Ambiguity is a common source of frustration and risk in business.
Why do two reports, both measuring Sale per Region, give different results? Which is correct?
It may be as simple as that one report shows invoiced and collected revenue only – the other shows all orders including those that have not yet been invoiced. Or maybe one report looks at projected sales (from CRM) and the other is showing completed sales (from ERP).
As discussed in The Importance of Meaning, the business glossary provides definitions for common business terms. As such, the glossary plays an important role in reducing ambiguity and creating trust.
Yet, in itself the business glossary does not provide data authority. To achieve authoritative data companies must move from what Forrester Research calls data Governance 1.0 to a Data Governance 2.0 approach.
In a Data Governance 1.0 approach data management is viewed as a project driven IT function.
The business glossary is an extension of the technical data dictionary, the data model and the ETL processes. The focus is on understanding where and how IT touch data – how it is moved in an ETL process and how it is represented in a database fields.
According to Forrester Analyst, Michelle Goetz, “An IT mindset has dominated the way organizations view and manage their data. Even as issues of quality and consistency raise their ugly head, the solution has often been to turn to the tool and approach data governance in a project oriented manner. Sustainability has been a challenge, relegated often to IT managing and updating data management tools (MDM, data quality, metadata management, information lifecycle management, and security). Forrester research has shown that less than 15% of organizations have business lead data governance that is linked to business initiatives, objectives and outcomes. But, this is changing. More and more organizations are looking toward data governance as a strategic enterprise competence as they adopt a data driven culture.”
Forrester defines Data Governance 2.0 as the “new reality of business ownership, business strategy, and emphasis on business outcomes.”
In the Data Governance 2.0 world the business glossary provides context for business strategy, goals and outcomes.
This means that we need to understand how business touches the data – how it affects keys programs, business processes, critical reports. We must include the technical dictionaries but go beyond this to understand manual interventions. Did Finance make a once-off adjustment to the sales figures in anticipation of a bad debt? How do we track that?
Data governance 2.0 aims to provide knowledge workers and decision makers with all the knowledge that they need to deliver on their business goals.
Leading data stewardship platforms focus on managing business interactions, ownership and the data governance processes themselves.
The goal is to embed data governance as the foundation of the data-driven enterprise. The business glossary shifts from being a technical tool to provide strategic context for information.
A Data Governance 2.0 glossary looks up, as well as down.
We link our glossary to business outcomes – which reports use a term, which compliance programs are dependent on it, how does it impact on key business processes?
We focus on ownership, accountability and collaboration. Who needs to be involved when we are defining each term? What are the different contexts in which it will be used. Who is ultimately responsible for the final, published definition? Can we show the auditors and the regulators that we have involved the right people to ensure that we have accurately managed ambiguity and risk?
We provide multiple contexts – we recognise that profit may mean different things to Sales and to Finance – and we accommodate each definition, in context, and provide the aggregated enterprise understanding.
We provide context for both automated and manual movements of data. In most companies a lot of data is moved by manual updates (cut and paste) or export and import. These movements are invisible to IT and the ETL process and must be documented and tracked manually.
We move beyond the business glossary to embrace the concept of a data authority – an organisational capability to ensure trusted data.
In the data-driven organisation each individual lives and breathes data: from initial data entry all the way to aggregated reporting and predictions. Stewardship drives and maintains that ideal data culture, but the key is to get it kicked off correctly, and to make it sustainable.
Organizational complexity, maturity, culture, targeted business outcomes – these all factor in how a company starts its stewardship journey, and the requirements affect the daily demands on stewards. In addition to their day job, they juggle data quality issues one day, define ambiguous data terms the next, and agree on data-sharing service levels the day after. Every day they juggle the needs of the broader business community to ensure that business outcomes are not affected by data issues.
Data stewards. like any other critical job function, need a platform that supports and monitors the myriad functions that they are required to deliver on a daily basis. Just as no two companies run their sales or fiance operations in exactly the same way, no two companies will deploy identical data governance organisations.
However, each company has a similar set of data responsibilities and processes that must be:
- Defined and agreed upon
- Implemented and deployed
- Measured and monitored
Just as your accounting package supports more that the journals – your Data Governance 2.0 platform must support all data stewardship responsibilities and processes, not just those that are technically oriented. The business glossary is one link in the chain. By itself it does not give data authority.
Companies that break down data governance silos and provide a single, integrated view to freely share all data related knowledge across the organisation are able to reduce rework; identify and manage data risks before they become issues; and deliver trusted reports to auditors, regulators and decision makers. They have created the solid foundation of a data-driven organization