Your CEO knows what a database is and probably thinks of a data warehouse as a big data vault used for reporting and analysis. They know little NoSQL data stores, why they need one cluster of sparksor how data lakes are used to ingest structured and unstructured data.
CEOs and business leaders are focused on the business value of data, analytics, and machine learning and care less about the underlying technologies.
But therein lies a paradox, because they want to understand the value of investing time and money in new technologies. Try explaining the latest data management technologies, including data meshes, data structures, and distributed data clouds, and watch your CEO’s head spin.
It’s not just CEOs either. Data technology has exploded since the early days of the web, when the main debate was whether to build your data warehouse on top of Oracle, Microsoft, or open source. Today, many non-IT managers are content to believe that data is “in the cloud” and that data integration, quality and performance are “IT issues”.
Anyone working with data should be prepared to explain the most critical technologies and practices in accessible language. In my book, Digital pioneer, I share a story about explaining what a browser cookie is to our startup board members when the web was new. You never know when the microphone will be returned to you to answer a technical question. Responding with technobabble can easily discourage or slow down key investments.
Gordon Allott, President and CEO of K3suggests starting with a simple answer: “Data Lake, Data Warehouse, Mesh, and Structure all simply refer to the overall enterprise data strategy.”
What is a data mesh?
Keeping your answers simple is important, but it’s often not enough. When an executive asks me about a technical term, I want to answer the question in a way that encourages curiosity and follow-up questions.
Let’s start by explaining what a data mesh is. Steven Lin, product marketing manager at semarchyshared this concise answer: “A data mesh is a decentralized approach to data management, where multiple teams within a company are responsible for their own data, promoting collaboration and flexibility,” he shared. declared.
There are no complex words in this definition, and it outlines the problems that data meshes aim to solve, the type of solution, and why it matters.
However, expect to be asked for more technical details, especially if the executive has prior knowledge of other data management technologies. For example, “Weren’t data warehouses and data lakes supposed to solve the data management problem?” »
This question can be a trap if you answer it with the technical differences between data warehouses, lakes and meshes. Instead, focus your answer on the business objective.
Satish Jayanthi, co-founder and CTO of Melt, offers this suggestion: “Data quality often affects the accuracy of business analytics and decision making. By implementing data mesh paradigms, data quality and accuracy can be improved, resulting in increased trust between businesses to use data more broadly for informed decision-making.
I like this answer and hope the exec will want to dig deeper into how data mesh paradigms help improve data quality. Jayanthi responds, “One of the fundamentals, domain ownership, ensures that the team producing the data is responsible for quality and accuracy. This principle of data as a product ensures that data shared with other groups is accurate, reusable, self-documenting and conforms to high standards.
If you’re new to data meshes and want to dive into the technical details, I suggest you check out Zhamak Dehghani’s key article at move from a monolithic data lake to a distributed data mesh.
What is a data factory?
The CFO has heard the conversation about data meshes and now wants to know why the CDO would rather invest in a data structure than a data mesh.
The CFO actually asks three questions:
- What is a data structure?
- How is it different from a data mesh?
- Why is the Chief Data Officer looking to invest in a Data Fabric?
When faced with a complex question, I suggest slowing down, taking a deep breath, considering the context of the person asking the question, and providing a deconstructed answer. I could start with: “Let’s first talk about the data structure and its importance”.
Ross Stuart, Senior Solutions Architect at BEFORE, suggests helping the CFO work on the visual of what a fabric looks like and how it works. “A data structure is a term used to describe the architecture of taking disparate systems and weaving them together, like a structure, to create a cohesive layer on top of an organization’s data,” he says.
Ivan Batanov, senior vice president of engineering at Nodeadds, “A data structure architecture can deliver enhanced insights and analytics efficiently and supports the interconnected nature of data from disparate sources.
At this point, you should pause and give your audience a few seconds to understand the relationship between data meshes and data structures, including the apparent conflict between the two approaches. How could you bring them together? I suggest saying something like this:
Data meshes help business teams use data for analysis and improve data quality, while data structures help the data steward and data governance team manage access to sources of connected data wherever it is stored, including data warehouses, data lakes, file systems and SaaS. apps.
What we unpack in these questions and answers are the different organizational roles and their data responsibilities. We want sales teams to adopt citizen data science And use data for decision making, while organizations need the chief data officer to focus on proactive data governanceaiming to reduce friction and risk when democratizing data.
What is a Distributed Data Cloud?
We now come to a third data management group, which is responsible for storing and structuring data to meet usage needs, performance goals, and security requirements. “Where should we store dataset X” is the challenge, and the answer is not straightforward. In most enterprises, there is no single architecture for storing, managing, and using data.
James Malone, director of product management at Snowflake, says: “Instead of specifying the ‘how’ behind information storage, a data cloud represents ‘what’ someone gets with the right combination of technologies,” he says. “The data cloud allows organizations to choose what works for them rather than prescribing and pushing one way of doing things. Use cases change, needs change, and technologies change, which is why the data cloud focuses on flexibility and utility.”
Hillary Ashton, Product Manager of Teradata, adds an important detail to share with the CFO. “Data clouds can be deployed on any combination of public clouds, on-premises private clouds, hybrid clouds, and multi-clouds,” she says. “But the ‘brain’ of any data cloud is the cloud analytics platform that processes and connects data from all sources and architectures. To get the most out of your data, what matters most is the ability to scale your analytics engine and capabilities across the organization, enabling teams other than data scientists to access, to interrogate and transform data into information.
Tie it all together
At this point, the CEO and CFO may be looking for an easy button to press, so I remind them of the expertise required in the simplest things. “To make a great loaf of bread, you need five ingredients: flour, water, yeast, salt and sugar, in the right proportions, made with proper techniques, baked for the correct amount of time, and elegantly presented for the experience. desired.”
Anyone who has ever tried baking bread knows how difficult it is to consistently bake good bread. Bread books contain hundreds of recipes and techniques continue to evolve.
Storing, managing, integrating, governing, and using data sounds simple, but you need the right ingredients, tools, and practices to empower the data-driven organization.
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