At the dawn of the modern computer age, "machine teaching" was a labor-intensive process that included feeding stacks of punch cards into computers the size of Buicks in order to program them.
The phrase GIGO (garbage in, garbage out) was coined to account for missteps that corrupted the process, such as incorrectly punching holes or entering cards out of order.
Now, as data and computer science progress farther into the age of big data, analytics and machine learning (ML), GIGO remains a critical concept.
Harvard Business Review, for instance, declares that bad data is "public enemy number one" in the integration of machine learning (ML). Bad data is the contemporary equivalent of incorrect punch cards in the 1960s.
"The quality demands of machine learning are steep, and bad data can rear its ugly head twice — first in the historical data used to train the predictive model and second in the new data used by that model to make future decisions," it warns.
What Role Do Data Analysts Play in Machine Learning?
Teaching a machine how to learn begins with creating files of existing data to build models capable of recognizing patterns in unstructured data that it has not seen before, evaluating them and making predictions based on those interpretations.
The role of analytics in teaching machines how to learn begins with clarifying what types of data meet the business objectives of its automated data pipeline. Once that is determined, analysts collect historical data, remove corrupted or irrelevant sets and replace, name and format them.
Analysts play a critical final step before the ML process goes operational: testing the model, optimizing it and validating the results.
"Model evaluation can be considered the quality assurance of machine learning. Adequately evaluating model performance against metrics and requirements determines how the model will work in the real world," according to TechTarget, an ML services provider.
Once operational, ML accelerates the organization and evaluation of new, unstructured data, the flow of data through the pipeline and the availability of actionable intelligence that drives data-based decision-making. "Think seconds instead of weeks," notes AnswerRocket, an analytics services provider.
Still, analysts will have a role in the automated computation era: assuring the quality of the ML predictive analytics and ensuring it meets business objectives – a critical thinking skill that machines cannot (yet) perform.
ML is an iterative process, with the model using continuous inputs of new, unstructured data. Therefore, bad data is an ongoing threat to ML's analytic capabilities.
"The risk is that a minor error at one step will cascade, causing more errors and growing ever larger across an entire process," Thomas C. Redman, president of Data Quality Solutions, writes in Harvard Business Review.
How Do Businesses Use Machine Learning?
Businesses in industries across the economy realize the advantages of ML's capacity to mine data in real time to:
- Anticipate customer behavior to innovate new products and services, optimize existing ones and enrich the customer experience
- Manage risk by monitoring transactional data as it occurs to spot trends that indicate cybercrime and fraud
- Reduce costs through automation that supports more efficient allocation of human, technical and financial assets
"Although machine learning is a type of predictive analytics, [it] is significantly easier to implement with real-time updating as it gains more data. Predictive analytics usually works with a static dataset and must be refreshed for updates," according to Microsoft's cloud computing service, Azure.
What Are the Career Prospects in Machine Learning?
As businesses in high-growth sectors such as healthcare, financial services, cybersecurity and software development expand, demand for data scientists, analysts and engineers in ML is likely to follow suit.
Data professionals can advance their careers by earning a Master of Science in Data Analytics, such as the one offered online by Northwest Missouri State University. The curriculum explores industry-leading methods to identify, collect, analyze and transform data in the era of machine learning.
The Applied Machine Learning course in Northwest's online program provides hands-on use of ML models to perform analyses of data sets, as well as data acquisition and cleaning, selection and evaluation strategies and methods.
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