2022 Data Industry White Paper

December 27, 2022

The 2022 data industry white paper consists of five parts. Part 1 discusses data sharing between individuals, across countries and within companies, under this year's theme of 'Data Sharing.' Part 2 summarizes the major policies, legislation, and systems related to the data industry in Korea and abroad. Part 3 deals with the current status of Korean and global data industry markets. In Part 4, the status of each industry based on business data is examined. Part 5 reviews technologies related to data sharing and utilization. Overall, this white paper uses infographics to summarize the flow of related policies, legislations, and institutions that have been promoted over five years, and highlights the main data issues experienced in Korea this year based on data analysis.

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OTHER WHITEPAPERS

Everest Group PEAK Matrix® for Data and Analytics (D&A) Service Provider 2022

whitePaper | September 21, 2022

Global macroeconomic conditions indicate the rising probability of a recession. Despite the resulting cost pressures, enterprise demand for D&A services is rising as enterprises realize that datadriven solutions focused on cost optimization and building competitive advantage are critical to tackle and survive the slowdown. In this regard, service providers can help enterprises in their digital transformation journey by formulating a vision for D&A, modernizing the D&A landscape, and providing the right talent and niche skill sets to achieve direct business outcomes.

Be (More) Wrong Faster – Dumbing Down Artificial Intelligence with Bad Data

whitePaper | June 24, 2022

The reactive approach to discovering, analyzing, and correcting data quality issues in business applications has been marginally effective throughout the era of data consolidation for analytics (data warehousing, etc.) and the subsequent onset of “big data.”

Drive Analytic Innovation Through SAS and Open Source Integration

whitePaper | May 27, 2021

In many organizations, we need more collaboration between businesses, analytic teams, application developers and IT operations. These teams often work with data in silos and end up duplicating efforts, failing to integrate, or missing opportunities to deliver value from data. In addition to siloed efforts, data scientists are also faced with ever-increasing volumes and speeds of data. And the reality is they’re expected to answer questions just as fast as - or faster than - before. It’s important that we’re using the right data and the right techniques to ensure optimal outcomes. Download this guide to learn more about SAS and open source.

Data & AI Survey Report 2021/2022

whitePaper | September 2, 2022

This year, unlike previous years, we asked professionals the most difficult elements of a successful data strategy instead of the most important.

The 2022 State of Data and What’s Next

whitePaper | February 24, 2022

Today’s business relies on access to critical data more than ever to remain ahead of the competition. Due to the challenges over the last two years, organizations must respond to greater demand for data access and a shift to supporting AI/ML in a complex, hybrid multi-cloud environment. Organizations need to become nimble in providing fast, reliable access to data anytime, anywhere, throughout their increasingly complex, integrated enterprise.

Data Analytics Techniques for Internal Audit

whitePaper | April 27, 2023

Data analytics are used to test controls and validate that business risks are managed. This would generally occur at a point-in-time when an assurance activity is scheduled. Rather than test a number of transactions, the entire population of transactions can be reviewed for greater coverage. Data analytics includes automated tools such as generalised audit software, test data generators, computerised audit programs, specialised audit utilities and computer-assisted audit techniques (CAATs).