Intelligent CIO Europe Issue 54 - Page 47

CIO OPINION
With operational data integration , the goal is to synchronise and replicate operational data and make it available across applications , systems and databases .
Analytical data integration involves the integration of data for all your systems of record , transforming and summarising data so it can be pushed to your analytics stack , which is usually comprised of a data warehouse and a business intelligence endpoint . order to ensure that all data is robust , free of errors , duplication and inconsistencies .
The risks of fragmented data
Research from Freeform Dynamics shows that 82 % of IT managers feel decision-making is hampered by data fragmentation , particularly data availability and inconsistency issues .
Hybrid data integration is – as the name suggests – a combination of operation and analytical data integration , where insights from BI tools are fed into operational systems to improve customer experience and drive operational efficiency .
Why is data integration important ?
This is evidence that CIOs need to ensure that all departments are aligned and the data integration process is followed carefully to avoid any mishaps . It will benefit the organisation , while largely avoiding the risk of data being managed by the dreaded shadow IT , which only causes further data fragmentation , with the added possibility of data loss .
As it goes , data integration has a number of benefits : aside from reducing costs , it can also reduce data complexity , make data more readily available , provide a seamless and easy data collaboration process and ensure smarter business decisions are made overall .
Also , when data is fragmented it is likely that it will be stored in multiple places , leaving no room for other information . This will also increase storage costs and cause ongoing overhead management , based on the complexity of handling large amounts of data .
Some reading this might say that they have ‘ done ’ data integration within their business already . However , data integration is an ongoing process , not a ‘ once and done ’ project . And when struggling with data overload and the feeling that different departments have started to build their own mini-siloes , a back-tobasics focus on data integration recalibrates the entire business . Alongside this , organisations can guarantee that their data will be cleansed and validated in
With operational data integration , the goal is to synchronise and replicate operational data and make it available across applications , systems and databases .
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