Strategic and Tactical Data Asset Management

Design governance processes and oversight for managing corporate data

Baseline’s Data Governance service provides a structured approach to designing and implementing a data governance framework that socializes the idea of managing data as an asset.

Baseline defines data governance as the processes and oversight for establishing policies and definitions for corporate data. Data governance puts structure around the decision-making process. It assigns accountability, prioritizes investments, allocates resources, and sets performance metrics. The data governance team ensures the data deployed on projects is aligned with corporate objectives, supports desirable business actions and behaviors, and creates value.

It’s a tall order for most companies, and an underestimated one. The Data Governance service gauges your company’s existing data management and governance capabilities, identifies gaps, and recommends structured, tactical, and incremental steps that not only formalize a governance process, but position its importance with management.

» Your Challenges
» The Problem
» The Baseline Approach
» Your Value
» Why Baseline

Your Challenges

• Explaining: “data is a corporate asset”
• Altering the “data hoarding” mindset
• Finding someone to “own the data”
• Impact of a merger or new enterprise application on data
• Losses suffered due to bad data
• Same reports—different results across lines of business
• Deciding: will a tool fix the problems

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The Problem

Lack of structure for making decisions about corporate data is a business issue

Data governance is a hot topic in business intelligence and data warehousing circles. Companies today face a host of data-related issues, and none of them are new.

Business management and boards of directors demand policies for dealing with compliance, security, access, and usage requirements. Poor data quality can sabotage an otherwise promising strategic project. Constant changes to existing data—for instance, the U.S. Census claims that about one in seven people change addresses in a given year—drive unsynchronized and often contradictory actions.

Non-integrated data results in operational inefficiencies and bad decision making. And arguments abound about access rights, systems of record, data ownership, data delivery processes, and even the meaning of key corporate data.

Data Governance and Data Management

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The Baseline Approach

Addressing five change levers to customize your data governance structure

Our Data Governance service begins with an analysis of your capabilities across multiple change levers.

Strategy: For which corporate strategies is information a critical component? Does the company have customer-focused initiatives that require well-managed and quickly deployed customer data? How are strategic objectives like “growth through acquisitions” operationalized at the data level?

Process: What data policy-making exists today? How formalized is the policy-making process? How do multiple lines of business share in the definition of corporate data and metadata?

Organization: Are committees in place to manage data governance issues at any level? Do key executives recognize that data quality and data management are business issues? Who are the influencers? The decision makers?

Data Enablement: Who participates in data definition and decision-making across the company’s “data supply chain”? Are business units, third party vendors, or outsourcers involved? Are key technologies such as data matching, data profiling, and data integration used to decide what “good data” means? Who maintains key data elements?

Culture: Does the company have a culture of innovation which encourages individuals to work independently and form their own processes? Or does a consensus-driven culture create barriers to making difficult decisions and adopting authoritative processes? Are there corporate-level philosophies, such as Six Sigma, that influence data governance mechanisms?

Baseline uses the findings gathered from our analysis to customize a data governance structure for your company that includes:

• A data governance organizational structure and responsibility matrix.
• A diagram of data governance process workflows.
• Recommendations for involvement of key executives and business stakeholders.
• A set of templates for inputs and outputs.
• A communications plan for data governance.
• A rollout plan and timeframe for data governance.

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Your Value

Data aligned with corporate objectives, driving desired behaviors, and creating measurable value

The ultimate goal of data governance is to provide a formalized process which enables your company to manage data as an asset across lines of business, organizational hierarchies, and systems. It provides a sustained and proven way to define data policies, find the root causes of data errors, drive corrections to broken processes, and measure ongoing success.

You begin to build a vocabulary and speak the language of corporate data. You understand your company’s readiness to make the time and resource investments necessary to manage data as an asset.

Our findings and recommendations, when presented to key management, help establish the business importance of data, as well as the potential risks of not implementing a data governance process.

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Why Baseline

Data – the common denominator of all our engagements – and a top to bottom governance framework

Regardless of the technology platform, type of business, or entry-point, data is the common denominator of all Baseline engagements. Our core consulting activities revolve around data management and integration. As such, we have experience with data governance activities from both a “top down” and “bottom up” perspective.

Baseline’s unique view of data governance is broader than other consultants and analysts. We distinguish between data governance and data quality, data management, data modeling, and data stewardship. While these latter activities are necessary subsets of a bona fide data governance plan, Baseline’s more comprehensive framework encompasses all the critical success factors – business strategy, people, process, data, and technology.

Data Governance Scope of Control

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