Data science, a fairly new, complex, and rapidly growing field boils down to a somewhat simple concept: the scientific method.
The scientific method is a process for experimentation that explores observations and answers questions.
There are as many versions of the scientific method as there are scientists, but with every variation the goal remains the same: to discover cause and effect relationships by asking questions, carefully gathering and examining evidence, and combining and analyzing available information into a logical answer.
Here is a quick refresher on the scientific method:
Ask a Question: The scientific method begins when you ask a question about an observation: How, What, When, Who, Which, Why, or Where?
Do Background Research: Rather than starting from scratch, you want to use existing research to bolster your experiment. Many times best practices already exist.
Construct a Hypothesis: A hypothesis is an educated guess about how things work. It is an attempt to answer your question with an explanation that can be tested. A good hypothesis makes a prediction and is easy to measure: “If _____[I do this] _____, then _____[this]_____ will happen.”
Test Your Hypothesis by Doing an Experiment: An experiment tests whether your hypothesis is supported or not. For an experiment to be valid, it must be a fair test. Change only one variable at a time while keeping all other conditions the same and repeat several times to make sure the results weren’t an accident.
Analyze Your Data and Draw a Conclusion: Once your experiment is complete, collect and analyze your measurements to see if your hypothesis is supported or not.
Communicate Your Results: Finalize an experiment by communicating the results to others.
While the process of a data scientist is the scientific method, the lifeblood of data science is data.
Data is more valuable than ever for businesses so it’s no surprise businesses are racing to record customer data whenever possible. In the race to collect as much data as possible, businesses are gathering data before knowing what questions to ask or how to organize their findings.
80% of a data scientist’s valuable time is spent finding, cleaning, and organizing data, leaving 20% to perform analysis. That is, only 20% of the average data scientist’s time is spent on value-added tasks.
Companies need to flip the ratio so data scientists can spend their time with what they do best: data science. That is, using data streams to power the scientific method and discover actionable insights for the business and customers. Then communicate those findings to their fellow humans, rinse, and repeat.
We would rather see our data scientists spend their time on tasks that machines can’t compete on, while letting the machines take care of the rest. We can begin the ratio flipping process by applying machine learning to data cleanup, and by involving intelligent agents in the data gathering process so data needs less cleanup in the first place.
An intelligent agent can be trained to gather and organize customer data such that a data scientist is empowered to shift their time away from organizing data and back into the more valuable area of running experiments, performing analysis, and improving customer experience. With the power of today’s machine learning frameworks and cloud computing platforms, companies can collect and organize thousands of data streams in parallel, often with the help of a data scientist.
While advances in Artificial Intelligence have lead to impressive data crunching and natural language processing abilities, machines are not yet as capable of human interaction as fellow humans. A data scientist can recognize patterns and empathize with fellow humans beyond the statistical abilities of algorithms, but algorithms are more efficient at scrubbing large data sources.
By implementing AI in the data science process, businesses can build a self-improving cycle. With more resources available to improve the product and customer experience, as well as improved data collection methods, businesses can provide more value and thereby attract more customers. A larger customer base creates a larger dataset to derive insights from. The data is gathered, cleaned, and analyzed by the Intelligent Assistant in parallel to data scientists and the cycle repeats itself. Here is a diagram representing a new AI-enhanced version of the data science process:
Data scientists can more easily discover and share insights with fellow team members, and provide value to customers when working in parallel to AI.
The data science process is as simple as applying the scientific method to large data sets, but data scientists today are bogged down by the data gathering and cleanup processes. We can augment data scientists by offloading much of the data gathering and cleaning work to machine learning-driven AI, leaving more time for data scientists to generate and share valuable insights.
The team at swivl is building a toolset that allows companies to easily label unstructured data to personalize and optimize customer experiences in an AI-First world. Retroactively review and train your models to make interactions smarter over time.
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