How Data Analytics is Not Changing But Transforming Business?

11/29/2019

'Change' and 'Transformation' are two words that have often been used synonymously in the technical landscape. Lately, however, the ever-pervasive impact of the digital is setting new benchmarks that segregate 'change' and 'transformation' from one another. So much so that, it is no longer good enough to change if you want to survive the digital age. Rather, it is the willingness to transform that is driving success in the digital landscape.

Change Vs Transformation
Change uses external means to modify actions whereas transformation modifies beliefs so that actions become natural and thereby achieve the desired result.

The Rise of The Internet Planet
The rise of the internet has forced a monumental spike in digital footprints of every entity on earth, including humans. To give you an idea about how much data is being generated daily, here's a small infographic of '1 minute of the internet in 2019'.

This is just the tip of the iceberg when we consider the millions of IoT devices, enterprise data centers, financial and banking sectors, e-commerce apps, etc that generate zillionth of data daily.

Moving Beyond Excel Sheets

For long, data analytics was restricted primarily to excel sheets. However, excel sheets have their limitations which include:

High probability of data loss.
Limited functions.
Incapacity to handle large data volumes.
Difficult to identify patterns and insights from large data sets.

Data Analytics: Transforming Data For Transforming Business

Most of the digital information i.e data collected through customer or operational interactions is in raw form. As data volumes keep increasing, so does the need for businesses to inspect, clean and transform this data into patterns and insights.

1. Proactive Business Strategies
It is now possible to predict and prioritize business challenges by creating data models that account for all if and buts of the business landscape. This prioritization plays a key role in equipping businesses with the right roadmap and strategies to overcome these challenges while staying competitive.

2. Mitigate Risks
One of the best ways to avoid failures or incidents is to learn from the past. With data analytics to their aid, companies can now use past and present data to foresee economic slowdowns, change in consumer trends and preferences, etc.

On a much larger scale, data analytics companies can play a key role in saving businesses millions of dollars in incidents. It can:

Exceptionally improve safety in autonomous cars.
Improve the incident management process.
Organize and Improve data flow across enterprise cross-functions.
Identify potential churn and improve retention rates.
Reduce Employee Attrition rates.
Reduce risks associated with business expansion in new product categories, demographics, etc.

3. Real-Time Insights
Data analytics is enabling enterprises to make smarter business decisions in real-time. Business intelligence enables companies to capture data from multiple sources and get real-time insights for finance, sales, marketing, product development, and other processes.

The Importance of Asking The Right Questions
Data analytics is viewed by many as some kind of magic spell. But it is far from that. Data engineering services suggest that the most critical factor for success in data analytics is data quality. Unless data is effectively processed and analyzed, the data analytics goals will never be reached.

To begin with, it is essential that an enterprise or company asks the right set of questions regarding their analytics goals and then works backward towards re-aligning existing data pipelines to serve those goals. Insights are useful only if they can be easily converted into practical decisions. Companies must not be overwhelmed and remember that data analytics is a tool that thrives on the quality of data it works upon.