Posted on July 27th, 2022
Using Machine Learning capabilities, an organization can significantly improve the quality, usability, and value of their important data assets.
In describing how Machine Learning compares to related concepts such as Software Engineering, Data Engineering, and Data Science, it can be difficult to ascertain where one discipline ends, and another begins. The boundaries tend to be fuzzy, and definitions can vary from article to scholar to expert.
One way to tie several of these umbrella terms together is the concept of "Applied" Machine Learning. In this way, one can utilize a portion of each of these areas in practical use cases. Since a primary purpose of Machine Learning is to use data to improve performance and value of a task or process, the combination of multiple concepts is a great way to demonstrate the value of each when leveraged together.
Thinking about a particular challenge in the abstract, as is the case with Applied Machine Learning, enables one to step back and think about which components, models, approaches, and concepts of Machine Learning, combined with other perhaps overlapping areas in the software and data world, can best be utilized to solve a specific challenge. This can be particularly useful in solving organizational challenges and creating new business opportunities.
In our case, the specific challenge is how to utilize these concepts to help significantly improve the quality of data in databases and datasets that are the foundation of customer information systems, marketing applications, analytics, data science, AI & ML, and other data-driven applications. This is a cornerstone of a strategy to improve the quality, usability, and therefore value of an organization's data assets, so everything that is done with the data is more effective, useful, and successful. A focus on better data ultimately drives more ROI for an organization on behalf of its data lifeblood and can become an important competitive advantage.
Using various techniques of Machine Learning, including specific models, scoring, iterative analysis, contextual learning, and reference bases of encoded knowledge all working together, combined with a lot of rolled-up-sleeves experience, we have been able to come a long way in solving many of the issues around data quality that affect an organization operationally and also in a decision-making capacity.
The useful thing for us is that in most cases, regardless of vertical industry that is the source of a given dataset, many of these data challenges are very similar across organizations. For example, a company name might exist within a database fifty different ways (various permutations, inconsistent spellings, etc.), and there might be tens of thousands of company names with the same challenge in the dataset. This can make any meaningful data analysis nearly impossible.
Because of the ubiquity of a specific set of challenges across vertical problem sets, we can build many sophisticated Machine Learning models and processes to help us provide significant value for our customers within different industries. And besides, HOW we do it is of less interest to a given customer - they want better data for better outcomes, and that is what we are able to deliver.
We have achieved this, both with database connected products, as well as data services we provide where it is just easier for an organization to send us datasets for analysis and processing that they need help with, so they can focus on their core business. The latter enables us to perform and perfect multiple Machine Learning techniques and to slice data many different ways to get the highest possible performance value and results.
A major epiphany for us was understanding that existing poor data quality can be leveraged to improve the quality of that data. This can be counter-intuitive, but it turns out that approach is very effective at increasing data quality, data usability and data value of corporate data assets. These analytical approaches within Data Engineering can go a long way towards success.
Let us know at email@example.com if you'd like to dig deeper with your organization's specific data challenges.
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