What Is AI Without Data?

SiliconANGLE’s head of research, Peter Burris, summed up the future of AI this way: “The goal…is to put more data to work.”[1]

AI & Data

An understanding of AI is not without a focus on, reference to, or mentions of the term Data. The simplest way to understand this? AI does not exist without Data. It is the food source to keep AI systems running and learning. Machine learning will learn from data, while deep learning will create neural networks in which data such as images and sounds are processed.

According to Gartner, AI technology will be “virtually everywhere over the next 10 years.”

AI works best when large amounts of rich, big data are available. The more facets the data covers, the faster the algorithms can learn and fine-tune their predictive analyses. [2]

Data or Big Data

Big data refers to the growth in the volume of structured and unstructured data, the speed at which it is created and collected, and the scope of how many data points are covered. Big data often comes from multiple sources and arrives in multiple formats.[3]

In understanding what Big Data represents, how is this information useful to business when collected?

Businesses need to understand the following equation:

With vast amounts of data becoming readily available through purchase histories, demographic data, geo-location movements and other sets of consumer data and trends, it is critical for businesses to understand how to transform these insights into actionable business value.

“Data has no value. Value is created when data is used by someone in context. When the data is put to use, that’s where the value comes from. So the responsibility is not on the data creator, but on the value creator to determine how to leverage the data.” [4]

Tarry Singh, Founder and AI Neuroscience Researcher of AI Startup succinctly explains how companies should proceed, “Instead of only focusing on using a consistent set of data to measure past performance and report for business planning, businesses must also focus on a combination of analytics and machine learning techniques so they can draw inferences and insights out of the massive sea of data. This will help you solve the higher order questions and derive far greater business value that you could have ever imagined!”

Extra Learning

Interesting insight from Andrew Ng, Baidu chief scientist, Coursera co-founder, and Stanford adjunct professor on how AI creates value for businesses through actionable insights from Data. Watch here:

The combination of Big Data and AI

Once businesses fully appreciate the importance of analysing and storing the data they collect from customers or processes over time, the power of the AI layer can be applied over this intelligence foundation.

The direct use cases for AI with data are:

  1. Data transformation
  2. Data Preparation platform
  3. Data Management and monitoring
  4. Data Integration

What does this mean? It means disruption in various industries, some of them are:

  1. E-commerce and retail: AI can predict upcoming shopping customer orders for the next season. This will give retailers the ability to better engage in inventory planning and purchasing, and to predict and control costs. [5]
  2. Digital Marketing and Content: Associated Press is already using AI in its content creation process. Properly curated and correct targeting will add power to digital marketing efforts.
  3. Automotive: Autonomous vehicles will work off software that applies control to radar systems, lane control systems, accident avoidance features, cameras etc. This AI based technology relies heavily on data to function.
  4. Manufacturing: One of the most heavily impacted industries to improve automation through AI is manufacturing. Achieved through Robotics, data improves training and smarter machines.

The current challenge for organisations

Too much of data can create an overload of noise. And not having the right skill in the form of data scientists to determine the validity and cleanliness presents a direct challenge too. There is a real need for organisations in understanding the storage implications and requirements for structured data (consisting of numeric values) and unstructured data (emails, text documents and videos).

So how do organisations rise to the challenge?

  1. Understand where the data is coming from into the organisation
  2. Look at the data sets and their correlation in value to one and other.
  3. Once the value of the data is determined, AI tech can use this for predictions and customer service
  4. Check if the skill set on the team is equipped to follow the above process