“Problems are solved not by giving new information, but by arranging what we have known since long,” said Ludwig Wittgenstein, a famous mathematical philosopher, and professor at the University of Cambridge. Wittgenstein may have taught in the early-to-mid-1900s, but his quote reigns true for most data-driven businesses today.
Businesses that consider themselves “data-driven” will have some quantity of historical data available. This is data that provides insight into past performances, like how successful a marketing campaign was or how a website change affected traffic. By collecting, analyzing, and interpreting this data, businesses can make more informed and impactful decisions about the future.
There are a variety of ways to work with data for better decision-making. Two of the more “buzzwordy” ones you’ll often hear about are business intelligence and big data analytics. And while the two terms are sometimes used interchangeably, they’re not the same. Let’s start by explaining business intelligence.
What is business intelligence?
A simple way to describe business intelligence is “the processes and software used to analyze data for better decision-making.” This enables businesses to understand “what” happened and “why” it happened.
Business intelligence is actually a culmination of a few things. When referring to processes, this can mean focusing on data management, which is the practice of collecting, validating, storing, and processing data. This ensures there is a single “source of truth” for businesses to reference. For businesses with many sources of data – like CRM, ERP, and marketing automation tools – data management can be crucial.
Another process is the creation of dashboards and scorecards for reporting. These options are a great way to visualize how a business is meeting its goals and which areas to focus on moving forward. The creation of dashboards and scorecards is a process, but it’s made possible using business intelligence software.
Not all business intelligence software serves the same purpose. For example, a business may license data visualization software to translate metrics into interactive charts which can be easily understood by non-technical users. Maybe a business needs a full-scale business intelligence platform to connect all data-points in a centralized space. This all depends on the business’ unique requirements.
One of the most trending ways to utilize business intelligence today is through self-service options. As a matter of fact, 64 percent of business leaders say self-service business intelligence creates a significant competitive advantage.
Self-service business intelligence essentially makes querying and reporting much more approachable for the everyday user. Some tools even have drag-and-drop capabilities. While this doesn’t completely remove the need for data analysts, it’s still a good option for businesses with limited resources.
To summarize, business intelligence can be taken literally as “making the business more intelligent” by harnessing all of its data. This is made possible using both the right processes and software. Now, onto big data analytics.
What is big data analytics?
Business intelligence may answer the “what” and the “why,” but data analytics is more concerned with hyper-personalization, forecasting, and answering “what is likely to happen next?”
In data analytics, it’s common for data scientists to work with big datasets, apply advanced mathematics, and build predictive models. Data analytics also consists of data mining, referred to as “knowledge discovery within databases,” to uncover patterns and trends that aren’t initially recognizable.
Then there is the use of unstructured data, as opposed to structured data with business intelligence. The main difference between structured and unstructured data is how it’s stored and processed. Structured data can be easily formatted in fixed fields and spreadsheets, unstructured data, like social media comments and audio transmissions, cannot. Instead, more modern approaches are required to work this raw data, like the use of NoSQL databases or data lakes.
If this all sounds very technical, that’s because it is. Big data analytics is much more of a scientific process compared to business intelligence. The use of statistics and algorithms helps data scientists find unique relationships in data. This is important for deconstructing unstructured data and applying it in meaningful ways.
The problem is, most of today’s data is unstructured, and user-generated content plays a prominent role in the scaling of big data. Think of how many Tweets are sent per hour, how many YouTube videos are posted every day, and how many blog posts are published monthly. This is why big data has become so “big” and a challenge for businesses to utilize – on top of being expensive to manage.
To summarize, big data analytics is a difficult endeavor, but businesses that are able to harness it gain a significant competitive edge. There are examples of Spotify leveraging big data to power its recommendation engine and provide personalized content to its users. There is also Starbucks, which uses big data and artificial intelligence to understand customer buying habits and preferences. Big data analytics opens up many opportunities for businesses and goes far beyond reporting.
What are the main differences between the two?
Let’s look at what we now know about both business intelligence and big data analytics so we can examine the differences.
- Business intelligence uses historical data for reporting. It tells us the “what” and the “why” and provides context to business problems. Big data analytics uses both historical and real-time data. It tells us “what is likely to happen next” and finds deeply-hidden trends and patterns within data.
- Business intelligence insights are typically visualized using bar/line graphs, histograms, and pie charts. Big data analytics visualizations are often much more complex and consist of fever charts/heatmaps, 3D computer models, cartograms, scatter plots, and more.
- Data analysts are the typical users of business intelligence. These analysts are strong in data querying, visualization, and possess business acumen so they can translate results. Data scientists are the typical users of big data analytics. These scientists are strong in programming, machine learning, and statistical modeling, just to name a few skills.
- Business intelligence is used across many industries where reporting and analytics are crucial for decision-making. Big data analytics is (currently) mostly used in industries where prediction and hyper-personalization are key. For example, early detection of diseases in healthcare or stock price forecasting in finance.
Business intelligence and big data analytics may be different, but are similar in the sense where they promote a data-driven culture within companies. In today’s competitive landscape, it’s important to have answers that are backed by data and remove any second-guessing. It’s also important to have business users who are able to interpret the results and take action. This is what it truly means to be “data-driven.”