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Why Data Management Matters ?

Data is a valuable asset that could be beneficial for company in running its business. However, data could also raise additional cost as a result of poor data management. Therefore, it is crucial for company to improve its data quality and have a good data management, so data can provide advantages for business operation.

How Data Architecture is Supposed to Be?

Data architecture is the process of standardizing how organizations collect, store, transform, distribute, and use data. It is aims to deliver relevant data to organization and create a better understanding of it. A good data architecture should be user-driven to make business users confidently define the requirements and build on data structures to encourage collaboration.

Moreover, a good data architecture should also be automated; thus, data architect can quickly design and integrate the data as well as complemented with AI to create a smart data architecture that can adjust, alert, and recommend solutions to new conditions. In addition, data architecture is should be elastic that allows administrators to focus on troubleshooting and problem solving rather than exacting capacity calibration. Lastly, a good data architecture should also recognize existing and emerging threats to data security and ensures the regulatory compliance with the legislation.

Key Benefits of Data Management Maturity Assessment

A Data Management Maturity (DMM) assessment could be used by organization to quickly evaluate its current state of data management maturity, so organization could achieve the actionable improvements. DMM assessment can help organization understand their capabilities as well as leverage their data assets to improve business performance. It also provides vision and understanding of data assets for organization and also could be used as a tool to identify strengths and gaps to be fixed, mitigate risk, improve auditing, controlling cost, strengthen data governance, and improve data quality to create strategic decisions.

Steps of Data Quality Improvement Process

Data that have a good quality should be relevant to their intended usage, have sufficient details and quantity, with a high degree of accuracy and completeness, consistent with other sources, and presented in appropriate ways. Since the quality of data is crucial for business process, organization needs to know how to improve data quality.

First, develop plan for data quality process improvement by investigating current processes, controlling data, and evaluating possible root causes. After that, implement data quality improvement as well as implement recommended solution in a controlled fashion. Then, evaluate the impact of data quality improvement as well as standardize data quality improvement by replacing old and defective processes.

Elements to Address on Your Data Governance Model

Data governance is critical for organization to capture value through analytics, digital, and other transformative opportunities. Many organizations are struggled to scale data governance effectively, yet some have excelled. An effective big data governance model should have accountability, inventory, process, and rules elements.

Accountability means that data governance model should establish clear lines and role of accountability and responsibility across the enterprise. Next, inventory is a situation in which Data Stewards maintains inventory of all data in the Hadoop data lake using Metadata tools. Then, data governance model should establish processes for data management and usage in Hadoop Data Lake, Big Data analysis tools, Big Data resource usage, and quality monitoring. Lastly, establish and train all users on rules & code of conduct by creating clear rules for data usage, such as Data Usage Agreements (DUA).


Managing data is an important thing that should be done by every organization and therefore it can have value and provide advantages for business operation in the company.


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