The transparent business barometer: Preparing for the end of easy data

The rapidly growing amount of data created in digital societies has led to an increase in personal data collection and usage for commercial purposes. Many large international companies use data to sell targeted advertising and services, while smaller organisations may also collect and sell such information to other businesses, or for other reasons. For instance, data gathering can reduce costs, help improve existing services and provide opportunities for innovation, such as geo-location information to match supply and demand in certain fields, like ride-sharing.

Unlocking big data with data science

Although the exponentially increasing amount of data in the world can be difficult to weave through, financial services firms can capitalize on this additional information if they have the right people, processes and technology in place.

Data quality: the foundation of effective data governance

A survey of more than 500 business executives in North America and Europe, conducted by The Economist Intelligence Unit and sponsored by Collibra, finds that the objective of data governance programmes is accuracy above all. Improving data quality ranks as the most important benefit of data governance for 38% of survey respondents, equalled only by the related goal of data security. Fifty-one percent of respondents say data accuracy is an important metric of success for their data-governance programmes, the highest percentage for any metric.
 

Championing data throughout the business

Management considered data governance protocols successful if they protected customer data, complied with applicable law and met basic standards of reliability. Underpinning this approach was the widespread view among executives that data governance was a cost centre rather than value driver. Today, more companies recognise that their vast troves of information represent an untapped source of business value, and as a result they’re looking to data governance with new aspirations.

Getting a handle on big data takes technology and talent

The new world of regtech in managing regulatory data

Regtech can help by automating what would otherwise be tactical and manual processes—such as data collection and reporting—and making these tasks more cost effective, efficient and strategic. Advances in areas like artificial intelligence (AI) and machine learning make it possible to automatically parse through regulatory filings, track employee compliance and more. Advanced regtech analytics solutions can also match and cleanse data from various legacy systems quickly and accurately.

Data optimization key for asset owners that insource

Across the globe, many asset owners ranging from large sovereign wealth funds to small university endowments have looked for efficiencies by managing investment portfolios in-house. Globally, pension funds manage nearly half their assets in-house, according to PwC.(1) 

AI key to optimizing institutional investment data

Creating better retirement outcomes using data, technology and transparency

In the US, 95% of salaried new hires have defined contribution (DC) plans as their only employer-sponsored retirement plan option, according to Willis Towers Watson (1). Other markets around the world are following a similar path. But the societal shift from defined benefit to DC plans has not always been smooth for employees. Thus, there’s a clear impetus for retirement stakeholders to forge plans that work for DC participants.

Leveraging data to improve governance

Because governance metrics vary among companies and encompass qualitative issues like independence and accountability, investors and boards are still learning how to clearly convey and analyze governance indicators. However, with increased data capabilities, driven by new technologies and a culture of transparency, investors and boards are beginning to better understand and act on governance issues.

Fundamentals of governance

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