Why Data Integration Is Crucial For Big Data and Analytics Success?
Organizations are producing more and more data everyday, which has propelled the use of Big Data technologies. In today’s online business realm, data is the crucial factor companies rely on for high-end data analytics and decision-making.
What is Big Data?
According to Oracle - “Big data is larger, more complex data sets, especially from new data sources. These data sets are so voluminous that traditional data processing software can’t manage them. But these massive volumes of data can be used to address business problems you wouldn’t have been able to tackle before.”
Here are some mind-boggling facts about big data.
From the above statistics you can see that companies are willing to spend a tremendous amounts of time and money on Big Data to get valuable insights that can help enhance customer experience. But The quality of data and timely availability of data is crucial for any big data investment to succeed. This is where data integration plays a vital role.
Data Integration
Data integration combines data collected from various platforms to increase its value for your company. It enables your staff to collaborate more effectively and provide more for your clients. You cannot access the data collected in one system in another without data integration.
What are the business benefits of Data Integration?
Here are 4 benefits based on the projects we have worked on with various clients.
Increase the ROI of your CRM
Our client a top construction company in Minneapolis had acquired Procore (A cloud solution for construction project software) which they wanted to integrate with Oracle EBS. The master data was maintained in Oracle and needed to integrate data like Employee (team members), Vendors, Cost Issues, Projects and Commitments (contracts) with Procore to perform Change Events, which was the core module used by the business. There was compatibility issues between Procore and Oracle due to which the team had to manually enter data into both softwares which led to duplications.
With dataZap we were able to solve all the above mentioned problems and saw an increase in data quality of 30% and saved close to $100,000 annually.
Clean data is the backbone of your organization
Clean data is the basis for analytics and the decisions that management take. Ideally companies want their data to be clean but that is not the case. One of our clients faced a similar problem where their master data was not clean. They wanted a solution to first clean their data and ensure the data stays clean.
Enter Chainsys, we first used dataZen our Master Data management tool and then introduced dataZap which comes with prevalidation to ensure only clean data in uploaded into the master data.
Operational excellence and improved competitiveness
Companies will have data in multiple formats and in different places. It also will have large amounts of transactions happening. In many companies there is a time delay in integrating data from various sources. Such problem was faced a client of ours who is a leading lens manufacturer. They have Point-Of-Sale (POS) solutions in 78 locations with 10,000 transactions happening everyday. It was taking them 24 to 48 hrs to to integrate all this data into a single source.
dataZap was implemented as an enterprise-wide integration platform that can ingest and store this business knowledge to enable the integrations. The client saw immediate results where
Improved decision making
The reason for companies to spend a lot of money on big data and analytics to make the right decisions. But to make the right decisions they need quality data. One can regularly reconcile their master data but new data coming in will reduce the accuracy of any analytics program setup in the organization. One of our clients was doing a complete digital transformation moving from on premise to Oracle cloud. Businesses wanted all their current analytical reporting to continue without any hindrance and invest in a technology that will cater to their future needs.
Chainsys implemented dataZap and took a target for Data quality at 99% clean. Quality processes were instituted to achieve the same. Our data quality engine ensured total profiling and validation of all the data. The profiling process was repeated until data quality reached 99%.
Next step to ensure your big data project is a success
Now you know the reason "why data integration is crucial". Do you want to learn more about the particular advantages of data integration for your company?
