Data analysis is now a central component of decision-making in most aspects 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. However, with the exponential growth of big data from various 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 applying 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 (TAMU-CC) now offers an online Master of Business Administration (MBA) with a Concentration in Business Analytics program to meet this need. Central to TAMU-CC’s 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 rapidly 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 integrating disparate data points and processing data efficiently.
A company may be “data rich” with access to a wealth of data, but without the tools and knowhow to rapidly process and unlock the meaning behind data, it may be of little use. Companies use data warehousing to address these challenges. Granted, aging enterprise data warehousing solutions come with their own problems due to the rapid escalation of big data and advancements in analytics technologies and applications, requiring an even greater degree of processing speed and accuracy. Thankfully, emerging technologies also increase the capabilities of cutting-edge data warehousing and analytics tools, helping solve problems as they arise.
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 to determine the data’s applicability and measurable values that can be correlated. Is the data valid, is it useful for gaining insight and is it in a form that can be compared to similar data? “Transform” refers to converting useful data into consistent values and structuring it into a uniform schema. “Load” is self-explanatory, the final step of inputting data that is now organized and conformed to definable parameters. In other words, ETL gathers and aggregates disparate data to be comparable, analyzable and usable in gaining insight.
How Does Data Warehousing Use Aggregated Data?
Once effectively aggregated, the data in data warehouses can be queried for specific data patterns and analyzed in mass by software, rendering results in statistical form. Many modern forms of “dataware” accomplish similar tasks. But, in general, dataware-driven processes can collect and aggregate data in real-time, bypassing integration. In contrast, aging enterprise data warehousing solutions tend to focus on historical data, with the need for intensive integration slowing down processing speeds.
Both data warehousing and dataware support the process of analyzing aggregated data from initial query to intelligent, targeted analysis through rendering formats specified by the end user. Essentially, using business analytics and business intelligence tools, users can query aggregated data and receive analysis in a form that is most useful for drawing insight and understanding its application to decision-making. Data visualization is a common example of this.
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 analytics tools 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 (such as a human resources manager).
How Might Generative AI Impact Data Analytics?
Emerging technologies like generative AI advance the potential of the analytics tools that rely on data warehousing and dataware. The first applications of generative AI to hit the mainstream — chatbots like ChatGPT — use machine learning models and Large Language Models (LLMs) to identify patterns in training data and generate outputs. The predictive capabilities of a well-trained LLM can generate appropriate natural language responses to queries.
Similar to generating natural language responses, LLM-driven generative AI applications can generate programming language, code and even highly accurate synthetic data for machine learning use. Generative AI tools can also help analyze and visualize data in complex ways. According to Transforming Data With Intelligence (TDWI) from the Data Warehousing Institute, these and other evolving capabilities of generative AI tools make them “a worthy addition to many data professionals’ self-service, no-code business analytics toolkits.”
What Types of Professionals Work With Data Warehouses?
Data warehouses are used to gain actionable insights from data at multiple levels within a business. 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 TAMU-CC’s online MBA with a Concentration in Business Analytics can play an integral role in enriching the content and impact of business education. Degree candidates can develop expertise in applying data-driven insights to decision-making and planning. Integrating data analytics and traditional business education is a crucial part of developing successful future business leaders.
Learn more about TAMU-CC’s online MBA with a Concentration in Business Analytics program.
