Best Practices for Enterprise Data Warehouse Governance

In an advancing age of improved data analysis, more and more enterprises are adopting business intelligence and analytics tools to benefit the company. Despite the variety of the sectors involved, all of these companies have a single common goal to use business intelligence software to transform data into actionable insights and competitive initiatives.

As the primary competitive advantage, data analysis should deliver an increased understanding of the factors that shape markets, influence businesses, and help companies to act on that knowledge. Ultimately, the hope is to be able to outmaneuver and outsell competitors, while proactively addressing customer needs.

With business intelligence and data analytics becoming the go-to approaches for enterprises, many companies are choosing to invest in developing their own data warehouse, commonly talked about as a data lake. In order to generate valuable insights from deep data analysis, enterprises need to have a reliable data warehouse as the foundation.

Today, a data warehouse is used to do more than just integrating data from multiple sources for better, more accurate analysis. A data warehouse must also be reliable, traceable, secure, and efficient at the same time. It needs to offer these advantages to differentiate itself from a simple database, especially in business intelligence.

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Minimum educational required – 10+2 passed in any stream from a recognized board.

The age limit is 18 to 25 years. It may differ from one airline to another!

 

Physical and Medical standards –

• Females must be 157 cm in height and males must be 170 cm in height (for males). This parameter may vary from one airline toward the next.
• The candidate's body weight should be proportional to his or her height.
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• Physical fitness is required of the candidate.
• Eyesight requirements: a minimum of 6/9 vision is required. Many airlines allow applicants to fix their vision to 20/20!
• There should be no history of mental disease in the candidate's past.
• The candidate should not have a significant cardiovascular condition.

You can become an air hostess if you meet certain criteria, such as a minimum educational level, an age limit, language ability, and physical characteristics.

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You can still apply for this job if you have a higher qualification (such as a Bachelor's or Master's Degree).

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What Exactly Is Data Governance?

The first step is to understand what is data governance. Data Governance is an overloaded term and means different things to different people. It has been helpful to define Data Governance based on the outcomes it is supposed to deliver. In my case, Data Governance is any task required for:

• Compliance: Data life cycle and usage is in accordance with laws and regulations.
• Privacy: Protect data as per regulations and user expectations.
• Security: Data & data infrastructure is adequately protected.

Why is Data Governance hard?

Compliance, Privacy, and Security are different approaches to ensure that data collectors and processors do not gain unregulated insights. It is hard to ensure that the right data governance framework is in place to meet this goal. An interesting example of an unexpected insight is the sequence of events leading to leakage of taxi cab tipping history of celebrities.

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Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

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Best Practices for Enterprise Data Warehouse Governance

In an advancing age of improved data analysis, more and more enterprises are adopting business intelligence and analytics tools to benefit the company. Despite the variety of the sectors involved, all of these companies have a single common goal to use business intelligence software to transform data into actionable insights and competitive initiatives.

As the primary competitive advantage, data analysis should deliver an increased understanding of the factors that shape markets, influence businesses, and help companies to act on that knowledge. Ultimately, the hope is to be able to outmaneuver and outsell competitors, while proactively addressing customer needs.

With business intelligence and data analytics becoming the go-to approaches for enterprises, many companies are choosing to invest in developing their own data warehouse, commonly talked about as a data lake. In order to generate valuable insights from deep data analysis, enterprises need to have a reliable data warehouse as the foundation.

Today, a data warehouse is used to do more than just integrating data from multiple sources for better, more accurate analysis. A data warehouse must also be reliable, traceable, secure, and efficient at the same time. It needs to offer these advantages to differentiate itself from a simple database, especially in business intelligence.

#data #data warehouse #data warehouse architecture #data ware house solutions #data warehouse engineer.

Best Data Governance Practices That Enterprises Should Adopt

As much as seeking and acquiring data is essential for an enterprise, equally important is its management. Data management and governance have become an integral part of company functions. In plain words, data governance concerns itself with the data privacy requirements, big data technologies and associated responsibilities, data governance approaches regarding the IT infrastructure. It also defines who within the organisation will have the authority and control over the data assets and encompasses the people, resources and technologies required to manage and protect the data.

Implementing a data governance framework impacts data management processes and models, its proper execution, and lays the foundation for smarter and faster decision making. Different organisations adopt different methodologies to govern their data. While these frameworks may differ from case-to-case, there are a few best practices that we discuss below.

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