Data volumes are continuing to grow and the use of streaming analytics therefore is becoming even more widespread, so many companies are beginning to realize that existing Data Governance practices must also evolve to remain relevant.
“More is being asked of an organization’s governance platform than ever before,” says industry insider Robert Chernesky, about the importance of transitioning to Data Governance 3.0:
“The evolution of Data Governance spans a continuum, and each step builds upon the previous step in order to progress towards an automated Data Governance program focused on business process automation … Although the technical lineage is extremely valuable and necessary, the organization is hamstrung unless it can evolve Data Governance to a business audience who is yearning to understand data governance from a business context.”
Governance: The Regulatory Framework for Data Management
These days, Data Governance often focuses on regulatory compliance, encompassing multiple data functions such as Data Quality, Master Data Management (MDM), Metadata Management, and data security. An organization’s Data Governance strategy charts out the rules and regulations of data naming, sharing, storing, and processing, and defines the different roles and responsibilities assigned for handling governance tasks within an organization.
A Data Governance strategy becomes all the more critical when an organization chooses to move to a pure cloud or hybrid cloud model. In that scenario, governance plays an important role in mapping out a secure data migration process through governed policies and procedures. The ultimate objective of a sound Data Governance strategy is to ensure smooth and efficient sharing and management of data across the enterprise.
In The Evolution of Data Governance, Olivia Hinkle predicts Data Governance will become even more important to the enterprise as data proliferates in the years to come:
“Data Privacy, Data Security, and Data Governance are all tightly intertwined, and each must be executed properly to achieve business success … especially as new advancements in technology, new requirements around privacy, and new trends in consumer behavior emerge.”
Streaming Analytics: Technology for Dealing with IoT Data
Data Governance also plays an important role in analytics related to the Internet of Things (IoT) device data, which broadly means data generated by sensors, smart meters, tags, live social data, and other connected devices. The significance of this type of data is that it is available in real time – and many different technologies like big data, Hadoop, NoSQL, and in-memory processing converge to prepare the streaming data for both real-time and later analytics. The consistently high investments in streaming analytics by businesses indicate the relative importance that “sensor data” analytics receive in corporate budgets. This trend is likely to continue in a hyper-connected business ecosystem.
Most business functions, including sales,
customer service, operations, and manufacturing, use IoT devices to collect and
conduct real-time or near real-time analytics. As IoT data is quite different
from conventional business data (for example, historic or descriptive data), it
has to be handled differently in terms of collection, storage, cleaning, and
parsing. In most cases, IoT data is unstructured, containing mixed text,
images, and even video footage.
Data Governance and
Streaming Analytics: A Necessary Duo
In Enterprise Analytics Trends, Evan Terry notes that:
“Enterprise analytics technology is increasingly sophisticated, due to machine learning, artificial intelligence, and streaming analytics, but you can’t benefit from these advancements without a solid foundation.”
That includes “brilliant basics” such as
Data Quality and Data Governance.
In the current business landscape, more
and more business owners and operators cannot think of functioning without
streaming analytics. Data Governance and streaming analytics are closely linked,
since well-governed analytics means more business users can trust the results.
Whether it’s preventive maintenance in manufacturing units, timely monitoring of critical patients, location-based marketing for retailers, accurate weather predictions, or the self-driving car, all modern analytics use cases are entwined with streaming analytics. Streaming analytics enable data to be collected from sensors and analyzed in real time for immediate action. Building a data-driven culture goes a long way in ensuring Data Governance benefits the enterprise analytics efforts.
Transforming Streaming Analytics with Data Governance
With streaming analytics, incoming data is processed via smart data models and algorithms so quickly that in many cases, the streaming data does not get an opportunity to get stored. This is an important change from conventional analytics process. Another significant characteristic of streaming analytics is that during the data processing phase, the relevance and urgency of action is instantly determined for generating alerts.
Managing large data volumes is not an easy task, especially when the data volume is exploding. On one end of the spectrum, you are witnessing the growth of advanced analytics platforms to tackle streaming data; on the other hand, your business may be dealing with the challenges of large-volume data access and sharing, while adhering to the data privacy regulations like GDPR or CCPA. Indeed, the recent growth of privacy regulations have slowed down enterprise analytics and machine learning projects, which typically benefit streaming analytics. Businesses are looking for end-to-end Data Governance solutions to handle and process large volumes of data without violating strict regulations.
In Data Streaming: Seven Unexpected Paths It’s Taking Today, Ori Rafael emphasizes the role of Data Governance:
“Today, as interest grows in Data Science and advanced analytics, and as SaaS and web and mobile applications continue to flourish, the number and range of companies using data streaming has expanded … The complexity of streaming data means significantly more emphasis needs to be placed on Metadata Management, Data Governance, and ETL testing, compared to traditional relational databases.”
In such a case, knowing the source of
the data, the access controls, and the details of data usage becomes critical.
That is the function of Data Governance in a typical streaming analytics use
case. The centralized database is often used to mitigate the challenges of Data
Governance – namely, data flows and data correlation tasks by analysts. A data warehouse
can also come handy in implementing a sound Data Governance strategy in a large
As data lakes expand and streaming data increases, Data Governance policies have to continuously adapt and evolve, says Thomas Brence in Transforming Analytics with Data Governance:
“By helping to build a data-driven culture that focuses on collaborating instead of controlling, and by choosing the right level of governance for your data lake, your analytics projects can succeed with trusted, governed data at their core. This level of trust will help to ensure that your analysis can be relied upon throughout your organizations to make major business decisions and fuel strategic initiatives.”
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