Articles by: Dru Wynings

Why Are Bots Still So Dumb? And How Can You Make Them Smarter?

Dru Wynings • February 20, 2017

“Hey Siri, call me an Uber.”
“Hey Alexa, schedule my next haircut.”
“Hey Cortana, play my favorite song.”

Smart AI is no longer a novelty worthy of science fiction; it’s now ingrained into our everyday lives. You could probably recognize Siri or Alexa’s voice in a crowded room, and chances are good that you’ve requested their services on more than one occasion.

As tech giants like Apple, Amazon, and Microsoft continue to enhance their products using AI techniques to keep up with real life demands – integrating with third-party apps like Uber or Spotify, for example – the capabilities of these smart bots seems endless.
In fact, more and more companies are using smart bots and even chatbots to create customized experiences for their shoppers. The ecommerce company Spring, for instance, uses chatbots to connect their website with Facebook Messenger so that customers can browse their inventory directly from Facebook.

But if you’ve ever used any of these voice or chat assistants, you know that the technology is still far from perfect.

Siri, for one, still has problems understanding thick accents. She can perform simple math, but gets hung up on more complicated equations. While she’s great for telling the occasional joke, she still has trouble pinpointing exact locations to make restaurant or shopping suggestions.

But if smart AI is everywhere, why are supposedly “smart” bots still struggling? Why are so many companies still battling to get their bots to do what they want them to do?

The answer: data.

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How Bots Learn

The issue isn’t that bots are stupid, exactly, it’s that they don’t always have enough of the right kind of data to learn properly.

Bots are a lot like children: You can’t just give a kid an encyclopedia and expect him to understand everything about how the world works.

First, he has to learn how to read, then he has to learn how to comprehend the things he’s reading, and then he has to learn how to give that information context and apply it to his world. Bots are the same way.

[Tweet “Bots learn the same way people learn – through progression.”]

You can’t just give bots data and expect them to understand what it is, why it’s there, and how to use it. Bots learn the same way people learn – through progression. And, like humans, the more data bots receive, the better the results will be.

AI uses algorithms that take in data and then determines what you want to do with it. The second half of that equation – the “what you want to do with it” – is the context, and every piece of data a bot receives needs to have it.

If you touched a stove, for example, you would learn very quickly that it’s hot. That’s input, or the first half of the equation. The context, in this case, is that you shouldn’t touch the stove again because burning metal on skin causes serious harm.

In order for bots to truly learn, they need the context, not just the input. But that context can’t come from just any type of data.

The Trouble with Data

When people talk about “Big Data”, they generally mean unstructured data – streaming data that comes from social media platforms, mobile applications, location services, or the “Internet of Things”.

Most “unstructured” data sets are actually structured on a micro-level –for example, one data set could show all customers that have purchased a certain product while another data set shows all those who have returned it – problems arise because they may not be structured the same way, making them essentially “unstructured” as a whole.

Unstructured data is useful for many things, like aggregated data for sentiment analysis , multimedia content, sales automation and social media tracking, though the prime example of its usefulness is Google search: unstructured data helps pinpoint keywords in the web pages where they appear.

But the biggest problem with unstructured data is that there’s a lot of it, which can make mining that data for useful information difficult.

If you were going to ask Alexa for articles about Apple the company, for example, but you only asked her to search for “Apple” as a keyword, you might get mixed results between Apple, Inc. and articles about apples, the fruit.

Bots, as smart as they are, can’t always pull the context of your search directly from your brain. If they can’t find the context in the words you use, you have to sift through the search results yourself in order to find what you’re looking for (or use more specific keywords).

But if you were to ask Siri to “Find me shoes under $50”, for example, she would return local search listings with worse results because she does not have direct access to structured data from Amazon. On the other hand, If you were looking for articles about Apple, Inc. on Google, you would probably get results about the company and not the fruit because Google understands the context of those keywords and has access to your search history and other relevant context.

But without access to structured data, neither Alexa nor Google would be able to produce the right results. Sifting through mountains of unstructured data without context to find the right data is like having a child guess the best answers to a complicated question. Sure, they might get it right some of the time, but they won’t learn much from the process.

How to Make Bots Smarter

Structured data makes a bot’s job significantly easier because it’s organized by fixed fields, so search operations or algorithms can find (and interpret) information better and faster. This not only goes for AI assistants like Siri or Alexa, but for any type of internal algorithm in your company.

Because structured data is targeted to specific functions and tasks, bots don’t have to work so hard to understand the process. It essentially takes the guesswork out of it, making the bot smarter in the process.

It can also make businesses smarter by saving time and money, since they can gather useful data quickly and at a much lower expense.

Most businesses simply don’t have the manpower or finances to sort through the proverbial needle in a haystack of unstructured data to find exactly what they’re looking for. And unless you’re a big enough company like Amazon or Google, chances are that you don’t even have access to enough unstructured data to get proper results.

By taking advantage of structured data, on the other hand, you can train your bots to understand the context of your business and your customers more quickly and cost effectively. You’ll know exactly what data to grab and when, and so will your bots.

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Final Thoughts

The ultimate goal of any bot is to help you find exactly what you’re looking for and to do exactly what you want to do, whether that’s find an article about gluten-free cooking, playing your favorite music, or ordering up a ride to the airport.

In order to do that, bots will need continued access to a knowledge graph of structured data so they can grow and learn at a steady pace.

For businesses, a bot’s goal is to use structured data to help customers find what they need, when they need it. But more so, by giving customers the chance to find exactly what they need, it can help companies move ahead of their competition.

How the Role of Chief Data Officer Is Changing in 2017

Dru Wynings • February 13, 2017

FEATURED_Why-You-Still-Need-a-Chief-Data-Officer-in-2017

How in-demand is the role of Chief Data Officer? According to recent data studies, very.

2017 is set to be one of the biggest years yet for data analytics, and that means the demand for CDOs is on the rise.

Because companies are using data across multiple silos in a variety of functions, data is now much more of a commodity than it was a mere decade ago, and along with the ever-changing growth of data, the role of a CDO is changing along with it.

While some of the primary responsibilities of CDOs hasn’t changed – tracking and interpreting data trends, for example – organizations are now implementing data-driven strategies like never before, which means that CDOs are no longer the sole gatekeepers for big data.

For those who have either been in the role for many years and are now wondering what lies ahead, or those just stepping into it, this means facing a bevy of new challenges in the world of data management.

Here’s what you should know…

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Changes to the Role of CDO

According to Mark Gambill, CMO of MicroStrategy, the CDO was originally born “as an attempt to create a bridge between functional leaders who need information in real time and the IT department.”

He argues that in a perfect world, there wouldn’t be a need for the role at all, but because data is “challenging, frustrating, and expensive” organizations need someone who can dedicate their time to sift through the complexities.

As Omri Kohl, founder of Pyramid Analytics, notes, organizations will need to take advantage of this role in 2017 like never before. He believes that companies looking to gain a competitive edge will need to shift their views on how to use data as well as leverage the capabilities of CDOs.

“As data becomes more robust, organizations are realizing that deploying a business analytics platform is not a nice-to-have anymore but instead a must-have,” he says. “And creating one role responsible for the centralized ownership of the overall data strategy will be critical to an organization’s success.”

This will require CDOs to understand the full capabilities of data and develop strategic solutions to utilize throughout the entire organization.

Because CDOs work with data across multiple departments and divisions, they will need to deal with more than just numbers and figures – they will need to deal in strategy.

CDOs in 2017 will be change agents: knowing how things operate, where and why there’s resistance to change, and how to help people understand the applications of complex data to drive growth.

[Tweet “CDOs in 2017 will be change agents”]

And as data continues to become more prevalent and complex in the years to come, the primary role of the CDO will be not only to help organizations understand data, but to implement it in new and creative ways.

Challenges Faced by Modern CDOs

With such a shift in responsibilities, there are certain challenges faced by the modern CDO compared to earlier counterparts.

CDOs will be tasked with communicating and stewarding data in ways previous generations never did. They will need to take advantage of existing data while using it to drive practical innovation, all while setting priorities for the use of data throughout the company.

Essentially, modern CDOs will be responsible for:

  • Establishing the organization’s data strategy – CDOs must lead the transformation to becoming a “data-driven organization” and ensure that data is being valued and understood properly
  • Integrating data across multiple silos – Because data will need to be organized throughout many departments, CDOs are responsible for making sure it integrates well and drives tangible results
  • Monetizing and creating value from data – CDOs will need to monetize data to drive marketing and sales funnels instead of simply analyzing and reporting on trends
  • Understanding data security and risks – CDOs will be responsible for protecting data and understanding any and all potential risks and threats

They will also need to be able to communicate the “what, why, and how” of data to both the leadership and the technical members of their organizations. This means working closely with upper management and not just IT teams.

But how does a CDO do that, exactly?

How to Overcome Data Challenges in 2017

Tony Fross, VP of digital advisory services at Capgemini Consulting, believes that CDOs will need to position themselves as authorities in their respective companies more than ever before.

“CDOs need to be chiefs, and not buried four layers under the CEO. They have to have ownership of an enterprise strategy with broad horizontal input. Otherwise, they’re not truly CDOs.”

According to Fross, CDOs will need to create clear objectives and incentives for companies that still don’t value data, and they will need to promote the capabilities and potential of data for companies that already value it.

Part of that will be using fact-based evidence to support the usage of data alongside a level of emotional resonance to help communicate their message.

According to Gartner’s 2016 report, there is currently a lack of meaningful metrics to measure the effectiveness of the CDO, but CDOs can overcome this by delivering clear value and by positioning themselves as data authorities.

This means CDOs will need to:

  • Connect departments across the organization – Expanding the use of data beyond just “technical” departments is essential
  • Develop a data roadmap – Data strategies will need to capture the right data for use in areas like sales, marketing, and other customer-related channels
  • Turn data into action – Data should be harnessed in real-time so that organizations can run with it
  • Anticipate data needs and attack challenges head on – CDOs will need to take on leadership roles, not merely analytical ones

In order to face the many challenges of modern data, CDOs will need to be flexible enough to work with both technical and non-technical teams to develop data strategies that offer practical value.

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Final Thoughts

In order for CDOs to be effective in helping to create data-driven organizations, they will need to not only monitor data, but also help leaders understand its value and apply it to multiple processes throughout the company.

This means creating strategic roadmaps to guide upper management, working closely with C-Level leadership to understand how data can be used to drive growth. This also means working with technical (and non-technical departments) to implement data in creative ways.

But most importantly, this means dedicating the time and energy to understanding the true value of data, communicating that value to others, and turning numbers into practical and measurable results.