As a CIO, you have many responsibilities—managing the department and budget, planning and maintaining operations and systems, and aligning IT strategy with overall company goals (just to name a few).
Arguably, the most important is ensuring the business’s needs are met. The business has many needs, but none are as impactful as data. When people are provided the data necessary to answer business questions, they can make better, more data-informed decisions.
This is building a data culture – people who value and trust data to make informed decisions.
Data is integral to many processes and decisions when a data culture thrives. It becomes the cornerstone of many aspects of the organization. More complex analyses can be performed on trusted data as the analytics capability matures to gain further insight.
Data as the foundation of what the business does is great – but how do you support that? What technology or platform can meet the needs of the business, from basic report creation to complex document analysis to machine learning workflows?
The Snowflake AI Data Cloud is the platform that will support that and much more! Snowflake provides many capabilities to support business needs and various value-generating use cases. It is the ideal single source of truth to support analytics and drive data adoption – the foundation of the data culture!
In this blog, we’ll walk you through how to build a sustainable data culture with Snowflake.
Understanding Data Culture
A data culture is really about people having trust in the data. If the data can’t be trusted—it’s inaccurate, incorrect, or useless—people won’t use or rely on it. The business will find other means to answer their questions. This oftentimes leads to shadow IT processes and duplicated data pipelines. Data is siloed, and there is no singular source of truth but fragmented data spread across the organization.
Establishing a data culture changes this paradigm. Data pipelines are standardized to ingest data to Snowflake to provide consistency and maintainability. Data transformation introduces data quality rules, such as with dbt or Matillion, to establish trust that data is ready for consumption.
With this approach, only fresh, cleaned, and accurate data reaches the consumer, and data issues can be corrected early in the pipeline.
As data becomes more trusted and part of business processes, the analytics capability will mature. A data culture allows users to move beyond descriptive analytics to more complex analysis, like predictive or prescriptive analytics.
There will always be a need for lower-difficulty analysis, but a data culture, especially one built on Snowflake’s capabilities, enhances analytics and provides the business with the tools to ask and answer those more difficult questions.
Role of CIO in Building a Data Culture
As the CIO, it’s important to understand the gaps in the current environment and where you need to go to achieve a flourishing data culture. This is done through several activities.
The first step is to assess the current environment and understand what is already in use. The next is to speak with the business to learn what they’re trying to do today that isn’t supported by the current architecture.
Lastly, building from the previous step aligns with the overall business goal. Where does the business want to be in 6 months? 12 months? 3 years? Aligning with the strategy will ensure the data is impactful and the architecture provides what is needed as the business demands. Snowflake, as the foundation of the data culture, ensures that businesses will have what they need when they need it.
Introduction to Snowflake
Snowflake is feature-rich and has many capabilities, making the dream of establishing a data culture a reality. Its platform runs completely in the cloud, with Snowflake supporting the underlying maintenance and management. The platform can be quickly deployed to start, but you will want to plan for the future so it’s scalable and performant as the data culture matures.
A core concept of Snowflake’s architecture is the decoupling of computing and storage. A database storage layer is a central repository for the data, while computing is in another layer. The nodes in the compute layer store a portion of the central repository data for processing locally. This means end users will see increased performance gains from legacy, on-premise systems without impacting other users or processes.
This is a key advantage of Snowflake. On-premise computing limitations no longer restrict queries and end users. Snowflake provides virtual warehouses to support many workloads, each with a compute resource set. Warehouses can be scaled up and down as needed to meet business demand. Users don’t need to worry about scaling their environment—they can focus on getting the data they need to drive the business forward!
Another key advantage of Snowflake is its continuously adding new features and capabilities to the platform. From building a fact and dimensional model to building hybrid tables to support both operational and analytical workloads to building Streamlit applications, Snowflake has features to support many types of use cases.
One such feature is Document AI. This recent addition (as of this writing) has moved into Public Preview. This new feature enables the processing and extracting of data from documents in various formats and structures. It is an incredibly powerful feature with many use cases in business, such as claims processing and order handling.
There are so many features to Snowflake that it’s impossible to list them all here. phData has many resources and information about Snowflake and how the AI Data Cloud can turn your business into one with a thriving data culture.
Establishing a Foundation for Data Culture
Data governance is going to be the foundation of every data culture. There are many reasons to implement data governance policies, such as auditing and compliance, data protection, monitoring, quality checks, and data classification. Data governance is a broad subject, and covering all aspects in a single blog is impossible. To speak generally, data governance aims to ensure data is trustworthy, including (but not limited to) being secure, accurate, accessible, standardized, and usable.
In a strong data culture, users trust that the data is secure and that they can also access it when they need to without being slowed down or hampered by additional layers of security. If a team regularly waits for access or jumps through hoops to query data that should be available to their business team, the team will lose trust in the data platform and look elsewhere for answers.
The data culture begins to break down with the introduction of more roadblocks and obstacles that impede the user experience.
This is where Snowflake comes to the rescue! Not only does it have the awesome features discussed above, but it’s also built for data governance. Snowflake simplifies the implementation of data governance processes and protections.
Data masking is an important capability for safeguarding data and preventing unauthorized viewing. Dynamic Data Masking in Snowflake is a column-level security feature that masks data in a table or views at query time based on a defined data masking policy.
Policies are straightforward to set up and allow users access based on their Snowflake-defined role. The best part is that the security check is transparent to the user! If the user’s role is in the accepted role group, that user will be allowed to view the data. Otherwise, the user sees masked text. Combining data masking with Snowflake’s object tagging is a flexible and powerful way to protect your data!
Because Snowflake role-based access controls (RBAC) are integral to data masking (and security in general), it’s important to have a solid model defined and established. With phData’s toolkit, implementing RBAC at scale has never been easier!
Aside from masking, a new Snowflake feature recently introduced (in Preview as of this writing) is about quality monitoring – Data Quality and data metric functions. This capability focuses on providing visibility to and understanding of the state of the data. A data metric function (or DMF) measures key metrics, including accuracy and freshness. Snowflake provides system-built DMFs and the ability to create custom user-defined DMFs. DMFs can be scheduled and provide alerts when there are unexpected changes in the data quality (however, quality is defined for that dataset).
These are just a few ways that Snowflake provides to implement data governance policies. Snowflake continuously strives for enhanced user experiences, both for the data consumer and the data governor, while maintaining data security and integrity.
Data Democratization with Snowflake
A common misconception is that data democratization means data is open to everyone at all times. Data is still governed and secure (the foundation of a strong data culture!). Data democratization means data consumers are familiar enough with data to understand how to use it daily. Another way to put it is that these users become data citizens within your organization.
Data democratization is the crux of self-service analytics. To self-serve, users must be able to access reliable and governed data when they need it. Data silos must be broken down, and users must be enabled to access data that is relevant to their use cases and are trained on how to use data effectively. Removing silos and obstacles to make the analytics experience smoother drives value for the business and enables the data culture to flourish.
Snowflake provides an easy-to-use platform where business users can run their own analyses without relying on others, equipping them to pursue high-value analytics. And because Snowflake’s architecture decouples storage and computing, the platform can scale to meet the users’ performance expectations. Teams can reliably perform their analytics without worrying about impacting the performance of others or having their analysis cut short due to computing restraints.
When business teams know they can go to Snowflake to query data to answer any question, they more fully harness the data. There is less time spent asking others to create reports for them or asking for processing time and more time spent realizing value.
When Snowflake’s capabilities are combined with a data catalog, such as Atlan, data users are provided with a business glossary to understand what data is in the platform, enhancing data discoverability without compromising data security.
Collaboration is promoted across teams when users see what other data is available. There is a knowledge sharing that happens during this collaboration, further breaking down those silos.
Another way Snowflake is well-suited for data democratization and collaboration is through data sharing. This capability allows for the secure sharing of data between Snowflake accounts. No data is copied or transferred between accounts; it is merely accessible to your Snowflake account. Sharing data is a great way to build collaboration between teams further when those teams have separate Snowflake accounts.
Promoting Data Literacy
Snowflake is an accessible platform. While it runs on an innovative architecture with a newly designed query engine, SQL is still the entry point. This makes it more approachable for newcomers and veterans alike. However, promoting data literacy and education to users is still important.
Training programs to equip users with the tools and concepts necessary to use data support data democratization and strengthen the data culture. These programs ultimately empower users to be more efficient with their self-service analytics. They learn how to ask better questions about the data, being more effective in their role.
By raising the floor for the business’s data literacy, less time is spent on trivial tasks or basic questions surrounding data. The business spends more time focused on driving decisions and potentially revenue-generating analytics.
Not only is data an important part of data literacy, but so are the platforms and technologies adjacent to it, like Snowflake or Power BI. Understanding the capabilities of these technologies unleashes the full potential of the business and what they can drive. phData provides training and enablement for Snowflake and other analytical technologies and can empower your business teams to thrive!
Driving Data-Driven Decision Making
Establishing a data culture should enable the business to make data-driven decisions. Snowflake’s analytics capabilities provide the means to reach those decisions. Snowflake’s performance and scalability are unmatched, allowing the business to perform all analytics, from descriptive and diagnostic to predictive and prescriptive.
Snowflake can work with many analytic tools in the marketplace, including Tableau and Power BI. This integration means a data solution can be developed in Snowflake for a business domain and then consumed from an analytical tool of the business’ choosing and experience. We have worked with many customers to develop this approach (fusion pods), which successfully enhances the business decision-making process!
As the business matures in the data culture, it will rely more heavily on data to drive decisions. Through collaboration, co-development of use cases, and data sharing, other teams will be encouraged to participate in this data culture supported by Snowflake.
Measuring Success and ROI
So, how do you determine whether your data culture is strong and thriving or needs a rehaul? KPIs are the best way to measure any change in the organization. Before implementing the data culture, these should be defined to get a baseline understanding of the business’s current state.
Once defined, we recommend starting with a pilot, maybe a business team or domain. The idea here is to start small to establish processes and work through the nuances of your business. The KPIs can also be refined here as necessary.
Being your business expert, you likely know what KPIs would be applicable to tell you how the data culture is progressing. Here are a couple of ideas to help:
Time saved on trivial or mundane questions (e.g., how do I join these data points?)
Ad-hoc report requests by the enterprise team
Report usage
Snowflake analytics/query load
Snowflake data product creation (e.g., purpose-built data marts)
Revenue realized from a particular project or team
Case Studies and Success Stories
Check out phData’s real-world examples!
Building a data culture is hard work, and it requires commitment from the organization and from leadership. One company we’ve worked with embraced Snowflake as the platform for building its data culture. The business users initially resisted change but eventually became comfortable through continued encouragement, training, upskilling, and enablement.
One of the key lessons here is that this takes time. It wasn’t an overnight change and took months of effort. The keys to the process are patience, consistency, and empathy. Often, this is a big change for how users operate in their roles today. Understanding where the users are can help meet and guide them on this journey. As users become more comfortable with the changes and see how Snowflake improves how they use and analyze data, the further the individual and the business can go.
Another takeaway is to start small and focused. A data culture is often a paradigm shift within an organization. Starting small allows you to show value more quickly and show how this new data culture will benefit everyone, enabling them to be better at what they do. They’ll be able to ask more complex questions and, through Snowflake’s advanced AI features, find answers to those questions.
Closing
It’s clear that the Snowflake AI Data Cloud is best equipped to serve as the foundation for building a data culture for your organization. From establishing data governance and enabling data democratization to enhancing collaboration and making better data-driven decisions, Snowflake can enhance your business and help you implement your data culture vision.
Make the most of Snowflake AI Data Cloud with phData. Build a solid data culture and improve your decisions. Reach out to our experts for additional assistance.
FAQs
I want to build a data culture. Where should I start?
Great question! We recommend starting with a data strategy to understand the organization’s current state, where the gaps are, and how to move forward. Establishing your data strategy from the beginning is key to sustained success.
How can I get a resistant business unit to embrace the data culture?
When a business unit or team is often resistant to a data culture, they don’t fully understand how this will improve their roles. One idea is to show them the “art of the possible” on Snowflake. For example, you could implement a chatbot on Snowpark Container Services (preview feature) to demonstrate to the team what they could accomplish within this Snowflake-enabled data culture.
