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What Is Data Warehousing?

Data analysis is now a central component of decision-making in most every aspect of a business's operations, from human resources management to logistics. Real-time data analytics provides management with the actionable insights they need to develop competitive advantages and scalability amidst the uncertainty of ever-changing markets. But with the exponential growth of big data from a multitude of sources and media types come inconsistencies and acute challenges in the actionable analysis of data.

This is where data warehousing comes in — being the bridge between collecting and using data. Data warehousing is essential to the application of data analytics to business management and administration. Yet there is a distinct shortage of people with the necessary combination of business acumen and current data analytics proficiencies in areas like data warehousing.

Texas A&M University-Corpus Christi now offers an online Master of Business Administration (MBA) with a Concentration in Data Analytics to meet this need. Central to this program is the advanced study of data warehousing and its incorporation into effective business intelligence architecture.

What Problems Can Data Warehousing Address?

Big data is so immense that it can be difficult to process quickly enough to be usable on the scale necessary for real-time business decision-making. This data also comes in many inconsistent forms which are hard to collate into targeted data sets that are defined, measurable, comparable and trackable over time. Traditional databases are ineffective in both integrating disparate data points and processing data efficiently. Companies use data warehousing to address these challenges.

How Does Data Warehousing Collect Data?

Data is gathered into data warehouses through a grouping of processes often referred to as Extract Transform Load, or ETL. In ETL data is first extracted or collected from data sources and evaluated as to the data's applicability and measurable values that can be correlated. Is the data valid, is it of use for gaining insight and is it in a form which can be compared to other similar data? "Transform" in this case refers to taking useful data, converting it into consistent values and structuring it into a uniform schema. "Load" is self-explanatory, being the final step of inputting data that is now organized and conformed to definable parameters. In other words, ETL gathers and aggregates disparate data so as to be comparable, analyzable and usable in gaining insight.

How Does Data Warehousing Make Use of Aggregated Data?

Once effectively aggregated, the data in data warehouses can be queried as to specific data patterns and information and analyzed in mass by software, rendering results in statistical form. Many modern forms of "dataware," often cloud-based services, can take this process from initial query to intelligent, targeted analyzation through rendering formats specified by the end user.

For instance, a human resources manager may want to create an interactive chart representing a measure of employee productivity as correlated with different performance incentives over time. Effective dataware can search and analyze the appropriate data sets and render them visually using parameters input by the data scientist, analyst, specialist or even the end user (human resources manager in this case.

What Types of Professionals Work With Data Warehouses?

Data warehouses are used to gain actionable insights from data at multiple levels within a business. And effective data warehousing is a team effort. Managerial personnel assess business goals and decision-making needs, giving data analysts the targeted information and rendered output desired. The data professionals manipulate data warehouse analysis tools to provide that targeted information in usable formats. Management uses insights gained from data to make decisions based on accurate, real-time information.

Employees at all levels use data-driven insights directly to adjust strategy and operations. Data warehouses provide usable data to end users in numerous roles within any organization, from retail operations to educational systems.

The lack of management professionals competent in the use of essential data analytics components like modern data warehousing has clear implications for modern business education program design. Programs like Texas A&M-Corpus Christi's online MBA with a Concentration in Data Analytics can play an integral role in enriching the content and impact of business education. Degree candidates can use what they learn through this study to develop their ability to apply data-driven insights to decision-making and planning. The integration of data analytics and traditional business education is a crucial part of developing successful future business leaders.

Learn more about TAMU-CC's MBA with a Concentration in Data Analytics online program.


Sources:

Informatica: What Is Data Warehousing?

Informatica: What Is Extract Transform Load (ETL)?

Guru99: What Is Data Warehousing? Types, Definition & Example

Forbes: The Top 10 Trends in Data Warehousing

Forbes: The Maturation of the Data Industry: Then, Now and in the Future

Data Science Central: Why Every Manager Needs These Data Science Skills

ADT Mag: Big Data Skills Getting Harder to Find

ADT Mag: New Survey Shows Big Data Skills Shortage, Low Adoption Rate

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