The more digital our world becomes, the more data we create. The process of digital transformation will not reach its full potential if an organization does not properly analyze the data that each digital solution collects. A data repository provides deep data analysis and helps organizations derive valuable insights from terabytes of diverse data sets.
Data management comes with many challenges. But the right data warehouse can help. Below are common problems that data warehouses can solve.
- Scattered data in isolated systems. Business data scattered across multiple databases is a serious bottleneck to effective data management and analysis. Data warehousing platforms serve as a single source of truth for your organization and help prevent meaningful data from being trapped in isolated legacy systems.
- Complex data access. The more data access points you have, the harder it becomes and the longer it takes to find the data you need. A data repository allows your analytics team to easily access, through a single point of entry, any datasets they need to discover dependencies between data elements. In addition, a central data repository makes it easy to manage access permissions.
- Limited data analysis capabilities. If you’ve been using a particular approach to data analysis for years, you may have reached a place where your data reports no longer meet your current business needs. Creating a data warehouse makes it easy to analyze your business data from multiple perspectives. Without a data warehouse, you will have to spend much more time creating separate pipelines to extract data directly from your corporate systems.
- Limited data scalability. As your business grows, so does the amount of data. It becomes harder to even store this ever-increasing data stream, let alone catalog and analyze it. You can implement a data warehouse that can scale with the growing volume of data to ensure that you never run out of capacity to store and analyze business data.
- Lack of unified access to data from different sources. Today, anything can be a source of data: news websites, Twitter posts, Instagram reactions, IoT, and telematics devices. With modern data warehouse solutions, such as those provided by AWS and Microsoft Azure, you can integrate data from external sources (as described above) and internal sources (your ERP, CRM, etc.). This allows you to effortlessly look at your business from different perspectives.
- Inefficient decision-making process. Ideally, every business decision should be supported by reliable facts. A data-driven business environment can allow executives to be more confident, more ambitious, and make less risky decisions. So, the more data you collect and analyze, the better foundation you’ll have to achieve better business results.
Now that we’ve looked at the general definition of data warehouses, let’s discuss each type of data warehouse individually to determine their differences, similarities, and use cases. The best data warehouses all have one goal in common: to give your organization more data analytics capabilities.