What is Big Data and The Importance in Business

The term ‘big data’ is used to refer to two different things. It can either mean a huge set of data (individual nuggets of information about anything from business performance to customer preferences). It can also refer to the technology used to handle huge amounts of data also known as big data technologies. However, for this article, we will use the first definition.

What is Big Data

We mean really, big. Think about everything you have stored on your phones and computers. These devices measure storage space in gigabytes or, for really heavy lifting, terabytes.

Big data is measured in petabytes (one million gigabytes) and exabytes (one billion gigabytes). A petabyte is big enough to store 500 billion pages of text (even at one page per second it would take a person over 16,000 years to read that much text) and an exabyte holds the equivalent of 1.5 billion CD-ROMs worth of data.

These numbers may already seem big, but in actuality, they are just the beginning. Take a single action on any social media platform as a piece of data. Now consider that more than 55,140 photos are posted on Instagram, 511,200 tweets are sent, and 4.5 million YouTube videos are watched every minute. That’s big data.

Where does big data come from?

Social media is just one of the many sources for big data. Data is also regularly harvested from databases, applications, websites, sensors, internet of things, and more. In business, how frequently people click on your ads, how long they take to read a blog post, whether they sign up for new product notifications, all add to your pool of customer data.

Data can be structured (such as input from a spreadsheet) or unstructured (from presentations, emails, social media posts, etc.). Structured data is organised, making it easy to search, arrange and analyse. But, for the most part, big data is unstructured.

Mining for gold in big data

Big data is only as valuable as the insights it can provide. Getting these insights is not easy because most big data is unusable or ‘dark’ data. The business intelligence you can gather from big data is scattered and buried deep among the dark data (imagine having to sift through hundreds of billions of document pages to find the handful of information you can actually use).

Obviously, this is a task too great to be carried out manually. Luckily, we have the support of big data technologies such as cloud analytics platform, software designed to scan through huge amounts of information at super speeds. Findings are then presented as useful, actionable insights so you can make faster and smarter decisions for your business. This is essentially what data analytics is in practice.

Four Types of Data Analytics

1. Descriptive Analytics

Descriptive analytics is by far the most widely used form of business analytics, making up about 80% of all analytics in practice. It looks back at what has already happened in your business, uncovering patterns within vast amounts of data collected from multiple sources.

For instance, it can reveal which factory (or even which machine) experienced a drop in productivity, whether a social media campaign delivered results, or the exact time of day customers most often visited your store last February.

The insights are usually presented in accessible formats dashboards, reports, or graphs so managers, sales teams, executives, and investors can quickly understand the situation at a glance.

2. Diagnostic analytics

Diagnostic analytics goes a step further, answering the critical question: why did something happen? By drilling deeper into historical data, it helps uncover the reasons behind past outcomes. For example, if a new product underperformed in sales, diagnostic analytics might point to a competitor slashing prices just weeks before your launch, offering context that descriptive analytics alone can’t provide.

3. Predictive analytics

Predictive analytics, along with prescriptive analytics, falls under the category of “advanced analytics.” It uses the data you already must anticipate outcomes you don’t yet know, applying statistical models and machine learning techniques to make educated forecasts.

A simple example: based on historical click-through rates, predictive analytics may suggest that customers are more likely to open your emails if they’re sent early in the week, right after lunch. Or it may highlight that demand for your service consistently spikes on rainy days valuable insight for planning promotions or adjusting resources.

4. Prescriptive Analytics

Prescriptive analytics is the most complex form of analytics requiring very advanced computing and a high level of human expertise. Instead of merely predicting what might happen, it allows you to examine multiple scenarios and make choices that may result in the best outcome.

For example, an analytics tool could predict what first-day sales would look like if you launched in July rather than late August; in Mid Valley rather than in KLCC, or both locations simultaneously. And then create a plan that entails all the best choices.

Where do you start?

Big data analytics is more than just a buzzword, it’s a tool that reveals what customers are doing, what they’re thinking, and how you can influence them to choose your brand over the competition. Its importance lies in its ability to shape strategy and fuel growth.

The applications are already all around us. Insurance companies use it to detect fraudulent claims. Healthcare providers explore its potential to accelerate cancer research. Businesses leverage it for everything from streamlining supply chains and boosting sales to reducing risks and improving employee retention.

But here’s the surprising truth: big data analytics begins with two very human decisions.

The first is clarity. What do you really want to know? Are you seeking insights into customers, productivity, revenue, or something else entirely? Is your focus on the past, understanding what happened, or the future predicting what might happen? Analytics tools are powerful, but they still rely on the questions you ask.

The second decision is about relevance. Do you actually have the data to answer your question? Big data may involve millions or billions of data points, but if you’re not tracking the right metrics, no software can magically fill in the gaps. For instance, if you don’t measure responses to an email campaign, analytics can’t tell you whether it succeeded or whether it’s worth repeating.

This is where the human element remains indispensable: deciding what you want to learn and ensuring you collect the right data to uncover it. Once those pieces are in place, big data analytics takes over delivering insights, highlighting patterns, and presenting answers in seconds.