Algorithms. Algorithmic. Machine learning. Deep learning. If you’re reading this piece, there is a good chance you have come across these terms at some point. An algorithm probably recommended this article to you. The umbrella term for all of the above is artificial intelligence (AI), which takes data of different flavors and provides you with predictions or answers based on that. There is a good chance you have benefited from this technology in some way, whether in a map application, image search from your favorite retailer, or intelligent autocomplete. 

However, I will let you in on a little secret. Sometimes, perhaps most of the time, the success of any given AI project lies not in the algorithm you choose. Rather, the key lies in the data you have, the state it is in, and the labels it has.

At G2, we have seen two trends that highlight this:

• Rise of tools focused on the data stage of the AI journey
• Rise of no-code and low-code AI solutions

Squeaky clean data is key

Data is the brain of your organization. It gives life and meaning to your business, whether through company data analysis or with the use of data in AI.

However, the saying “garbage in, garbage out” (or “rubbish in, rubbish out” for our British friends) should be heeded. An algorithm is only as good as the data it is trained on. Suppose the data is of low quality, i.e., it is not properly labeled, riddled with errors, data type mismatches, etc. In that case, it will most likely not make accurate or useful predictions.

With that in mind, data quality and data preparation software can help companies take control of their data and ensure it is squeaky clean.

Unlocking the power of data

Once the business has recognized the power and potency of data, they can and should start thinking bigger. Even if they do not have the largest dataset in town, if it is proprietary, they can still have a competitive advantage. When it comes to datasets, the data-driven company of 2022 has access to a host of open, readily available ones, such as those available on Dataset list. However, since anyone has access to this data, it does not provide a competitive advantage. If a company has access to proprietary data, they can ensure its quality and have it all to themselves. 

Data is not like a chia pet, inasmuch as you cannot pour water on it to make it grow. However, what you can do is explore various resources to expand your already squeaky clean data, such as:

• Synthetic data is useful since it is fake yet statistically identical to the original dataset, thus allowing for data analysis and machine learning without privacy concerns.
• Data enrichment helps companies find related data or datasets via data exchange software and some data science and machine learning platforms. This data enrichment can help improve the accuracy of models as the enriched dataset contains new and expanded data.
• Data labeling is key for training models on unstructured data. Unstructured data, like images, audio, and text, is essentially like a mystery wrapped up in an enigma without labels. As seen below, how can a computer gain the necessary context to understand the difference between a chihuahua and a muffin? The answer is in data labeling. Through this process, one can build a dataset of thousands of images labeled definitely as chihuahua and muffin. This will, in turn, help the algorithm determine between the two inputted images.

Source: Twitter

We love you models, but…

The focus on the data step in the machine learning journey is prudent and on the rise. Historically, especially when looking at structured data, there was much focus on the actual training of models, using tried and tested methods like linear regression. This included feature selection (choosing which features are essential for the model) and model selection. These tasks were critical in ensuring that predictions were accurate and that the best models could be chosen and put into production.

However, we are seeing the rise of easier-to-use technology, such as low-code and no-code machine learning and related technology like automated machine learning (AutoML).

As G2's associate market research analyst Amal Joby notes:

With this proliferation comes the commoditization of algorithms, as data scientists and citizen developers can pull an algorithm off the shelf and deploy it quickly. At G2, we've seen the importance of prebuilt algorithms. Reviewers in G2’s Grid® Report for Data Science and Machine Learning Platforms for Winter 2022 rated the top products on the Grid® highly for their prebuilt algorithms. The number one product, based on the G2 score, scored a 9 out of 10 for prebuilt algorithms. This shows that for a data science product to be highly rated, it must have prebuilt algorithms.

Prebuilt algorithms can be used to both conduct rapid analysis of data or use that data to make predictions. For example, a product manager at a shoe retailer can use these tools to easily optimize their mobile application, dynamically changing the banner on a product page based on user behavior.

This shift away from models (steps 3 and 4 in the data science journey below) also leads to a different trend: the collision of analytics and AI. 

For example, on G2, the former director of product marketing for Kraken (now Qlick AutoML) said:

 "Kraken is primarily a platform built for data analysts or business analysts without a deep understanding of data science. As such, we try to automate as much of the data science work as we can and do not currently support more expert-level features like hyperparameter tuning." 

As analytics tools provide users with predictive models off-the-shelf, data analysts and data scientists of varying expertise can collaborate on using that data to derive insights and build data-powered applications. This is something that will pick up in the near future.

Edited by Sinchana Mistry