Top 5 Data Collection Trends for Data-Driven Businesses

Data collection is becoming common practice for many businesses. Whether for implementing deep tech or conducting analytics, business leaders are continuously involved in gathering or using data to improve their operations.

As people realize the power of harnessing data, the regulations and practices of gathering and using it change. Considering that, business leaders must stay up to date regarding data collection and usage trends to maintain a consistent and useful flow of data across their business value chain.

This article explores the top 5 data collection trends to keep your data-driven business growing and to keep you informed of the latest developments.

Development in AI/ML models

As businesses try to automate more business operations, AI/ML models become more sophisticated and capable. For instance, a deep learning model can figure out its own parameters and learn how to improve itself. However, this means that not only do these models require a significantly larger amount of data to learn from, but they also have a much longer learning curve.

For instance, Facebook’s facial recognition system was trained with 4 million labeled images from 4000 people. This was back in 2014. Current facial recognition models require even larger datasets. The increase in dataset size is a trend that will continue to be observed.

You can check our data-driven list of data collection/harvesting services to find the best option that suits your project.

Development in rules and regulations

Data, a double-edged sword, can both be a powerful asset and a harmful liability. And to keep data usage and collection in check, there are regulatory measures being enforced. 

Many countries are regulating data usage and sharing, making the rules more strict and comprehensive. The developments in regulations related to data collection, sharing, and usage will be another trend that will continue to be observed. Therefore, local companies need to thoroughly go through country-specific rules and policies that they operate in regarding data collection and usage before initiating any practices.

Rise of unstructured data

To understand this trend, let’s first have a look at structured and unstructured data.

Structure data

Structured data is normally stored in relational databases. It can be easily searched for by humans or software and can be placed into organized, designated fields. Examples include addresses, credit card or phone numbers. Unstructured data

Unstructured data is the opposite of structured data. It does not fit into predefined data models. And it can’t be stored in a relational database. Due to the various formats, conventional software can not process and analyze this data.

In other words:

In the past, structured data was the king. However, that has changed now, and unstructured data is more commonly used. This is because unstructured data is much more diverse than structured data and can provide more in-depth insights into things. Thanks to new technology such as AI, ML, computer vision, etc., unstructured data can now be analyzed and used in various ways to benefit a business.

Studies show that the volume of unstructured data was 33 zettabytes in 2019 and is projected to grow to 175 zettabytes (175 billion terabytes) by 2025. With the surge in the adoption of AI/ML-based solutions, the use of software to organize unstructured data rises as well, and companies continue to gather unstructured data.

Data stored in different tiers

Since the volume of data being generated and used continues to increase, business leaders are refocusing their efforts on data management strategies, including data storage and protection technology. Another trending practice to better manage data is data tiering. Organizations with strong digital maturity are tiering their data based on: 

• Data volume: How much they have, and the growth rate.
• Data variety: The type of data they have, data storage details, and the accessibility of the data.
• Data velocity: The speed at which data is generated.
• Data priority: The impact of the data on the business operations.

Based on these considerations, data is stored in different tiers.

Data diversity

Bias in AI is becoming an increasing concern among businesses. For instance, studies show that AI-enabled facial recognition systems show more erroneous results for darker skin women, men, and children as compared to people of lighter color.

This bias can be reduced through reevaluating training of AI/ML models and diversifying training datasets. Diversifying the data collected for training AI/ML models is another trend that is being observed. For instance, IBM and Microsoft are taking steps to optimize their facial recognition system toward racial and gender neutrality. 

Further reading

• Top 4 Data Collection Methods
• Top 6 Data Collection Best Practices