Data Governance boosts Analytics and Big data

Organizations, seeking efficiency and effectiveness, have been improving processes by Business Analytics and Big Data. Now, information assets´ governance has become crucial for their implementation. If we take data as the most valuable information asset of organizations and companies, then we should be able to keep it safe throughout its life cycle.

Usually, 4 key action areas are defined to make the best of corporate data.

Building basic skills
Corporate members should develop and/or acquire skills such as data gathering, managing and analyzing, to turn these data into knowledge, intelligence and vision, aiming at supporting their strategies.

Data wholeness
All master data related standards and policies should be properly managed. It is crucial to know who handles data and what for. It is not a question of having more systems, but rationalizing data gathering, and improving how they are shared and analysed. Often, we cannot know customers’ preferences, accurately assess the effectiveness of actions or campaigns, or the return on investment made. To find an answer to these questions all we need is to follow adequate practices to gather customers’ data, trends analysis and digital technology.

Activating quality data
Research on quality data activation and Data Analytics, have shown that volume, variety, and data generation speed are crucial to obtain the most of analysis. However, data accuracy and/or wholeness are usually a major hindrance to make savvy decisions. Additionally, data sharing is a problem. More often than not, data provided by an induvial or company is given under certain terms and conditions, so it cannot be automatically shared with another organization without prior consent. First and foremost, this kind of situations should be analysed to make the best of Big Data.

Pondering rights
There should be a perfect balance between individual rights, privacy, and confidentiality, on the one hand, and the benefits obtained from sharing information, on the other. It is pivotal to have good understanding of what the value proposition consists in, foster trust and reduce uncertainty as to how data will be used. At the same time, we must ensure that they will be used, and administered following strict security and control standards. Data have enormous potential to improve the services offered to customers, buy it requires investment, innovation and imagination.

Image byAg Ku , Pixabay

Data Governance

The practice used to support these initiatives is Data Governance. It is defined as the orchestration of people, processes, and technology in order to manage data as a corporate asset. Underpinning this definition is the true and core purpose of this practice which is but building reliability and trust.

To this end it is necessary to identify:
• where data are created,
• who needs them,
• where they are used,
• what they go through,
• their quality, and
• who copies this information and who actually needs it.

The answers to these questions will yield the following benefits:
• reliable data,
• timely decisions, and
• trustworthy regulatory and financial reports.

Image by Gerd Altmann, Pixabay

A Data Governance programme involves a whole organization: everyone involved with data (creators, consumers, executives and technicians), as well as corporate decision makers.
The functions involved in Data Governance applied to different areas are:
• defining policies, standards, and strategies;
• defining data base standards;
• supporting migration and data conversion;
• ensuring data model consistency and its definitions;
• Providing support to all corporate solutions architecture: BI/DWH, Metadata, SOA, Master Data Management (MDM), Enterprise Data Management (EDM), etc;
• supporting profiles, cleaning and unification of data to ensure quality:
• defining and monitoring quality metrics;
• defining access and security for sensitive data protection;
• supporting risk management;
• ensuring compliance with regulations, laws and agreements’ requirements;
• determining master data information needs;
• supporting Master Data Management (MDM) solutions;
• integrating and maintaining data solutions incorporating new internal or external corporate sources;
• defining and maintaining metadata standards;
• implementing a metadata management system;
• creating, capturing, storing, and maintaining metadata;
• metadata repository management;
• managing metadata access and distribution for data glossary, reports and Datamart.

In future articles we will analise how to implement a Data Governance programme.

Eng. Gustavo Mesa    @gmesahaisburu

Data & Analytics consultant

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