Data Governance and Data Integration using SAS
Sankhyana Consultancy Services Pvt. Ltd.
Data Driven Decision Science
What is Data Integration?
Data governance (DG) is the process of managing the availability, usability, integrity, and security of the data in enterprise systems predicated on internal data standards and policies that withal control data utilization. Efficacious data governance ascertains that data is consistent and trustworthy and doesn't get misused. It's increasingly critical as organizations face incipient data privacy regulations and rely more and more on data analytics to avail optimize operations and drive business decision-making.
A well-designed data governance program typically includes a governance team, a steering committee that acts as the governing body, and a group of data stewards. They collaborate to engender the standards and policies for governing data, as well as implementation and enforcement procedures that are primarily carried out by the data stewards. Ideally, executives and other representatives from an organization's business operations take part, in integration with the IT and data management teams.
What are the benefits of data governance?
1.????Make better, more timely decisions
Users throughout your organization get the data they require to reach and accommodate customers, design and ameliorate products and accommodations and seize opportunities for incipient revenues.
2.????Improve cost controls
Data helps you manage resources more effectively. Because you can eliminate data duplication caused by information silos, you don’t overbuy—and must maintain—expensive hardware.
3.????Enhance regulatory compliance
An increasingly intricate regulatory climate has made it even more consequential for organizations to establish robust data governance practices. You eschew risks associated with non-compliance while proactively anticipating incipient regulations.
4.????Earn greater trust from customers and suppliers
By being in auditable compliance with both internal and external data policies, you gain the trust of customers and partners that you will forfend their sensitive information, so they feel positive about doing business with you.
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5.????Manage risk more easily
With vigorous governance, you can allay concerns about exposure of sensitive data to individuals or systems who lack opportune sanction, security breaches from malevolent outsiders, or even insiders accessing data they don’t have the right to optically discern.
6.????Allow more personnel access to more data
Strong data governance allows more personnel access to more data, with the confidence that this personnel get access to the right data and that this democratization of data does not negatively impact the organization.
What is Data Integration?
Data integration is the process of bringing data from disparate sources together to provide users with an amalgamated view. The premise of data integration is to make data more liberatingly available and more facile to consume and process by systems and users. Data integration done right can minimize IT costs, free-up resources, ameliorate data quality, and foster innovation all without sweeping changes to subsisting applications or data structures. And though IT organizations have always had to integrate, the payoff for doing so has potentially never been as great as it is right now.
How does data integration work?
One of the most astronomically immense challenges organizations face is endeavoring to access and make sense of the data that describes the environment in which it operates. Every day, organizations capture more and more data, in a variety of formats, from a more immensely colossal number of data sources. Organizations need a way for employees, users, and customers to capture value from that data. This designates that organizations must be able to bring pertinent data together wherever it resides for the purposes of fortifying organization reporting and business processes.
But, required data is often distributed across applications, databases, and other data sources hosted on-premises, in the cloud, on IoT contrivances, or provided via 3rd parties. Organizations no longer maintain data simply in one database, instead of maintaining traditional master and transactional data, as well as incipient types of structured and unstructured data, across multiple sources. For instance, an organization could have data in a flat file, or it might want to access data from a web accommodation.
The traditional approach of data integration is kenned as the physical data integration approach. And that involves the physical kinetics of data from its source system to a staging area where cleansing, mapping, and transformation takes place afore the data is physically peregrinated to a target system, for example, a data warehouse or a data mart. The other option is the data virtualization approach. This approach involves the utilization of a virtualization layer to connect to physical data stores. Unlike physical data integration, data virtualization involves the engendering of virtualized views of the underlying physical environment without the desideratum for the physical kineticist of data.
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