Thanks to innovations in mobile technology and WIFI, the creation and consumption of data continues to grow at an extraordinary rate. On average, every human created at least 1.7 MB of data per second in 2020 alone[1]. A large percentage of this data was created in, and for, the workplace. As businesses became inundated with massive amounts of data, they needed a single place to store and analyze this critical information. Enter data warehouses.
In computing, a data warehouse (DW or DWH), also known as an enterprise data warehouse (EDW), is a system used for reporting and data analysis and is considered a core component of business intelligence (BI)[2]. DWs are central repositories of integrated data from one or more disparate sources. They store current and historical data in one single place[3] that are used for creating analytical reports for workers throughout the enterprise[4].
The data stored in the warehouse is uploaded from the operational systems (such as marketing or sales). The data may pass through an operational data store and may require data cleansing[3] for additional operations to ensure data quality before it is used in the data warehouse for reporting.
You’d be surprised at how many people within an organization collect and analyze data to perform their daily duties. Data scientists and analysts leverage business intelligence to identify improvement opportunities and provide strategic recommendations to company leadership. Sales analysts analyze complex data to calculate discounts, measure CSAT, NPS and customer’s lifetime value. An operations manager analyzes data such as supply chain metrics to optimize processes and improve distribution routes.
Now imagine if all these people could access this critical information from multiple sources in one place through an easy-to-use dashboard at the same time. With marketing data in a single location, marketers can view real-time campaign metrics and improve overall performance for existing and future marketing activities. The financial team can blend their data with marketing, operations, and sales teams to pull valuable insights around cost and revenue and understand factors that impact profit and loss.
With companies being overwhelmed with a constant flow of data from multiple sources, it is difficult to truly leverage the critical insights this information can provide. Businesses need a consolidated, easy-to-use data repository that frees them from the burden of unifying data and helps them gain valuable business intelligence to enhance operations.
Evolve IP’s data warehouse solution provides customers the ability to access, analyze, and better understand the data generated by their Evolve IP UCaaS and CCaaS solutions. While customers can currently access much of this data by themselves, it resides in different locations. By aggregating these multiple, disparate reporting solutions into a single location directly accessible to customers across all voice platforms, customers save time, can better analyze their data, and discover critical insights beyond traditional reports to meet their team’s business needs.
By utilizing Evolve IP’s data warehouse solution, one of our customers leveraged their own business intelligence tools to analyze complex data and make predictions and strategic decisions around their specific telephony needs. This saved them time and money as they were able to better understand the impact of their telephony users on business operations and quickly react accordingly.
Here’s how they did it. The customer’s voice data, such as Call Detail Record (CDR), Call Center data, and SMS, is stored in a single, easily accessible location in Evolve IP’s data warehouse. Each row of that data is associated with an individual telephony user. From this data, the customer could clearly see the impact a particular telephony user had on their business.
Additionally, the customer benefited from Evolve IP adding an external field to this telephony user dimension, which allowed the customer to link Evolve IP’s direct data with their internal data. This provided insights not only on a single telephony user, but on all telephony users within the company. The comprehensive business intelligence gained from Evolve IP’s direct data also allowed our customer to better understand how their company performed based around a selected time frame and headcount. The customer could make real-time, data-backed predictions on how their business efficiency is impacted if they modified their staffing requirements. The customer now has a succinct way to merge two data sets and extrapolate key information easily to make better business decisions.
Business data generated and captured from multiple sources is one of the most valuable assets a company has at its disposal. However, the vast amount of data a business receives at any point in time can quickly become overwhelming to manage and critical insights are lost. By leveraging a powerful BI solution like Evolve IP’s Data Warehouse, teams can focus on analyzing issues, optimizing solutions, and pulling rich insights instead of spending their time retrieving and compiling data. In the case of data warehouses, knowledge truly is power.
For more information on data warehouses and Evolve IP’s data repository, download our Data Sheet here.
[1] “Data Never Sleeps 6.0”, Domo, https://www.domo.com/assets/downloads/18_domo_data-never-sleeps-6+verticals.pdf#:~:text=By%202020%2C%20it%E2%80%99s%20estimated%20that%20for%20every%20person,are%20just%20getting%20started.%20DATA%20NEVER%20SLEEPS%206.0 Retrieved 2021-11-01.
[2] Dedić, Nedim; Stanier, Clare (2016). Hammoudi, Slimane; Maciaszek, Leszek; Missikoff, Michele M. Missikoff; Camp, Olivier; Cordeiro, José (eds.). An Evaluation of the Challenges of Multilingualism in Data Warehouse Development. International Conference on Enterprise Information Systems, 25–28 April 2016, Rome, Italy (PDF). Proceedings of the 18th International Conference on Enterprise Information Systems (ICEIS 2016). 1. SciTePress. pp. 196–206. doi:10.5220/0005858401960206. ISBN 978-989-758-187-8.
[3] "9 Reasons Data Warehouse Projects Fail". blog.rjmetrics.com. Retrieved 2017-04-30.
[4] "Exploring Data Warehouses and Data Quality". spotlessdata.com. Archived from the original on 2018-07-26. Retrieved 2017-04-30.