. And he wants to put together a crack engineering team who can work with this employee on a futuristic project. Imagine the number of people, who have to work on this query to get the most up to date information about this employee and present to the CEO.
What if the CEO had follow-up questions or wanted more aggregate information?
Behind the scenes we will need many people with different skill sets to find, read, summarise and put together the best information that exists in numerous systems. As a matter of fact, it is very likely that hundreds of hours would be spent till they get enough information for the CEO to take the decision.
What this problem points to is the fact that there is no easy way for the CEO to directly access the information which is locked away in hundreds of data systems. Now let’s imagine the CEO has a chatbot through which he/she can ask a query in natural language and get all the answers he/she needs at whatever level it matters. This can be a powerful tool at the hands of everyone in an organisation to get access to locked away data in seconds and help in quick decision making. Plus it will be a big step to democratise data in the enterprise, which is sorely missing today. A problem that Engati is actively solving for CEOs and corporates.
What does it take to get there?
The chatbot is the interface through which the user will enter the queries in the language he/she is most familiar with. Behind the scenes we will need a backend system that can translate this natural language query into a system specific query. Then aggregate the query responses from multiple systems and respond back in text, image or voice.
Let’s take the first challenge. Translating the natural language query into a system specific query. It is in the purview of the chatbot platform.
This is how data is scattered in an enterprise-
And this is what the chatbot needs to do the translation-
This problem is not a simple one and involves many techniques. In fact, it involves technologies to come together such as NLP, NLU, NLG, Deep Learning, Reinforcement Learning. This is for them to be able to understand the natural language query. Therefore, they can translate it into a backend system specific query.
As an example, let’s say we have the employee information in a RDBMS. We have to translate the user query into an equivalent SQL query. Hence, it will need a Deep Learning model to translate Natural Language to SQL.In subsequent blogs we will explore more on this. For instance, what machine learning models and tools we can use to solve this problem in the best possible manner. For that matter, the team here at Engati is working towards solving the problem.
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