If you’re new to the concept of business intelligence (BI)—or need a refresher—it helps to understand what the term actually refers to.
The term ‘business intelligence’ has been around for decades. Some say it was IBM researcher Hans Peter Luhn who coined the term in 1958. Others say it was Gartner analyst Howard Dresner who used the term in 1989 to describe the concept of improving business decision-making using fact-based support systems.
Over the years, the concept of BI has evolved; these days, it’s about turning data into insights. But it isn’t a single technology. Rather, it’s a set of tools and processes that combine advanced analytics with capabilities like data mining, data visualization and reporting. These tools and processes can help users extract value from their data—from inventory and sales data to customer demographics and preferences.
So, whether you’re looking to inform decision-making, pinpoint trends or quickly react to changes in the market, BI can provide a data-driven approach rather than one based on ‘gut feelings’ or what was done in the past.
Understanding the Basics of Big Data
Deloitte projects that, by 2025, the global data volume will reach 175 zetabytes. But BI can leverage big data, which is defined by three Vs: volume, variety and velocity.
There are three types of data that fall into the big data bucket: structured, unstructured and semi-structured. Structured data refers to data stored in a standardized format, such as Excel spreadsheets, CRM systems and SQL databases. It’s organized, so it’s easier for machine learning algorithms to decipher.
But a lot of data is unstructured, such as documents, text messages, call logs, social media posts and sensor data. That means it can’t be processed in conventional ways, so it’s typically stored in its raw form and needs to be ‘prepared’ before it can be used for analysis. Yet, it’s an important part of the mix, because it can provide deeper insights than structured data alone.
Then there’s semi-structured data, which falls somewhere in the middle: It’s more complex than structured data, but easier to manage than unstructured data. Basically, it uses metadata to identify similar characteristics in the data, allowing it to be catalogued, searched and analyzed.
Since data is stored across a variety of business systems (both on-premise and in the cloud), BI begins with data preparation. This involves collecting raw data, combining data sources and ‘cleaning’ the data for analysis. Datasets are then stored in applications, data warehouses or the cloud. But to extract value out of those datasets, organizations need BI.
Understanding the Basics of Business Intelligence
Organizations are drowning in the sheer volume, variety and velocity of data. Trying to find insights is like trying to find a needle in a haystack.
BI tools help to ‘mine’ that data for insights: Users make a query, and BI tools ping predefined analytic models, which already have the data sifted and calculated, based off the query.
For example, you could quickly compare your current sales performance to historical performance—though you could also do that on an Excel spreadsheet. However, with BI, you can take this a step further by drilling down into the data to understand factors or anomalies that may have impacted sales performance during certain quarters. This could lead to insights that could then boost future sales performance.
Visualization tools, such as charts, graphs and ad hoc reports, make it easy for users to understand and interpret this data. Customizable dashboards display visual data in one place, so users can interact with it in real time, drill down into it further and even share the results of their queries with others.
Another advancement is embedded BI, which embeds BI capabilities directly into applications, portals or websites that users are already familiar with, so they don’t have to toggle between systems. This can help to boost adoption and generate more value out of a BI platform.
The Benefits of Modern-Day BI
Back in the day, users sent their queries to the data analytics team or the IT department. Their request would be put in a queue, and they’d have to wait for an answer—for hours, days or longer—which usually came in the form of a static report. If they had follow-up questions, they’d have to resubmit their request.
That’s changed. These days, many BI platforms are cloud-based and provide self-service capabilities, so the average user can run queries on data—no data science expertise required—to make decisions or take action in the moment. IT departments are still needed to manage the data and the BI platform, but users are empowered to run their own queries and even customize their own dashboards.
Organizations are drowning in data. But they’re not always getting value out of that data. With BI, they can gain better visibility to set targets, track progress, uncover insights, spot trends and identify patterns. Stay tuned for Part 2 of Data Insights 101 to learn more about data visualization and interpretation.
Vawn Himmelsbach
Vawn Himmelsbach is a writer and editor specializing in enterprise IT, writing for national newspapers and technology trade magazines on everything from AI to zero-day threats. She also spent three years working abroad as an Asian correspondent, covering all things tech.
