Tamr, Inc., a leading cloud-native data mastering solution, announced the findings of its research of over 500 data leaders on their enterprise data initiatives for 2023. Over 90% of participants said their organizations have data initiatives planned for 2023. Yet more than half said they still face challenges as they strive to realize business value from their company data.
Becoming data-driven is an imperative for any modern business that desires digital transformation. While more organizations are becoming data-driven, many still face enormous challenges in mastering their core data because it is incorrect, incomplete, out-of-date, duplicative or siloed, which makes it unreliable and difficult to use.
“Digital transformation is more important than ever before in the enterprise. But as countless leaders have learned, building a data-driven culture centered on managing data as an asset/product is the cornerstone of successful digital transformation,” said Andy Palmer, co-founder, chairman and CEO of Tamr. “Although we’ve seen companies make significant strides over the past decade, our research shows that many organizations still have a long road ahead. From data culture and modern data mastering to consumption-based data governance and streaming/Kafka-like architectures, organizations must embrace change to deliver clean, curated, continuously updated data products assembled from thousands of source systems to all potential data consumers in their enterprise.”
Data organizations are changing to address the growing volume, velocity and variety of data and meet the growing demands of their business partners. Investments in data and data-related initiatives are rising. New approaches to managing data at scale are emerging. And data technology is evolving to support the new regulations, strategies and priorities of modern organizations focused on digital transformation. As we head into 2023, Tamr predicts that companies looking to become more data-driven will continue to focus on data culture, data integration, data governance and data architecture as priorities for success.
Data culture
Compared to traditional non-digital native companies, data-driven companies think differently about roles and organizational structures. Over the past 40 years, CIOs struggled to deliver on the remit of using data as a strategic weapon. CDOs have now emerged and evolved to take responsibility for and realize the value of data in their enterprise. But too often, newly-minted CDOs mistakenly focus solely on the data ecosystem and the technology that supports it. Many CDOs are evolving to realize that they must broaden the role and scope of data teams to embrace the context in which the people at their enterprises consume data and treat frontline business owners as true partners. Tamr’s research revealed that close to 60% of respondents think the CDO role needs to expand beyond data stewardship for their organization to become more data-centric. And more than 55% responded that focusing on integrating data scientists into the rest of the business is also a key imperative to becoming data-driven.
Data integration
When an organization attempts to integrate data from multiple, siloed source systems, continuously cleaning and organizing the data for use by a broad population of consumers in an enterprise is a significant challenge. Legacy tools such as rules-based master data management (MDM), traditional data warehouses and data lakes have attempted to make messy, dirty data usable. But in reality, they have only aggravated the situation because their manual processes and the limited scope created more data silos. Additionally, the manual methods to integrate, organize and clean data that are dogma in the industry have created an insurmountable and underappreciated quantity of manual effort to prepare data for broad consumption.
Tamr found that on average, 46% of organizations clean their data using manual processes. And, to no one’s surprise, respondents reported data cleaning as the least enjoyable part of the job. Insights derived from dirty data are unreliable and untrustworthy. In fact, nearly 60% of respondents stated that their organization faces challenges in realizing business value from its use of data and 70% said their company needs help turning data into valuable business insights.
That’s why improving data quality is a top priority for organizations wanting to become data-driven— 75% of respondents said their companies are focused on improving data quality and 65% stated that investing in data management technologies will help achieve better data quality. Modern technologies such as human-guided machine learning consolidate messy source data into clean, curated, continuously-updated, analytics-ready core datasets that companies can use to unlock the valuable insights needed to achieve business outcomes.
Data governance
Data governance has been in the spotlight recently, and many predict that the interest in data governance will continue to grow. But the focus is shifting away from source-based governance (which mainly focuses on data cataloging and governance workflows) toward consumption-based data governance (which focuses on appropriate use and control of access to data downstream).
Data privacy has taken center stage, too. Between new regulations, including the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), data privacy and security have evolved to adhere to the intention of GDPR and CCPA organizations need to focus on consumption-based data governance. Over 95% of respondents agree that data security and privacy will become even more critical over time as the volume and complexity of data increases.
Data Infrastructure
Companies have more data – and more data sources – than ever before. Data silos proliferate and data sources are idiosyncratic, making the task of integrating and aligning data across an organization extremely difficult. Impossible, in fact, without the help of the machine. Tamr found that 95% of respondents expect their organizations to make more significant investments in AI and machine learning technologies, indicating that companies are looking for new approaches and strategies to tackle the challenges of their data architecture.
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