Close this search box.

Data governance strategy is key to effective business management

Data governance

An effective data governance strategy is critical for unlocking the full benefits of data-driven decision-making, enhanced business innovation, and improved collaboration.

Data governance requires a system. It’s important to have a solid framework of the people, processes, and technologies involved. This uncovers actionable intelligence, maintains compliance with regulations, and mitigates risks. Here are the key steps for building an effective data governance strategy according to data specialists Alation.

1. Identify and Prioritise Existing Data

To implement a data governance strategy, a company needs to know what data it already has. As part of this process, the organization should start by: Inventorying data: Create a complete record of information resources with relevant metadata; Classifying data: Analyse structured and unstructured data to organize it by relevant categories; Curating data and knowledge: Organize and manage datasets with active metadata management and data catalogues.

2. Choose a Metadata Storage Option

Traditionally, departments within a business have their own databases for metadata management. This has led to siloed data which limits the sharing and reusing of metadata assets. Choosing a storage option that centralizes metadata is key for: Collection across many platforms; Productive reuse of metadata; Visibility into data history; Effective governance and stewardship; Centralized metadata ensures scalability and flexibility needed for analytics. It also helps different departments understand the value of data lineage.

3. Prepare and Transform the Metadata

This is one of the most time-consuming steps. It requires going back to the raw metadata, reformatting it, correcting it, and combining datasets into data catalogs. The three primary activities are: Cleansing and validating data: Removing outliers, filling in missing values, standardizing the data, masking sensitive entries; Transforming: Updating values or formats so that all data can be understood and used across the organization; Creating templates: Create templates for a business glossary, data dictionary, and business metadata. This will help organize data vocab and track how many data assets or terms you upload within your network

4. Build a Governance Model

A one-size-fits-all data governance model does not exist. Historically, companies used passive, compliance-oriented frameworks. These defined how users created, stored, maintained, and disposed of data. However, to take advantage of all that analytics offer, companies need modern data governance models that: Respond to multiple styles and are sensitive to contexts; Encourage innovation; Provide a flexible, dynamic strategy across enterprise and ecosystem; Incorporate distributed decisions rights connected to value; Take an active approach to managing risk proactively.

The governance model must also be centralized or federated. And the model you choose should be dependent upon the needs of your organization: Centralized Governance Model: A centralized governance model is when one group determines the rules of how to govern data. This group defines critical data elements and how to approve business terms around data. It also addresses core processes that every team member must adhere to.

Federated Governance Model: A federated governance model is when several groups have authority over data. This is helpful when departments have different data needs.

5. Establish a Process for Distribution

Modern data governance strategies should democratise data. Data governance policies only work when people follow them. This is why policies are most effective when they are embedded into people’s normal activities, workflows, and tools. To mitigate regulatory risk effectively, organizations should consider: Appropriate employee onboarding; Training employees around usage policies and guidelines; Encouraging knowledge sharing among employees; Creating processes for requesting and making changes

6. Identify Potential Risks

New security laws and compliance requirements are continuously emerging. For example, the General Data Protection Regulation (GDPR) require companies to put appropriate security controls in place. Potential risks that companies need to account for in their data governance strategy include: Excess access: everyone should have the least amount of access necessary, even at the data field level, to do their job; Secure storage locations: all sensitive data storage locations need to have security controls in place to prevent cybercriminals from accessing or stealing data

7. Constantly Adapt Your Data Governance Framework

Businesses change, and so too their data strategies. Companies need to continuously adapt and improve their data governance processes. This allows them to respond to issues like rising data privacy risks as they emerge. Organizations need automation that helps track and measure the effectiveness of their strategies to: Determine policy conformance; Measure data usage; Enforce consistent data quality; Analyse curation; Throughout the Process: Facilitate a People-Centric Approach

CTS The industrialisation of IT
CTS - Industrialisation of IT
Related Posts
CTS The industrialisation of IT
Others have also viewed

UK businesses see boosting connectivity as integral to growth

Study reveals a great opportunity for alternative network providers (AltNets) to meet growing demand for ...

Germany Energy Efficiency Act demonstrates importance of data centre supply chain collaboration

Following the signing into law of Germany’s Energy Efficiency Act (EnEfG), energy solutions specialist Aggreko ...

Systemair look to Infor’s cloud solution to deliver more sustainable products

Systemair is moving its core business system to Infor CloudSuite Manufacturing, aiming at smoother integration ...
Data Centre

Vertiv collaborates with Intel on liquid cooled solution

Vertiv is collaborating with Intel to provide a liquid cooling solution that will support the ...