Using dashboards to get broad visibility into business is gaining popularity. Because of advancements in technology, organizations can use BI without a full business intelligence infrastructure. For organizations that want to keep historical records of operations, meet compliance, identify risk, implement governance initiatives, etc. implementing a data warehouse becomes essential. When companies look for their first data warehouse, they may not know where to start. Some general considerations include:
- Identifying the business purpose – Any BI project or data warehouse evaluation needs to start with a business pain or the identification of a gap within the business. Whether this includes the ability to identify trends over time, increase analytics capabilities, or meet compliance requirements, it is impossible to successfully implement a data warehouse without defining a business need.
- Data sources – Looking at where data comes from and…
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Using dashboards to get broad visibility into business is gaining popularity. Because of advancements in technology, organizations can use BI without a full business intelligence infrastructure. For organizations that want to keep historical records of operations, meet compliance, identify risk, implement governance initiatives, etc. implementing a data warehouse becomes essential. When companies look for their first data warehouse, they may not know where to start. Some general considerations include:
- Identifying the business purpose – Any BI project or data warehouse evaluation needs to start with a business pain or the identification of a gap within the business. Whether this includes the ability to identify trends over time, increase analytics capabilities, or meet compliance requirements, it is impossible to successfully implement a data warehouse without defining a business need.
- Data sources – Looking at where data comes from and how many sources are required may affect overall solution choice. In addition to the purposes behind implementing a data warehouse mentioned above, many companies require a full view of what is happening within the organization that the use of operational systems doesn’t give them. For instance, information related to customers and their overall lifecycle might reside in multiple systems. Within a data warehouse, this data can be consolidated so that decision makers can see customer actions over time and link them to marketing campaigns, identify general trends in demographics, or identify potential gaps in operations.
- Data volumes – How much data and how often data needs to be updated can affect solution choice. Some data warehousing solutions are optimized for larger data sets, while others pride themselves on query performance. There really is no one size fits all solution when it comes to data warehousing so organizations should try to match their business requirements to the solutions available in the market without trying to implement something that is outside the scope of their needs.
- Outputs – Identifying the output required means looking at whether the data warehouse will be used as a business intelligence back-end, to stream operational data for general analytics, or to perform extensive analytics. Information requirements for general reporting or dashboards will be different than those used for predictive analytics or risk identification and mitigation.
These four items don’t represent all of the considerations when looking at a data warehousing solution, but do identify some preliminary requirements that should be considered to identify the right solution for the organization.
