Statistically, around 2.5 quintillions bytes of data are created every day. Yet many experts cannot say confidently whether the data analysis on which they base their multi-million dollar decisions is meaningful. 

 

First, it is important to understand, what entitles data? And how do we differentiate between clean and dirty data?

 

Simply data can be classified as any facts that are collected over time and are quantitative or can be turned into qualitative. Numbers, statistics, symbols, etc. 

In order to provide valuable results we don’t just need any data but clean data, which is data that is not duplicated, inconsistent or incomplete, and can be used for analysis.

We have so much data that it is difficult to identify which one is best suited for our decisions. Even if we can differentiate between clean and dirty data, it is still difficult to choose from the two different analyses done by two different datasets.

Even in this era, several companies are still not making data-driven as they fail to understand the importance of such decisions. Before any data analysis, it is important to understand the importance of data analysis and why it is promoted exponentially throughout.


Importance of Accurate Data Analysis:

Targetting the correct audience:

Marketing strategies cost companies in millions and companies can incur huge losses if their message is not correctly conveyed or is targeted to the wrong audience. Data analysis helps companies to do correct market segmentation, as well as understand which form of advertisement will have the maximum conversion rate. It helps in building cost-effective marketing campaigns. For example: Starbucks used its data to create promotions suitable for their target market to increase sales and foot traffic in their stores. 

 

Expansion and product diversification: 

In the digital age, the market study is a common practice for organizations that are targeting new markets for their products. Data analysis helps companies identify the best-suited market for their product, and can help them save cost and probability of product failure. 

 

Innovation:

When data is exploited correctly it opens doors for innovation. In today’s competitive market companies are focusing on high investment in R&D  to derive and understand the market’s pain point and current state in order to innovate a product for which demand/ market already exists. 

 

To be more cost-efficient:

Instead of losing billions of dollars due to poor analysis, companies can mitigate risk by efficiently using their data to have a better predictive analysis. For example, Big data analysis combined with machine learning helped American Express $2 billion in potential annual incremental fraud incidents before a single cent was lost.

 

For a better decision making:

Companies that are making data-centric decisions are 12.5% more profitable than their counterparts, according to a study by Splunk. For instance, several companies have started incorporating “uncertain events” into their supply chain models in order to lower the risk and prepare in a better way. 

 

Gaining competitive advantage:

With customer feedback and innovative strategies, accurate optimization of data gives companies the opportunity to have a competitive advantage and have the first movers advantage. They can easily and efficiently leverage real-time analysis to base their strategies on. 

 

Conclusion:

Accurate data analysis encourages companies to not only have an external outlook but an internal one as well. This can help them identify their errors, improve operations, and gain a fair share of the market. The more data mature company gets, the more benefits they reap. Hence, it is vital in the digital age to identify and assess their data correctly.


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