Data analysis

How data analytics will help us understand chatbots

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:

The concept of human to machine interaction via natural language processing can drive immediate analytics responses, rather than waiting on human analysis…

Data: Do you see numbers or opportunities?

data analytics

There are only two ways of playing a strategy game, be it chess, billiards, or poker. You can wait for your turn and devise the next move, or you can plan the next 10 moves for every step you take. Needless to say, good players never wait for their turn.

In the tech world, we have a slightly different game. It is called the data game. We’ve come up with several terms for it that you may have heard of, such as machine learning, advanced analytics, behavioral analytics, big data etc. In the end, they share the same goal, planning and predicting the next move.

Playing this game, like any other game, requires practice, but it is one of the most versatile and fast changing games you can play. The rules are not really set, and what might have been true yesterday may no longer be valid today. Your benchmarks and targets should change their levels frequently because your solution needs to be optimized constantly! And if you don’t… make no mistake, your competitor will change them for you.

It is important to reiterate that data analytics unfortunately has become another buzzword in today’s tech world. Many people talk about it, but few understand its importance, and how to apply it. Big companies like Facebook and Google repeat over and over again that they do not take a single step which is not data-driven and that might resonate as a simple principle, but have you ever found yourself reading a report that only gives you numbers? That you look at it and just try to react by seeing if they are growing or declining.

As stated before, a good player needs to plan in advance the next 10 moves, play out scenarios and contextualist the data. So how do you apply it to your enterprise and products?

If you are an enterprise trying to thrive in the digital world either via web or mobile, the first step is to know what to use data analytics for:

Refine the value proposition

What you want to offer the users can be very different from what they actually want.

  • A successful solution is an ever-changing one. It has to always be optimized also in terms of a value proposition, and data analytics can show you what to “sell” based on what the customer wants to “buy”.
  • Re-evaluate if what you are trying to sell is beneficial to the customers at that point in time and place.

Understand the actual user behavior

When defining the features and…

Dataiku Offers Advice on how to Create Data Team Harmony

Harmony and analytics are two terms not often found together, especially when executing data science chores using a team of diverse data professionals. A data science team is often made up of people from diverse backgrounds, with diverse skillsets – from the machine learning specialist to the master Python coder, to the beginning data analyst. To successfully build and execute any sized data science project requires harmony across all of the team members. Everyone needs to work effectively and efficiently, using the tools they know best.

GigaOM recently had the opportunity to discuss the dynamics of building data teams with Florian Douetteau, CEO of Dataiku. Douetteau offered some sage advice and was able to point out what challenges face those trying to build teams.

Douetteau said “one of the biggest challenges is finding, hiring, and keeping people with a background in machine learning. There is a high demand for experienced data scientists, meaning that it can cost a lot to hire one, especially considering the opportunities data scientist have.”

Douetteau added “yet so many of those data professionals are specialized in their area of expertise, meaning that it is difficult for a business to maximize the return on investment of hiring a data scientists. In other words, to build a team, businesses need to move beyond a single individual’s domain of expertise and hire individuals with different skill sets and enable them to work cooperatively and productively.”

The growing deficit of Data Scientists, along with the closed nature of many analytics tools have made building effective teams a near impossibility. Yet all is not lost. Douetteau said, “there exists a vast ecosystem of opensource tools that are available to the masses, which can help to level the playing field, and bring data analytics capabilities to professionals of all stripes.”

However, much like the cola wars of the 80’s, there is an almost infinite variety of flavors and formulas that drive tastes, at least when it…