The Role of Experimentation in Artificial Intelligence

Today, Artificial Intelligence is being used to build data-driven organizations, initiate digital transformation, and help organizations leverage data to increase customer experience; the 21st century is finally aligning with what many would have expected from it a decade or two ago.

We are at a standstill in time, where data is influencing every decision and forming the foundation of tomorrow. A lot of attention is being put on data collection methods. Organizations that are letting data drive their policies realize that for data to lead them, it needs to be authentic and accurate. Data that isn’t would barely provide useful interpretations and the systems would not be able to generate actionable insights. Inaccurate data leads to decisions based on flawed information, which inevitably leads to poor results and disasters businesses simply cannot afford.

While there is a lot of focus being put on data accuracy and collection methods, there is currently a dearth of areas where the extracted data is being used. As part of my study with Godatadriven, I found out that a staggering 80 percent of all organizations use their data for reports and dashboards, but only 50 percent use this data for prediction models. These figures need to change so that organizations derive better results out of their efforts in this field.

To better leverage the potential of their data, organizations need to ensure three things:

  1. Cultivate the right organizational culture
  2. Influence the right attitude for learning in employees
  3. Ensure that employees are empowered to experiment with their data (we will further be elaborating this point within this article).

I recently got a chance to work with GoDataDriven to create their Data Survey for 2018-19. The focus of the study was to form a benchmark for the data-driven enterprises of the future. Over the course of the study, we collected data from over 1,300 professionals and studied their experiences and insights pertaining to the topics of data science implementation, data strategy and technology, efficient talent acquisition, and the cloud.

What Does Experimentation Mean?

Companies in today’s world can gain a lot of actionable insights by conducting simple business experiments. Experimentation here means augmenting the insights from the data you have and planning business processes on a test and learn basis to see how they respond. The process of experimentation can reap multiple rewards for businesses, considering how they will find themselves at a better position to continue with a given strategy, if it proves to be successful.

Who is Involved?

An experiment can have multiple representatives working together to achieve the desired end results. In layman’s terms, we can suggest that the Research and Development (R&D) team would be at the forefront of a business’s experimentation. Since experiments require focus groups and different participants, the process might require getting the assistance of external participants to ensure that the results aren’t biased. Additionally, experiments could be assisted through the presence of analytics for substantial reasoning and proof.

How to Approach an Experiment

Approaching an experiment isn’t an easy task, which is why you should think of ways that can facilitate your desired results. Below are some of the rules that you should follow for your experiments at the beginning:

1. Think of individuals and make short-term plans

If you’re experimenting with a new offering for your customers, try a short-term trial and analyze the yield before you make long-term production and distribution plans. You need to know the kind of return on the manufacturing you are doing before you make long-term plans.

2. Keep it simple

The simpler you keep your experiments, the more you can leverage them in the future. Don’t make experiments overly complicated for your team or yourself. People don’t want to work on experiments that are extremely time-intensive only for the idea to be scrapped later on.

3. Follow the proof-of-concept test

Get proof for whatever experiment you are running beforehand. For instance, if you, as a retailer, think that lowering the price for one good would decrease the demand for another, then test this concept and see how it goes. When you’ve tested the concept and seen it in action, then you can use that proof to run a future experiment based on your inference.

4. Shoot big

Don’t let the scope of your current operations keep you from shooting outside of the box. For instance, if you want to revamp the prices you have for specific items, try thinking of new supplier chains altogether. Don’t just think of optimizing what you have now, try implementing completely new systems and see how they evolve.

5. Conduct natural experiments

Trygve Havelmo, who won the Nobel Prize in Economics in 1989, has specified two basic types of experiments around the world as, “those we should like to make” and “the stream of experiments that nature is steadily turning out from her own enormous laboratory, and which we merely watch as passive observers.” 

Organizations need to realize the right time for a natural experiment and work accordingly to get the best out of the situation.

Why Experimentation is Key

Experimentation is the key to developing and forming new insights that help you innovate fast. Two-thirds of the professionals that were interviewed as part of our survey mentioned that they provided plenty of room for natural and thorough experimentation. However, one in five organizations from the education, telecom, and public sector wasn’t open to the idea of experimentation on a regular basis.

Additionally, 83 percent of all UK-based organizations were unsure or neutral regarding their experiments. Thirty-one percent of all professionals surveyed mentioned that their organizations had plenty of room in place for experimentation. Only 2 percent of all professionals surveyed said that there wasn’t room for experimentation within their organization.

Experimentation not only bridges the gap between analytics and the real world but also ensures that organizations can turn their expectations into reality. 

Challenges in Implementing Data Driven Solutions

Forty-four percent of all participants believed that the building up of knowledge pertaining to data science and big data was the biggest roadblock in analytics for them. However, second only to this was the challenge of making time available for conducting experiments. Around 40 percent of all participants believed that finding time for challenges was the second biggest challenge for them when it came to starting a data-driven culture.

Experimentation is, therefore, an important part of the data culture and ensures that the organization is able to get the results it desires from the efforts being put into analytics.

With attention to experimentation and finding the right learning curve from them, organizations can conquer the world of data in its true form.

About the Author

Ronald Van LoonRonald Van Loon

Ronald is named one of the 3 most influential people in Big Data by Onalytica. He is also an author for a number of leading big data & data science websites, including Datafloq, Data Science Central, and The Guardian, and he regularly speaks at renowned events.

View More
  • Disclaimer
  • PMP, PMI, PMBOK, CAPM, PgMP, PfMP, ACP, PBA, RMP, SP, and OPM3 are registered marks of the Project Management Institute, Inc.