Databases Reference
In-Depth Information
3.
Monitoring compliance with the data policy. In the
abovementioned example, the organization might use
text analytics tools to identify instances where call center
agents' notes contain social security numbers.
4.
Defining acceptable thresholds for data issues. In the
example, the information governance team might
determine that the acceptable threshold needs to be zero
instances because of the potential privacy implications of
having social security numbers in clear text.
5.
Managing issues especially those that are long-lived and
affect multiple functions and lines of business. Taking the
example further, the information governance team might
create a number of trouble tickets so that the customer
service team can eliminate any mentions of social
security numbers within agents' notes.
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Big Data Warehouses and Enterprise Data Warehouse.
As
organizations adopt big data, they will increasingly follow a hybrid
approach to integrate Hadoop and other NoSQL technologies
with their traditional data warehousing environments.
•
Big Data Analytics.
Analytics models will increasingly
incorporate big data types. Besides development of sophisticated
algorithms for structured data that can work on large volumes of
data, you will need analytics capabilities for the unstructured data
types as well. Concepts like social listening specialized analytics
on streaming data are critical for the big data platforms.
•
Big Data Reporting and Advanced Data Visualization.
Traditional reporting solutions will not work on the scale and
variety of data types as big data. You will need advanced data
visualization solutions to visualize and analyze big data.
•
Big Data Lifecycle Management.
Information lifecycle
management (ILM) is a process and methodology for managing
information through its lifecycle, from creation through
disposal, including compliance with legal, regulatory, and
privacy requirements. The components of a big data lifecycle
management platform are listed below:
1.
Information archiving.
As big data volumes grow,
organizations need solutions that enable efficient
and timely archiving of structured and unstructured
information while enabling its discovery for legal
requirements, and its timely disposition when no longer
needed by the business, legal, or records stakeholders.
