Data Analytics VS Business Intelligence - A Comparative View
Imagine, not long before, if someone were to tell you that you can make big decisions, decisions that would create a huge impact, in a matter of seconds. Decision making was, and still is, one of the critical stages in any process. What if it could be less ‘human-interacted’? What if we could leverage software and complex pieces of code to do all the hard work of decision making just by feeding the right data and information. This is where terms like Business Intelligence or Data Analytics come in.
Often, these two words are used interchangeably, since it encompasses similar results and metrics. Business Intelligence is used to enhance data, to enable the decision makers. The term itself is as old as the late 1800s. On the other hand, Data Analytics is about finding insights from data, by cleaning and preparing it.
To put things into perspective, we can divide analytics into 4 major categories:
1. Descriptive analytics
‘What happened’ sums up descriptive analytics. Questions like how much revenue, customer churn, product popularity, productivity helps companies understand current trends. These analytics use historical data.
2. Diagnostic analytics
Usually concerned with ‘why did it happen’, it also utilizes historical data to probe into decrease in revenue or increase in customer satisfaction.
3. Predictive analytics
The data is used to create forecasts, but the predictions are based on data science, and often on algorithms that use multiple data sets. Generally, the more data, the more accurate the predictions. Some of the examples include sales forecasts, consumer credit scores, and retailers' suggestions of what you might want to read, watch, or purchase next.
4. Prescriptive analytics
The final pillar of analytics, based on if the action taken is going to guide the company to the goal or not. In many industries (e.g., oil and gas, clinical healthcare, finance, and insurance), prescriptive analytics is found with well-defined use cases because it requires strong competencies in descriptive, diagnostic, and predictive analytics.
Business Intelligence - A world driven with facts
In aforementioned categories of analytics, most of the BI heavily consists of descriptive analytics. Nowadays, predictive analytics is another part of Business Intelligence, usually known as Augmented BI. After all, what’s the point of having all the analytics without having to take actionable measures?
It is becoming a norm to introduce BI to your businesses. However, without enough planning of what to look for and just to keep up with the competition, it is implemented quite hastily. More than 2 Quintillion data is generated everyday, with more than 80% of data all around the world took mere 2 years to be created. It is imperative that before implementing BI, one must have some hypothesis on how it can solve issues in the current setting, rather than creating one.
PowerBI, Tableau and cloud provided platforms like AWS QuickSight, Google Data Studio are a few examples of BI Tools, used by companies and organizations all around the globe. No coding required, just the domain knowledge will get you up and running!
Data Analytics - Realm of insights
Yes, you can have reporting and real-time analytics of your operations using BI, but is that going to keep you ahead of the curve? This is where Data Analytics comes in.
Data analytics will not only provide you advanced insights like correlation and regression, but also keep you future ready i.e help you garner insights to remind you of your shortcomings to your goal. If you want to make data-driven decisions and those too, intelligent ones, Data Analytics fits right into your roadmap to success.
It does require one to have keen knowledge of programming languages like Python, R or experience with tools like MATLAB, AWS Sage Maker or IBM Watson, which leverage data science models and machine learning algorithms to create options you need to make.
From an eagle eye view, it might seem that Business Intelligence and Data Analytics have the same goal: to mitigate bad choices. Should one want to go down this data-driven journey, it solely depends on:
What problem to solve
Understanding the stakeholders
What kind of data is required
Systems and process to turn data into action
This discussion will always be at an impasse. People would still carry on using the terms to their likings. Maybe some might even go as far as bringing ‘business analytics’ to play. Amid all this, one thing is for sure: the point is to make data meaningful and improve decision making - everyone agrees to this. Sudofy is home to various data and business solutions and is aiding countless organizations out there that are conquering the market with exceptional insights.