How is Data Warehouse Indispensable for Business Intelligence

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Data Warehouse Indispensable
Data Warehouse Indispensable

Do you use spreadsheets or siloed databases with non-standardized formats and structures to make business decisions? Is there inconsistency in your data across business units? What ways do you adopt to determine access levels and permissions to restricted data? Well, data warehousing is the ultimate solution to all such concerns, and you have easy access to data analytics companies providing you with data warehousing consulting services and solutions. 

In this blog, we talk about data warehouse, cloud data warehouse, differences between traditional and cloud data warehousing, as well as benefits of cloud data warehousing.

How can you define a data warehouse?

Better understood as an online analytical processing system (OLAP), a data warehouse can be defined as a data repository that is extracted, transformed, and loaded from one or more operational source systems. Data modelling here is done in a manner that makes data analysis and reporting possible. 

A data warehouse is known to run on a special kind of database. You specifically design and optimize the database to carry out data warehouse operations. Focused on the quality and presentation of data, a data warehouse offers tangible data assets that are not only action-oriented but can also be consumed by the business. A data warehouse gets data from a wide number of sources. These sources include the line of business applications, relational databases, etc

The three most common kinds of data warehouses include:

Enterprise Data Warehouse –

This type of data warehouse offers a central repository customized to support decision-making for the entire organization.

Operational Data Store –

In terms of scope, an operational data store is the same as an enterprise data warehouse. But, in this type of data warehouse, data is refreshed in near real-time and may be used for operational reporting.

Data Mart –

Data Mart refers to a subset of a data warehouse known to support a specific region, function area, or business unit.

What defines a cloud data warehouse?

In business data analytics, a cloud data warehouse is the focal point of an analytics system that is well-structured. It acts like a central information repository that can be analyzed to enable your business to make well-informed decisions.

A cloud data warehouse delivers agility and scalability, which implies it is worth considering a company providing you with data warehousing consulting services. Such companies help you modernize your legacy on-premises data warehouses by moving them to the cloud, thus, delivering fit and value into a modern analytics ecosystem.

Traditional Data Warehousing vs. Cloud Data Warehousing

Designed employing obsolete data management practices, traditional data warehouses fail to keep up with today’s rise in end-users, processing workloads, data volume, and data analysis use cases. Most importantly, legacy, on-premises data warehouses are not only difficult but expensive to scale. They are incapable of tackling raw, complex, or unstructured data.

If an organization has a cloud warehouse in place, it can simplify as well as speed up the development of their data warehouse, while mitigating IT costs and the total cost of ownership (TCO). As you take full advantage of cloud technology, you can easily overcome these challenges:

  • scalability
  • data variety
  • elasticity
  • adaptability
  • data latency
  • data science, and
  • data silos.

Besides, cloud data warehousing enables you to enhance data governance and security by putting into place an integrated data management solution.

Why do you need a data warehouse?

Data warehousing is a crucial aspect of Enterprise data analytics Services due to the following benefits it brings to data and thus, business decision-making

Improves Business Intelligence – As you integrate multiple sources, your decisions should be based on all data.

Ensures Data Access – A data warehouse enables you to access critical data in one centralized location faster.

Enhances Data Quality and Consistency – Through data warehousing, data across the organization is standardized and stored in the same manner and format. This way all departments can draw decisions based on uniform data. 

Builds on Historical Intelligence – As a data warehouse stores huge amounts of historical data, you can easily detect trends through month-over-month and year-over-year analysis. 

Guarantees Faster Query Response Times – Data warehouses are modeled, built, and optimized for read access. This leads to quick report generation.

Other benefits include:

  • Data warehousing enables data mining, wherein you explore big data to forecast trends in the future.
  • It makes provision easy by granting access to specific data to qualified end-users, excluding others at the same time. 
  • Analytical tool support comes in handy with data warehousing. Analytical tools offering drill-down capabilities work best at the time of extracting data from the warehouse.
  • Data stored properly in a data warehouse allows for a comprehensive audit trail of exactly when data was loaded and from which sources. 
  • It becomes easier for you to comply with Sarbanes-Oxley and other related regulations than in the case of some transactional systems.
  • Merge disparate data sources with capabilities to preserve history with data availability.
  • Data warehouse allows for the storage of descriptions of the data. Only then can users comprehend the data in the warehouse, simplifying report creation.
  • With volumes of historical data that need consolidation, a data warehouse makes for easy access and the ability to scale up and down when required.

Role Data Warehousing Plays in Business Intelligence (BI)

In business intelligence, it is the data warehouse that acts as the foundation of data storage. BI depends on complex queries and compares multiple data sets. This fosters effective communication, right from day-to-day decisions to shifts in focus that are organization-wide. 

To alleviate this, BI consists of three all-encompassing activities, such as data wrangling, data storage, and data analysis. Data warehouses enable data storage using an online analytical processing system, integrating, summarizing, and transforming data. This ultimately makes analysis easier. Extract, transform, load (ETL) technologies facilitate data wrangling, and BI tools help in data analysis, without which digital transformation is impossible. 

BI tools integrate with multiple varied sources, including your data warehouse. They then offer an easy means to query the data to analyze it for trends and insights. Visualizing and sharing data via reports and dashboards thus becomes simple.

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