Many people believe the terms machine learning, big data, AI, neural networks and data science are interchangeable. There are distinctions to each that will be critical to understand in order to tactically architect your data-driven programs.
Data Science is a discipline that involves the study of data and the methods used to capture, store and analyze in order to mine valuable insights and unearth patterns, correlations and other key understandings.
Big Data involves the systems and processes utilized to manipulate, manage and analyze high volume and complex data sets.
Machine Learning encapsulates the algorithms and statistical models that computers apply to data to execute tasks, forecast outcomes or identify trends, patterns and precedents.
AI (artificial intelligence) is the growing science of machines demonstrating intelligence using information from which it learns, reasons and makes independent corrections.
Neural Networks are a system of algorithms, considered to be somewhat configured like the human brain, designed to find patterns by processing, interpreting, labeling and clustering data points.
Data is meant to be action-oriented with a value extraction. Data science involves several areas of expertise including data engineers, analysts, researchers and designers.
Common goals are to create pathways to problem solve, reach peak performance, develop business tactics based on sales patterns, garner project insights or other defined objectives.
Essential to any data science initiative is to evaluate the usability and application of the results to ensure its benefit and ROI.
Cloud computing has significantly advanced conditions and accessibility for data science to be utilized by companies of all sizes.
Data science is designed to handle, optimize, manipulate and effectively manage the four Vs of information:
1. Volume [quantity]
2. Veracity [quality and accuracy]
3. Variety [range of types and diversity]
4. Velocity [speed]
Important Considerations
• Data frame sets and structure – look at whether the data is standardized and labeled or raw and unstructured
• Throughout any project, there will be requirements for data cleansing, processing and refining
• With the understanding that there are numerous variables within data, several iterations and validation of outputs are necessary
• Evaluate patterns, classifications and correlations using predictive or prescriptive practices
• Don’t underestimate the time and resources required for preparation, standardization and cleansing of data to make it actionable.
• Pay close attention to ethics, privacy rights, regulations and other critical factors when utilizing data and know when you must expressly share sources and obtain informed consent.
While data science may appear to be vast and dense – there is a viable blueprint for developing a practical and scalable application that can powerfully serve your company by providing otherwise unknown insights. Grasp the opportunity to make data a fundamental tool that can drive far more formidable strategies – giving you a real boost in competitive positioning and smart spending.
