The market can be categorised into 2 domains: producers and consumers. Every industry is based on the balance of producers and consumers. The same can be said for today’s digital world. A lot of producers generate a lot of data, and similarly this data is consumed by many consumers. However, there is always a gap between data production and data consumption. A gap in quality, a gap in trust, and a gap in how much data is actually usable. This can be named as the Data Production Consumption gap.
In this article, you will gain information about Data Production Consumption Gap. You will also gain a holistic understanding of Production Consumption gap, the reasons behind Data Production Consumption Gap and the ways to reduce it.
Read along to find out in-depth information about Data Production Consumption Gap.
Table of Contents
What is the Production Consumption Gap?
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The Production and Consumption gap is not new in this world. However, it is a critical issue. Maintaining a balance between the production and consumption of items is very important for sustainable development. Sometimes production is not adequate, while demand is more, and sometimes supply is more than demand. But it is essential to maintain a demand-supply balance for the sufficient growth of our society.
Let’s take the example of food. Food is the requirement of every human being. But there has always been a gap between the production and consumption of food in our society. Moreover, food production and consumption vary within a geographical area. In some localities, food production is significantly less than its consumption, and in some areas, food production is in bulk, but consumption is considerably less. Maintaining the balance between the two is crucial for human survival.
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What is the Data Production Consumption Gap?
The phrase ‘data is the new oil‘ is probably the most well-known to draw global attention to the importance of data. It happens because the amount of data produced every day is massive and growing at an exponential rate. Every day, we are inundated with data because every human uses various internet-connected devices.
The Internet continues working and producing quintillion bytes of data every day. As per DOMO, ‘Data never sleeps.’ In addition, the pandemic generated more data than expected in 2020 and 2021. It has forced the world to go online. With online classes and working from home, internet usage has increased manifold.
If we gather some facts, the below image shows the amount of data generated by the most-used digital apps every minute. As per Statista.com, the world produced 79 zettabytes of data in the year 2021. However, out of this enormous amount of data, only 10% is new and genuine, with the remaining 90% being duplicate data.
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It is the responsibility of data engineers, analysts, and scientists to separate genuine data from total data and use it for analyses and industrial growth. Unfortunately, not all generated data is suitable for consumption. As a result, consumers must identify trustworthy, accurate, and new data in order to put it to use for industrial growth.
Major Data Sources
In the digital world, there are an infinite number of applications and websites. However, certain apps and websites rule the world. Most major data producers’ apps and websites are created by these widely used apps, which are listed below.
- Google
- Amazon
- Twitter
- Facebook
- Instagram
- Snapchat
Why is there a Data Production Consumption Gap?
There has always been a gap between data production and data consumption. It is difficult for data analysts and data scientists to bridge the gap. Most large organizations employ data scientists to deal with all types of data. Google, Amazon, Airbnb, Uber, Facebook, and many other companies deal with large amounts of data on a daily basis. However, not all information can be consumed. As a result, obtaining valuable data and validating it before use is difficult.
The primary reasons behind the data production consumption gap are as follows:
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1) Data Production and Consumption Gap: Data Duplicacy
There has been a massive amount of raw data production. As a result, there is an enormous amount of data to consume, manage and thus protect. Among the whole data generated, approx 90% of data is duplicate data, and only 10% of data is new and genuine. Extracting this 10% of new data from raw data is problematic and time-consuming. Data duplicity is one of the major causes of the data production consumption gap. Data duplication misuses the storage and misleads the users and models.
2) Data Production and Consumption Gap: Discovering Real and Trusted Data
Trust is a costly affair nowadays. And in the era of fake news and reports, it is tough to trust and identify accurate data. Moreover, from the vast raw data, filtering and discovering actual and trusted data takes approximately 30% time of data scientists and data analysts.
3) Data Production and Consumption Gap: Data Governance
Data governance is the problem at the organizational level. Organizations need to comply with regulations for data security. Online users unawarely give their information to the companies like their location, payment details, IP address, web search history, and other personal information. The government checks data security, what kind of data is accessed by the organization, where it is stored, and the usage of user’s data. Thus, all type of data is not readily available for consumption.
How to Reduce the Data Production Consumption Gap?
There are some sustainable ways to reduce the data production and consumption gap.
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1) Data Production Consumption Gap: Reduce Internet usage
In order to reduce the data production consumption gap, you first need to reduce the production of data. If the amount of data produced decreases, it also reduces the redundant data. Therefore, users need to limit internet usage to generate fewer data. But there is some contradictory information. If the number of users in the digital world keeps increasing exponentially, then as per statistica.com, the prediction is that by the year 2025, annual data will grow over 180 zettabytes.
2) Data Production Consumption Gap: Data Uniqueness Validation and Deduplication
Data Scientists must validate the uniqueness of data before consuming data to nullify duplicate data and use accurate data. Some algorithms are used for data deduplication, such as Binary Search Tree structures, Simple Map-reduce algorithm, MD5, or SHA-1. Analysts need to implement appropriate algorithms according to the organizational data for deduplication.
3) Data Production Consumption Gap: Data Mining
Data mining is a deliberate method of distilling raw data into actionable insights to meet business requirements. It demonstrates tremendous progress in extracting useful information from raw big data for use in organizations. Raw data is transformed and modeled using various machine learning algorithms like regression, classification, and clustering to add value to the business, reduce the data production consumption gap and increase ROI (Return on Investment).
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4) Data Production Consumption Gap: Safeguarding Sensitive Data
The safety of sensitive data like location, payment, and personal information is critical. It is obvious that people will be concerned about the protection of their sensitive data. Many new data protection regulations have been introduced in recent years, introducing new compliance requirements for organizations. It is critical to strike a balance between data security and profitable data consumption.
Conclusion
It is challenging to reduce the gap between production and consumption data, but at the same time, it is exciting too. Organizations have already staring implementing solutions to decrease the gap and maintain a balance between raw data production and its usage. Furthermore, with more government involvement, users’ data will maintain privacy, and the organizations will not access every piece of data. This will lead to gaining users’ confidence and natural growth of organizations. Furthermore, many machine learning techniques help extract new and valuable data for consumption, leading to declination in the data production consumption gap.
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