Three Governance Trends to Watch in 2019
By thoughtfully applying RPA, prioritizing data quality, and catering to the changing workforce makeup, data professionals can guide their organizations effectively and efficiently into the data-driven future.
- By Tracy Ring Cryder
- December 19, 2018
The massive growth in data and automation across all industries and sectors continued in 2018 and shows no signs of slowing. Data management is a heavily complex field growing more so by the year. Although the breadth of challenges and needs is tremendous, so, too, are the opportunities.
As data professionals contemplate future priorities and needs, three primary trends will likely impact their efforts in the year ahead. Putting robotic process automation to work, ensuring data quality, and adapting to the evolving “gig economy” all present challenges -- and opportunities -- for data professionals in 2019.
Trend #1: Data quality is at the heart of data management, compliance, security, and analytics challenges -- and opportunities
The insights data yields are only as valid as the data itself. Poor data quality can derail efforts to extract meaningful value, capture new efficiencies, and comply with evolving standards and regulations. Data quality suffers from a variety of issues. One common cause is complicated integration architecture, where system changes have cascading affects, notably from multiple, sometimes-overlapping applications with non-integrated data sources.
Data quality can also be hampered by unclear responsibilities for data quality at each system or application integration point, redundancy in how data quality is addressed, and mismatched data syntax, formats, and structure. In addition, without clearly defined data quality escalation processes, issues may not even be addressed.
Recognizing the vital importance of high-quality data, data professionals should prioritize governance as a method for ensuring consistent data for people, processes, and systems. An effective data governance solution enables an enterprise to connect and access data across different source systems, to match and merge duplicate records, and to yield pristine datasets. Data professionals may find value in incorporating a modern master data management (MDM) solution, which is highly automated, can be implemented far more quickly today than in years past, and provides a comprehensive method for enhancing data management and ensuring data quality.
Trend #2: New talent models are compelling greater reliance on data governance, security, and accessibility
The way people work is changing rapidly. Whereas most employees were previously employed full-time and performed their jobs on site, today a growing portion of many companies’ workforces are “contingent workers” -- freelancers, independent contractors, and those participating in the so-called “gig economy.” In the United States today, one in three Americans performs some form of freelance work, and a recent 2017 study reports that overall self-employment is likely to triple to 42 million workers by 2020.
Although this change can provide new benefits and opportunities for organizations, it also underscores the importance of effectively managing data, securing it, and providing accessibility to a remote workforce. Indeed, the multitude of devices, systems, and user competencies can vary dramatically when organizations draw on freelance workers. At the same time, these contractor workers require system and data access to work off-site. And throughout, data professionals are challenged to ensure data security in the face of evolving cybersecurity threats and potentially poor cyber hygiene on the part of freelancers.
In the year ahead, data professionals will need to continue adapting to the changing nature of the workforce and anticipate the tools and processes they will need as the freelance workforce continues to grow and more companies turn to contract workers for various enterprise functions.
Trend #3: Forward-thinking organizations are using robotic process automation (RPA) to drive data governance
As the volume of enterprise data grows, data professionals will continue to face the challenge of aggregating and curating so much information. In some cases, robotic process automation (RPA) is a valuable tool for enhancing and improving data governance. RPA is particularly suited to repetitive tasks such as data cleansing, normalization, and metadata creation or updates.
As a means to aggregate data from multiple sources in varying formats, RPA offers numerous potential benefits, among them: greater efficiency in regulatory, non-financial, and risk reporting; streamlined and more effective data sampling; improved outcomes by virtue of greater process quality and consistency; and enhanced productivity and resource capacity resulting from new efficiencies. RPA also yields a record of data transformations, which is itself important for enterprise goals such as process optimization and regulatory compliance.
Looking ahead to 2019, RPA’s potential benefits and use cases will continue to grow -- but not all processes are suited to automation. Data professionals and other organization leaders should take a critical look at their needs and operations and conduct cost/benefit analyses to determine whether there is a business case for incorporating RPA. In some instances, an organization may find comparable benefits through people or process initiatives.
About the Author
Tracy Ring Cryder, vice president, alliances at Deloitte Consulting LLP, is responsible for driving go-to-market strategy with Deloitte’s analytics and cognitive (A&C) alliances. She collaborates across the A&C portfolio to build powerful ecosystem plays. You can reach the author via email or LinkedIn.