Trusting Data: Finding Truth, Building Transparency

Trust is hard to come by in the digital age -- where fake news, AI-altered content and shifting standards of ethics and quality in publishing reign -- it’s easy to see why senior executives should inherently trust their own data.

A mature data infrastructure and culture is marked by transparency in the data source, the ability to easily track lineage (who developed it and how) and advanced data governance practices fostering data quality, privacy and security.

A report by HFS Research found while eight in 10 senior executives trust their organization’s data, less than half said they believe that most enterprise data can be operationalized.

The Benefits of Trusted Data

By actively maximizing the value of trust through robust data practices, businesses can strengthen their competitive advantage and build long-term relationships with their customers and stakeholders.

Conversely, failing to uphold the value of trust in data can lead to reputational damage, loss of customers, and potential legal and regulatory consequences, making it crucial for businesses to prioritize data integrity, security, and responsible data use.

“Organizations gather, store, and build processes on top of data derived from consumer activity, financial transactions, operations, and market research,” says Nate Holiday, CEO and cofounder of Space and Time. “Data represents a significant investment of time and resources for a business and serves as a base source of truth for its decision making and strategy.”

However, he notes operationalizing this data presents a myriad of challenges, from dealing with the sheer volume of collected data, to managing it effectively, to deriving actionable insights from it.

“Effective data management requires an array of tools and skills that are difficult, time consuming, and expensive to bring together,” Holiday says.

Michael Heath, technical solutions engineer at SHI International, says while the implementation of robust data governance practices ensures data consistency, accuracy, and security, it doesn’t mean it will be automatically operationalized.

“Data monitoring and achieving business objectives creates trust and a warm and fuzzy feeling, but in the end it’s a false sense of data usage,” he says. “The fact of the matter is that 80% of data lies dormant because of poor data empowerment or usage of the tools to take it from dormancy to action.”

As data moves through its own lifecycle from management to governance, one of the most missed pieces is data empowerment.

“You’ve got the correct strategy, management and structure for the data, but it’s locked up,” he says.

From his perspective, the key is to take data from analysis to action by making the data available and empowering a community of citizen developers to use it to deliver real value and ideation to the enterprise.

“Meld the business users with the data stewards, then you have something magical,” Heath says.

Data Trust Starts with a Complete Picture

Krishna Subramanian, president and COO of Komprise, explains to truly gain trust in internal data, it must not only be of high quality -- it also must be complete.

“The data infrastructure to deliver modern executive requirements can no longer solely focus on structured and semi-structured data from ERP, CRM, HR, finance and other operational systems,” she says.

Most enterprise data that is piling up across clouds, edge and hybrid data centers is unstructured data, from documents and texts and chats to imaging, video, audio, IoT, and research data.

This unstructured data is too often stored away and rarely analyzed or executed as part of a business intelligence workflow.

“Discovering unstructured data sources and delivering the right data to engineering and research teams for further data preparation and exploration is essential to a holistic, trusted data services platform,” she says.

Improving Data Cycle Management

Heath says to ensure effective data cycle management, companies can take crucial steps, the first of which is to establish a comprehensive data governance framework that defines roles, responsibilities, and data quality standards.

“Secondly, invest in data monitoring and analytics tools to proactively identify anomalies and track data performance,” he explains. “Lastly, implement data integration and security measures to make data accessible, reliable, and protected across the organization.”

With all steps, it’s important to understand and to account for the human in the loop--the people that make up your organization drive the data.

“There are a specific set of unique behaviors and norms that make up the way data gets created and used within the organization and ensuring a data culture that drives governance and management is what nurtures a healthy data lifecycle,” he says.

Subramanian says for executives, it all comes down to the ability to access quality data when they need it.

This requires AI-driven analytics and business intelligence solutions to analyze real-time and historical data to understand what’s happening now and how that compares with trends over time.

This should be combined with data management technologies that feed the right data to these analytics solutions by continually indexing data and providing search, query and data mobility no matter where the data lives, across different data silos both in datacenters and the cloud. 

“This ensures that users can find the right data and send it to business intelligence and AI/ML solutions without delay,” she says. “The field of unstructured data analytics is just emerging.”

Tapping the Power of AI

Subramanian points to new AI and machine learning techniques that are improving the tagging of unstructured content of various types.

In addition, data management tools are evolving to provide a unified global data index, deep analytics to query and search for the right data across petabytes of data and data workflow capabilities to automate the analysis and operationalization of unstructured data. 

Heath says in their organization, they successfully addressed data trust challenges by establishing a strong data governance framework driven first by culture and second by technology to meet those needs.

“By collaborating with business stakeholders, we aligned data strategies with business objectives,” he says.

Holiday says he agrees as the world moves deeper into the digital era and technologies like AI increasingly drive business processes, verifiable data is more important than ever.

“Executives need to know that the data powering business decisions is accurate, tamperproof, and actionable,” he says.

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