In this blog we will discuss how Alation helps minimize risk with active data governance.
Now that you have empowered data scientists and analysts to access the Snowflake Data Cloud and speed their modeling and analysis, you need to bolster the effectiveness of your governance models. Governance influences how an organization’s objectives are set and achieved, how risk is monitored and addressed, and how performance is optimized.
But governance is a time-consuming process (for users and data stewards alike). Data scientists and analysts waste hours verifying the data. Stewards spend hours enforcing the right policies with little to no certainty they have implemented everything comprehensively.
According to Entrepreneur, Gartner predicts, “through 2022, only 20% of organizations investing in information governance will succeed in scaling governance for digital business.” This survey result shows that organizations need a method to help them implement Data Governance at scale.
So why are organizations not able to scale governance? This is because they have resorted to a traditional approach. This command-and-control approach puts an emphasis on compliance, but not on the people. Imposing restrictions and controls may seem right at first glance. However, this not only slows enforcement of policies at scale, but also limits free-flow of information.
An active approach to data governance is a better option. This is a people-first approach that scales your governance initiative – while encouraging data-driven decision-making. It is supported by a deep integration with Snowflake to guarantee that all data – legacy and Snowflake Data Cloud – is governed accurately.
The Alation data catalog enables active data governance. Through automation, Alation helps organizations harness the power of data intelligence at scale. Deep integration with Snowflake guarantees that all data – legacy and Snowflake data – is governed accurately. It combines a set of defined data governance business processes with autonomous capabilities that support business practices.
This results in quicker turnaround and greater trust between teammates. For example, joint Alation-Snowflake customer Farm Credit Services of America have accelerated the delivery of data for strategic decision-making. Users trust the data, as well as the analysis drawn from that data, with BI analysts spending 75% less time searching for data – and more time on fulfilling, productive projects.
Key Attributes of Active Data Governance: You Must Be Able to:
- Find Trusted Data
- Meet Governance Requirements
- Operationalize governance
In the next section, let’s take a deeper look into how these key attributes help data scientists and analysts make faster, more informed decisions, while supporting stewards in their quest to scale governance policies on the Data Cloud easily.
Find Trusted Data
Data scientists and analysts need deep insights into data quality to trust data in the Data Cloud. Before they can use data to build their model or perform day-to-day analysis – they need to understand its quality.
Verifying quality is time consuming. Without knowing if the data is good, bad or has empty columns, they would have to manually view the data to verify – adding a larger burden to their work. They need context about data if they are to trust it or use it correctly. If the data is bad, they need a flag to caution them.
A data catalog must provide functionality so that data scientists and analysts can easily see the status of the data (if it’s endorsed, has a warning, or is deprecated). They also need the ability to profile and sample to build deep trust in data. Status flags enable them to get a feel for data at a glance. Profiling can help them take further action, such as joins, remove nulls etc. Sampling can help them see a small part of data and give them a quick indication of what the whole dataset looks like – without much effort.
Meet governance requirements
Complex and ever-changing compliance rules make it challenging. Failure to comply can result in costly fines. A range of regulations exist: the General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), as well as industry regulations like the Health Insurance Portability and Accountability Act (HIPAA) and Sarbanes–Oxley Act (SOX). Organizations that run afoul of such laws risk damaging their reputation.
Industries of compliance are under intense scrutiny. Leaders must attest to the fact that they’re governing data in every location, from on-premises to the cloud. To keep up with these rules, data stewards need an easy way to create new policies for data in the Snowflake Data Cloud and other sources.
Organizations need to ensure that data use adheres to policies (both organizational and regulatory). In an ideal world, you’d get compliance guidance before and as you use the data. Imagine writing a SQL query or using a BI dashboard with flags & warnings on compliance best practice within your natural workflow. Automation like this will help organizations comply – while enabling data scientists and analysts to make decisions quickly.
To drive effective business decisions (while following compliance), organizations need to empower data stewards to automatically identify data assets that are sensitive to change. This requires the ability to see details about the origin of data — information that is rarely visible and time-consuming for stewards to furnish.
Operationalize Data Governance
At a high-level, data stewards are focused on improving data governance and guiding users to adhere to data policies. But this is an arduous task.
Data stewards are challenged by an ever-increasing volume of data. They often lack guidance into how to prioritize curation efforts. Furthermore, spend a major amount of time associating business terms with the technical data. In order to quickly meet the business needs, they need clues on data to start their curation process, and automation to augment manual curation.
With Snowflake, data stewards have a choice to leverage Snowflake’s governance policies. However, most stewards soon realize that this is not scalable.
Two problems arise. First, stewards are dependent on data warehouse admins to provide information and to create and edit enforcement policies in Snowflake. Second, managing policies in SQL is simply not scalable. Stewards need an easy way to manage data governance policies in the Snowflake Data Cloud in a comprehensive manner.
Once you have policies in place, you need to ensure that data stewards can track and measure their activities. This will help stewards not only prioritize and delegate curation, but also gain feedback on the impact of their stewardship efforts.
Alation Reduces Risk with Active Data Governance
Alation helps you:
- Empower anyone to find trusted data
- Meet governance requirements with ease
- Operationalize data governance at scale
Empower anyone to find trusted data
Alation makes it easy for anyone – from data scientists & analysts to business users – to find trusted data. Alation TrustCheck provides quality flags that signal endorsement, warning, or deprecation; this gives you instant understanding of quality and helps you trust data.
Data quality details signal to users whether data can be trusted or used.
TrustCheck can be integrated with popular business intelligence BI tools, like Tableau, which supply quality information as you use these tools. In addition, Alation provides a quick preview and sample of the data to help data scientists and analysts with greater data quality insights.
Alation’s deep data profiling helps data scientists and analysts get important data profiling insights. Insights such as characteristics, stats, and graphs help you get quick and thorough information without much effort. Depending on the data’s characteristics and stats, users can quickly customize the data to suit their needs. This means you can accept, reject or resolve data quality issues before exhausting too much time on heavy analytical work.
Data profiling gives users a birds-eye view of an asset, enabling them to understand it quickly.
Through sampling, data scientists and analysts can gain data quality insights more quickly. Gone are the days when you had to worry about query wait-time — now you can easily run a sample instead. You can quickly view a small number of entries (e.g., extract the first 500 rows from millions of rows) to get an understanding of the whole dataset and quickly decide if you can trust the data.
Meet governance requirements with ease
As you find relevant and useful data in Snowflake, Alation surfaces intelligent suggestions to surface approved data assets, current policies, and right guidelines to ensure you use that data the right way. The Alation Data Catalog helps data scientists and analysts to see and understand relevant policies and build models and perform analytics in real-time without any resistance.
Stewards can use Alation’s SQL query writing interface, Compose, to create new data policies with ease. A key feature of Compose, SmartSuggest, automatically surfaces meta-information gleaned from your peers to help make your queries more powerful. This enables stewards to create policies and get feeds in intelligent cues in real-time.
Alation’s data lineage helps organizations to secure their data in the Snowflake Data Cloud. Data lineage empowers data engineers to quickly identify all upstream and downstream impacts of a particular data asset in the Data Cloud – enabling them to more effectively protect against data misuse and address the requirements of data regulations like GDPR and CCPA.
Data can change drastically over time! Lineage captures who changed what, when, and how.
Operationalize data governance at scale
Alation’s Analytics Stewardship enables data stewards to prioritize data based on importance. Stewards are able to apply policies and documentation in bulk to meet growing demands. Through features like agile approval, Analytics Stewardship facilitates direct communication of policies to data scientists and analysts within their day-to-day workflow.
The catalog automatically suggests new business glossary terms using AI/ML and links those terms to relevant data, saving valuable time and effort for stewards.
Alation provides a deep integration with the Snowflake Data Cloud. The catalog marries business-level metadata with data governance information from Snowflake – making the life of a steward simpler.
Policy Center simplifies policy enforcement across your entire data landscape.
Alation Policy Center helps data stewards manage data governance policies in Snowflake from within the Alation platform itself. All Snowflake policies, such as row-level and data masking, that are enunciated in Snowflake are automatically extracted & ingested into Alation and centralized into one location – making it much easier to discover and apply policies.
Alation’s Stewardship dashboard aligns data processes with business practices.
Alation’s Stewardship Dashboard measures, monitors, and tracks stewardship activities. The Stewardship Dashboard enables stewards to prioritize, delegate, and report metrics on curation progress and delivers crucial feedback on the impact of stewardship efforts.
In this day and age, data governance is critical for effective data modeling and analytics. Active data governance helps organizations meet growing and every-changing compliance requirements with ease. Active data governance introduces a foundation for data scientists and analysts to trust the data. Its people-first approach and deep integration with Snowflake Data Cloud helps drive adoption and empowers stewards to easily create, manage, and audit policies at scale.
In conclusion to this 4-part blog series (Introduction, Accelerate Data Migration, and Boost Data Scientists and Analysts Productivity), let’s quickly recap: Snowflake provides the most comprehensive modern cloud data warehouse on the market today. With Alation, organizations can go-live faster, adopt sooner, govern effectively, and realize greater value from the Snowflake Data Cloud.