Data science expands business analytics

Source: Pixabay

We continue our journey through data with data science. In our last blog, our data explorer discovered everything there is to know about business analytics. This world seems familiar, but underneath it lies a more vast approach to analyzing data: data science. Business analytics is like the crust of the earth, but data science is the mantle.

What is data science?

Data science uses different algorithms, processes, and systems to make sense of large amounts of data of any kind. Where business analytics specifically explores business data to make decisions within the business environment, data science can ingest vastly different data types to answer many additional questions.

That doesn’t mean data science doesn’t belong in the world of business. Just as the mantle determines different types of land masses and arrangements in the earth’s crust, data science is a larger discipline that supports robust business analytics. We may see more of business analytics, but the reality is that data science makes up a much larger piece of the ecosystem—just like the earth’s mantle makes up a majority of the earth’s mass.

Data science versus business analytics

The earth’s mantle forms a large part of the earth, sandwiched between the often mysterious and uninhabitable core and the life-supporting environment of the earth’s crust. Without this layer, life would need to evolve in a much different manner in a much more hostile environment.

Data science also exists to support the aims of business analytics. Business analysts are able to analyze their business data and statistics based on the pipelines built by data scientists. Business analytics doesn’t need to know what’s happening or even see the vast majority of what comprises data science to make quality business decisions from data. As a result, data scientists can perform the role of business analysts, but it doesn’t always go the other way around.

Business analytics doesn’t require much coding, instead relying on a foundation of tools with prebuilt capabilities to ask questions and understand data. It makes up a small part of everything data science offers, just as the earth’s crust is a small part of the overall earth composition while the mantle spreads far and wide just underneath.

Why is data science so important?

Data science is the key to surviving in the new era of business. We’re creating more data now than we ever have before. Businesses, organizations, and governments are sitting on treasure troves of data just waiting to be analyzed. Data science helps us make sense of all this data in ways our human mind can understand.

Data science can transform business by allowing it to become truly data-driven. This concept moves beyond business analytics, which often focuses on past data to understand where a business is now. Instead, data science can analyze data to look to the future, find trends before they happen, and offer strategic business choices that differentiate from the competition.

Some examples of how businesses can use data science in everyday operations:

  • Better predict customer churn: Data science can reveal habits that predict which customers will leave and when, helping businesses prevent it from happening in the first place.
  • Weather disruptions to logistics: Data science can help retail companies determine inventory levels or help shipping companies plan for weather-related events.
  • Improve fraud detection: Data science is making waves in the finance industry by better identifying instances of fraud with fewer false positives. This can help improve overall customer satisfaction.
  • Improve timing: Companies are fighting for customer attention in a noisy field. Data science can inform communication, recommendations, business offerings, new services, and many other common occurrences using data-recommended timing for maximum impact.

These examples are just a few options for how businesses of all kinds can implement data science into their operations. Moving into the “mantle” of our data environment can open up a world of new possibilities beyond business analytics that allows businesses to get ahead of the curve.

Data science is the right choice for evolved business

Source: Pixabay

How is data science conducted?

The process of analyzing data and putting data products into production requires several critical steps.

  • Planning: It’s not enough to simply ask a question. Planning a data science initiative ensures the right, business-value-focused question provides the foundation for the project.
  • Building the model: Data scientists and engineers then look to the logistics of answering the question with the right model.
  • Finding the right data: This step ensures only quality data informs the model. The old adage “garbage in, garbage out” has real-world consequences, so businesses want to start off right.
  • Deploying the model: Taking trained models to production happens through avenues such as secure APIs and may require buy-in from multiple departments.
  • Evaluating and monitoring models: Once models deploy, they require monitoring to ensure optimal operation and evaluation to ensure the model is still relevant to the business need. Teams may go through multiple iterations.

The benefits of data science for business

It pays to have a data science strategy. Companies can uncover larger applications for their data using data science. Here are some benefits of deploying data science models.

  • Improves business predictions: Data scientists can deploy machine learning models, artificial intelligence, and other tools to handle massive data loads. This helps businesses stay ahead of the curve.
  • Aids sales and marketing: Marketing dollars can be a mystery unless you can predict what customers want and need before they do. Data science helps reduce waste in marketing and sales spend and builds better relationships with customers through personalization.
  • Increased security: Data science can also help companies implement robust cybersecurity strategies that protect the data in their care.

Automation of mundane tasks: Data science pipelines can automate tasks that take up valuable time—think documentation, customer service responses, monitoring for regulatory compliance, etc.

Don’t be afraid to explore deeper

The mantle of the earth offers structure and protection for those existing on the surface. Data science, likewise, can offer us tools to build data products and move deeper into data insights than business analytics. It may require more specialized skills, but in today’s business world, it pays to understand what’s underneath the surface.

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