Common Data Integration Issues You Should Be Aware Of
Data integration can be defined as the process of combining data from different sources to form logical and valuable information by using both technical and business procedures. This process, however, comes with its own share of concerns. Here are key issues that you should focus on to guarantee a successful data integration process that yields information that can be trusted.
One of the major global concerns around technology today is cyber security. Cases of data leakage have cost many corporations hefty losses in fines and reputational damage. Therefore, when undertaking data integration, the processes and tools used should guard against possible loss or leakage of important information.
Unfortunately, when carrying out standard ETL procedures, many organizations find themselves dealing with distinct relational databases from which information is extracted to create a new database. Often, multiple tools are used to extract data and transform it into a new data silo. With more databases, there are increased opportunities for cybercriminals to exploit. Moreover, there are the ETL tools to worry about. These applications must have clean and secure code that do not leak any information.
Even as companies set out to extract data from various databases, there is the problem of defining what information will be extracted and from which source. This highly depends on the diversity of data available in various databases. You will also have to contend with the issue of cleaning data and ensuring that only useful information is extracted.
Remember that if any irrelevant information is passed over to the new database, the quality of data and its usefulness for critical decision making will be greatly undermined. To overcome these odds, experts should embrace data mining methodologies that are guided by the knowledge of the available data and ways of handling any irrelevant information that may exist therein.
Some of the methods commonly used to ensure fast data integration include memory partitioning, using disk arrays, CPU balancing, and system monitoring. However, in cases where large data sets are being processed, these methods may not be adequate as the system will evidently become slow. To ensure that your system is operating optimally, use tools that allow you to quickly assess its performance and initiate troubleshooting procedures when need be.
4.Multiple Data Sources
When it comes to data extraction, the main problem lies in determining which sources are to be used and what type of information to access. In many cases, experts end up with excess data that makes data mining processes extremely slow. For an efficient process, it is necessary to use tools that allow you to specify what information you want to collect from each source. This ensures that only relevant data is collected.