Data has risen to the level of being a key corporate asset, with the speed, confidence, and effectiveness of business decisions increasingly rooted in data transparency and trust. To improve data transparency, chief data officers (CDOs) and other stakeholders must focus on automated harvesting of data assets, data search and discovery and crowdsourced curation for data classification and description.
These capabilities represent a lifecycle of collection, improvement, and reuse that supports enterprise-wide data awareness and transparency. They can be stitched and integrated together using individual technologies, however most organizations prefer to use a SaaS-based catalog that serves as a platform to provide these capabilities and support various user types, from the non-technical to very technical.
“There are several best practices that should be part of a CDO’s playbook,” says John Wills, field CTO with data governance specialist Alation. “First, the goal of creating a data culture with data transparency is one dimension of it.”
He points out this goal is a strategic, corporate initiative and allows you to define the benefits in ways that positively impact the business. “It also sets expectations for data providers and consumers,” he adds. “Second, it’s important to measure and report on business impact.”
While he admits it’s not easy, Wills says measurements that key business stakeholders will attest to are vital for sustaining investment and support.
Organizations can start by capturing the “as-is” state of data assets and providing search capabilities. “Don’t slow down the process by starting with ‘conforming’ and ‘aligning’ — that can come later,” Wills says. “Great value is delivered quickly by reflecting back to everyone what already exists.”
Finally, he notes it’s important to include data-related assets such as metric descriptions, process descriptions, terms, briefing books and BI reports. “Connecting all these assets provides value for a broader community of non-technical and technical users,” he says. “That’s how you drive data culture — everyone is participating.”
Breaking Down Silos to Boost Agility
Adrian Carr, CEO of Stibo Systems, explains that traditionally, different types of data are managed by different departments using function-specific applications for specific business needs.
“In this kind of technology landscape, data is hard to share and not used in the same way,” he says. “Siloed data hampers business agility — it puts your data at risk of being duplicated across different systems, maintained separately and governed independently.”
He explains data transparency depends on the availability and control of clean, accurate, consistent, updated information. “Without high-quality data, even the most well-intentioned transparency initiatives are bound for failure,” he says. “The results could be worse than that – a negative customer experience can breed uncertainty and distrust. In that sense, it’s crucial for a business to take steps toward data transparency.”
Moreover, customer experience, time to market and the ability to compete all depend on the quality and availability of a company’s data. He noted data silos are a serious roadblock to a business’s ability to operate efficiently and deliver the level of trust and customer experience people have come to expect.
Justin Richie vice president of data at RevUnit, explains that many organizations struggle to remove silos, so the logic is often not up to date with current business practices and will need to be adjusted. “Governance is an issue alongside data quality because a common plague of data silos is how the data gets updated,” he says. “For example, one team looks at one type of grouping of products, but another group looks at the categories slightly differently.”
Data silos result because they start tracking data separately and can’t reconcile the other business logic, so it’s all maintained independently.
To develop a roadmap to greater data transparency, Richie says the best place to start is data governance — understanding where data inputs are coming from and how data is stored, and what type of people access what information. “Even documenting these processes can unearth potential risks to business value or even more severe security risks,” he says. “Cloud computing is an ideal way to grow adoption with increased data availability and transparency mechanisms.”
Wills points out decision accuracy suffers when organizations have data silos because it’s unclear and unknown if the best and most complete data is being used to represent the current state of reporting and future projections.
Secondly, significant time is spent searching for data and trying to understand if it’s trusted, accurate, and reliable each time a new question is asked–there is no reuse or retained corporate knowledge.
“The approach to resolving data silos, and the challenges that come from them, begins with an executive commitment to develop a data culture, which is something that has to be a valued, strategic initiative,” Wills says.
Look to Data Literacy, Training, and Certification
A data culture creates standards for employee data literacy and provides open and transparent access to what assets exist, as well as standards for curation, quality, and certification so employees have a shared understanding of the data within an organization. “This will not resolve the silos, but it will create a transparent view of the entire enterprise data fabric,” Wills explains.
He adds some of the approaches Alation has seen work well include things like providing an enterprise-wide data literacy training and certification program, so that everyone shares the same perspective, vocabulary, and basic analytic skills.
Each functional business unit and area should include data training as part of their employee onboarding as it provides a review of an organization’s authoritative data and data-related assets, the process used to maintain them, and sets expectations for how employees should participate.
“Also, recognition: Nothing motivates more and sends a stronger message than employees seeing each other be recognized and rewarded for their contributions,” Wills says. “Organizations should use mechanisms like newsletters, awards, and executive callouts as a way to reinforce the data culture.”
Abe Gong, co-founder and CEO of Superconductive, says to create a more data-conscious company, you need to get away from the culture of following the highest-paid person’s opinion. “It’s not about reaching for the data that exists, it’s people actively trying to construct the data that would allow them to answer questions that matter to the company,” he says. “You’re building a supply chain to make decisions based on real data.”
From his perspective, learning about the existence of data is becoming the easiest part–what’s hard is sharing context on what the data really means. “Data is an echo of something that’s happening out there in the real world,” he says. “To really understand data, often you have to learn something that you didn’t know about that real-world effect.”
While data engineers are the ones responsible for making data transparency a repeatable, sustainable, automated process, Gong warns from turning data transparency into a “technological priesthood”. “Business leaders and domain experts are the people who really get where the data’s coming from and how it will be used,” he says. “We need them, too.”