The Importance of Machine Learning and of Building Data Sets
In this special guest feature, Chris Haddock, a software product manager with ADEC Innovations, discusses how the foundation of a machine learning project for companies large and small is training data that will be used to teach the machine to recognize patterns. The quality of the training depends on the quality of the data input. Creating this data set is not always a simple matter. Chris is responsible for bringing new ideas and solutions to environmental compliance and sustainability applications. After graduating from the University of California, San Diego, he worked as a programmer on Environmental Information Management Systems, a client advocate and, for the last 5 years, a product manager.
Machine learning can significantly contribute to business’ efficiency. It can help identify which products and services sell well during defined periods, allowing companies to adjust their sales operations accordingly to improve their bottom line. Machine learning can also identify which parts of a business process are taking up too much time and resources, enabling companies to make improvements before profits are undermined.
Small companies can start with small machine learning projects, which they can eventually expand and improve. In the process, they gain access to valuable data which can help them grow their business.
Machine Learning Can be Leveraged by Small Businesses
An owner of a restaurant, laundromat or corner store may believe that their business does not need machine learning, thinking that the amount of data they generate does not require the use of machine learning, or that machine learning projects are too complex and expensive for small businesses.
Machine learning projects are actually beneficial to businesses of all sizes. They help businesses make better decisions and save on operating costs. A payroll application, for example, can process payroll on a smartphone.
Some businesses may already be using small machine learning projects without knowing it. Social media sites, databases and cloud-based platforms are all small machine learning projects that are used in almost every business. In addition, they are downloadable and can be used on computers, tablets and smartphones, rendering them easy to start and use.
Barriers to Building a Data Set
The foundation of a machine learning project is training data that will be used to teach the machine to recognize patterns. The quality of the training depends on the quality of the data input. Creating this data set is not always a simple matter.
Some companies do not know if they are using the right data to begin with. In other cases, companies are unsure if the amount of data they have is sufficient for evaluation. There is also the need to verify the accuracy of the data. These concerns can be addressed by deriving the following subsets from a data set:
- Training data set – A data subset that is used to form a predictive model
- Test data set – A data subset that determines how the predictive model will perform in the future; a predictive model is likely to be overfitted if it aligns more closely with the training set than with the test set.
- Validation data set – A data subset that measures the predictive model’s adherence to a given quality standard.
Using these data subsets, a company can tell if the data set they have is complete, accurate and relevant, enabling the organization to generate valuable insights that will boost business sustainability.
Methods for Building a Data Set
There are various methods that can help companies build a data set. The first is the “do-it-yourself” approach. This method often involves using online applications and information learned from online tutorials. This approach can help save money, as well as provide company owners firsthand experience in creating a data set.
The flip side is that it is largely a trial-and-error process. Information provided online is largely unverified. Unless the business owner is a data mining expert, they are likely to commit mistakes, which can cause delays and additional expenses.
The second method is hiring temporary workers. If they are proficient in building data sets, they can immediately help to start and operate a machine learning project. Hiring temporary workers also enables companies to save on wages and benefits.
Due to the nature of their employment, though, temporary workers may come and go. Often, the implication is that a company’s data sets and machine learning projects can be created by one temporary worker and maintained by another, which can lead to disruptions.
The third method is managed outsourcing. A company can outsource the building, operation and maintenance of its data sets and machine learning projects to a managed services provider. This option is cost-effective and provides reliable service.
Outsourcing to a managed services provider, however, can compromise data security and confidentiality. Many company owners are not fully aware of how managed services providers handle the information that is transmitted to them.
Means to an End
Whatever method is chosen, business owners need to remember that a machine learning project and data set are tools that can help them achieve business sustainability. Whether or not they will serve their purpose depends largely on whether the company uses them effectively. Before venturing into a machine learning project and creating a data set, a company must first identify its goals and verify the data it has is relevant to its goals. Big data is good, but companies need to know how to use it.
“Originally published in insideBIGDATA