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Bots can augment human interaction, create greater business efficiencies, and remove friction from customer interactions.

It’s also a market that’s attracting impressive investment dollars, with 180 bot companies raising $24 billion in funding to date. Industry leaders from IBM to Facebook are making big efforts to take advantage of this trend, spending significant resources encouraging developers to create new bots that enable more personalized customer interactions. In March of 2016, Cisco announced the Spark Innovation Fund, a $150 million investment in bots and developers who want to make new products for Cisco endpoints in offices around the world.

Some of the most obvious uses for bots revolve around communication, customer service, and ecommerce. Chatbots are at the center of the way people communicate today, with over 2.5 billion people worldwide using a messaging platform like WhatsApp, Facebook Messenger, or Telegram. Twitter recently rolled out a bot-like feature within its DM service to enable brands to interact more frequently with customers, with the goal of ultimately improving the customer experience. Facebook is testing a service to enable users to make payments on Facebook Messenger that are facilitated via the use of bots built on its platform. Gaming companies are using bots to help ward off trolls that might interfere with the natural progression of the game.

All this is happening while we create almost unfathomable amounts of data — data that is expected to reach 35 zettabytes by 2020. So how can companies outside ecommerce take advantage of bots to automate these new data sets and deliver smarter, faster analytics access in the process? Let’s take a look:


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The concept of human to machine interaction via natural language processing can drive immediate analytics responses, rather than waiting on human analysis for a report. This is what BI companies like Sisense are focused on: the ability for users to instantly interact with data via a bot interface. One can immediately see the practical value of this with larger data sets that would take a longer time to process — the efficiency of the bot architecture combined with the underlying power of big data analytics can deliver significant value in a short period of time.

Another business arena in which bots can enhance the relevance of big data is through automated data collection. Bots could automatically add to the knowledge about a customer by progressively asking for more information during interactions with your application or website. Certain banks are already using bots in the back office to automate data look-ups. This concept can also extend to information-gathering for application testing, especially because bot testers can execute far more automated tests.

There are natural consequences of any bot-driven approach to collecting and processing ever larger quantities of data. The first is that bots are not infallible, since they are still subject to human biases in their development, so data collection/analytics efforts could be compromised. This is why human reviews are still essential to the business process even when using bots to collect and analyze data.

The second issue has to do with privacy. Are the humans on the other end of the bot interaction explicitly aware of the specific privacy standards governing their interaction with the bot? There should be an explicit contract spelled out between company and consumers/customers to ensure the latter are fully aware of how their interactions are governed and protected.

So what happens next as bots become a bigger part of the B2B landscape? There’s a real opportunity for companies to go broader and deeper in their data analysis — and to obtain the kinds of insights that will optimize nearly all aspects of their business.

We’ve only scratched the surface when it comes to bots. But with the help of artificial intelligence (AI) and machine learning, the landscape is changing in a big way — and quickly. Most companies would be wise to start thinking about a bots strategy and consider the ways in which bots can augment the data technology stack. As we continue to understand just how business critical data is, bots will play an important role in digging deeper into new and growing data sets. In the months and years ahead, I think we can expect that bots will play a big role in improving business processes and helping us better understand our customers and what drives them.

Nitin Donde is the founder and CEO of Talena, a Big Data management company.

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