More than 2.5 quintillion bytes of data—as much as 250,000 times the printed material in the U.S. Library of Congress—come into existence every day. What this data means for the average enterprise is opportunity: the opportunity to improve fraud protection, compliance and personalization of services and products.
But first, you need to make sure you are working with the right data and that your data is consistent and clean.
While data governance itself is not a new concept, the need for significantly better data governance has grown with the volume, variety and velocity of data. With this need for better data governance has come a need for better databases. Before we get into that, let’s make sure we’re clear on what data governance is and how it’s used.
Data governance is the establishment of processes around data availability, usability, consistency, integrity and security, all of which fall into the three pillars of data governance.
In an age when data silos run rampant and “bad data” is blamed for nearly every major strategic oversight at an enterprise, it’s critical to have someone or something at the ready to ensure business users have high-quality, consistent and easily accessible data.
Enter data stewardship and the “data steward.” A data steward ensures common, meaningful data across applications and systems. This is much easier said than done, of course, and quite often the problems with data stewardship arise from a lack of clarity or specificity around the data steward’s function, as there are many ways to approach it (i.e., according to subject area, business function, business process, etc.).
Nevertheless, properly stewarding data has become a key ability for today’s enterprises and is a key aspect of proper data governance at any organization.
Where data governance itself is the policies and procedures around the overall management of usability, availability, integrity and security of data, data quality is the degree to which information consistently meets the expectations and requirements of the people using it to perform their jobs.
The two are, of course, very intertwined, although data quality should be seen as a natural result of good data governance, and one of the most important results that good data governance achieves.
How accurate is the data? How complete? How consistent? How compliant? These are all questions of data quality, and they are often addressed via the third pillar of data governance: master data management.
Master data management, or MDM, is often seen as a first step towards making the data usable and shareable across an organization. Enterprises are increasingly seeking to consolidate environments, applications and data in order to:
MDM is a powerful method used to achieve all of the above via the creation of a single point of reference for all data.
Considering the recent Facebook fiasco with personal data, and with big regulations like the General Data Protection Regulation (GDPR) now in effect, it’s impossible to understate the importance of data governance.
NoSQL databases were designed with modern IT architectures in mind. They use a more flexible approach that enables increased agility for development teams, which can evolve the data models on the fly to account for shifting application requirements. NoSQL databases are also easily scalable and can handle large volumes of structured, semi-structured and unstructured data.
Graph databases can be implemented as native graphs, while non-native graph databases, which are slower, store data in relational databases or other NoSQL databases (such as Cassandra) and use graph processing engines for data access. Graph databases are well-suited for applications traversing paths between entities or where the relationship between entities and their properties needs to be queried.
This relationship-analysis capability makes them ideal for empowering solid data governance at organizations of all types and sizes. From fraud protection to compliance to getting a complete view of the customer, a NoSQL graph database makes data governance much easier and much less costly.
To learn more about how to use a NoSQL graph database for data governance, click here.
Senior Account Executive, OrientDB, an SAP Company