Regardless of your industry or function, the ability to access, analyze, and make use of your data is essential. For many organizations, however, data is scattered throughout the organization in various applications (data silos), often in a format that’s unique to that system. The result is inconsistent access to data and unreliable insights. Some organizations may have a data management solution in place, such as a legacy or on-premise data warehouse, that is not able to keep up with the volume of data and processing speeds required for modern analytics tools or data science initiatives. For organizations striving to become data-driven, these limitations are a major roadblock.
The solution for many leading companies is a Modern Data Warehouse.
Over the course of several blogs, we’ll tap into our extensive data warehouse experience across industry, function, and company sizes to guide you through this powerful data management solution.
In this series of blogs, we’ll:
- Define The Modern Data Warehouse
- Outline The Different Types Of Modern Data Warehouses
- Illustrate How the Modern Data Warehouse Fits in the Big Picture
- Share Options on How to Get Started
A modern data warehouse, implemented correctly, will allow your organization to unlock data-driven benefits from improved operations through data insights to machine learning to optimize sales pipelines. It will not only improve the way you access your data but will be instrumental in fueling innovation and driving business decisions in all facets of your organization.
Part 1: What is a Data Warehouse?
At its most basic level, a data warehouse stores data from various applications and combines it together for analytical insights. The integrated data is then evaluated for quality issues, cleansed, organized, and modeled to represent the way a business uses the information – not the source system definition. With each business subject area integrated into the system, this data can be used for upstream applications, reporting, advanced analytics, and most importantly, for providing the insights necessary to make better, faster decisions.
Mini Case Study:
A great example of this is JD Edwards data integration. Aptitive worked with a client that had multiple source systems including; several JDE instances (both Xe and 9.1), Salesforce, TM1, custom data flows, and a variety of flat files they wanted to visualize in a dashboard report. The challenge was the source system definitions from JDE, with table names like “F1111”, Julian-style dates, and complex column mapping, it was nearly impossible to create the desired reports and visualizations.
Aptitive solved this by creating a custom data architecture to reorganize the transactional data, centralize, and structure the data for high-performance reporting, visualizations, and advanced analytics.
Image 1: The image above illustrates a retailer with multiple locations each with a different point of sale system. When they try to run a report on the numbers of units sold by state directly from data housed in these systems, the result is inaccurate due to data formatting inconsistencies. While this is a very simple example, imagine this on an enterprise scale.
Image 2: The image above shows the same data being run through an ETL process into a data warehouse. The result is a clear and accurate chart with the business users’ needs.
Data warehouses then . . . and now
There was a time when a data warehouse architecture consisted of a few source systems, a bunch of ELT/ETL (extract, transform, load) processes, and several databases, all running on one or two machines in an organization’s own data center. Companies would spend years building out this architecture with custom data processes that were used to copy and transform data from one database to another.
Times have changed and traditional on-premise data warehousing has hit its limits for most organizations. Enterprises have built data warehouse solutions in an era where they had limited data sources, infrequent changes, fewer transactions and low competition. Now, the same systems that have been the backbone of an organization’s analytical environment are being rendered obsolete and ineffective.
Today’s organizations have to analyze data from many data sources to remain competitive. In addition, they are also addressing an increased volume of data coming from those data sources. Beyond this, in today’s fast-changing landscape, access to near real-time or instantaneous insights from data is necessary. Simply put, the legacy warehouse was not designed for the volume, velocity, and variety of data and analytics demanded by modern organizations.
If you depend on your data to better serve your customers, streamline your operations, and lead (or disrupt) your industry, a modern data warehouse built on the cloud is a must-have for your organization. In our next blog, we’ll dive deeper into the modern data warehouse and explore some of the options for deployment.
Jason Maas is the COO of Aptitive and head of the Data Practice. He is a seasoned technology leader with experience spanning several industries such as transportation, accounting, insurance, retail, software, healthcare, and financial services.