Working with data in today's companies, where numerous systems and applications are in use, it's essential to have a clear strategy for data standardization and validation. This approach ensures that only high-quality data circulates within processes, thereby accelerating data flow and facilitating rapid, high-quality decision-making.
In a classic situation, a company receives daily or weekly dozens and hundreds of emails, PDF documents, CAD (BIM) design files that need to be integrated into the company's business processes. The forest of systems must receive nutrients, in the form of multi-format data, to produce the results the company needs.
Ensuring the healthy vitality of a company's ecosystem requires a rapid and quality supply of resources to all of its systems
Since data has different structures and content, it often seems that such a task of streaming and integrating incoming data simply cannot be physically handled without utilizing a large number of specialists and managers.
To effectively deal with the flow of data, it is not necessary to hire an army of managers, the first thing to do is to develop strict data requirements and use appropriate tools to validate, unify and process them.
Data quality is described through the data requirements of a particular system. To work with data, it is necessary to clearly understand what data specialists need to receive and what requirements the company has for the incoming data to be used in its systems. The same process happens in the "Gather Business Requirements" phase of data modelling and creating databases and tables that we discussed in the previous chapters. Only often databases and tables already exist in the company, and we have to re-build the Business Requirements to work with them according to the new rules.
Data requirements describe criteria for the quality, structure and completeness of the information received and processed. This includes, for example, the accuracy of technical data in a PDF document or compliance with industry standards. Elements- entities from CAD (BIM) formats require checks to meet certain technical attributes, while checks in the form of scanned images require the correct date and the total amount attributes of the contract.
Data validation and quality assurance is the most costly and difficult step in preparing data for integration into other systems
Successful data management in a construction company requires a comprehensive approach that includes creating data quality requirements and utilizing the right tools to validate them.
