What is data and why does everyone want it?
- Asad Naqvi
- Apr 5, 2019
- 3 min read
Updated: Jun 10, 2019
Whichever industry you work in, or whatever your interests, you will almost certainly have come across a story about how “data” is changing the face of our world. According to Forbes, there was close to 2.5 quintillion bytes of data created each day in 2018. Thats nearly 2.5 million times the amount of data you would store in a 1 TB hard disk and this is only going to increase given the increased digital adoption by the year.
Facebook, Google and Square are a few examples of companies that collect user personal, transactional or web data from their users to fine tune their offerings for their customers. With a combined user base of 4.3 billion, it is simply impossibly for any human to read and interpret all this data. It is one thing to create a pivot in a spreadsheet table, but its a different predicament to analyze such copious amount of data. This is very machine readable data kicks in.
What is machine readable and big data?
Big data is a term that describes the large volume of datas - both structured and unstructured. However, for big data to be of any use to an enterprise it needs to be machine readable or structured. Machine readable data can simply be a small set of data points a computer program can read. Alternately, the program can also be designed to read and manipulate a large amount of data to identify recurring patterns. Companies collect user data and for mining to seek a deeper understanding of their customer habits and preferences. In many cases this data is also disintegrated into different parts and sold to third party organizations to target customers with specific products.
© Cross Data Technologies
How are companies using big data?
According to Forbes, 53% of the companies are using big data analytics in 2017, which is substantially larger than the 17% reported in 2015. Financial, Telecom, Technology and Healthcare are some of the industries with the highest adoption rates (Forbes, 2017). I will focus here on how data usage can help build quality software.
Define product strategy:
Data can help define the right strategy to scale a software solution. By carefully monitoring user workflows and usage, management can better adapt to the dynamic needs of the market and industry trends. Understanding customer engagement can also help determine the best way to scale your product by providing key insights required to define resource planning, release goals and subsequent investments into the product.
Increase customer engagement:
Data can help stack rank and define the most popular solutions in an enterprise software. More importantly, it can also help determine the exact pain points and most frequent issues faced by the customers. Understanding what delights and upsets the customer can often change the competitive landscape of a software product. Management can use these points to build business cases that are targeted to eliminate recurring issues and provide a seamless experience to the customer.
Predict future needs of customers:
In a tech savvy world, users are getting more dependent on software than ever before. The rapid acceleration of innovation, hyper competitive market landscape and evolving customer needs has made it harder for software products to have a larger lifetime. The threat of a new, better looking, software product is real and always around the corner. In such an aggressive environment, developing a culture of data driven decision making can help uncover common synergies between an enterprise core competencies and customer needs.
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