Artificial intelligence (AI) and machine learning (ML) are emerging fields that will transform businesses faster than ever before. In the digital era, success will be based on using analytics to discover key insights locked in the massive volume of data being generated today.
In the past, these insights were discovered using manually intensive analytic methods. Today, that doesn’t work, as data volumes continue to grow as does the complexity of data. AI and ML are the latest tools for data scientists, enabling them to refine the data into value faster.
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Historically, businesses operated with a small set of data generated from large systems of record. Today’s environment is completely different where there are orders of magnitude more devices and systems that generate their own data that can be used in the analysis. The challenge for businesses is that there is far too much data to be analyzed manually. The only way to compete in an increasingly digital world is to use AL and ML.
AI and ML apply across all verticals, although there is no universal “killer application.” Instead, there are a number of “deadly” use cases that apply to various industries. Common use cases include:
- Healthcare — Anomaly detection to diagnose MRIs scans faster
- Automotive — Classification is used to identify objects in the roadway
- Retail — Predictions can accurately forecast future sales
- Contact center — Translation enables agents to converse with people in different languages
Regardless of the use case, AI/ML success depends on making the right infrastructure choice, which requires understanding the role of data. AI and ML success are largely based on the quality of data fed into the systems. There’s an axiom in the AI industry stating that “bad data leads to bad inferences”- meaning businesses should pay particular attention to how they manage their data. One could extend the axiom to “good data leads to good inferences,” highlighting the need for the right type of infrastructure to ensure the data is “good.”
Data plays a key role in every use case of AI, although the type of data used can vary. For example, innovation can be fueled by having machine learning find insights into the large data lakes being generated by businesses. In fact, it’s possible for businesses to cultivate new thinking inside their organization based on data sciences. The key is to understand the role data plays at every step in the AI/ML workflow.
AI/ML workflows have the following components:
- Data collection: Data aggregation, data preparation, data transformation, and storage
- Data science/engineering: Data analysis, data processing, security and governance
- Training: Model development, validation, and data classification
- Deployment: Execution inferencing
One of the most significant challenges with data is building a data pipeline in real-time. Data scientists who conduct exploratory and discovery work with new data sources need to collect, prepare, model, and infer. Therefore, IT requires change during each phase, and as more data is gathered from more sources.
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It’s also important to note that the workflow is an iterative cycle in which the output of the deployment phase becomes an input to data collection and improves the model. The success of moving data through these phases depends largely on having the right infrastructure.