A proper data governance strategy has become more vital than ever, thanks to the ratification of data protection regulations like the GDPR and CCPA. Despite this consensus, the CIO WaterCooler Data Governance Survey revealed that 53% of companies had only just started working on their enterprise data governance strategy in 2017.
This sudden increase in attention to enterprise data strategy is primarily fueled by the explosive growth of data generated over the past decade. Even still, a lack of urgency with data governance can come with severe consequences, making it essential to expedite the implementation of frameworks and procedures that maximize data security.
Sadly, many businesses are developing data governance frameworks out of necessity, rather than the desire to maintain security standards.
While data governance does improve regulatory compliance at an operational level, you can also simultaneously enhance the quality of your datasets with a comprehensive framework. This combined outcome can have a positive effect on business growth through increased customer trust and improved insight validity.
So how do you start building a data governance framework?
The Design Phase – Initially, you’ll need to design a preliminary framework and establish a dedicated team.
Your dedicated team should include internal stakeholders across the business, and data stewards for compiling the framework. Collaborative feedback will relate the requirements of different departments to your data governance strategy, allowing you to formulate a technology-agnostic framework that eliminates IT siloes.
Start by performing an audit of your data management procedures, so you can highlight potential problems that you will aim to solve with your data governance strategy. This will allow you to create a solid business case for data governance, highlighting its importance against business objectives.
Defining Requirements and Building Policies – Now, you should have an understanding of what you aim to accomplish through your data governance strategy.
You may wish to improve data accessibility, improve visibility into data flows across your business, or centralize your data storage for a single source of the truth. By creating a list of objectives, you can prioritize these goals and determine which are easiest to implement. This will allow you to quickly realize the benefits of data governance, further bolstering your business case for increased investment in this space.
Lack of Expertise and Overcoming Technological Hurdles – After developing your framework, you may realize that you lack the infrastructure or internal expertise to implement your data governance strategy.
By understanding what you lack, you can prepare for long-term adherence to your data governance strategy. Without preparation, you may hurt your business case for data governance through mismanagement and insufficient infrastructure capacity. Overcoming these hurdles will require an increase in the short-term investment needed to realize your data governance objectives. At the same time, upgrading your underlying infrastructure will broaden your IT capabilities and ensure data governance success.
Orchestrating your data governance strategy is a difficult task. If you need to perform a cloud migration first, it is even more complicated. You will need to decide upon a cloud provider, develop a working server instance, link your applications to the new database, and migrate your datasets.
Thankfully, the Snowflake data warehousing and governance platform can simplify this process. Snowflake offers a serverless computing platform, which simplifies cost management and improves data security in the cloud.
Individual compute clusters perform database processing, with a master node abstraction layer that centralizes data access across your business. With automatic scaling, data encryption, and cloud-native management tools, many points of contention are handled through Snowflake, strengthening your business case for data governance implementation.
Trianz is an industry-leading data governance consulting firm who has partnered with Snowflake to deliver simple, secure data warehousing in the cloud for our clients. If you are already in the cloud, we can integrate your services into Snowflake and simplify your IT operations management. For those that are still on-premise, we can help you assess and migrate your databases so that you can upgrade your data security without any service interruptions.
Get in touch with our data governance consulting team and join the serverless computing revolution with Trianz today!
Contact Us Today
The idea of data warehousing was first explored in detail back in 1988 by a team of researchers at IBM. In their whitepaper, Barry Devlin and Paul Murphy went into detail about the environments in which companies were maintaining their database instances. They highlighted the growing need at IBM for further integration to simplify access and improve data consistency for end-users. This was during a time where “big data” was unheard of, with businesses managing much smaller datasets.Explore
With the California Consumer Protection Act (CCPA) and General Data Protection Regulation (GDPR) now in full swing, the regulatory landscape for businesses has never been more complicated. Consumers are increasingly aware of cybersecurity in their daily lives, and they are demanding better protection against these threats when using online services.Explore
With businesses collecting massive quantities of information daily, it is crucial to maintain data accessibility for employees. The art of data integration involves combining datasets from different sources to create a unified focal point for analysis and insight generation. This achieves a state of data confluence, which can be beneficial in a variety of use cases including commercial applications and scientific research.Explore
As internet usage continues to grow, so does the generation of raw data. Businesses need to process this raw data into useable structured data, so they can extract insight to guide their enterprise strategy. Without a unified, integrated IT infrastructure, data processing will become more complicated due to growing service quality demands and an increased number of data sources.Explore
With the explosive growth of big data and cloud computing, we’ve seen a remarkable increase in the quantities of data we generate each day. According to a study by EMC Corporation, it is estimated that we will generate 1.7 megabytes of data every second, of every day, for every single person on the planet in 2020. That is equivalent to 40 Zettabytes, or 40 billion Terabytes per year. This is a goldmine of information, but even more surprising is the fact that only 12% of this data is ever properly analyzed—namely due to a lack of effective data governance.Explore
The cloud has become one of the most popular hosting destinations for businesses, thanks to the decentralized provision of modern, cost-effective computing resources. In particular, there has been a sharp rise in cloud-based data warehousing, due to the abundant storage capacity and easy scalability of these server instances.Explore