Introduction

Data is an essential resource for any organization. Without accurate and timely data, organizations cannot make informed decisions or optimize their processes. To ensure that the data within an organization remains relevant and useful, a Data Steering Group may be established to provide guidance and direction. In this post, we will discuss what a Data Steering Group is and why an organization may want to have one.

Section 1: What is a Data Steering Group?

Definition: What is a Steering Group

A Steering Group, also known as a Steering Committee, is a group of individuals responsible for providing strategic direction, oversight, and decision-making for a specific project, initiative, or organization. The group typically includes representatives from various stakeholder groups and serves as a central point of communication and coordination to ensure the success of the project or initiative.

A “Data Steering Group” is a team of individuals within an organization responsible for setting and governing the data strategy, ensuring that data is used as effectively as possible. The group typically consists of representatives from across the business and will include roles such as Chief Information Officer (CIOs), Chief Technology Officers (CTOs), line-of-business heads, or departmental heads. In addition to technology experts, members of the Data Steering Group should also have broad business knowledge and experience. This ensures that decisions on data usage are taken with the wider context in mind.

The primary role of a Data Steering Group is to provide guidance on data initiatives, taking into consideration both short-term needs and long-term goals. It is responsible for setting data strategies and policies, including developing standards that ensure the quality of data. It also works to ensure compliance with relevant data security and information privacy regulations, such as GDPR and CCPA. In addition to this, the Data Steering Group is tasked with identifying opportunities for data-driven innovation, developing plans to implement them, and ultimately determining which initiatives should be pursued and which should be abandoned.

The Data Steering Group is typically chaired by a senior executive in the organization (such as a CIO or CTO), who will set the agenda for each meeting. Members of the group bring different skillsets and expertise to bear on decisions about how best to use data within an organization. Working together, they can create effective data strategies that benefit the organization as a whole.

In summary, a Data Steering Group is an important part of any organization and can be invaluable in helping to set data strategies that are both effective and compliant. By bringing together individuals with different skillsets from across the business, it can provide valuable guidance on how best to use data for the benefit of the organization.

Section 2: Why have a Data Steering Group?

A Data Steering Group (DSG) is an important organizational tool for ensuring data quality and security. It provides a forum for stakeholders from across the enterprise to come together and make informed decisions about data management issues. The DSG is responsible for setting the data governance strategy and ensuring it is aligned with the organization’s overall business objectives.

The benefits of having a Data Steering Group are numerous. By providing a forum for stakeholders to collaborate, the DSG can help ensure that all data initiatives, or business cases, are compliant with applicable regulations while also improving data quality. This improves trust in data-driven decision making and helps teams produce more accurate results. Additionally, the presence of an oversight body like a Data Steering Group leads to greater accountability for mistakes and ensures that important issues are addressed quickly and effectively.

Organizations such as Google, IBM, GE, Microsoft and Intel have implemented successful Data Steering Groups with positive outcomes. These organizations have seen improved data quality, more effective processes for data governance and compliance, and better alignment of data initiatives with business objectives.

In summary, having a Data Steering Group can provide significant benefits in terms of data quality, compliance, and alignment with business objectives. Organizations that have implemented DSGs have seen successful outcomes and results from their efforts. With the right stakeholders and commitment to collaboration, a Data Steering Group could be highly beneficial for any organization looking to optimize the management of its data resources.

Therefore, creating a Data Steering Group is an important step in ensuring proper data management in any organization or company. The DSG provides an oversight body that ensures data initiatives are compliant with applicable regulations and that data quality is never compromised. With the right stakeholders in place, a Data Steering Group can be an invaluable tool for improving data governance and achieving better alignment with business objectives.

Section 3: How to establish a Data Steering Group

The first step to establishing a Data Steering Group is to identify the stakeholders from within the organization who should be part of it. The group should include leadership from IT, operations, finance, marketing, and any other departments that are heavily reliant on data. Additionally, stakeholders outside the organization such as customers or vendors may need to be involved depending on the scope of the data initiatives.

Once all necessary stakeholders have been identified, a charter can be created which outlines the governance structure and objectives of the Data Steering Group. This document should clearly establish roles and responsibilities for each member as well as objectives for guiding data projects through their lifecycle. It should also include metrics that will measure progress towards these objectives in order to ensure accountability across the board.

Finally, the Data Steering Group needs to be effective and remain relevant over time. This can be done by regularly reviewing the charter and metrics to ensure they are still aligned with the organization’s objectives, addressing any changes that may need to be made. Additionally, ensuring open communication among all stakeholders is key for a successful Data Steering Group. Regular meetings should be held in order for members to share updates on their respective initiatives as well as discuss any potential obstacles or opportunities that have arisen. By doing this, the Data Steering Group will continue to make meaningful contributions in guiding data projects through their lifecycle and helping shape the future of an organization’s data-driven decisions.

Conclusion

Having a Data Steering Group is an effective way to ensure data quality and governance are managed in an organization. The group provides the oversight needed to manage data, identify issues, and make decisions that will improve data management practices. With the right resources in place, such as a Data Steering Group, organizations can have confidence that their data is well-managed and secure. Furthermore, having a Data Steering Group can lead to improved decision-making and greater efficiency within an organization. Organizations should consider establishing a Data Steering Group in order to reap the many benefits it has to offer.

Data Cloning, sometimes called Database Virtualization, is a method of snapshotting real data and creating tiny “fully functional” copies for the purpose of rapid provisioning into your Development & Test Environments.

The Cloning Workflow

There are four primary Steps

1. Load / Ingest the Source Data
2. Snapshot the Data
3. Clone / Replicate the Data
4. Provision of the Data to DevTest Environments

Under the Hood

Cloning is typically achieved/built using ZFS or HyperV technologies and allows you to move away from the traditional backup & restore methods, which can take hours.

By using ZFS or HyperV you can provision databases x100 quicker and x10 smaller.

What is ZFS?

• ZFS is a file system that provides for data integrity and Snapshotting. It is available for most if not all major OS platforms.

What is HyperV?

• HyperV is a Microsoft virtualization platform that can be used to create and manage virtual machines. It supports Snapshotting as well.

Problem Statement

Backups are often taken manually and can take hours or days to complete. This means that the data isn’t available for use during this time period, which can be problematic if you need access to your data immediately.

There is also a secondary issue with storage. A backup & restore is, by its nature, a 100% copy of the original source. So if you started with a 5 TB database and wanted x3 restores then you are up for another 15 TB in disk space.

What are the Benefits of Data Cloning?

Data cloning is the process of creating a copy, or snapshot, of data for backup, analysis, or engineering purposes. This can be done in real-time or as part of a scheduled routine. Data clones can be used to provision new databases and test changes to production systems without affecting the live dataset.

Advantages

– Clones can be used for development and testing without affecting production data

– Clones use little storage, on average about 40 MB, even if the source was 1 TB

– The Snapshot & Cloning process takes seconds, not hours

– You can restore a Clone to any point in time by bookmarking

– Simplifies your End to End Data Management

Disadvantages

– The underlying technology to achieve cloning can be complex.

However, there are various cool tools on the market that remove this complexity.

What Tools are available to support Data Cloning?

In addition to building your own from scratch, commercial cloning solutions include:

• Delphix
• RedGate SQL Clone
• VirtualizeMe (vME)
• Windocks

Each is powerful and has its own set of features and benefits. The key is to understand your data environment and what you’re trying to achieve before making that final decision.

Common Use Cases for Data Cloning

• DevOps: Data cloning is the process of creating an exact copy of a dataset. This can be useful for several reasons, such as creating backups or replicating test data, into Test Environments, for development and testing purposes.
• Cloud Migration: Data cloning provides a secure and efficient way to move TB-size datasets from on-premises to the cloud. This technology can create space-efficient data environments needed for testing and cutover rehearsal.
• Platform Upgrades: A large majority of projects end up going over the set schedule and budget. The primary reason for this is because setting up and refreshing project environments is slow and complicated. Database virtualization can cut down on complexity, lower the total cost of ownership, and accelerate projects by delivering virtual data copies to platform teams more efficiently than legacy processes allow.
• Analytics: Data clones can provide a space for designing queries and reports, as well as on-demand access to data across sources for BI projects that require data integration. This makes it easier to work with large amounts of data without damaging the original dataset.
• Production Support: Data cloning can help teams identify and resolve production issues by providing complete virtual data environments. This allows for root cause analysis and validation of changes to ensure that they do not cause further problems.

To Conclude

Data cloning is the process of creating an exact copy of a dataset (database). This can be useful for many reasons, such as creating backups or replicating data for development and testing purposes. Data clones can be used to quickly provision new databases and test changes to production systems without affecting the live dataset.

This article provides a brief overview of data cloning, including its advantages, disadvantages, common use cases, and available tools. It is intended as a starting point for those who are new to the topic. Further research is recommended to identify the best solution for your specific needs. Thanks for reading!

Every aspect of our daily lives involves the usage of data. Be it our social media, banking account, or even while using an e-commerce site, we use data everywhere. This data may range from our names and contact information to our banking and credit card details.

The personal data of a user is quite sensitive. In general, all users expect a company to protect their sensitive data. But there is always a slight chance that the app or service you are using might face a data breach. In that case, the question that comes to mind is how the company or app will keep your data safe.

The answer is data privacy regulations. Nowadays, most countries have their individual data privacy laws, and companies operating in those countries generally follow these laws. Data privacy laws protect a customer’s data in production. But did you ever think about whether your dev or testing environment is safe and secure?

In this post, we’ll discuss why you must follow data privacy regulations in a non-production environment. We’ll take a look at the challenges faced while complying with privacy rules, solutions to these challenges, and strategies to follow while implementing privacy laws in non-production. But before that, we’ll discuss a bit about privacy regulations. So, let’s buckle up our seat belts and take a deep dive.

What Do You Mean by Privacy Regulations?

Data privacy regulations ordata compliance is a series of rules that companies must abide by to ensure that they’re following all the legal procedures while collecting a user’s data. Not only that, but it’s also the company’s job to keep the user’s data safe and prevent any misuse.

There are various data privacy laws. For instance, companies operating under the European Union follow GDPR. On the other hand, the United States has several laws like HIPAA, ECPA, and FCRA. Failing to follow these rules results in potential lawsuits or penalties. The goal of these rules is to keep a user’s sensitive data safe and secure from malicious activities.

Now that we know what data privacy regulation is, let’s discuss why we need to follow these rules in non-production.

Why Privacy Regulations in Non-Production Are Important

While deploying an app or a site in production, we add various security protocols. But often, the environment where we develop or test our apps is not that secure. In 2005 and 2006, Walmart faced a security breach when hackers targeted the dev team and transferred sensitive data and source code to somewhere in Eastern Europe.

This kind of incident can happen to any company. Currently, many companies use production data for in-house testing or development. So, how does a company ensure that a user’s sensitive data is safe? The answer is data masking, which is one of the mandatory rules of data privacy regulations.

However, implementing data privacy rules comes with many challenges. Let’s explore some of them and the ways to resolve these challenges.

Challenges Faced While Complying With Privacy Rules

Adapting to something new always comes with certain challenges, be it some new tool, technology, or regulation. Data privacy is no exception. However, the challenges are not that complicated. With proper planning, overcoming them is quite straightforward.

Adapting to New Requirements

Data privacy regulations are generally process-driven. While implementing privacy rules in non-production, your team must welcome changes in the way they do things. This may involve data masking, generating synthetic data, etc. Your team will take some time to adapt to the new processes.

Chalk out a plan before the transition. Train your team and explain why they need to follow these regulations. With proper training and clarification of individual roles, adapting to the new changes won’t take much time.

New Rules of Test Data

If your testing team is using real user data for testing the essential features of your product, beware. The process is going to change. As per data privacy regulations, you cannot use real user data for testing, so the challenge comes while rearranging or recreating your test data.

However, with a proper test data management suite, the task becomes a lot easier than doing the entire thing manually.

Adjusting Your Budget Plan

Implementing any new process often involves spending a lot of money. While implementing privacy laws, you have to think about factors like

• the research your teams need to do
• the purchase and implementation of data compliance tools that will help you generate privacy-compliant test data
• the arrangement of training sessions for your team
• the hiring of resources to monitor or enforce compliance laws

All of the above and more will affect your budget, so it’s best to have a discussion with your finance and technical team. Figure out the zones where you should focus spending and calculate an approximate amount. Planning is beneficial if you want to avoid overspending. On that note, in the following section, we’ll discuss some strategies to follow while implementing privacy regulations in non-production.

Strategies to Implement Privacy Regulations in Dev and Testing

Although there is no end to planning strategies while implementing data privacy regulations, there are some important steps that we can’t miss.

Sorting Data

Before following privacy laws, you must know everything about your data. If the project is at a starting phase, there will be a lot of customer data. Discuss this with your team to categorize the data and clarify what data is sensitive to the user. Once you categorize the data and separate sensitive data from general data, it’s time for the next steps.

Encrypting Sensitive and Personal Data

GDPR and other data privacy laws make it mandatory for you to secure any sensitive data. Ensure that if you have any such data in a non-production environment, it’s secured by layers of encryption. Even if you’re not using the data, you must still secure it in your database. This is because no matter how strong your firewall is, hackers can always breach it. So it’s wise to protect sensitive data with layers of encryption apart from just a firewall.

Restricting Access to Database

As per most data privacy rules, your database should not provide overall access to all users. Since a database has multiple types of data, you must create roles and grant specific permission to each role. For instance, a tester should have access to test data only and not production data. Imagine if a fresher on your team deletes a table from the production database. The incident may happen by mistake, but it will cost the company a lot. Enforce these rules to prevent similar unfortunate mishaps.

Change the Policies of Cookies

If you’re developing a site, you’ll need to think about how your cookies work and whether they comply with the data privacy law you’re following. For instance, what if your website is operating outside the EU and the target audience is in the EU? In that case, apart from standard compliance, you need to comply with GDPR as well. As per GDPR, a website should collect a user’s personal data only after they agree to cookie consent. That means you should inform the user about the data used by your site’s cookies to perform specific functions. The information must be clear, and your cookies can collect data only after the user gives permission.

Use of a Compliance Monitoring Solution

Generally, companies often appoint a data protection officer (DPO) whose job is to monitor the processes, analyze the risk, and suggest measures so that your company never fails to comply with privacy laws. But a DPO is a normal human being. When it comes to large data sets, a human mind can always miss something. The solution? Provide your DPO with a compliance monitoring solution.

Enov8 provides such a solution that addresses the needs of compliance managers. The tool monitors your data and identity risks. Not only that, but the tool also helps you to find compliance breaches and points out processes that you need to optimize in order to protect the data.

Disclose Important Information to Users

Data privacy laws ensure that users should have all the knowledge about how companies are using their data. You must disclose everything about data usage while signing the agreements. Situations may arise later for which you may need to revise the agreement. For instance, suppose you’re monitoring the logs of a system that’s connected with the customer’s network. If the logs contain the user’s IP address or other sensitive data, inform the customer.

Synthetic Test Data Generation and Data Masking

There are some cases where you need real data to develop or test something. But what if the data compliance standard that your company follows prohibits you from using real data? Don’t worry. Synthetic data is the next best thing. Synthetic data is data generated by an algorithm and closely imitates the original data. You can also use data masking, where sensitive data is hidden and replaced by similar dummy data. The advantage? You can continue your work without any risk of failing to comply with privacy laws.

Train Your Team on Privacy Regulations

When it comes to complying with privacy laws, there is no end to learning and adapting to new things. It’ll be quite hectic for your team if you enforce a lot of rules on your team all of a sudden. Make the transition smooth by arranging training sessions for your employees to explain the need for compliance with privacy laws and the consequences if they fail to abide by these laws. In addition, train them on using data compliance suites. You can take a look at Enov8’s data compliance suite, which monitors your data and ensures you’re compliant with GDPR, FCRA, ECPA, and multiple other standards.

Keeping your test and dev data compliant with privacy laws may prove to be a little challenging at first. But if planned and executed in a phased manner, your team will adapt easily.

Author

This post was written by Arnab Roy Chowdhury. Arnab is a UI developer by profession and a blogging enthusiast. He has strong expertise in the latest UI/UX trends, project methodologies, testing, and scripting.