Making Machine Learning Accessible and Beneficial for Everyone

The past few years have been revolutionary for organizations that were hesitant to embrace technology as a way to do business. As technology cemented its position in the economic value generation of any business, these organizations realized that it was imperative for them to harness the potential of new era technologies. For example, machine learning (ML) can be of significant value to companies learning the right way to approach it. One can only imagine the kind of assistance ML can provide if made accessible to anyone in the organization who needs to make decisions based on truths provided by numbers. Enterprises are gradually leveraging ML, and this is supported by an IDC study that predicts that by 2024, 40% of large enterprises will expand the use of AI/ML to all business-critical horizontal functions. business such as marketing, legal, HR, procurement and supply chain logistics. The only difficulty so far was that many ML technologies required special skills or tools to achieve this. Instead, companies should view modern analytics platforms as a way to provide methods that can use ML, seamlessly integrated, suitable for different levels of user experience.

For ML to be productive, it must be accessible to all levels of users within a single analytics platform. It is imperative to have built-in and configurable ML models for citizen data scientists. Additionally, data scientists should have the ability to publish custom, certified models to the platform for all users to run on their own datasets. ML has been around for decades; however, with all of his abilities, he was not exploited as much for a long time. As the cloud era has dawned over the past few years, a resurgence of ML has been observed, catalyzed by the provision of relatively inexpensive and powerful computing power with little or no upfront outlay to get started. and no ongoing administration for onsite hardware. Some features designed for business users can also be leveraged by citizen data scientists and data scientists, for example, one-click forecasting. This capability is accessible to any user without ML skills, but at the same time a data scientist could also benefit from it to identify unseen trends and create a much more complete and advanced custom model if a problem is detected.

ML technologies can enable stakeholders to scale up and be effectively focused on analytics. There should be a mix of backtracking and making decisions on historical information, as well as taking advantage of future machine-generated recommendations and predictions.

Making ML Beneficial for All

Organizations should aim for a provider that offers business users access to a variety of secure, governed datasets through the data feeds of a dedicated Analytics platform. This would allow users to use pre-programmed ML algorithms to draw conclusions from their data. Users could

Quickly and easily run pre-released models with no experience or coding ability required and make analytics-based decisions faster, resulting in higher revenue numbers, more effective marketing campaigns, an efficient recruiting pipeline, or cost savings costs for the company. ML-powered data visualizations also provide automatic, unaided pattern recognition that makes it easier to predict based on historical data trends. ML algorithms also power AI-based natural language processing (NLP) and natural language generation (NLG), which help business users find relevant data and draw key conclusions. Sentiment analysis is another powerful ML-enabled tool that businesses can leverage. Allowing ML to take over these tasks will help these teams save hours of manual analysis of survey and market research results and allow them to make changes faster.

Do the prep work

Effective and accurate ML capabilities result from the quality and availability of data fed into a specific model. An organization must first properly prepare relevant data sets for input into specific data streams so that analytics-driven behavior becomes second nature to the business user. The process of maintaining data storage and moving data is handled by a data engineer, making connecting and sourcing the correct datasets from the correct sources the real first step in data preparation. , in different departments or storage mechanisms.

Ultimately, leveraging ML is not about letting the data science team work with more sophisticated or more complicated algorithms, but rather about making ML easier to use and accessible to everyone within the company. Every organization agrees that being data-driven improves decision-making, but it’s not enough. Using built-in ML to turn numbers into future projections and recommendations, becoming analytics-driven, is where the real business opportunities lie. The more ML-enabled users there are, the more accurate the business decisions.



The opinions expressed above are those of the author.


Sherry J. Basler