Most small- or midsize-business owners admit they aren’t great at data analytics. They are simply passionate individuals, working hard to build the business of their dreams.

But in today’s age of big data, data analysis is important to build and expand your business. As your business grows, the volume, velocity, and variety of the data you handle will also increase. This makes data analysis a more complex and challenging task since it requires more knowledge of coding and statistics.

This is where business intelligence (BI) chatbots or BI bots can come to your rescue. BI bots understand data queries in spoken languages such as English and provide results. Natural language processing (NLP) and chatbots are emerging technologies that will make data analytics accessible to everyone.

Adopting BI tools with NLP-driven chatbots will help small and large businesses develop a culture of analytics, improve adoption of data tools, and make data-driven decisions to grow revenue and improve business performance.

In this article, we define NLP and BI bots, list features you need to look out for, and share some market developments.

NLP and BI bots simulate human conversation around data and analysis

NLP is a sub-field of artificial intelligence technology that helps to interpret, recognize, understand, and process user requests in the form of any human language. For BI, this means that you no longer have to code in SQL or .NET to extract data results. You can type in simple English to get answers from your data set.

Interactions between the BI bot and user in QlikSense

How an NLP query works in QlikSense (Source)

NLP is the core technology driving chatbots, helping them decipher and process human queries.

Chatbots are computer programs designed to simulate a conversation with human users. Chatbots are especially helpful and used widely in customer support conversations, initial sales conversations, and solving customer queries.

An emerging use of chatbots is in BI applications. BI applications support data-driven decision-making, and BI bots help make the process easier by enabling users to have a real discussion about their data. BI bots also read through your data, do necessary analysis, and provide you with results to your queries.

BI bots work like any normal chatbot. You just need to input your dataset and ask (or type!) your queries to the chatbot. The chatbot will share relevant results and insights.

Here is a sample BI bot to help you understand how they work.


BI bots make data analytics a cakewalk

BI bots help make the process of data analytics and insight generation easier and give them a human touch. Some of the key benefits of BI bots are:

Ease of use: Chatbots within BI applications increase their ease-of-use. It is as easy as asking your friend for the data and getting the results immediately, without you having to pore over tons of data sets and read visual data.

Democratization of data: Chatbots help boost user adoption of BI tools at the workplace. Gartner predicts that by 2021 conversational analytics (chatbots) and NLP will help more than 50% of employees work with analytics, from the current 35%. BI chatbots will make data analysis easily accessible and doable for a large majority of the employees.

Cost savings: Customer service chatbots help save $23 billion annually. BI bots can also provide similar cost savings for small businesses. Chatbots can save companies millions which they otherwise would have had to pay to data scientists or to data analytics outsourcing companies.

Why are BI bots gaining traction?

BI chatbots are an easy gateway for quick and in-depth data analytics and will soon become a common feature of analytics tools because of the need for “analytics for everyone.”

According to Gartner’s Hype Cycle for Analytics and Business Intelligence 2018 (content available to clients), search-based data discovery using natural language query is becoming an important interface for BI content creation and is expected to become a feature of modern analytics tool rather than a separate category.

BI solution providers also are working on improving their tools’ interface with chatbot features to make analytics easier and accessible to all.

Common BI chatbot features

BI bots are still in their early stages of development and may not offer all or many capabilities. The common features of BI chatbots include:

  • NLP and machine learning (ML): Smart learning and natural conversations help bots understand the user’s intent and adapt to office jargon quickly. Advanced NLP, AI, and ML technologies will help the chatbot learn with each new query and become more efficient and accurate.
  • Real-time alerts: The chatbot must send key data analytics updates and results relevant to the user. It must be able to personalize alerts based on the user’s departments, location, and role. It must also alert the user to any data anomalies.
  • Collaboration features: Collaboration features enable the chatbot to share data insights with other users via email, chat attachments, or message. They also should be able to include other users’ in a group chat.
  • Security: The chatbot secures data through features such as user authentication, authorization, encryption, and access controls.
  • Integration: The chatbot must be able to seamlessly integrate data between BI tools and other applications such as ERP, CRM, and other data warehouses. It must also integrate quickly with your BI tools, if not an inbuilt function.
  • Deployable on messaging apps: You must be able to add the BI bot to popular third-party messaging apps such as Slack or Microsoft Teams. This way you can get data analysis results without even opening your BI systems or switching multiple apps to access data.
  • Multi-device deployment: The chatbot must be available and functional on multiple device types including laptops, desktops, mobile phones, and tablets. It must also support different operating systems such as Windows, iOS, and Android.

Recent chatbot developments in the BI world

Natural language processing, AI, and chatbots will drive the future of business intelligence. Conversational AI is considered the last step that’ll help connect strategic business data to all employees.

Here are what some popular BI vendors are doing in the conversational analytics space.

Tableau: Tableau has added a range of smart capabilities including natural language processing using a new “Ask Data” query tool to its Tableau 2019 version. Its key features include the ability to enter questions in text format in the Ask Data tool and get relevant results. Both market analysts and developers at Tableau agree that the product’s NLP features are still work-in-progress and much needs to be done to improve its capabilities.

Qlik: Qlik acquired CrunchBot in early 2019 to expand its conversational analytics capabilities. The acquisition provides Qlik with the technology that enables users to type questions in conversational language through QlikSense UI or with other collaboration tools such as Slack, Skype, Salesforce Chat, or Microsoft Teams. The users will receive insights to their queries directly within those applications along with auto-generated charts, predictions, and data interpretations.

Sisense: Sisense Boto is Sisense’s analytics chatbot that uses machine learning to uncover and share data insights. Users can add Sisense Boto as a contact to their Slack, Skype, or Messenger tools and share CSV sheets that need to be analyzed. The bot will provide you the results as well as share them with other users if needed.

BI chatbots and NLP will be mainstream technologies within the next 5 years

NLP technologies are still in their early stages of development, and their precision rate is around 60-70%, making them currently unsuitable for many use cases. But there is growing interest and research efforts to make NLP smarter and more accurate.

Gartner believes that conversational chatbots for analytics, BI search, and natural language generation and querying will provide organizations with high benefits in the next two to five years. The use of natural language programming in BI tools will make it easier for less sophisticated workers to get data-driven insights from analytical tools.

“We are early in this trend, but I believe conversational analytics will be a critical and standard way users consume and interact with analytics content in the next two to five years.”

—Rita Sallam, Gartner analyst

With BI solution providers racing to add chatbot capabilities to their tools, we expect that chatbots will be a common feature in majority of BI solutions in the next five years. We also expect it to be available at a reasonable price points for adoption by the majority of the businesses, both small and large.

Next steps for trying out BI bots and NLP

Though it is important for your business to be aware of developments in the BI space regarding chatbots and NLP, you must also take some of these proactive steps to ensure that the transition to using these emerging technologies is smooth for all employees.

  1. Test trial versions: Sign-up for trial versions of BI bots with your BI vendor or any other provider offering the feature. Let a select few members of your team use the feature to study its benefits and challenges. This will make it easier for your teams when they transition to using BI chatbots on a more regular basis.
  2. Build a “culture of data:” You must invest to build a culture of data analytics within your business. This will make employees aware of the benefits of data-driven decision making over gut-based decisions, making them adopt BI tools more. They’ll also be able to understand and use new BI features like chatbots easily when made available.
  3. Engage with your BI vendor regularly: Engage with your BI vendor regularly to check for updates, new features, and upgrades. You must also work with them to add new features and customizations.

For a list of all BI software, visit GetApp’s


Note: The applications selected in this article are examples to show a feature in context, and are not intended as endorsements or recommendations, obtained from sources believed to be reliable at the time of publication.