Data Governance Tool: What to look for?
Data Governance Tool
Imagine you purchased a computer and expensed it to your company. You follow up on the expense report and carry out the correct procedure, so you expect to get paid back soon. However, on the accounting side of your company, someone sees an unusually high expense report which prompts them to give you a call. When they call, you get back at them rudely, saying, “you are the money guy; you figure it out.” The accountant is indeed the money guy, but your response will make it harder for you to get paid back and will not answer why you have a computer. The same is true for data guys. They are in IT, but they do not create the data. So when the data in the system is inconsistent, the data guys are bound to call you, the business owner. When questioned, the business owners will, in turn, claim that that is the data guy’s work, and this leads to a lot of inconsistencies in data management until a data governance strategy is laid out. So
What is Data governance?
Data governance is the total management of an organization’s data availability, usability, integrity, and security. Data governance, according to Gartner, is the specification of decision rights and a framework for accountability to assure acceptable behavior in the value, generation, consumption, and control of data and analytics.
Why do organizations need it?
It guarantees that data is consistent and reliable, which helps to avoid data inconsistencies or errors, which can lead to difficulties including data integrity, poor decision-making, and a number of organizational concerns.
It helps in regulatory compliance that helps in ensuring that organizations are consistently compliant with all levels of regulatory requirements. This is key for minimizing risks and reducing operational costs.
It leads to improved data quality, decreased data management costs, and increased access to data for all stakeholders, which translates to better decision-making and better business outcomes.
Critical pillars in data Governance
Consider the following when looking for a modern data governance tool to support your data governance framework and enable your pursuit of being data-driven:
- Data stewardship – Consider using a data governance platform with data stewardship capabilities to see the impact. Your workforce should be able to comprehend policies and procedures. To work effectively with the data governance council or data governance committee, they’ll need to know the stakeholders. They’llThey’ll need to connect the technical metadata to the business context to comprehend the data. And, perhaps most important of all, they’ll need a place to feel like they can trust the data.
- Data quality – Successful governance projects require data accuracy, completeness, and consistency across platforms. Suppose one of the pillars of being a data-driven organization is that your team can trust the data. In that case, an integrated data quality solution may be the most critical capacity. Data quality tools provide those capabilities through parsing, data profiling, and matching functions, among other features.
- Master data management. Another data management discipline that is strongly linked to data governance processes is master data management (MDM). MDM projects provide a master collection of data about customers, products, and other business entities in order to ensure that data is consistent across several systems inside an organization.
- Data governance use cases. Data governance is essential for controlling data in operational systems and BI and analytics applications supplied by data warehouses, data lakes, and data marts. It’sIt’s also a crucial part of digital transformation projects, and it can help with other corporate operations like risk management, business process management, and mergers and acquisitions. Data use continues to increase, and new technologies emerge, so data governance will likely gain more comprehensive applications.
DQLabs presents a data governance tool with the following features;
- A greater data comprehension; Utilize a machine learning-enhanced data catalog to streamline and automate the process of discovering, inventorying, profiling, tagging, and developing semantic associations between data assets, as well as determining data quality.
- Traceback to the source using Data Lineage; Build trust in data-driven business outcomes by automating the process of data lineage to trace from the data source to data consumption.
- Ability to monitor and report data assets over time, as well as many areas of data management such as trend, quality sensitivity, curation impact, and so on.
- Minimize risk with proper access; Easy and Simple but Centralized Data Security and Permissioning of information assets across data sources, datasets, and even attribute levels.
- Automatic identification of personally identifiable information across diverse information assets using semantic type identification to avoid revealing sensitive data.
Check out DQLabs’ Agile Data Governance for more details on data governance and how your organization stands to benefit from this service. You can also signup for a 7-day free trial and request a demo.