Data Everywhere: The Future of Data Science and Business Intelligence

Data Everywhere: The Future of Data Science and Business Intelligence

In today’s world, digital data drives nearly every facet of business—from ideation and marketing to operations and management. Nearly every industry uses some form of digital data in order to do everything from making smart market decisions to increasing efficiency within the organization.

Truly, data is everywhere. This modern world of data presents a notable opportunity for those who are interested in learning to become a data scientist or data engineer. But what exactly does data science look like in business? And, what does the future of data science and business intelligence look like? Will there be jobs in data science? Let’s explore.

What is data in business?

What exactly are we talking about when we talk about data in business? In business, usually the data that’s important is data that provides actionable insights for the organization. For example, while the timestamp on a text document created by accident is technically data, it likely isn’t useful to the organization. However, data such as the number of times customers purchased a certain product, and how many of them first saw an ad for that product might be extremely useful data.

Who uses data science? You. Software engineering courses start soon. Learn to code.

Data science in business

You might already understand where we’re going here; somebody needs to collect this data, store it, organize it, find useful insights from it, and present them to organizations. That’s where data science comes in. Through robust data analytics driven by data science, data can become highly useful. For the industry of data science, this can present many opportunities.

Who uses data science?

Nearly every industry has some use for data science and in some ways relies on accurate data in order to make informed decisions, but to get a better understanding of how ubiquitous data is in business, and how important it can be, it can be helpful to explore some of the industries that have seen significant changes to the way they operate through the integration of data sciences. Industries like this include:

  • Finance: the finance industry widely uses data science to identify and better understand trends, which can help financial institutions enhance the way they avoid risk, manage assets, and more. The impact of a risk and the statistical likelihood of that risk impacting an organization are both calculated using data science, making it a critical factor in risk management for the finance industry.
  • Automotive: the automotive industry uses data science in order to better understand how vehicles perform and gain valuable insights about automotive manufacturing that they can use in each new generation of vehicles. As the trend towards autonomous vehicles continues to grow, data science is essential to power IoT indicators and diagnostics tools.
  • Logistics: the logistics industry utilizes data science to predict variables such as the weather and to find the most affordable and fastest routes through the sea, on the road, and through the air. For example, many organizations utilize IBM Watson Analytics to automatically streamline logistics operations, assisting with inventory management, route planning, and forecasting.

The truth is, data science, in some form, is used in nearly any industry. While different industries may have different focuses in adopting data analytics–for example, financial institutions might be focused on market trends, while marketing companies might be more interested in the behavior of consumers driving those trends–collecting and interpreting data and generating actionable insights can be potentially useful to nearly any business or organization. For many industries and many businesses, the potential benefits of business intelligence driven by data science should not be overlooked.

The future of data science

While nobody can predict the future (though it should be stated that data science is, in some regards, very much geared towards predicting the future), we can take a look at some current trends in order to get a better understanding of the ways data science is evolving now. From there, we might be able to better understand what the future of data science and AI might look like.

Current trends in data science

  • Integrating machine learning and AI to process data: one notable current trend in data science is that organizations, increasingly, are using AI and machine learning to better manage, analyze, interpret, collect, and present data. AI and machine learning can be used to identify trends and analyze vast amounts of data very quickly. For example, Netflix uses machine learning to make recommendations to existing customers, as well as to reach new potential customers.
  • Data democratization: with the vast amounts of data that organizations now possess, the concept of data democratization has emerged; by making data more accessible to users regardless of their technical capabilities, some organizations hope that they’ll be able to get more use out of data they already have access to. This concept is known as data democratization. An organization utilizing data democratization might break down the silos between sales data and marketing data. This allows teams from both sides of the organization to monitor leads and determine the effectiveness of a new marketing channel.

Where these trends could take us

  • Increased automation and accessibility: as technology evolves, we may see organizations increasingly adopting user-friendly and accessible technologies designed to deliver powerful insights and capabilities to users even with minimal technical training. The future of data science jobs also may involve helping create and maintain increasingly accessible and automated systems. Business intelligence analyst jobs may focus even more on creating accessible reports for teams across every level of the organization.
  • More insights in less time: as data science evolves alongside the technologies driving it, we may see data science become faster and more accurate, with novel technologies such as AI and machine learning being able to process even more data in less time. The future of data analytics may be much faster and more efficient.
  • Increased adoption of data science across the board: as some organizations that adopt data science in their strategies see success, other organizations may follow suit either aspiring to see similar success or in order to remain competitive.

The bottom line

While it may be impossible to predict the future, we can take a guess that data science will probably remain an important part of doing business for many organizations. Many businesses now are embracing data and data driven decision making. As data can be a valuable asset to organizations of all kinds, the future of business will likely involve lots of data science driven business intelligence.

As AI and machine learning enhance the way organizations are able to process data, we may also see accurate data become more affordable. The democratization of data could lead to not only smaller businesses being able to use data to make better decisions, but team members within organizations across more levels being able to use data to make decisions. To learn about a potential path to data science, be sure to check out Hackbright Academy. Hackbright’s full-stack curriculum is rooted in Python language, a useful tool for data analysis and visualizations as well as a breadth of machine- and deep-learning applications, among other core data science emphases. A Hackbright Academy Software Engineering bootcamp may provide the programming foundation or breadth of tech skills helpful for pursuing a career in data science.

Who uses data science? You. Software engineering courses start soon. Learn to code.

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