Data has risen to the level of significant corporate assets, along with the speed, reliability, and effectiveness of business decisions that are increasingly rooted in data transparency and trust. To improve data transparency, Chief Data Officers (CDOs) and other stakeholders are curated by automatic collection of data assets, data retrieval and detection, and crowdsourcing for data classification and description. You need to focus on.
These features represent the collection, improvement, and reuse lifecycle that supports data awareness and transparency across the enterprise. These can be stitched together and integrated using individual technologies, but most organizations provide these capabilities to support a variety of user types, from non-technical to highly technical. I prefer to use a SaaS-based catalog that acts as a platform to do.
“There are some best practices that should be part of the CDO playbook,” said John Wills, Field CTO of Data Governance Specialist Alation. “First, the goal of building a data culture with data transparency is one aspect.”
He points out that this goal is a strategic corporate initiative that can define benefits in a way that has a positive impact on the business. “It also sets expectations for data providers and consumers,” he adds. “Second, it’s important to measure and report on the business impact.”
Wills admits that it’s not easy, but says the measurements proved by key business people are essential to maintaining investment and support.
Organizations can start by capturing the “as-is” state of their data assets and providing search capabilities. Do not slow down the process by starting with “fit” and “alignment”. This can happen later, “Wills says. “Reflecting what already exists for everyone quickly brings great value.”
Finally, he states, it’s important to include data-related assets such as metric descriptions, process descriptions, terminology, briefing books, and BI reports. “Connecting all these assets provides value to a wider community of non-technical and technical users,” he says. “That’s the way to drive a data culture. Everyone is involved.”
Disassemble silos for agility
Adrian Carr, CEO of Stibo Systems, explains that traditionally different types of data are managed by different departments using feature-specific applications that address specific business needs.
“In this kind of technology environment, it’s difficult to share data and it’s not used in the same way,” he says. “Siled data hinders business agility. There is a risk that data will be replicated between different systems, maintained separately, and managed independently.”
He explains that data transparency depends on the availability and control of clean, accurate, consistent, and updated information. “Without high-quality data, even the most deliberate transparency initiatives would fail,” he says. “Results can be worse than that. Poor customer experience can create uncertainty and distrust. In that sense, it’s very difficult for companies to take steps towards data transparency. Is important to. ”
In addition, customer experience, time to market, and competitiveness all depend on the quality and availability of your company’s data. He said data silos are a serious obstacle to a company’s ability to operate efficiently and provide the level of trust and customer experience people expect.
Justin Richie, Vice President of Data at RevUnit, explains that many organizations struggle to remove silos, so logic often doesn’t correspond to current business practices and needs to be tuned. “Governance is a problem with data quality because a common problem with data silos is how to update the data,” he says. “For example, one team considers grouping one type of product, while another group considers categories in a slightly different way.”
Data silos occur because you can’t start tracking data individually and adjust other business logic. So everything is kept independent.
To create a roadmap for data transparency, Ritchie understands data governance: where data is entered and how it is stored, and what types of people access what information. It’s best to start with. “Even documenting these processes can uncover potential risks to business value, or even more serious security risks,” he says. “Cloud computing is an ideal way to improve data availability and transparency mechanisms and increase adoption.”
Wills made a decision because if an organization has a data silo, it is unclear and unclear whether the best and most complete data is used to represent the current state and future expectations of the report. Point out that accuracy is compromised.
Then, it takes a lot of time to search the data and try to understand if the data is reliable, accurate, and reliable every time a new question is asked. There is no reuse or retention of corporate knowledge.
“The approach to solving data silos and the challenges that arise from it begins with executive commitment to develop a data culture, which must be a valuable strategic initiative,” Wills said. say.
Focus on data literacy, training, and certification
Employees share data within their organization because the data culture creates standards for employee data literacy and provides open and transparent access to existing assets, as well as curation, quality, and certification standards. You can get an understanding of. “This doesn’t solve the silo, but it creates a transparent view of the entire enterprise data fabric,” Wills explains.
He believes that Alation works well, including providing enterprise-wide data literacy training and certification programs, so everyone has the same perspective, vocabulary, and basic analysis. He adds that he can share his skills.
Each functional business unit and domain provides a review of the organization’s trusted data and data-related assets, the processes used to maintain them, and to set expectations for how employees participate. Data training should be included as part of onboarding.
“Also, Awareness: Nothing is more motivated and sends a stronger message than employees who see each other recognized and rewarded for their contributions,” says Wills. “Organizations need to use mechanisms such as newsletters, awards, and executive callouts as a way to enhance their data culture.”
Kobo Abe, co-founder and chief executive officer of Superconductivity, says that in order to create a more data-sensitive company, we need to escape the culture of following the opinions of the highest-paying people. “It’s not about reaching for existing data, it’s about people who are actively building data that can answer important questions for the company,” he says. “We are building a supply chain to make decisions based on real-world data.”
From his point of view, learning about the existence of data is becoming the easiest part. The difficulty is sharing context about what the data really means. “Data is a reflection of what’s happening in the real world,” he says. “To really understand the data, we often have to learn what we didn’t know about the actual effect.”
While data engineers are responsible for making data transparency a repeatable, sustainable and automated process, Gong warns that it will turn data transparency into a “technical priesthood.” “Business leaders and domain experts are people who really understand where data comes from and how it is used,” he says. “We also need them.”
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