Firms Look To Become More Data Driven
December 13, 2022
of research of over 500 data leaders shed light 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incorrect, incomplete, out-of-date, duplicative or siloed, which makes it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.
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
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
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.
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.
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.