data governance platform Archives

With business process modeling (BPM) being a key component of data governance, choosing a BPM tool is part of a dilemma many businesses either have or will soon face.

Historically, BPM didn’t necessarily have to be tied to an organization’s data governance initiative.

However, data-driven business and the regulations that oversee it are becoming increasingly extensive, so the need to view data governance as a collective effort – in terms of personnel and the tools that make up the strategy – is becoming harder to ignore.

Data governance also relies on business process modeling and analysis to drive improvement, including identifying business practices susceptible to security, compliance or other risks and adding controls to mitigate exposures.

Choosing a BPM Tool: An Overview

As part of a data governance strategy, a BPM tool aids organizations in visualizing their business processes, system interactions and organizational hierarchies to ensure elements are aligned and core operations are optimized.

The right BPM tool also helps organizations increase productivity, reduce errors and mitigate risks to achieve strategic objectives.

With insights from the BPM tool, you can clarify roles and responsibilities – which in turn should influence an organization’s policies about data ownership and make data lineage easier to manage.

Organizations also can use a BPM tool to identify the staff who function as “unofficial data repositories.” This has both a primary and secondary function:

1. Organizations can document employee processes to ensure vital information isn’t lost should an employee choose to leave.

2. It is easier to identify areas where expertise may need to be bolstered.

Organizations that adopt a BPM tool also enjoy greater process efficiency. This is through a combination of improving existing processes or designing new process flows, eliminating unnecessary or contradictory steps, and documenting results in a shareable format that is easy to understand so the organization is pulling in one direction.

Silo Buster

Understanding the typical use cases for business process modeling is the first step. As with any tech investment, it’s important to understand how the technology will work in the context of your organization/business.

For example, it’s counter-productive to invest in a solution that reduces informational silos only to introduce a new technological silo through a lack of integration.

Ideally, organizations want a BPM tool that works in conjunction with the wider data management platform and data governance initiative – not one that works against them.

That means it must support data imports and integrations from/with external sources, a solution that enables in-tool collaboration to reduce departmental silos, and most crucial, a solution that taps into a central metadata repository to ensure consistency across the whole data management and governance initiatives.

The lack of a central metadata repository is a far too common thorn in an organization’s side. Without it, they have to juggle multiple versions as changes to the underlying data aren’t automatically updated across the platform.

It also means organizations waste crucial time manually manufacturing and maintaining data quality, when an automation framework could achieve the same goal instantaneously, without human error and with greater consistency.

A central metadata repository ensures an organization can acknowledge and get behind a single source of truth. This has a wealth of favorable consequences including greater cohesion across the organization, better data quality and trust, and faster decision-making with less false starts due to plans based on misleading information.

Three Key Questions to Ask When Choosing a BPM Tool

Organizations in the market for a BPM tool should also consider the following:

1. Configurability: Does the tool support the ability to model and analyze business processes with links to data, applications and other aspects of your organization? And how easy is this to achieve?

2. Role-based views: Can the tool develop integrated business models for a single source of truth but with different views for different stakeholders based on their needs – making regulatory compliance more manageable? Does it enable cross-functional and enterprise collaboration through discussion threads, surveys and other social features?

3. Business and IT infrastructure interoperability: How well does the tool integrate with other key components of data governance including enterprise architecture, data modeling, data cataloging and data literacy? Can it aid in providing data intelligence to connect all the pieces of the data management and governance lifecycles?

For more information and to find out how such a solution can integrate with your organization and current data management and data governance initiatives, click here.

For regulatory compliance (e.g., GDPR) and to ensure peak business performance, organizations often bring consultants on board to help take stock of their data assets. This sort of data governance “stock check” is important but can be arduous without the right approach and technology. That’s where data governance comes in …

While most companies hold the lion’s share of operational data within relational databases, it also can live in many other places and various other formats. Therefore, organizations need the ability to manage any data from anywhere, what we call our “any-squared” (Any²) approach to data governance.

Any²first requires an understanding of the ‘3Vs’ of data – volume, variety and velocity – especially in context of the data lifecycle, as well as knowing how to leverage the key capabilities of data governance – data cataloging, data literacy, business process, enterprise architecture and data modeling – that enable data to be leveraged at different stages for optimum security, quality and value.

Following are two examples that illustrate the data governance stock check, including the Any² approach in action, based on real consulting engagements.

Data Governance “Stock Check” Case 1: The Data Broker

This client trades in information. Therefore, the organization needed to catalog the data it acquires from suppliers, ensure its quality, classify it, and then sell it to customers. The company wanted to assemble the data in a data warehouse and then provide controlled access to it.

The first step in helping this client involved taking stock of its existing data. We set up a portal so data assets could be registered via a form with basic questions, and then a central team received the registrations, reviewed and prioritized them. Entitlement attributes also were set up to identify and profile high-priority assets.

A number of best practices and technology solutions were used to establish the data required for managing the registration and classification of data feeds:

1. The underlying metadata is harvested followed by an initial quality check. Then the metadata is classified against a semantic model held in a business glossary.

2. After this classification, a second data quality check is performed based on the best-practice rules associated with the semantic model.

3. Profiled assets are loaded into a historical data store within the warehouse, with data governance tools generating its structure and data movement operations for data loading.

4. We developed a change management program to make all staff aware of the information brokerage portal and the importance of using it. It uses a catalog of data assets, all classified against a semantic model with data quality metrics to easily understand where data assets are located within the data warehouse.

5. Adopting this portal, where data is registered and classified against an ontology, enables the client’s customers to shop for data by asset or by meaning (e.g., “what data do you have on X topic?”) and then drill down through the taxonomy or across an ontology. Next, they raise a request to purchase the desired data.

This consulting engagement and technology implementation increased data accessibility and capitalization. Information is registered within a central portal through an approved workflow, and then customers shop for data either from a list of physical assets or by information content, with purchase requests also going through an approval workflow. This, among other safeguards, ensures data quality.

Data Governance “Stock Check” Case 2: Tracking Rogue Data

This client has a geographically-dispersed organization that stored many of its key processes in Microsoft Excel^TM spreadsheets. They were planning to move to Office 365^TM and were concerned about regulatory compliance, including GDPR mandates.

Knowing that electronic documents are heavily used in key business processes and distributed across the organization, this company needed to replace risky manual processes with centralized, automated systems.

A key part of the consulting engagement was to understand what data assets were in circulation and how they were used by the organization. Then process chains could be prioritized to automate and outline specifications for the system to replace them.

This organization also adopted a central portal that allowed employees to register data assets. The associated change management program raised awareness of data governance across the organization and the importance of data registration.

For each asset, information was captured and reviewed as part of a workflow. Prioritized assets were then chosen for profiling, enabling metadata to be reverse-engineered before being classified against the business glossary.

Additionally, assets that were part of a process chain were gathered and modeled with enterprise architecture (EA) and business process (BP) modeling tools for impact analysis.

High-level requirements for new systems then could be defined again in the EA/BP tools and prioritized on a project list. For the others, decisions could be made on whether they could safely be placed in the cloud and whether macros would be required.

In this case, the adoption of purpose-built data governance solutions helped build an understanding of the data assets in play, including information about their usage and content to aid in decision-making.

This client then had a good handle of the “what” and “where” in terms of sensitive data stored in their systems. They also better understood how this sensitive data was being used and by whom, helping reduce regulatory risks like those associated with GDPR.

In both scenarios, we cataloged data assets and mapped them to a business glossary. It acts as a classification scheme to help govern data and located data, making it both more accessible and valuable. This governance framework reduces risk and protects its most valuable or sensitive data assets.

Focused on producing meaningful business outcomes, the erwin EDGE platform was pivotal in achieving these two clients’ data governance goals – including the infrastructure to undertake a data governance stock check. They used it to create an “enterprise data governance experience” not just for cataloging data and other foundational tasks, but also for a competitive “EDGE” in maximizing the value of their data while reducing data-related risks.

To learn more about the erwin EDGE data governance platform and how it aids in undertaking a data governance stock check, register for our free, 30-minute demonstration here.