Tag: data quality

Five Pillars of Data Governance Readiness: Delivery Capability

Post author By Bunny Tharpe
Post date May 3, 2018
No Comments on Five Pillars of Data Governance Readiness: Delivery Capability

The five pillars of data governance readiness should be the starting point for implementing or revamping any DG initiative.

In a recent CSO Magazine article, “Why data governance should be corporate policy,” the author states: “Data is like water, and water is a fundamental resource for life, so data is an essential resource for the business. Data governance ensures this resource is protected and managed correctly enabling us to meet our customer’s expectations.”

Over the past few weeks, we’ve been exploring the five pillars of data governance (DG) readiness, and this week we turn our attention to the fifth and final pillar, delivery capability.

Together, the five pillars of data governance readiness work as a step-by-step guide to a successful DG implementation and ongoing initiative.

As a refresher, the first four pillars are:

The starting point is garnering initiative sponsorship from executives, before fostering support from the wider organization.

Organizations should then appoint a dedicated team to oversee and manage the initiative. Although DG is an organization-wide strategic initiative, it needs experience and leadership to guide it.

Once the above pillars are accounted for, the next step is to understand how data governance fits with the wider data management suite so that all components of a data strategy work together for maximum benefits.

And then enterprise data management methodology as a plan of action to assemble the necessary tools.

Once you’ve completed these steps, how do you go about picking the right solution for enterprise-wide data governance?

Five Pillars of Data Governance: Delivery Capability – What’s the Right Solution?

Many organizations don’t think about enterprise data governance technologies when they begin a data governance initiative. They believe that using some general-purpose tool suite like those from Microsoft can support their DG initiative. That’s simply not the case.

Selecting the proper data governance solution should be part of developing the data governance initiative’s technical requirements. However, the first thing to understand is that the “right” solution is subjective.

Data stewards work with metadata rather than data 80 percent of the time. As a result, successful and sustainable data governance initiatives are supported by a full-scale, enterprise-grade metadata management tool.

Additionally, many organizations haven’t implemented data quality products when they begin a DG initiative. Product selections, including those for data quality management, should be based on the organization’s business goals, its current state of data quality and enterprise data management, and best practices as promoted by the data quality management team.

If your organization doesn’t have an existing data quality management product, a data governance initiative can support the need for data quality and the eventual evaluation and selection of the proper data quality management product.

Enterprise data modeling is also important. A component of enterprise data architecture, it’s an enabling force in the performance of data management and successful data governance. Having the capability to manage data architecture and data modeling with the optimal products can have a positive effect on DG by providing the initiative architectural support for the policies, practices, standards and processes that data governance creates.

Finally, and perhaps most important, the lack of a formal data governance team/unit has been cited as a leading cause of DG failure. Having the capability to manage all data governance and data stewardship activities has a positive effect.

Shopping for Data Governance Technology

DG is part of a larger data puzzle. Although it’s a key enabler of data-driven business, it’s only effective in the context of the data management suite in which it belongs.

Therefore when shopping for a data governance solution, organizations should look for DG tools that unify critical data governance domains, leverage role-appropriate interfaces to bring together stakeholders and processes to support a culture committed to acknowledging data as the mission-critical asset that it is, and orchestrate the key mechanisms required to discover, fully understand, actively govern and effectively socialize and align data to the business.

Here’s an initial checklist of questions to ask in your evaluation of a DG solution. Does it support:

Relational, unstructured, on-premise and cloud data?
Business-friendly environment to build business glossaries with taxonomies of data standards?
Unified capabilities to integrate business glossaries, data dictionaries and reference data, data quality metrics, business rules and data usage policies?
Regulating data and managing data collaboration through assigned roles, business rules and responsibilities, and defined governance processes and workflows?
Viewing data dashboards, KPIs and more via configurable role-based interfaces?
Providing key integrations with enterprise architecture, business process modeling/management and data modeling?
A SaaS model for rapid deployment and low TCO?

To assess your data governance readiness, especially with the General Data Protection Regulation about to take effect, click here.

You also can try erwin DG for free. Click here to start your free trial.

Tags data governance readiness, data modeling, GDPR, General Data Protection Regulation, data governance, data-driven business, metadata management, data quality, DG readiness, five pillars of data governance, data governance should be corporate policy, DG implementation, data governance tools, comparing data governance, data governance solutions, data quality management, enterprise data architecture

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Data Governance: Your Engine for Driving Results

Post author By Mark Peco
Post date January 4, 2018
1 Comment on Data Governance: Your Engine for Driving Results

In my previous post, I described how organizational success depends on certain building blocks that work in alignment with common business objectives. These building blocks include business activities, data and analytics.

Governance is also one of the required building blocks because it provides cohesion in the standards to align people, processes, data and technology for successful and sustainable results. Although it has been somewhat of an abstract concept, data governance is foundational to helping organizations use data as a corporate asset.

Assets are acquired and used to help organizations execute their business models. Principles of asset management require that assets be cataloged, inventoried, protected and accessible to authorized people with the skills and experience to optimize them.

Assets typically generate more value if they have high levels of utilization. In the context of data, this means governed data assets will be more valuable if they strengthen existing operations and guide improvements, supported by analytics.

As organizations seek to unlock more value by implementing a wider analytics footprint across more business functions, data governance will guide their journeys.

A New Perspective on Data

Becoming a data-driven enterprise means making decisions based on empirical evidence, not a “gut feeling.” This transformation requires a clear vision, strategy and disciplined execution. The desired business opportunity must be well thought out, understood and communicated to others – from the C suite to the front lines.

Organizations that want to succeed in the digital age understand that their cultures and therefore their decision-making processes must become more proactive and collaborative. Of course, data is at the core of business performance and continuous improvement.

In this modern era of Big Data, non-traditional data sets generated externally are being blended with traditional data generated internally. As such, a key element of data-driven success involves changing the long-held perspective of data as a cost center, with few if any investments made to unlock its value to the organization.

Being data-driven, based on analytics, changes this mindset. Business leaders are indeed starting to realize that making data more accessible and useful throughout the organization contributes to the results they want to achieve – and must report to their boards.

If traditional asset management concepts are applied to data, then objectives for security, quality, cataloging, definition, confidence, authorization and accessibility can be defined and achieved. These areas then become the performance criteria of the new data asset class.

So transforming an organization’s leadership and the rest of its culture to perceive and treat data as an asset changes its classification from “cost” to “investment.” Valuable assets earn a financial return and fuel productivity. They also can be re-invested or re-purposed.

Data governance is key to this new perspective of data as an asset.

Data Governance Definition and Purpose

Data governance is important to the modern economy because it enables the transformation of data into valuable assets to improve top- and bottom-line performance. Well-governed data is accessible, useful and relevant across a range of business improvement use cases.

But in the early stages of implementing data governance, organizations tend to have trouble defining it and organizing it, including determining which tasks are involved.

At its core, data governance is a cross-functional program that develops, implements, monitors and enforces policies that improve the performance of select data assets.

Implementing data governance ensures that “asset-grade” data is available to support decision-making, based on advanced analytics. Using this rationale, potential objectives to meet the strategic intent of the organization can be defined to derive value.

Following is a list of possible objectives for a data governance program:

Improve data security
Increase data quality
Make data more accessible to more stakeholders
Increase data understanding
Raise the confidence of data consumers
Increase data literacy and determine the data-driven maturity level of the organization

Building a Data Governance Foundation

The scope and structure of a data governance program are important to determine and include responsibilities, accountabilities, decision rights and authority levels, in addition to how the program fits into the existing corporate structure in terms of virtual or physical teams.

Structural options include top-down command and control and bottom up collaborative networks. Executive accountability also should be outlined.

It’s common for a data executive, such as the chief data officer, to be identified as accountable for overall data governance results. Data owners are business leaders who manage the processes that generate critical data. They’re responsible for defining the polices that support the program’s objectives.

Data stewards report to the data owners and are responsible for translating data policies into actions assigned to data specialists. The data specialists execute projects and other workflows to ensure that the governed data conforms to the intent of the policies.

Data stewards form the backbone of a data governance initiative. They influence how data is managed by assigning tasks to the specialists. Data stewards are responsible for cataloging, defining and describing the governed data assets.

These roles may be full-time or part-time, depending on the scope of the work.

Key processes carried out by the data governance team include:

Defining and planning the program’s scope
Data quality improvement
Data security improvement
Metadata creation and management
Evaluating the suitability of new data sources
Monitoring and enforcing compliance to data policies
Researching new data sources
Training to improve data literacy of staff at all levels
Facilitating and finding new data-driven opportunities to improve the business
Leading and managing cultural change

Data governance is based on a strategy that defines how data assets should look and perform, including levels of quality, security, integration, accessibility, etc. The design and implementation of a data governance program should start with a limited scope and then gradually ramp up to support the overall business strategy. So think big, but start small.

The next post in the series explores how data governance helps implement sustainable business processes that produce measurable results over time. Click here to continue reading on.