The Data Warehouse Explained: Benefits, Challenges, and Predictions
Overview
What is a Data Warehouse?
A Data Warehouse, or DW, is a system used for reporting and data analysis and is considered a core component of business intelligence. They store current, future, and old data for the user, to produce, later, analytical reports that are based on them. They are essential for any enterprise, as they act as a log that is fundamental in creating an enhanced future path for the company. This article will provide an overview of the data warehouse concept, review its main processes and use cases, mention some benefits and challenges of the data warehouse, and suggest a few cool predictions for data warehouses in 2018.
Main Processes & Use Cases
There are different versions and updates of data warehouses coming to the data industry every day. I am here to cover a few concepts related to data warehousing. To be clearer and more focused, we will view the main data warehouse concepts: BI, ETL and Analytics tools.
BI stands for Business Intelligence, and it is a tool used to make fact-based and insightful decisions that can enhance the client’s company’s performance. The data can be read from different sources, not exclusive to a data warehouse, to generate a report that brings the data to life.
ETL is known as the process in which the data is extracted from the data source and brought into the data warehouse. It stands for Extraction, Transformation, and Loading.
Analytics tools have three different types of analytics – Descriptive, Predictive, and Perspective analytics. Each of the different types offers a different kind of information for the company.
The three types can be used differently and for distinct purposes. A company might wish to get a visualization of their data, in which case they can use Charito – being powerful and richly usable for data pros. The company might want to keep all their data in one place and get a descriptive report, in which case they can use Blendo – a tool that uses ETL to consolidate all the data in one place and analyzes it in an innovative way.
Benefits & Challenges
Data warehouses have advanced a lot since the usage of the cloud system as a platform for some applications. The shift meant that companies can rely on external platforms to not only store and manage their data for them but to also read, analyze and organize their data to generate reports that can warn the company for a coming downfall or suggest a path for improving the company’s performance. Some of the benefits can be: Improved communications within the company itself, upgraded security, lowering the cost of data storage and maintenance and an increase in the revenues; that was affected by the evaluations done by Cloud Data Warehouses.
Some of the challenges that might come along using a data warehouse are:
Facing errors when combining inconsistent data from disparate sources.
Data quality challenges can come from duplicates, logic conflicts, and missing data.
Poor data quality can also generate faulty reports that might lead companies to take unproductive moves suggested by the reports.
All of those challenges can be spotted, recognized and fixed with an efficient system of data management and a supervised self-evaluation system.
Predictions
The future of technology is understandably hard to predict, but that doesn’t mean that viewers can’t give an insight into how might the future look like for data warehouses.
Over the past years, data warehouses have been revolutionized repeatedly, especially with the Cloud services being adopted by many platforms. New trends are mainly focused on Artificial Intelligence and Machine Learning, as they seem to be promising and fruitful. This can be groundbreaking for many companies, as this suggests that Data Warehouse platforms might be able to provide live dashboards in the future, helping businesses to monitor their performance at every second.
For more info check this blog.
Moreover, the Machine Learning part means that there will be algorithms created, to perform the analysis on the data supplied, but this algorithm will advance and learn from its mistakes. Furthermore, it can be customized to each company, for example, an algorithm can be fed with the historical records of the company with all the past data, so the algorithm can see what works for this company and what doesn’t.
On the other hand, working on Predictive analytics seems to be a top priority for some platforms and their clients. Enhancing the way we understand the present and the past, to be able to predict with higher precision and reliability for the future. This can have a big impact on the market in general, as companies would better understand their customers and use that to deliver better services where needed.
For a cool perspective, check this blog post!
Closing Thoughts
Data Warehousing is an essential step in the process of the development of any company around the globe. It reads and analyzes data, with massive scales, to build a deeper understanding of the company’s performance, past mistakes, and achievements and utilize them to generate a reliable and precise model that makes safe predictions for the future, and suggests new improvements in the company’s work. It has different types: BI, ETL and Analytical tools, each with its own mechanism and uses.
Data Warehousing has valuable benefits that include: better communications, higher security measures, and lower budgets for data management and storage. Some of the challenges might occur with inconsistent data, duplicates, logical contradictions, and poor-quality data. However, the future of DW seems to be bright, with lots of technologies being embedded in the modern platforms, including AI, machine learning, and predictive analytics.
Companies will realize, in the close future, the necessity of cloud data warehousing, as it simplifies the whole process and allows companies to use their resources in a more productive way.
References
An evaluation of the Challenges of Multilingualism in Data Warehouse Development
http://www.scitepress.org/Papers/2016/58584/index.html
Types of Analytics: descriptive, predictive, perspective analytics.
https://www.dezyre.com/article/types-of-analytics-descriptive-predictive-prescriptive-analytics/209
Top 10 Tools for a Dangerously Effective Data Stack in 2018
https://blog.panoply.io/the-top-10-tools-for-a-dangerously-effective-data-stack-in-2018
Enterprises Eye Big Benefits from Cloud Data Warehouse
https://blog.panoply.io/enterprises-eye-big-benefits-from-cloud-data-warehouses
7 Challenges to Consider when Building a Data Warehouse
http://www.onapproach.com/7-challenges-consider-building-data-warehouse/