Data Governance Solutions - Artha Solutions

3 Ways To Reduce Data Governance Failures

No organization is immune to data governance failures. They can be costly and embarrassing for businesses when things go wrong. Data governance is a complex process, and there are many moving data governance solutions for enterprises. For example, data governance solutions include data quality, data security, and data compliance. Even if a single part of the data governance process fails, it can have a ripple effect on the entire organization.

Fortunately, there are ways to reduce the risk of data governance failures. There are many measures that can help reduce the risk of data governance failures, such as:

Data Discovery and Profiling
Data Cleansing and Standardization
Data Security and Compliance

Here are three ways to reduce Data Governance Services failures:

1. Deliver trusted data to the people who need it

Data governance is about more than just managing data. It’s also about making sure the right people have access to the right data. For example, Data Discovery and Profiling can help you identify which data is most important to your organization. Once you’ve identified the key data, Data Security and Compliance can help you control who has access to it.

Delivering trusted data to the people who need it, can help reduce the risk of data breaches and other security threats.

Poor and uncontrolled data access is one of the main causes of data breaches. For example, in 2017, the Equifax data breach occurred when hackers gained access to the personal information of 145 million people. One of the main reasons the hack was successful was that Equifax had poor data security controls.

Data Discovery and Profiling can help you avoid a similar fate by delivering trusted data to the people who need it.

2. Ensure data quality across your organization

Data quality is another important part of data governance. Inaccurate or incomplete data can lead to problems down the line. Suppose you’re a retailer and you have a customer’s address in your database. If the address is inaccurate, the customer may not receive their purchase. Inaccurate data can also lead to compliance issues. For example, if you’re required to report data to a government agency and the data is incorrect, you could face fines or other penalties.

There are several measures that can help organizations ensure that the quality of their data is accurate and complete:

Data cleansing: This feature can help you clean up inaccuracies in your data.
Data standardization: This feature can help you ensure that all of your data is consistent.

As a result, these measures can help improve the accuracy of your data and avoid compliance issues. Please note that data quality is an ongoing process. You should continuously monitor your data for inaccuracies and take steps to correct them.

3. Automate data governance processes

Data governance is a complex process, and there are many moving parts. As a result, it can be challenging to keep track of everything. This is where automation comes in. Automating data governance processes can help you:

Save time: Automating data-intensive tasks can free up your time so you can focus on other things. For example, if you’re manually cleansing data, it can take a lot of time. But if you use automating data governance processes, you can automate the process and save yourself some time.

Improve efficiency: Automating repetitive tasks can help improve your organization’s overall efficiency. If you’re manually standardizing data, it can be easy to make mistakes.

Reduce errors: Finally, automating data governance processes can help reduce the risk of human error. Errors can be costly and time-consuming to fix. By automating data governance processes, you can help reduce the risk of errors.

Conclusion

Data governance is a complex process, but it’s important for any organization that wants to avoid data breaches and other security threats. The three data governance solutions mentioned can help reduce the risks of data governance failures in several ways. If you’re looking for a way to improve your organization’s data governance, these measures are a good place to start.

About Artha Solutions:

Artha Solutions is a premier business and technology consulting firm providing insights and expertise in both business strategy and technical implementations. Artha brings forward thinking and innovation to a new level with years of technical and industry expertise and complete transparency. Artha has a proven track record working with SMB (small to medium businesses) to Fortune 500 enterprises turning their business and technology challenges into business value.

Data Governance Vs Data Management The Difference Explained

People often wonder if there is any difference between Data Governance and Data Management. Well, the answer is yes. However, they are related.

Data governance is the definition of organizational structures, data owners, policies, regulations, processes, business terminology, and measurements for the end-to-end lifespan of data (collection, use, storage, protection, deletion, and archiving).

The technical implementation of data governance is data management.

Data Governance Solutions are little more than documentation if they aren’t put into action. Enterprise data management allows processes and policies to be executed and enforced.

Simply put, data governance solutions help develop policies and procedures surrounding data, whereas data management solutions implement those policies and procedures to assemble and use the data for decision-making. To better grasp how these notions work together in practice, it’s helpful to first understand what each of them is.

What is Data Governance?

Let’s look at some aspects of data governance, shall we?

People

People are essential to data governance because they are the ones who generate and manage the data, as well as the ones who gain from well-governed data. Subject matter experts in the business, for example, can identify the organization’s standardized business terms as well as the levels and types of quality standards required for various business processes.

Data stewards are in charge of resolving concerns with data quality. IT professionals take care of the architecture and management of databases, applications, and business processes. Data privacy and security are the responsibility of legal and security personnel. And cross-functional leaders, who make up the governance board or council are in charge of settling conflicts among various functions inside an organization.

Rules and Policies

If policies specify what should be done, rules specify how it should be done. Policies and regulations are used across processes and procedures by organizations; popular categories include consent, quality, retention, and security. You might, for example, have a policy that stipulates that consent for processing must be sought before personal data can be used. When personal data is acquired, one rule might outline the consent alternatives that users must choose (such as billing, marketing, and third-party sharing). Another rule can state that prior to providing a promotional offer to a customer, marketing consent must be confirmed.

Metrics

What is measured will be managed. The number of duplicate records in an application, the correctness and completeness of data, and how many personal data pieces are encrypted or masked are all common technical metrics. While these metrics aid in data technical management, leading businesses are also attempting to define how these technical metrics affect business outcome measurements.

For instance, Days Sales Outstanding (DSO) is a typical business indicator used by financial analysts and lenders to assess a company’s financial health. When client address data is inadequate or faulty, the billing cycle time and, as a result, the DSO will increase. Analysts and lenders may view a higher DSO than the industry average as an indication of risk, downgrading the company’s outlook or raising the cost of financing.

What is Data Management?

Let us now dig into some tools and techniques for data management.

Cleansing and Standardization

Data quality policies can be implemented and enforced with the help of cleansing and standardization. Profiling allows you to compare the data’s validity, correctness, and completeness to the data quality parameters you set. You may then rectify issues like invalid values, misspelled words, and missing values. To enforce data quality at the point of entry, you can also incorporate cleansing rules into data entry processes.

Profiling also aids in the identification of similarities, differences, and links between data sources so that duplicate records may be removed and consistency can be enforced across all sources.

External data, such as DUNS numbers, demographics, and geographic data, can be used to enrich internal data. Many firms also establish a centralized hub to assist in maintaining master data semantic consistency across data sources.

Masking and Encryption

Masking and encryption assist you in enforcing and implementing privacy and protection policies. Tools and techniques for data discovery and classification assist you in identifying sensitive and personal data and categorizing it as requiring protection based on internal requirements and external regulations such as the General Data Protection Regulation (GDPR), the California Consumer Privacy Act (CCPA), and the General Data Protection Law of Brazil (LGPD). These tags can then be utilized to implement suitable security measures. Some users may be authorized to access raw data, while others may require the data to be dynamically masked upon query, depending on classification and access regulations.

Internally and externally, data flow modeling can help you understand how data is acquired, processed, stored, and distributed. Based on classification and privacy policies, you can then decide on relevant protection mechanisms. Data masking, for example, maybe sufficient for access within your firewall, but data must be encrypted before being shared with other parties outside your organization.

Archiving and Deletion

The use of archiving and deletion aids in the implementation and enforcement of retention policies. When data is no longer required for day-to-day operations but is still required to meet regulatory requirements such as tax reporting or long-term storage, it is archived. Data archiving tools also keep track of how long data should be kept, index it for quicker retrieval for uses such as legal discovery, and enacts necessary access and data masking/encryption controls. Data is permanently destroyed after the predefined retention period has expired.

While this may appear simple, balancing the retention needs of industry rules (such as BCBS 239 and CCAR) with the erasing requirements of governmental and regional regulations is a difficult process (like GDPR and CCPA).

While data governance and data management are two separate entities, their goals are the same: to build a solid, trustworthy data foundation that allows your company’s finest employees to do their best job.

Unmask the 3 Levels of Holistic Data Governance Strategy

Gathering quality data is the first step towards business success. However, the growth of the same business relies on the usage of given data. The trick to any successful business nowadays is defined not by the data collected, but by the best use of data.

As important as data is to a successful business, it is not any good on its own. It is only as good as its usage, and that is governed by Data Governance.

It is not easy to decide the best use of collected data, keeping in touch with every single department of the organization, taking the needs into account, and ensuring that they are all met is confusing, and challenging.

However, organizations that cannot spare their resources on setting great data governance strategies, seek help from the experts who are behind the most successful businesses. Here are the three things that you will find common inside every single strategy.

A data governance strategy is like the foundation of the process that allows a company to base its operations on. Understanding the strategy truly allows the organization and the individuals to carry the business towards a successful outcome.

Data governance strategies are unique to every business model. Like every new idea for a business is unique, the strategy to make it work is unique too. However, these 3 strategies are the common denominator everywhere.

Framework

Taking into account the different departments of an organization and their needs, building a framework that accommodates the growth of every individual department, and building a framework that syncs up each department with the other while making use of the data that is collected is the goal.

Bring in the framework that supports a greater ROI. Changes will be common, the change in the collected data will change too. The framework should allow changes in the collection of data and the steps that will be taken.

Understanding the efforts that will be put in extracting the best out collected data and how individual teams are to meet their set goals is what building a framework is all about.

After the framework, comes the planning.

Planning

Setting expectations and requirements is tough, sure but drawing a route map of execution is rough too. Knowing what we want from a company from the beginning and understanding where to take it in the next time frame is the agenda of data governance strategy.

Drawing up a route map is however a step towards achieving the said ROI. A process on how each individual in the organization and each team will lead the company towards the desired goal of success.

Fixing how the individual teams will work and the operations being carried out every day, and on a bigger annual basis is what efficient planning looks like. Keeping in mind that there will be some unexpected circumstances and preparing the company as a whole for them includes the best use of resources.

This also means drawing up an execution strategy that supports the data growth and methods to imbibe the best data usage policies keeping in mind to adhere to the requirements of the organization.

Adherence

Building a strategy that is easy to execute is one of the most important aspects that help in adhering to it. Knowing that a strategy will be possible and is in fact a scalable target will help in adhering to it.

Keeping in mind that data governance strategies are the center of a company’s operations, it is important to notice that it is also just a plan that is a well thought out idea for a company’s growth.

These are the three levels of data governance strategies that decide the growth of a company. Now, there are many different approaches to understanding each of these levels and attempting to personalize the strategies at each level to suit the business model, but the intention of each level is to meet the company growth targets in the swiftest, most economical and efficient way possible.

What’s The Foundation of Hybrid Cloud Self-Service Automation?

In the last one decade, cloud application delivery has become extremely important but undeniably complex, sometimes getting out of direct control. This has become a roadblock towards achieving total self-service automation within budget control principles.

According to a report by the IDX, 69% of enterprises believe that they are overspending on the cloud and the lack of automation is the number one reason they cite. It all boils down to data governance because that’s what essentially, well, governs who can access what, where, and for how long.

This makes data governance not just essential but crucial for self-service automation. Naturally, the question arises as to what is the foundation upon which it rests.

Hybrid Cloud Self-service Automation

Since the cloud is not a singular destination, it must be adaptable to change and not averse to evolution. Self-service automation provides the necessary agility which enables the end users to provision their applications into the right cloud based on their needs, whether they want a public cloud or a private cloud – a truly hybrid synergy becomes the need of the hour.

However, governance becomes even more important with such cross-cloud environments where the control needs to be more poignant and strong.

Data Governance: The Foundation of Hybrid Cloud Self-service Automation

As mentioned above, data governance is key to better comprehend the value that a cloud provides which makes it the most important, foundational need of the cloud environment, especially a hybrid cloud or multi-cloud.

Having said that, developing and implementing a common governance model that’s adaptable to the various requirements and complexities in such environments is a challenge in itself. Therefore, there has to be a shared control plane that enables centralized governance across clouds and other associated technologies.

Most companies fall short with data governance when they treat it as just another tool in their cloud arsenal – it’s so much more than that. It includes all the required integrations into the existing technologies that organizations have deployed over the years along with any operational links that enable collaboration among them, across the lifecycle of an application.

With a foundational data governance framework in place, businesses can assign and manage the applicable multitenancy, role-based access controls, and policies.

Principles of Good Governance

Data governance isn’t confined to data. In fact, it blankets the people, the processes, and the technology that surrounds the data. As such, there’s a need for auditable compliance for these three areas that are well-defined and agreed-upon. When done correctly, this could help organizations make data work for them.

Moreover, organizations need to think macro, not micro. They must consider the entire data governance lifecycle instead of monitoring in siloes. Although this could prove to be overwhelming, especially for the small and medium enterprises, it’s also extremely important and worthy of detailed attention.

Some of the key areas organizations must focus on includes:

Data Discovery & Assessment
Classification of Sensitive Data
Data Catalog Maintenance
Data Sensitivity Level Assessment
Documentation of Data Quality
Defining & Assignment of Access Rights
Regular Audits for Evaluation of Security Health
Enabling Encryption & Other Additional Data Protection Methods

With these guiding principles, organizations are able to create a highly effective data governance strategy that enables them to achieve control over their data assets and maintain total visibility. This translates into a culture that’s data-driven, helping organizations make better decisions, improve risk management, and most importantly, maintain regulatory compliance as per industry standards.