What’s the difference between Business Intelligence and Big Data?


IT vendors, hardware companies and cloud services providers love to throw buzz phrases around. This often happens, even though much of the industry and most general end users don’t really understand what the terms mean. Here, we look at two of the biggest, hottest topics, and what they really mean for a business.

Business Intelligence vs Big Data Explained

The simple answer to the what’s the difference question is as follows, Business Intelligence (BI) is a suite or package of tools that your IT department, finance or analytics team use to gain insight into company performance. A BI system does this through microscopic analysis of data and information, using well understood methods to interpret it. BI tools can provide a neat dashboard, allowing at-a-glance snapshots of performance, trends and goals.

Big Data

Business intelligence (https://www.surecom.com.au/core-solutions/business-intelligence/) can be used to analyse historical or operational data to establish past or current states, with predictive questions finding out where those trends may take the business. A BI package can also provide information on why a situation happened, and what the business should do about it.

For a company in the process of modernising how it handles data, BI allows workers to save a lot of time, reduce the need to manually handle mundane tasks, and provide clear insights to the leadership, to help them better prepare the company for future market conditions. At the same time, while the bosses look at the pretty dashboard, engineers or analysts can drill down to the core data and see what any assumptions are based on.

Big data, on the other hand, is not something you can currently buy and expect results from within a few days. It is not a single product the business can just turn on and see magical predictions fly around the boardroom. A big data project sees the company investigate the information it produces. This usually flows from high-volume data streams of both structured (online transactions, machine-to-machine data or GPS signals) and unstructured data (email text, voice chats with customer service bots or agents and so on).

That data, when aligned and compared using large-scale databases, or cloud-based analysis can provide useful insights for the business, depending on how they choose to use it. Examples of real-world big data usage helps companies produce information for ideal costings and pricings, how to save time on a complex multi-department process, to what products the company should look to evolve and so on.

Deploying BI vs Big Data

A business intelligence product, particularly a cloud example can be installed by any business intelligence companies within a short period of time. Data scoops and translators can pick up the company’s existing data and feed it into any number of BI tools, with reports being produced in short order. In the era of the dashboard, a BI system can provide all manner of snapshots and slick graphics to reduce what used to be complex charts and spreadsheet data into something more digestible. Armed with visual insights and clear predictions, CIOs and CEOs or department leaders can make good knowledge-based decisions.

At the opposite end of the data spectrum, a big data project needs to establish what questions the leadership needs answering. This often requires hiring a big data scientist, specialist software or relying on another provider’s experts. They can analyse your business data and establish what can be done with it, to turn it into a form that can be analysed, and then provide the answers.

Big data started out as the preserve of banking institutions, massive retailers, global traders and manufacturers, and insurers. Their experiences, resulting in information that helps determine product failure rates, detect fraud or improve customer experiences online or in stores. Thanks to these successes, big data has filtered down to general enterprises and smaller businesses. In fact, any company that relies on data, especially in the Internet of Things or cloud era, may be considering using big data and business intelligence companies to resolve huge and disparate data sources to provide essential insights.

What to Expect from Business Intelligence Companies and Big Data

Business Intelligence has been with us as a concept since the 1950s, with modern computer-based services arriving in the 1990s and maturing rapidly to today’s various as-a-service solutions. With analytics a key part of many high-priority business initiatives, a BI solution and BI tools can prove an investment that is easy to sell to the business, based on years of strong results and references.

Big data remains the harder tool to deploy or even sell to a business. It is both a more complex technical endeavour, to get disparate sources of data together and to establish what end-users actually need from their data. Researchers started worrying about big data in 1998, and it hit the big time a decade later with an entire episode of Nature magazine dedicated to the subject.

As a more immature, complex concept, especially with one that requires “scientists” on the premises to help wrangle data streams, it is no wonder that big data is taking longer to see adoption. However, those in the business intelligence space can already see a clear point in the future when the two technologies and services will become one, so for those already invested in BI, it should not be long before a big data option becomes part of the service.

Along with the business intelligence companies and specialist vendors, the likes of IBM, Google, Microsoft and Oracle are all pouring research time and dollars into big data. They aim to help businesses deal with increasing volumes, velocity and volumes of information, from a growing range of connected devices including smart things, medical appliances or social media, and try to help them find the value within it.

That value may come from a huge variety of minuscule or wider-scale revelations or suggestions from any results. From revising an interest rate by 0.25% in some territories, having all a fleet’s trucks fill up at fuel stop “x”, or building their next product out of carbon fibre because futures markets for metals will cost them more. Such complex questions and answers cannot be easily teased from big data, but when they are, the results and cost savings or revenue improvement could be staggering.

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