At its core, business analytics is the exploration of the data of an enterprise, with a strong emphasis on statistical analysis and how the best practices and individual systems are selected for each business.
Increasingly, more companies are becoming data-driven, as enterprises of all sizes are becoming increasingly aware that their data is one of their most valuable assets to leverage as an advantage over the competition.
Once an end goal of the analysis is understood, the analysis methodology is chosen and company data is selected to support the analysis. This typically involves a feed from multiple data sources and systems, to then be cleansed and integrated into a unified space, such as a data warehouse.
The success of business analytics inherently relies on both the quality of the data (good data in, good data out) and the expertise of the analyst who understands the nuances of an individual business, as well as the technology on which everything is built.
The challenge of multiple sources
Many companies use a range of different business solutions and platforms, which may be great individually, but are stifled by their inability to collaboratively communicate with each other, or at least, flow to the same place. When you also throw in legacy paper-based data sources to the mix, it’s easy to see why, in many organisations, a lot of time is spent simply trying to find information – let alone doing anything constructive with it.
Multiple sources of data can be challenging to get in one uniform feed, especially when you consider a variety of formats, legacy systems, export times and availability that many enterprises face.
The challenge of real-time business analytics
As an example, real-time data analytics have been used in financial trading for quite some time now and now take on board more data streams than ever before.
To be useful, real-time analytics applications need to have good availability coupled with low response times. Systems should also be able to manage vast quantities of data, yet should still be expected to return queries within seconds.
The better your company knows where it is now, the better it can forecast for where it needs to be.
Predictive analytics is a part of business analytics & intelligence that’s becoming increasingly augmented by both Artificial Intelligence and Machine Learning, by using statistics and modeling to determine future performance and conclude potential outcomes, based on both historical and current data.
This allows organisations to decide where best to focus resources, thus, be able to make intelligent predictions about the future. One could argue that this level of insight is so valuable, that the systems that make it happen can easily pay for themselves in no time.
The exact applications vary from industry to industry, however the ability to make intelligent forecasts about future events has almost limitless applications.
Advanced Business Analytics is already used across a variety of industries, including telecoms, pharmaceuticals, defence, logistics, insurance, financial services and far beyond.
What are the main differences between Business Analytics & Business Intelligence?
It’s (understandably) fairly common for people to confuse BA (Business Analytics) with BI (Business Intelligence) as they both sound inherently similar.
Both BA and BI require data to be collected, cleansed and visually represented through data visualisation software for compelling storytelling and intel to be gained from the data.
There is, however, a few key differences between them:
BI deals with historical data, but data tends to be collated from a number of sources, eg. CRM software or automated marketing tools. The main function of Business Intelligence is to do with the reporting of a company’s performance, based on key metrics. It provides context to what’s previously occurred in the past, why it may have occurred, and what’s currently happening.
Business Analytics on the other hand takes the context deduced from Business Intelligence and applies predictive modeling, data mining, statistical analysis, and more. These methods are more advanced, so they’re more indicative of what you can expect in the future.
How can Business Analytics help your Organisation?
- Make Better Data-Driven Decisions
Typically, this is the most important reason why organisations utilise data science applications – to better understand their (quantifiable) data and put it to good use.
- The Ability To Better Identify Opportunities
Another capability of data science tools and analytics is opportunity identification. AI and ML can power predictive analytics to better identify patterns in data that can determine the likelihood of future emergence. This allows organisations to decide where best to focus resources, thus, be able to make intelligent predictions about the future. By using both historical and projected market data, decisions and predictions can be made to determine whether a new venture/product/service or investment is likely to have a healthy ROI.
- To Make Sure You Recruit The Best People
By using unique algorithms, data science can take in the data from CVs and determine whether a candidate is worth considering proceeding through to the next stage.
- To Gain A Better Understanding Of Customer Intentions
As an example, companies can now use data science to better understand the nature of a customer’s enquiry in a more autonomous way, thanks largely to advancements in NLP (Natural Language Processing), powered by data science.
The Latest Advancements In Business Analytics
Advanced Business Analytics is powered by GPU-accelerated databases enable users to instantly interactively visualise and query billions of lines of data. Older CPU-based systems however, rely on manual processes, like downsampling and indexing. It can take a huge amount of time and manpower when using these legacy systems, so many enterprises know that the business case for upgrading to newer GPU-based systems makes a truly compelling business case.
When your company decides to take the plunge into the world of Business Analytics, it’s almost certain that you’ll be making better decisions as a business overall.