- The building blocks for a trusted analytics foundation
- Data discovery and quality
- Data movement, transformation and synchronization
Data is multiplying rapidly in quantity and variety for enterprises of all kinds. In multicloud environments, a range of data sources is exponentially increasing the stream of incoming information, from the Internet of Things and social media, to mobile devices, virtual reality implementations and optical tracking.
While organizations are readily investing in artificial intelligence (AI), most haven’t done due diligence to understand their data or ensure the quality of data needed to benefit from AI solutions. In many organizations, their data is inaccessible, unreliable, or non-compliant with data privacy and protection rules.
Global regulations like the General Data Protection Regulation (GDPR), the California Consumer Privacy Act (CCPA) and Brazil’s Lei Geral de Proteção de Dados (LPGD) focus on personal customer and employee data. These types of regulations offer organizations an opportunity to transform and create new data-led business models, despite the severe penalties that may result from non-compliance, which can slow productivity, or damage brand value.
To meet privacy obligations and protect personal information, organizations must first discover and classify their various types of data. Businesses that struggle to gather or properly use customer data can experience urgent problems. To address this challenge, organizations are implementing governed information architectures that acknowledge regulations while continuing to support data-driven organizational performance and innovation.
The building blocks for a trusted analytics foundation
Approaching data privacy regulations as an obligation and an opportunity to modernize data infrastructures offers a significant benefit. Doing so can encourage organizations to implement data governance strategies that generate new business models and lead to datadriven insights.
Unified governance and integration (UGI) initiatives apply to data, unstructured and structured, in public and private clouds. Implementing UGI for compliance is significant on its own, but its value affects other areas of an organization, particularly the governing of AI models for data scientists.
When an organization uses data governance to trust its data, users know the data came from a quality source. They know how the data is being used across the organization and they know how it will enhance any analytics project. Analytics initiatives require trusted data to work effectively no matter how advanced the tools might be.
The benefits of trusted, business-ready data seem limitless. Analytics can suggest new product designs and marketing programs, and improve sales, supply-chain or customer-service initiatives. Analytics can even uncover operational inefficiencies that when eliminated, increase organizational agility and boost bottom-line revenue.
Implementing data and AI governance in your organization is comprised of several building blocks, as follows.
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Data discovery and quality
Organizations can be unaware of the large amounts of data stored within their business. The first step in data governance is to inventory organizational data. Start by focusing on data sets in a specific project, then expand to other business cases for broader organizational coverage.
Data that’s redundant, obsolete or trivial (ROT) is not only costly to store and manage, but also clutters decision making and operations. It can also make compliance more difficult and thwart analytics efforts. Data must meet and remain at certain quality measures to make downstream usage successful.
Once data is discovered and profiled, it’s cataloged using metadata tags to identify data types, usage, ownership, data lineage and more. Because companies in certain industries share common needs, pre-built industry models can expedite the cataloging process by using readily available business terms and taxonomy. With advancements in machine learning, business terms can be automatically mapped to build an enterprise catalog in a matter of hours.
The cataloging foundation UGI provides, enables organizations to govern their AI models, notebooks and other data sources, creating a central library for organizational knowledge. Such a foundation is a resource for many data users in the organization including data engineers, data stewards, and line-of-business users like analysts, data scientists and marketers.
Data movement, transformation and synchronization
Data from multiple sources can be easily integrated, transformed and shared with other systems as needed, physically or virtually. This process brings structured and unstructured data together and allows integration with open technologies like Apache Atlas and Hadoop.
Creating automated data flow and synchronization helps ensure that the most recent data is available in data lakes, data warehouses, data marts and point-of-impact solutions. As data quantities increase, replication supports large volumes with low latency. Organizations can use virtualization without moving data based on their need.
By 2019 the analytics output of business users with self-service capabilities will surpass that of professional data scientists.
Master data management
According to Gartner, by 2019 the analytics output of business users with self-service capabilities will surpass that of professional data scientists. It’s essential for organizations to rely on a comprehensive, trusted and unified view of critical entities like customers, products and accounts.
Modern master data management (MDM) implementations come with analytical graph-based exploration, a highly accurate matching engine, a data-first approach in selecting matching algorithms, and stewardship processes powered by machine learning. In addition, MDM solutions feature agile self-service access, governance tools and user-friendly dashboard capabilities.