PowerData Inc.

PowerData Solutions Inc.

Data Analytics

Why You May Need A Data Warehouse

How Your Organization Could Benefit from Data Warehouse

Your organization has multiple database that contain data related to customers, sales, HR and many other business functions. So why would you need a data warehouse?

The idea behind creating a data warehouse is to collect data from heterogeneous sources, aggregate those and create a normalized and a unified structure for the purpose of analysis, report generation and other business intelligence activities that look for patterns and insights within the data. Why not just analyze the data and create reports from the source transactional databases, you may ask?

First of all, different databases and sources of data have different and incompatible schemas, which means each data source has its own unique structure. While modern analytics and reporting tools can connect to multiple data sources using a technique known as blending, there are multiple issues with using this approach. Firstly, creating reports from transactional databases are subject to inaccuracies because of the constant nature of changing data within transactional sources of data. Then, the dissimilar structure of each data source allows for inaccurate and disjointed reports

Data warehouse resolve these problems by normalizing the data and creating stable and cleaned-up copies of data ready for reporting and intelligence gathering.

Data warehouse paired with ECTL solves this problem.  The Extract, Clean, Transform, Load technique paves the way to provide a homogeneous structure from disparate sources of data, which in turn allows for analysis and interrogation of the data.

What You Need to Know About Data Analytics

No matter the size or the type, your organization uses some type and level of analytics. This could be as simple as Excel or Google sheets and pivot tables, or as complex as tabular and multidimensional tables.

Analytics come in three flavors: Descriptive, Predictive and Prescriptive.

Descriptive analytics is a summary of data points collected over different time periods. For instance, tallying requests for a specific product or grouping numbers of incidents by different categories are examples of descriptive analytics.

With the advent of machine learning and the ability to collect massive amounts of data, also known as big data, organizations can predict scenarios and behaviors.

Prescriptive analytics uses a combination of machine learning, business rules, artificial intelligence, and algorithms to recommend options for a given scenario.

Your organization may already have all the necessary tools to achieve business intelligence (BI). Even if it doesn’t, acquiring the right tools shouldn’t discourage you from reach the intended business goals. Various tools are available at either very little or no cost. It’s a matter of knowing what to look for and how to use those tools to accomplish the intended goals.

Simply put, making decisions without data these days is going to result in less than ideal decisions that could put your organization at a disadvantage.

Depending on the size and the level of maturity, your organization may need to take any one or some combination of the following actions:

  • Build a data warehouse, 
  • Better use of Excel or Google Sheets,
  • Use a visualization tool,
  • Build a database to store transactional data,
  • Or some combination.

Ask yourself. How has data benefited your organization’s mission, direction, and its competitive edge?

Call us if you’re uncertain about any of these and with a free consultation, you won’t lose a dime.