data preparation
Artificial intelligence (AI) and machine learning (ML) has the potential to transform the world in ways we cannot imagine. I can’t stress this fact enough!

Data preparation for machine learning

Artificial intelligence (AI) and machine learning (ML) has the potential to transform the world in ways we cannot imagine. I can’t stress this fact enough! And, I am not alone in thinking so.

A survey of Fortune 500 CEOs revealed that about 81% of the respondents said that AI and ML are a “critical area of investment”.

Though the hype around AI and ML persists, much of the work related to AI is heavily dependent on fragmented data science teams. Enterprises want to use AI but many struggle to understand the know-how. A report by MIT Sloan shows that 85% of organizations believe that AI will give them a competitive advantage for their peers. However, only one in 20 companies has incorporated AI in product offerings or business processes.

What is causing this wide gap between ambition and execution in most companies? Often, much of the challenge boils down to the manual aspects of the machine learning cycle – data curation, feature engineering, model validation, and operationalization. Even if you had the most powerful cloud computing and ML algorithms, at the heart of it all is data.

You would argue that organizations have access to a lot of data which should only speed the process up. In truth, data that is available to companies is neither streamlined or structured in a way that can be easily processed by a machine. In order to reduce the ML cycle and leverage the power of AI, you need to find ways in which you can streamline the data mining process.

AI adoption and data challenges

In a survey by Gartner, results showed that some of the top challenges for AI Adoptionwere related to data, people, or business alignment. Understandably, since every company is different, it will experience the process of AI adoption quite differently. Let’s look closely at the challenges pertaining to data accessibility, preparation, and exploration.

Gaining access to the right data

There is no dearth for data which serves as both a challenge and an opportunity. The opportunity is that you can build better AI models by using more data. The challenge, however, is that data is often available in silos making it difficult to make sense of or use it for effectively using it for training the AI models. Data scientists need to clean up and streamline the data. This significantly increases the duration of the ML cycle.

Time spent on data preparation

Do you know how data scientists spend most of their time? They spend over 70% of their time in data preparation. This means they have less time to build the model itself. In a fast-paced and digital world, this only slows your overall AI efforts down. It also leads to job dissatisfaction amongst data analysts. All this is due to the large scale of manual data processing within the organization.

Data scientists Data prep

Choosing only the right data

Data exploration is the process of identifying relevant data that will be used to train your AI models. It’s critical that you get this right. It helps feed the feature engineering part of the process which equips the data scientist with more visibility into the most important variables required to solve the problem. Hence, data analysts end up spending a large amount of time exploring and trying to make sense of the data.

Validation and iteration

In order to be sure that the model output is accurate, you need to validate it. Data scientists explore the output, usually in Excel or by using simple charting functions. But this is a one-dimensional view of the results and isn’t enough to gauge the accuracy or efficiency of the model.

Model validation

Source: Moov AI

You will need to dig deeper into the testing data in multiple dimensions and exploration paths to fully comprehend how your model works. This is a time consuming and manual task. It also requires participation from the business team so that they have insight into how the model behaves and can provide feedback.

Well, with all these resource-intense processes, it should come as no surprise that ML development cycles are long and often don’t see the light of the day.

A modern approach to data

Taking the modern approach using an artificial swarm intelligence (ASI) platform like Brainalyzed Insight will provide you with the right data foundation and framework. This will help you simplify the machine learning cycle. Such platforms have the necessary tools to help you with data preparation, exploration, automation and various other aspects of the machine learning cycle.

As I mentioned earlier, one of the most important steps in the machine learning process is to ensure that you have centralized access to enough and more data. This way your team can find the right information needed to solve the problem at hand. With the use of an enterprise data preparation platform, you can avoid teams working in silos and bring data engineers and data scientists onto a single platform for collaboration. This way you can easily organize large data sets, streamline and make them accessible for analytic purposes.

A huge part of data engineering revolves around digging into various datasets and making sense of them and understanding how the data fits into the problem that you are looking to solve. An artificial swarm intelligence platform like Brainalyzed Insight drastically reduces time to insight by enabling teams to prepare and explore data at scale. After this, come model validation.

Model building is an iterative process. Once data is prepared, you will build a model, test it and validate the results not just once but multiple times within a cycle. Only by doing so, can you create the best possible model.

Closing thoughts

Where do you encounter snags in the data processing component of your organization? Are you still using manual processes to access, prepare, and explore data? To leverage AI and scale your efforts you need to switch away from manual processes and invest in a good ASI platform.

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