Good to know

Data Sovereignty

Data sovereignty is the concept that information which has been converted and stored in binary digital form is subject to the laws of the country in which it is located.

Many of the current concerns that surround data sovereignty relate to enforcing privacy regulations and preventing data that is stored in a foreign country from being subpoenaed by the host country’s government.

The wide-spread adoption of cloud computing services, as well as new approaches to data storage including object storage, have broken down traditional geopolitical barriers more than ever before. In response, many countries have regulated new compliance requirements by amending their current laws or enacting new legislation that requires customer data to be kept within the country the customer resides.

Verifying that data exists only at allowed locations can be difficult. It requires the cloud customer to trust that their cloud provider is completely honest and open about where their servers are hosted and adhere strictly to service level agreements (SLAs).

Data Stewardship

Data stewardship is the management and oversight of an organization’s data assets to help provide business users with high-quality data that is easily accessible in a consistent manner.

While data governance generally focuses on high-level policies and procedures, data stewardship focuses on tactical coordination and implementation. A data steward is responsible for carrying out data usage and security policies as determined through enterprise data governance initiatives, acting as a liaison between the IT department and the business side of an organization.

Some organizations have created formal data steward positions, in many cases filling them with workers drawn from business units, while others assign stewardship responsibilities to employees who have other duties as well. A data steward might function as both a “data coordinator” who tracks the movement of data inside an organization and a “data corrector” who understands and enforces internal rules on how data can be used. Regardless of how the position is structured, an effective data steward maintains agreed-upon data definitions and formats, identifies data quality issues and ensures that business users adhere to specified standards.

A corporation may use a data stewardship program as part of its overall data lifecycle management effort and/or to help with data quality improvement projects. A data steward will often collaborate with data architects, business intelligence developers, extract, transform and load (ETL) designers, business data owners and others to uphold data consistency and data quality metrics. Data quality tools, including data profiling software, are key technology components of many data stewardship programs.

Operational Intelligence (OI)

Operational intelligence (OI) is an approach to data analysis that enables decisions and actions in business operations to be based on real-time data as it’s generated or collected by companies. Typically, the data analysis process is automated, and the resulting information is integrated into operational systems for immediate use by business managers and workers.

OI applications are primarily targeted at front-line workers who, hopefully, can make better-informed business decisions or take faster action on issues if they have access to timely business intelligence (BI) and analytics data. Examples include call-center agents, sales representatives, online marketing teams, logistics planners, manufacturing managers and medical professionals. In addition, operational intelligence can be used to automatically trigger responses to specified events or conditions.

What is now known as OI evolved from operational business intelligence, an initial step focused more on applying traditional BI querying and reporting. OI takes the concept to a higher analytics level, but operational BI is sometimes still used interchangeably with operational intelligence as a term. Read more…

Data Management Platform (DMP)

A data management platform (DMP), also referred to as a unified data management platform (UDMP), is a centralized system for collecting and analyzing large sets of data originating from disparate sources.

A DMP creates a combined development and delivery environment that provides users with consistent, accurate and timely data. At its simplest, a data management platform could be a NoSQL database management system that imports data from many systems and enables marketers and publishers to view the data in a consistent manner. A high-end DMP might combine data management technologies and data analytics tools into a single software suite with an intuitive and easy-to-navigate executive dashboard.

A key role of a data management platform is to collect structured and unstructured data from a range of internal and external sources, and to then integrate and store that data. These platforms also analyze and organize data to provide insight to data-driven parts of the business, such as marketing and advertising campaigns.

Data incorporated into a data management platform can be first-party data — coming from an organization’s own applications, systems, websites and products — as well as second-party data from partners and other associates. In addition, DMPs use third-party data to fill in holes in a company’s own data and partner data.

The roster of data management platform vendors focused on marketing and publisher clients is long, with some bigger name vendors getting into the technology more recently through acquisitions. These offerings include Adform DMP, Adobe Audience Manager, KBM Group’s Zipline, Lotame, MediaMath DMP, Neustar Identity DMP, Nielsen eXelate, Oracle BlueKai, Salesforce DMP (formerly Krux) and Turn Digital Hub for Marketers. Read more…

Data Integration

Data integration is the process of retrieving data from multiple source systems and combining it in such a way that it can yield consistent, comprehensive, current and correct information for business reporting and analysis. Integration is the act of bringing together smaller components into a single system that functions as one. In computing, the source systems may be various types of digital devices and the data may be in a variety of formats.

Important data integration techniques include:

Data consolidation: Data is copied from multiple sources and integrated into a single data store.

Data propagation: Applications copy and push data from one location to another.

Data federation: Data federation software enables an organization to aggregate data from multiple source systems into a virtual database for use in business intelligence (BI) and other analyses.

Data warehousing: Data warehouses store data collected by various operational systems; the data is captured for access and analysis, rather than transaction processing.

Customer data integration

Customer data integration (CDI) was one of the first drivers for consolidating and managing information from disparate sources. Software for managing data gathered through customer interactions can help ensure that departments across a business are able to reliably access the most current and complete view of customer information available.

Data integration in the cloud

Integration as a Service (IaaS) is a cloud-based delivery model that strives to connect on-premise data with data located in cloud-based applications. This paradigm facilitates real-time exchange of data and programs among enterprise-wide systems and trading partners.

In business-to-business (B2B) integration, IaaS allows partners to develop, maintain and manage custom integrations for diverse systems and applications in the cloud. In this way, the enterprise can more effectively pursue process innovations without the need to constantly modify and maintain diverse and often incompatible application programs.

Reference: What is it

May 17, 2020

What is going on in the Data world (Georges N Bellangue)