Data Sharing is a Production Need

The concept of “Production” in the area of Information Technology is well understood.  It means something (usually an application or system) is ready to support business processing in a reliable manner.  Production environments undergo thorough testing to ensure that there’s minimal likelihood of a circumstance where business activities are affected.  The Production label isn’t thrown around recklessly; if a system is characterized as Production, there are lots of business people dependent on those systems to get their job done.

In order to support Production, most IT organizations have devoted resources focused solely on maintaining Production systems to ensure that any problem is addressed quickly. When user applications are characterized as Production, there’s special processes (and manpower) in place to address installation, training, setup, and ongoing support.  Production systems are business critical to a company.

One of the challenges in the world of data is that most IT organizations view their managed assets as storage, systems, and applications.  Data is treated not as an asset, but as a byproduct of an application.  Data storage is managed based on application needs (online storage, archival, backup, etc.) and data sharing is handled as a one-off activity.  This might have made sense in the 70’s and 80’s when most systems were vendor specific and sharing data was rare; however, in today’s world of analytics and data-driven decision making, data sharing has become a necessity.  We know that every time data is created, there are likely 10-12 business activities requiring access to that data.

Data sharing is a production business need. 

Unfortunately, the concept of data sharing in most companies is a handled as a one-off, custom event. Getting a copy of data often requires tribal knowledge, relationships, and a personal request.  While there’s no arguing that many companies have data warehouses (or data marts, data lakes, etc.), adding new data to those systems is where I’m focused.  Adding new data or integrating 3rd party content into a report takes a long time because data sharing is always an afterthought. 

Think I’m exaggerating or incorrect?   Ask yourself the following questions…

  • Is there a documented list of data sources, their content, and a description of the content at your company? 
  • Do your source systems generate standard extracts, or do they generate 100s (or 1000’s) of nightly files that have been custom built to support data sharing?
  • How long does it take to get a copy of data (that isn’t already loaded on the data warehouse)?
  • Is there anyone to contact if you want to get a new copy of data?
  • Is anyone responsible for ensuring that the data feeds (or extracts) that currently exist are monitored and maintained?

While most IT organizations have focused their code development efforts on reuse, economies-of-scale, and reliability, they haven’t focused their data development efforts in that manner.  And one of the most visible challenges is that many IT organizations don’t have a funding model to support data development and data sharing as a separate discipline.  They’re focused on building and delivering applications, not building and delivering data.  Supporting data sharing as a production business need means adjusting IT responsibilities and priorities to reflect data sharing as a responsibility.  This means making sure there are standard extracts (or data interfaces) that everyone can access, data catalogs available containing source system information, and staff resources devoted to sharing and supporting data in a scalable, reliable, and cost-efficient manner.  It’s about having an efficient data supply chain to share data within your company.  It’s because data sharing is a production business need.

Or, you could continue building everything in a one-off custom manner.

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About Evan Levy

Evan Levy is management consultant and partner at IntegralData. In addition to his day-to-day job responsibilities, Evan speaks, writes, and blogs about the challenges of managing and using data to support business decision making.

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