Now that the world is digitized, data is everywhere. Whether it’s the food we ate this morning or our shopping habits online, businesses have more information on consumers than ever before. But how can they manage all of this data effectively?

There are two fields that companies rely on to maintain and analyze all of these numbers and statistics. They are Business Intelligence (BI) and Data Science. While they are related to the same thing (interpreting numbers about consumers and industry), they operate in fundamentally different ways.

Today we’re going to break down the elements of both of these systems and compare how they can be utilized together to create a better business model for anyone.

Business Intelligence Overview

At its core, BI is all about understanding information as it is and as it was. Analysts will look at current and past data to draw conclusions about various elements of the business. For example, you may look at how consumers shopped for particular products around the same time of year for the past five years.

The goal of business intelligence is to provide a comprehensive picture of the marketplace for executives and business owners to better understand their customer base. This information is invaluable because it offers strategic insight based on hard facts, not bias or belief. With the numbers coming in from BI, any company can adjust its marketing strategy to match, allowing them to adapt.

Data Science Overview

While BI is all about analyzing what happened before, Data Science is the next logical step in this process. Once you have a complete picture of your consumers, where do you go from there? That’s the core question driving data scientists and how they work.

Overall, this practice is all about taking the numbers and analyses and formulating projections and models for what’s to come. Using our example from above, if you notice that shoppers are buying certain items around a particular season, you can anticipate their needs and deliver better products or services to meet them.

Comparing the Two

As you can imagine, BI and data science are technically two sides of the same coin. Both are necessary for a business to adapt to a changing marketplace, and both systems are highly valuable in their own way. You can’t do data science without hard numbers, which is why they are mutually beneficial.

To further help understand how both of these operations can work together (as well as how they differ), let’s break down some of the fundamental methods used for data gathering and intelligence.

Managing the Hard Numbers

For the most part, BI is an excellent way to take all of the data you have and present it in a consumable format. Things like graphs, dashboards, spreadsheets, and KPIs are all par for the course when using business intelligence.

However, there are some significant limitations. Usually, this data is transcribed and formatted in a particular way, which doesn’t offer a lot of room for changes and adaptations. Thus, if the method of data collection or presentation is flawed, you may not realize the issue until much later.

With data science, you have the flexibility to make adjustments as necessary so that you can adapt in real-time. Feature engineering may be necessary when analyzing metrics in Data Science. Feature engineering is the process of using domain knowledge to create new attributes that can be used to insert additional context to algorithms. Technology has evolved, and with it comes our ability to monitor, extract, and manage any information related to the marketplace.

Analysis vs. Prediction

When looking at BI graphs, you can get a clear picture of what’s going on right now in your business. Having quantifiable items can offer better insight into the potential of your company based on where you’ve been and what’s happened before.

Data science lives exclusively in the future. When you look at models and graphs with this system, they are all guesses and predictions, which can make them a little hard to master. Because you can’t see the future, you aren’t basing your knowledge off of real data, but simply what could be.

Data Discovery

One critical difference between these two strategies is that data science allows you to discover new elements of the marketplace and your business that may otherwise go unnoticed. BI is based on questions you already know, while data science can help you figure out different questions that you want to have answered. The inherent flexibility of the system allows for greater discovery and adaptation in the company.

Storage and Deployment

Traditionally, businesses that used BI had a very limited number of methods of distributing the data across the company. It was managed and analyzed by the IT sector, which stored it on proprietary servers and hard drives. To get access, you usually had to use legacy systems or wait for a presentation to get the information you needed.

Data science, however, is much more reliant on new technology like the cloud, which means that updates and changes can not only be interpreted in real time but distributed as necessary to keep everyone up-to-date. Since the data is not stored on legacy hardware, it’s much more accessible. And although many organizations still have Data Scientists reporting through the IT function, more and more organizations are embedding them throughout the organizations (i.e., Marketing, Finance, Manufacturing organizations).

To further blur the lines, BI software vendors are introducing Data Science elements into their BI solutions. Tableau has built integrations to allow users to execute R and Python code to make models more accessible to organizations. Microsoft PowerBI has also included visualizations in their marketplace that allow users to quickly incorporate K-Means clustering, an unsupervised machine learning algorithm, into their dashboards.


While business intelligence is a core component of any analysis procedure you may have, data science is the key to unlocking your company’s future. Rather than waiting for numbers to come in and be analyzed, you can get a real-time understanding of what’s happening and how it can change in the near future.

Data science will become more and more critical for businesses to stay ahead of their competitors, which is why so many top-tier companies are investing heavily in the technology and the people with this skillset.


For years now we have heard the term “big data” — experts have predicted that data will, in essence, take over our world and drive virtually every aspect of our existence, from how we run our businesses to how we conduct our free time.

While we haven’t quite reached that pinnacle just yet, there’s no question that data is becoming more and more of a factor in our day-to-day lives. As technology develops, the amount of applications that rely on big data, data science, and data-driven analytics only increases. The sheer volume of data collected these days is staggering and growing at an almost exponential speed. It is estimated that 2020, 1.7MB of data will be created every second for every person on earth. Because of this, the question for companies in today’s world is not, “should we use data” but “how can we bring data into more and more aspects of our operations?”

In short — how can we compete in today’s data-driven world?

Answering this question is no easy task, but based on current trends and developments it is possible to take at least an educated guess as to what today’s businesses need to think about when it comes to navigating the world of big data today and in the near future.

Artificial Intelligence

Artificial Intelligence, otherwise known as AI, is the term used to describe the complex simulation of human intelligence by machines. It’s been the stuff of science-fiction for years (usually, with a terrible outcome for the humans) but in reality, it’s been a slowly developing field for some time now. AI is a wide field that primarily deals with machines learning to apply logical reasoning and self-correction while taking in data and learning about the world around them. As machines learn, the AI they apply allows them to solve problems, learn about their users or change and refine how their applications work. This self-learning can have massive implications for the business world.

Because of its inherent complexity, a lot of what falls under the realm of AI is out of reach for current developers and businesses. However, as AI becomes more and more refined, the applications become obvious and important. Be on the lookout for the term “democratizing AI” to become part of our lexicon in the not too distant future. Whether you’re developing an app for your business or using your computers to help predict future customer interactions, the uses for AI are still being formulated, and experimentation will be key to gaining a competitive advantage.

Machine Learning

Machine learning is a specific category of AI. Machine learning deals with how computers are able to learn and adapt to the needs of the world around them without any explicit programming. To accomplish this, programmers/ data scientists/ ML engineers (the job roles continue to grow) make extensive use of algorithms which can learn and adapt based on user data and input.  Machine Learning typically falls into one of three categories: supervised, which makes use of labeled data to allow computers to recognize characteristics and use them in the future; unsupervised, which leaves data unlabeled and forces machines to understand and classify various characteristics on their own; and reinforcement learning, which use algorithms to analyze the environment and learn from various causes and effects.

In business, machine learning is a vital part of any model of data analytics today. Machine learning is the driving force behind such various applications as bank fraud detection, email spam filters, and product recommendations. All of these applications can be used to make a customer’s experience and interactions more convenient and pleasant. It’s not too hard to see how each of these skills, and others like them, could either help or truly harm customer relations. As businesses look to become more data-driven, employing Machine Learning will be a vital part of the equation for many years to come.

Deep Learning

Deep Learning is just a smaller sub-set of Machine Learning. Deep Learning is designed to more closely resemble the organic neural networks of the human brain. As a result, deep-learning algorithms can tackle much more complex problems that involve unstructured data like images, speech, and text. Typically DL requires significantly more data than ML models, but the benefit is that it has the potential to increase accuracy with less cumbersome fine-tuning or feature selection/engineering.

Deep learning is still a field that is hard to describe or understand — even for those who work within it. The expected high costs and technical expertise required to implement a Deep Learning solution will preclude most organizations from introducing the technology; however, the benefits will certainly be present for an organization that identifies the right use case (i.e., autonomous vehicle, facial recognition).

As companies contemplate ways to stay competitive, some implementation of Artificial Intelligence will be needed – whether building the solution in-house or finding an external partner with a more in-depth knowledge of the technology. However, the organizations that truly begin to embrace AI and forge ahead to develop a comprehensive strategy to experiment and introduce use cases will outpace those laggards that wait.