Industry experts, competitors and even your customers are talking about machine learning and artificial intelligence. As they continue to grow in popularity, more companies than ever before are seeking ways to use advanced solutions to extract data, connect it and employ it for meaningful insights and learning. But in order to ensure machine learning success, enterprise models need to ingest clean data sets. Otherwise, to put it bluntly, dirty data goes in and garbage analytics come out.
Machine learning at a glance
With the increasing amount of accessible data and the cost of high powered computing becoming more affordable, data scientists no longer need to rely on small, thoroughly curated data sets. Instead, large and even unorganized data sets can be used with thousands of parameters to train algorithms and generate predictions. Based on these modern workloads, machine learning is understood to be a form of artificial intelligence and mainly refers to computers that can learn and improve their analysis on data over time without reprogramming their core logic. Machine learning has taken a massive leap in adoption over recent years, with many businesses having adopted or planning to develop machine learning models.
Business use cases that can be improved with machine learning
Both machine learning and artificial intelligence have distinct and practical applications for your business that go beyond driverless cars. Machine learning allows your business to process and more importantly understand data faster, allowing you to run more effective marketing campaigns, maximize logistics operations efficiency, and significantly outpace your competitors.
Specific examples of machine learning and data include personalized marketing (using customer data to drive real-time recognition and personalized recommendations), fraud detection (using PII data to understand if someone is who they say they are) and predictive maintenance systems (using time series data to understand when equipment failures may occur).
Preparing your data for machine learning
Common data transformations are required before data can be processed within machine learning models. The better your data, the more valuable your machine learning. Here are some tips to help you properly harness the power of machine learning and AI models:
- Consolidate and transform data from various sources and types into a consumable format.
- Carefully identify and evaluate business objectives and align them with your data strategy to help reveal where machine learning fits into your overall data management framework.
- Hand picking the data that you specifically need will not only improve the speed at which your model trains but also helps when you come to analyze it.
- Remove characters like line breaks, carriage returns, white spaces at the beginning and the end of values, currency symbols, etc.
- Make sure your categorical data is in a numerical format. This means converting values such as yes and no into 1 and 0. However, be cautious not to accidentally create order to unordered categories such as converting mr, miss and mrs into 1, 2 and 3.
- Define a specific date/time format and convert all timestamps to the defined format.
- Determine how to resolve incomplete data based on your dataset. If you have missing data, you’ll need to proceed with caution when deciding between imputation or removal as it may create a bias in your model and/or skew your results.
Machine learning can help your business process and understand data insights faster – empowering data-driven decisions to be made across your organization. For machine learning to be successful, however, your models will need to consume clean data sets. As the quality of your data increases, you can expect the quality of our insights to increase as well. Transforming data for analysis can be challenging based on the growing volume, variety and velocity of big data. This challenge will need to be overcome to unlock the potential of your data and to mobilize your business to move faster and outpace competitors. When you’re ready for machine learning, consider deploying data transformation purpose-built for the cloud. This will help you increase the ROI on your data, transforming your data so it is machine learning ready.
About the Author
Damian Chan is an experienced data engineer and finance enthusiast with a passion for big data. Damian serves as a Solutions Engineer at Matillion, a provider of data transformation software for cloud data warehouses. His previous professional work includes building algorithmic systems for Seer Trading Systems where he was exposed to the stock, commodities, and foreign currency exchange market. He has led big data ingestion and deployment and is proficient in cloud data warehouse technologies.
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