erwin Expert Blog Data Governance Data Intelligence

Overcoming the 80/20 Rule – Finding More Time with Data Intelligence

The 80/20 rule is well known. It describes an unfortunate reality for many data stewards, who spend 80 percent of their time finding, cleaning and reorganizing huge amounts of data, and only 20 percent of their time on actual data analysis.

That’s a lot wasted of time.

Earlier this year, erwin released its 2020 State of Data Governance and Automation (DGA) report. About 70 percent of the DGA report respondents – a combination of roles from data architects to executive managers – say they spend an average of 10 or more hours per week on data-related activities.

COVID-19 has changed the way we work – essentially overnight – and may change how companies work moving forward. Companies like Twitter, Shopify and Box have announced that they are moving to a permanent work-from-home status as their new normal.

For much of our time as data stewards, collecting, revising and building consensus around our metadata has meant that we need to balance find time on multiple calendars against multiple competing priorities so that we can pull the appropriate data stakeholders into a room to discuss term definitions, the rules for measuring “clean” data, and identifying processes and applications that use the data.

Overcoming the 80/20 Rule - Analyzing Data

This style of data governance most often presents us with eight one-hour opportunities per day (40 one-hour opportunities per week) to meet.

As the 80/20 rule suggests, getting through hundreds, or perhaps thousands of individual business terms using this one-hour meeting model can take … a … long … time.

Now that pulling stakeholders into a room has been disrupted …  what if we could use this as 40 opportunities to update the metadata PER DAY?

What if we could buck the trend, and overcome the 80/20 rule?

Overcoming the 80/20 Rule with Micro Governance for Metadata

Micro governance is a strategy that leverages the native functionality around workflows.

erwin Data Intelligence (DI) offers Workflow Manager that creates a persistent, reusable role-based workflow such that edits to the metadata for any term can move from, for example, draft to under review to approved to published.

Using a defined workflow, it can eliminate the need for hour-long meetings with multiple stakeholders in a room. Now users can suggest edits, review changes, and approve changes on their own schedule! Using micro governance these steps should take less than 10 minutes per term:

  • Log on the DI Suite
  • Open your work queue to see items requiring your attention
  • Review and/or approve changes
  • Log out

That’s it!

And as a bonus, where stakeholders may need to discuss the edits to achieve consensus, the Collaboration Center within the Business Glossary Manager facilitates conversations between stakeholders that persistent and attached directly to the business term. No more searching through months of email conversations or forgetting to cc a key stakeholder.

Using the DI Suite Workflow Manager and the Collaboration Center, and assuming an 8-hour workday, we should each have 48 opportunities for 10 minutes of micro-governance stewardship each day.

A Culture of Micro Governance

In these days when we are all working at home, and face-to-face meetings are all but impossible, we should see this time as an opportunity to develop a culture of micro governance around our metadata.

This new way of thinking and acting will help us continuously improve our transparency and semantic understanding of our data while staying connected and collaborating with each other.

When we finally get back into the office, the micro governance ethos we’ve built while at home will help make our data governance programs more flexible, responsive and agile. And ultimately, we’ll take up less of our colleagues’ precious time.

Request a free demo of erwin DI.

Data Intelligence for Data Automation

erwin Expert Blog

Data Governance for Smart Data Distancing

Hello from my home office! I hope you and your family are staying safe, practicing social distancing, and of course, washing your hands.

These are indeed strange days. During this coronavirus emergency, we are all being deluged by data from politicians, government agencies, news outlets, social media and websites, including valid facts but also opinions and rumors.

Happily for us data geeks, the general public is being told how important our efforts and those of data scientists are to analyzing, mapping and ultimately shutting down this pandemic.

Yay, data geeks!

Unfortunately though, not all of the incoming information is of equal value, ethically sourced, rigorously prepared or even good.

As we work to protect the health and safety of those around us, we need to understand the nuances of meaning for the received information as well as the motivations of information sources to make good decisions.

On a very personal level, separating the good information from the bad becomes a matter of life and potential death. On a business level, decisions based on bad external data may have the potential to cause business failures.

In business, data is the food that feeds the body or enterprise. Better data makes the body stronger and provides a foundation for the use of analytics and data science tools to reduce errors in decision-making. Ultimately, it gives our businesses the strength to deliver better products and services to our customers.

How then, as a business, can we ensure that the data we consume is of good quality?

Distancing from Third-Party Data

Just as we are practicing social distancing in our personal lives, so too we must practice data distancing in our professional lives.

In regard to third-party data, we should ask ourselves: How was the data created? What formulas were used? Does the definition (description, classification, allowable range of values, etc.) of incoming, individual data elements match our internal definitions of those data elements?

If we reflect on the coronavirus example, we can ask: How do individual countries report their data? Do individual countries use the same testing protocols? Are infections universally defined the same way (based on widely administered tests or only hospital admissions)? Are asymptomatic infections reported? Are all countries using the same methods and formulas to collect and calculate infections, recoveries and deaths?

In our businesses, it is vital that we work to develop a deeper understanding of the sources, methods and quality of incoming third-party data. This deeper understanding will help us make better decisions about the risks and rewards of using that external data.

Data Governance Methods for Data Distancing

We’ve received lots of instructions lately about how to wash our hands to protect ourselves from coronavirus. Perhaps we thought we already knew how to wash our hands, but nonetheless, a refresher course has been worthwhile.

Similarly, perhaps we think we know how to protect our business data, but maybe a refresher would be useful here as well?

Here are a few steps you can take to protect your business:

  • Establish comprehensive third-party data sharing guidelines (for both inbound and outbound data). These guidelines should include communicating with third parties about how they make changes to collection and calculation methods.
  • Rationalize external data dictionaries to our internal data dictionaries and understand where differences occur and how we will overcome those differences.
  • Ingest to a quarantined area where it can be profiled and measured for quality, completeness, and correctness, and where necessary, cleansed.
  • Periodically review all data ingestion or data-sharing policies, processes and procedures to ensure they remain aligned to business needs and goals.
  • Establish data-sharing training programs so all data stakeholders understand associated security considerations, contextual meaning, and when and when not to share and/or ingest third-party data.

erwin Data Intelligence for Data Governance and Distancing

With solutions like those in the erwin Data Intelligence Suite (erwin DI), organizations can auto-document their metadata; classify their data with respect to privacy, contractual and regulatory requirements; attach data-sharing and management policies; and implement an appropriate level of data security.

If you believe the management of your third-party data interfaces could benefit from a review or tune-up, feel free to reach out to me and my colleagues here at erwin.

We’d be happy to provide a demo of how to use erwin DI for data distancing.

erwin Data Intelligence