The Business Process of Data – Why You Should Care

Recently I had a conversation with a good friend who works as an executive compensation and benefits consultant. We were talking about some of BackOffice’s recent data management solution updates. I spoke about how our platform comes together as a perfect storm to deliver a comprehensive pragmatic data governance solution:

  1. Automated closed-loop remediation
  2. Role-based data access and workflow
  3. Flexible alerts, reporting, and dashboards to provide people information how they want it
  4. Business-user friendly workflow design interface – based on registered systems, data, and people
  5. Economy of scope realized through the reuse of data management logic across all relevant phases of a data journey

After I had painted my picture he said, “Impressive…I assume that customers are falling over themselves to do business with you.” Although excited with the direction our solutions were going, my response was muted: “Yes and no. In the Data Quality Management and Data Governance space, there is still a lot of solution positioning, customer convincing, and education that needs do to be done.”

His next question was loaded: “What’s at stake if these companies don’t proactively address the data management capabilities that your solutions provide?” This question made me think of an HBR article I had read.  

The article discusses complex adaptive systems (“CAS”), making a comparison between similarities of companies and biological species. This led me to a couple other questions:

  1. What’s ultimately at stake if companies are unable to achieve a state of “Trusted and Assured” data?
  2. With respect to data, are companies really that similar to biological species? 

1. What’s at stake?

I recently contributed to a white paper that referenced the high cost and impact of some high-profile, public sector, IT project failures (the data migration approach was often found to be a root cause). Some project failures were categorized as Black Swans: where project costs spiralled to greater than 300% of the initial budget.

The HBR article took a longer term view, and the results were more dire:  "Companies are dying younger because they are failing to adapt to increasing complexity." Forty-five years ago the average lifespan of a publicly listed company in the US was 55 years. In 2010, the average lifespan has been reduced to 31.5 years. It is much less today. Large publicly listed companies are dying faster (none of us need to be reminded of near fatal - and fatal - missteps by Nokia, Blackberry, Lehman Brothers, etc.).

2. Company similarity to biological species?

For companies to be robust, resilient, and achieve corporate longevity, the authors discussed 6 principles. Although all are relevant (with respect to achieving "Trusted and Assured" data) I’ll focus on 3 of the principles:

a. Preserve redundancy: Living entities are under constant attack from pathogens. Our immune systems have evolved to multiple layers of defense and redundancy. Similarly, to prevent data decay, organizations need several layers of "data quality firewalls" to protect data critical to the success of their business: i.e., system of record configuration, passive data governance, and active data governance (this is in addition to data security firewalls).

b. Expect surprise, but reduce uncertainty: Organizations need mechanisms in place to collect signals, detect patterns of change, imagine plausible outcomes – and take action to minimize undesirable ones. Here, companies can leverage business process governance to predict future business workflow using relevant historic process performance.

c. Create feedback loops and adaptive mechanisms: Companies need to first detect the right signals from the organization, and second, translate those signals into action. Data-driven companies can learn from the discipline of Total Quality Management – more specifically, the concept of Gemba Kaizen. A culture and solution should be in place to ensure data is introduced correctly the first time at the system of record.

  • Defects created here must be resolved and prevented here.
  • Never delegate work to downstream customers.

I realize that for many working in the area of enterprise data management, objective, organization specific clarity is not readily available to imagine how successful data management practices can greatly influence organizational longevity. If you’re not convinced, I’m sure that you will agree that these recent high profile data breaches in the UK took their toll on corporate life expectancy.

We can learn from thinking of – and identifying similarities between –companies and biological species. We can link data management capabilities to complex adaptive systems. Like all those worker bees striving for the continued survival of the queen bee,  every person who interacts with data has some degree of data stewardship responsibility. We all need to treat data as a trusted and assured asset to ensure continued sustainability and success of our respective organizations. 

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