Big Data has become one of the most valuable assets held by businesses, and virtually every large organization is investing in Big Data initiatives.
It’s not exaggerated. A 2021 survey by NewVantage Partners found that 99% of C-level senior executives at Fortune 1000 companies said they were pursuing a big data agenda. Perhaps even more importantly, 96% said their companies were successful with big data and artificial intelligence programs, 92% said the pace of their investment in these areas was accelerating, and 81% expressed optimism about in the future of big data and AI in their organizations.
What is big data collection?
Big data collection is the methodical approach to collecting and measuring massive amounts of information from various sources in order to capture a complete and accurate picture of a company’s operations, derive insights and take critical business decisions. Data collection is far from new, of course, since information gathering has been an ingrained practice for millennia. Additionally, researchers for centuries have been confused in their attempts to manage and analyze overwhelming amounts of data.
Big data collection involves structured, semi-structured and unstructured data generated by people and computers. The value of Big Data is not in its quantity, but rather in its role in making decisions, generating insights, and supporting automation – all critical to business success in the 21st century.
“Companies need to invest in what data can do for their business,” said Christophe Antoine, vice president of global solutions engineering at data integration platform provider Talend. But organizations that want to reap the benefits of big data must first collect it efficiently, which isn’t so easy given the volume, variety, and speed of today’s data.
What data is collected?
Today the volume, variety and speed of data are so much more important than they deserve the title big data. The world now generates around 2.5 quintillion bytes of data every day, according to General Consensus statistics. This data comes in the following three forms:
- Structured data is highly organized and exists in predefined formats like credit card numbers and GPS coordinates.
- Unstructured data exists in the form in which it was generated, such as social media posts.
- Semi-structured data is a mix of structured and unstructured data like email addresses and text, respectively.
Data can generally be categorized as quantitative and qualitative. Quantitative data comes in numerical form like statistics and percentages while qualitative data has descriptive characteristics like color, smell, appearance, and quality. In addition to primary data, organizations may use secondary data collected by another party for different purposes.
Common Big Data Collection Methods
In big data collection, a company’s range of sources generating data musts to identify. Typical sources include the following:
- operational systems producing transactional data such as point-of-sale software;
- devices within IoT ecosystems;
- secondary and third-party sources such as marketing companies;
- social media posts from existing and potential customers;
- several additional sources such as smartphone location data; and
- surveys that directly request information from customers.
No company can collect and use all the data created. So, business leaders need to create a big data collection program that identifies the data they need for their current and future business use cases. Some experts believe companies should collect as much data as they can acquire to drive innovative use cases, while others advise organizations to be more selective to avoid increasing cost, complexity and complexity. compliance issues without getting business value in return.
Steps in the data collection process
Identifying useful data sources is just the beginning of the big data collection process. From there, an organization should create a pipeline that moves data from the build to locations in the enterprise where the data will be stored for organizational use. Most often, this data ingestion process involves three overall steps: extract, transform, and load (ETL):
- extraction — data is extracted from its original location;
- transformation — data is cleansed and normalized for business use; and
- loading — data is moved into a database, data warehouse, or data lake to make it accessible.
Data management teams face additional considerations and requirements at each of these stages, such as how to ensure that the data they have identified for use is reliable and how to prepare it for their usage.
“Data drives the uses you can have, and desired applications drive the data you’ll need,” said David Belanger, principal investigator at the Stevens Institute of Technology School of Business and retired chief scientist at AT&T Labs. . “Once you know the sources, you need to answer a number of questions: Where can I get the data I need? Is the source reliable? What are its properties, for example, velocity, the flow, the transaction, the purchase? What is its internal or external origin? etc.”
The Challenges of Big Data Collection
It’s no surprise that many companies struggle with these questions. “There are all kinds of challenges — technical challenges, organizational challenges, and sometimes compliance challenges,” said Max Martynov, CTO of digital transformation service provider Grid Dynamics. These challenges can include the following:
- identify and manage all data held by an organization;
- access all required datasets and break down internal and external data silos;
- obtain and maintain good data quality;
- correctly select and use the right tools for different ETL tasks;
- have the right skills and enough qualified talent for the level of work required to achieve organizational goals; and
- Properly secure all collected data and adhere to privacy and security policies while allowing access to meet business needs.
Such challenges within the data collection process reflect challenges that leaders cite as barriers to scaling up their big data initiatives as a whole. The NewVantage study, for example, found that 92% of respondents identified culture – people, business processes, change management – as the biggest challenge to becoming a data-driven organization, while only 8% identified technological limitations as the main obstacle.
Big Data Security and Privacy Issues
Experts advise business leaders to develop a strong data governance program to help address these challenges, especially security and privacy challenges. “You don’t want to hurt access, but you need to have the right governance in place to protect your data,” said Antoine de Talend.
A good governance program should establish the necessary processes to dictate how data is collected, stored and used and ensure that the organization does the following:
- identifies regulated and sensitive data;
- establishes controls to prevent unauthorized access;
- creates controls to audit those who access them; and
- creates systems to enforce governance rules and protocols.
These steps help secure and protect data to ensure regulatory compliance. Moreover, experts said that these measures help the company to trust its data, an important part of becoming a data-driven organization.
Best practices for big data collection
To create a successful and secure big data collection process, experts have come up with the following best practices:
- Develop a collection framework that includes security, compliance, and governance from the start.
- Build a data catalog early in the process to know what is in the organization’s data platform.
- Let business use cases determine what data is collected.
- Fine-tune and adjust data collection and data governance as use cases emerge and the data program matures, identifying missing datasets in the enterprise big data collection process organization and collected data sets that have no value.
- Automate the process from data ingestion to cataloging as much as possible to ensure efficiency and speed as well as adherence to protocols established by the governance program.
- Implement tools that uncover issues in the data collection process, such as datasets not displaying as expected.