Quality of The Source Data Is Not Known
In most organizations, the quality of the operational datais either unknown or grossly overestimated Skills Are Not Available
It is rare that the team initially has the right number of people in the right roles with the right skills, and that they are available at the right time Inadequate Budget
It is often difficult to know in advance how much a data warehouse will cost. Data warehouse budgets are often underestimated Lack of Supporting Software
In many cases, supporting software (ETL, cleansing, BI tools, RDBMS, etc.) have not been chosen or have not been installed in time Source Data Not Understood
Most organizations do not have a documented understanding of the source data Weak Sponsor
For the project to succeed it needs a strong, well-placed user sponsorwho makes reasonable decisions. Solicit the best – Research Users Not Comfortable with Technology
There will always be users who, based on their experience and their willingness, will be open to try something new. However According to the study, there are four key obstacles recurring in most businesses which are stalling data warehousing progress and success. These are
disconnected data silos, slow loading of the data warehouse, time-consuming data preparation processes, and a need for more automation of their core data management activities.
Common Issues Data Teams Face With Traditional Data Warehousing
- Data Quality It can be difficult to maintain data quality in a traditional data warehouse structure. ...
Top 5 Challenges of Data Warehousing
- Ensuring Acceptable Data Quality More often than not, a data warehouse consumes data from disparate sources. ...
- Ensuring acceptable Performance Prioritizing performance ...
Implementing a data warehouse is generally a massive effort that must be planned and executed according to established methods.
Construction, administration, and quality control are the significant operational issues which arises with data warehousing.