The human brain cannot comprehend large volumes of data. With our lives growing more data-centric by the day, collecting, sorting, filtering and organising data into comprehensible units is the need of the hour. This is exactly where data science enters. It is a multidisciplinary field synthesising data in predictable ways by combining computer engineering principles, statistics, and mathematics. There is a pool of opportunities for people adept in mining and interpreting data. Businesses rely on data scientists to make well-informed decisions and plan efficiently to remain competitive in this dynamic business landscape.
A career in data science is one of the most lucrative options for computer engineering graduates. A suitable data science certification course can help you kickstart your career and explore the many options this budding field offers.
Data analytics is useful for companies to gain insights into their processes and services. It helps them examine customer problems, develop targeted solutions and enhance customer experience. By shifting the criterion beyond data to align insights with action, companies can customise customer experiences, develop new products, and increase productivity. Data analysts are hired in a diverse mix of industries.
A career in data analytics is challenging and competitive but also flexible and offers on-the-job training.
This blog will focus on the concepts of effective data management and the need for a robust data governance framework.
Data management is the process of storing, collecting, organising, and integrating meaningful data from structured and unstructured data to support efficiency and strategic decision-making. Storing and collecting data cost-effectively helps people and businesses optimise data usage to make well-informed decisions. Every application and algorithm used in business depends on efficient access to data. Data management ensures that data is available and accurate.
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Importance of Data Management
Data management is an important part of data science, data analytics and data management. Theoretically, there are two kinds of data management— cloud and traditional data management. Below are a few reasons why efficient data management is a necessity.
Enhanced data quality
Effective data management ensures that data errors are minimised and data duplicity is avoided. This leads to data being more accurate, consistent and reliable. Measuring data quality using specified parameters helps businesses assess the effectiveness of the data management process and make appropriate changes.
Increased data security
Businesses and organisations should ensure that data is secure so it adheres to data protection laws. Data managers must draft data security protocols and data access controls to protect data from being prone to attacks, breaches and unauthorised access.
Organising data into meaningful data sets enables data managers to spot patterns to make better decisions backed by data-driven insights.
Data governance refers to setting data standards, protocols, and policies that allow data to be managed effectively and efficiently. Without data governance, data becomes disorganised, data duplication and error occur, and data security is breached. Data governance is one of the challenges of data management. It is more holistic than data management as it is a data strategy. Data science training delves deep into the tools required for data management, besides offering in-depth knowledge on data modelling and other related aspects
Data Governance Framework
A data governance framework is a collection of protocols, standards, and guidelines that enable organisations to manage data effectively. Establishing a data governance framework is essential for implementing data governance initiatives. Serving as the foundation of a data governance programme, it should visualise how to ensure the quality, integrity, and accessibility of data. A data governance framework streamlines the process of collaboration.
It will ensure that rules apply to all the available data in the organisation.
Data governance has the following pillars:
- Data Quality: Data governance methods relating to data quality involve setting standards and implementing data quality in decision-making processes. Data quality establishes policies for data validation, profiling, and cleansing.
- Data Integration: Data integration involves merging data from different sources into a single data set. It ensures that data from departments is complete, accessible, and can be used for data analysis, decision-making, and other applications.
- Data Privacy & Security: Data privacy entails deciding what data can be shared with the third party and the mode of sharing that data. Data security enforces a framework to prevent data from being prone to malicious threats and external hackers.
- Data Architecture: Data architecture involves designing and planning data systems to align with the organisation's objectives.
Importance of Effective Data Governance
Effective data governance enables better decision-making, improving operational support. It ensures decreased data management costs and equal access to data for all stakeholders. This results in better decision-making and increased return on investment (ROI). The objectives should align with organisational goals. Some organisations have designed a robust data governance framework but eventually failed as they didn’t address the cultural change.
The key objectives of data governance are as follows:
- Enforcing data security so that there is no data breach and unauthorised access.
- Preventing data errors and formulating policies for data usage and monitoring.
- Connecting multiple databases of various departments through centralised data governance.
- Formulating procedures that comply with industry standards as auditors are most likely to examine how the data was generated. This is important for reducing risks and operational costs.
There is no specific data management plan that fits all businesses. For the successful functioning of a business, a robust data management framework along with an appropriate data governance system should be in place. To explore this employment landscape, you can seek a career in data science.
A data analytics course can help you understand data management challenges and deliver critical business intelligence. Become a data analyst to learn how to build data analytical models to suit business needs. Imarticus offers a comprehensive, hands-on data analytics certification course to gain practical knowledge about the implications of data science and develop a successful career in data science. Enrol in the Postgraduate Programme in Data Science And Analytics today to unlock a future full of possibilities.