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CXOTech Magazine

Technology Magazine for IT Leaders & Enterprise Solutions

Todd Barber, Director of Enterprise Applications and Data Services, The University of Tennessee Health Science Center
Data Data Center Information Technology 

From Data Center to Data Management, Nothing Should Escape Transformation

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By Todd Barber, Director of Enterprise Applications and Data Services, The University of Tennessee Health Science Center

The data center has undergone its own “digital transformation” over the past decade or two. Networks have been extended into several clouds, both public and private; portions have remained as is. Infrastructure has gone from massive on-premise rooms to, well, lines of code. There are seemingly countless iterations, options, and opportunities of what a data center could look like during this transformation because, without a doubt, the transformation isn’t complete.

Some organizations are still not ready to make the necessary jump to the cloud due to staffing issues. Many seasoned staff members lack the necessary skills, but most junior staff members lack the expertise. Not to mention, the new roles that the cloud introduces. Internal collaboration, done right, could provide a way to upskill willing staff members. These skills are in high demand causing higher salaries which prices out numerous small to medium businesses, which leads to another issue. In addition to higher salaries, funding roadblocks could also cause issues as costs have moved from capital expenditures to the operational line. Some CFOs are harder to convince of the necessary yearly line item.

As the data center has fractured, so has the underlying data. However, savvy business leaders still want the data at their fingertips to drive business decisions. Data optimization has become front of mind to make data easier to work with and more reliable and to lower costs as data must traverse various systems in potentially various clouds.

In conclusion, as organizations move forward with fractured data centers and limited resources, efficiency becomes one of the key drivers.

Pandemic outbreaks, diversity, equity, and inclusion (DEI) initiatives, socioeconomic indicators, and political contests are just a few recent items that are using data to drive impact and/or change. Being in the higher education industry, student success is at the forefront as we dive into the data to help our students navigate through their journeys. No matter the driver, each likely requires different data from possibly different systems. The insights that are delivered don’t just happen. It is a marriage of business and technology, a fusion that can provide an organization – and its customers – a massive amount of value when done correctly.

It’s at this inflection point where over the past two decades, another transformation has occurred, the rise of data management offices (DMO). The insights needed to drive organizations are not easy to produce as there are pitfalls along the data life cycle. A data strategy and data governance program aligned with business goals and outcomes are vital to success. As a DMO engages with business and technology, it can help drive smart technology decisions that support the business goals. Enterprise architecture, storage, security, privacy, data and system integration, and data warehousing are just a few technologies that can benefit from a successful DMO. Data quality, document management, data literacy, business glossary, security, privacy, and business intelligence are just a few of the business needs that benefit from a successful DMO. Arguably the biggest challenge that a DMO will face is a common organizational foe, culture.

As we push toward greater maturity, we are facing some of the same culture issues others have faced. (A quick note here to say that while cultures may need to change, don’t read as the current culture is “bad” in a negative way.)

  • Often, reports or visualizations are incorrect due to data quality issues. However, culture can change as people are shown the value of the data and the holistic view.
  • Data quality issues inherently lead to a mistrust of the data. This mistrust leads to “shadow” systems that only introduce confusion, more quality issues, and a host of privacy and security nightmares. As data quality increases, the mistrust will steadily subside.
  • Inconsistent – or missing – definitions often lead to confusion as it is left to individuals to interpret the meaning of a metric. A business glossary that is consistently updated and utilized helps reduce confusion.
  • Siloed data that is hoarded is another difficult hurdle to overcome. Often this takes time and relationship building to show an understanding of the value of the data as well. As a DMO can show the value of leaving the silo and being combined with other data to provide even more insights, this barrier is slowly broken down.
  • Data accessibility is another hurdle that has massive implications. It is obviously important, but necessary to state, that data access and system access should be provisioned to the least level needed.

One more thing to note, is that as a DMO provides culturally changing value, the word inevitably spreads and each hurdle to jump hopefully becomes lower and lower.

If utilized correctly, the cloud can provide a DMO with valuable tools to fight the cultural issues that arise. As we have seen in the past few years, agility is key. However, the benefits of newer technologies in this space are often the drawbacks as well. Security may be enhanced, but a misconfiguration can open systems to attacks or expose data. Extra bandwidth can be added on the fly to help with peak times but could also be exploited – intentionally or accidentally – that have potentially expensive side effects. Even agility often means giving up some of the control you may have on-prem to move services around in the cloud quickly.

In conclusion, as organizations move forward with fractured data centers and limited resources, efficiency becomes one of the key drivers. A DMO that is working to change the data culture through an effective data strategy and governance can help technology move more efficiently, which accelerates the ability to deliver valuable insights to decision makers. Faster access to insights allows for quicker time to action. What that action does depends on each industry. For us in higher education, it’s ensuring students are returning each semester, knowing a student is taking the right course with the right professor, knowing when a student may be in over their head (preferably before they do), ensuring students graduate on time, and ensuring we have a diverse and inclusive campus, just to name a few.