Gage  Monahan

Gage Monahan

1618413360

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.

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Best Practices for Enterprise Data Warehouse Governance
Mikel  Okuneva

Mikel Okuneva

1600012800

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.

#databases #big-data-and-governance #data-lineage #data-governance #what-is-data-governance #data-governance-explained #data-governance-and-privacy #data-governance-problems

Siphiwe  Nair

Siphiwe Nair

1620466520

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.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

Gage  Monahan

Gage Monahan

1618413360

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.

Siphiwe  Nair

Siphiwe Nair

1622608680

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.

#opinions #data classification #data governance #data governance practices #data management framework #data stewards

Gerhard  Brink

Gerhard Brink

1620629020

Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.

Introduction

As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).


This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.

#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management