Data warehousing is used to gather data from different sources to form a singular database that provides a holistic view. It is particularly useful for analysts to gather insights more easily for better business decision-making.
However, many businesses face certain hurdles while trying to incorporate data warehousing for business intelligence (BI).
Also Read: Demystifying “Data Governance vs Data Management”
Discover potential hurdles with integration and solutions to help you leap them.
Businesses can address these challenges by utilizing the potential solutions given below.
Integrating Data to One Database
There could be many different sources from where a small company collects and receives data. But combining the information to create a comprehensive database can be challenging.
Many firms have data across a range of spreadsheets, CRM, and more.
How to Resolve This
The first step is to identify all the critical sources of data. Then, it must be checked whether they will be compatible for transfer.
By utilizing ETL (Extract, Transform, Load) tools, data can be easily extracted and converted to create a singular database.
Maintaining Data Quality
BI depends on having quality data to generate smarter analytics for better decision-making. Thus, it is important to maintain data quality.
Unfortunately, any sort of errors and anomalies in the data can lead to ineffective BI tools.
How to Resolve This
Before working with data, it is important to clean it. That means removing duplicates, blanks, filters, errors, and anything that incorrectly impacts reporting.
It is also important to set specific standards and criteria to ensure data quality. Regular audits of data should be conducted for better data governance.
Changes in Needs
With the growth of the business, the data warehousing needs will change. Naturally, the firm has to have the capacity to efficiently scale their data infrastructure.
Many firms struggle when it comes to flexibility and scalability requirements.
How to Resolve This
Cloud infrastructure can effectively help manage and store data. For businesses with limited resources, it can be done without the need for a high initial investment.
Additionally, the data infrastructure itself can be designed in a manner that can handle expansion in the future. This type of architecture allows a firm to scale components as required.
Resistance to Change by Employees
New technology and changes in operations always invite resistance from some employees. Adopting and getting used to new BI systems can be a potential issue.
How to Resolve This
Providing adequate training to each personnel is key. Make sure they clearly understand the reasons and benefits of the new system.
Try to expand at a slow and steady pace with changes rolled out over time rather than all at once.
Additionally, the data infrastructure itself can be designed in a manner that can handle expansion in the future. This type of architecture allows a firm to scale components as required.
