Data Analytics - A Game Changer for Creating Business Value
- K. Murugesan
Murugesan has worked in HAL, specializing in Manufacturing, Supply chain and Quality systems AS 9100. He has experience in Six sigma projects to improve the quality of the products.
Data is the new oil
Data is referred as the new oil, an economic resource that is fueling the fourth industrial revolution. It is the new capital in the digital age. Data is perhaps a single most important ‘fact’ for helping business get insights for developing strategy and have an edge in operations.
Data has been treated as important as wealth for a long time. The census survey that occurs once every decade is one example of ‘big data’. Strategic government policies are founded on the analysis of this ‘big data’. Today computerization and digitization has given corporates the same basic information that the government was physically collating. The end use of data is of course is more commercial.
What is Data Analytics?
Data analytics is the science of extracting business insights for value creation, from various data sources. Value creation may be in the form of increasing sales volume, profitability, enhancing customer satisfaction etc., Data Analytics has vast potential to transform businesses, cutting across industry sectors including social organizations.
Our life today is driven by data analytics. There is much data to be analyzed right from our food habits, purchasing behavior, travel preferences, or social media behavior, to mention few examples. Data is a mine of wealth for the businesses to tap in to their advantage. In addition to commercial value, data can help organizations become more effective and efficient in their processes. It is all there to see when one logs into the ubiquitous internet and Amazon promptly suggests various goods for purchase.
How does Amazon or any other ecommerce entity do this? The primary
requirement for data analytics is data collection from the business processes. In case the data is not readily available, necessary systems and technologies are implemented in the processes to harvest the data. The data then needs to be cleansed for errors, duplicates, invalid and unwanted information etc., Care must be taken to ensure that data is genuine, authentic, and free from distortions.When we speak about data, it is not something that is small in size. To get meaningful interpretations analysis tools like R Programming, Python are extensively used. In conjunction with visualization tools such as Tableau or Power BI, data helps business users make informed decisions. The output of the analysis can bring to the table new insights which can become a critical input in drawing up the business strategies.
Big Data Vs Data Analytics
Big Data and Data Analytics are closely similar, but not the same even though both deal with creating Business Value through data. There is a subtle difference on some aspects as contrasted in the table below.
Huge volume of data -Structured, unstructured or semi-structured
Relatively smaller volume of data
Nature of Analysis
Exploratory analysis and looking for business insights with the voluminous data
Testing known hypothesis, assumptions and solving specific business problems
Exploratory analysis, building dashboards and visualizations. Complex tools like parallel computing
Simple statistical and programming tools like MS Excel, R, Python etc.,
Data is the new capital that businesses can capture and use it for maximizing the business outcomes. With the rapid advancement in technology, the demand for analytics is growing exponentially to the extent that it can be safely said it is a business imperative. The market forecast for Big data according to Wikibon, for 2020 is US$60.91 Billion. And that is not a small number.