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7 Best Practices for Data-Driven IT Operations Excellence

Aug 11, 2022
TOPIC: IT
3 min read
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Enterprise IT environments are growing in scale and complexity, and customer demands, both inside and outside, require better approaches to asset monitoring, maintenance, and problem-solving. That means moving away from an order-taking, ad hoc philosophy toward a proactive, architected approach that aligns stakeholders with enterprise goals … and with each other.

And the path toward operational excellence is paved with data. On one hand, vast volumes of data are available in real time. On the other hand, without the right IT inventory and asset tracking system, tools and solutions, all that real-time data can lead to paralysis rather than progress.

Transitioning to a data-driven organization isn’t simple, but it is essential. Handled correctly, data becomes knowledge and awareness, so the better your data—and the better you handle it—the more evidence-based your decisions can be.

Here are 7 key ways IT ops managers put data to work helping to drive operational excellence across entire organizations:

  1. Recognize data as an asset with enterprise value
  2. Begin with a coherent strategy that focuses on data
  3. Collect, analyze, and report the data that serves your internal customers
  4. Ensure data compliance at all levels and locations
  5. Make the most of automation
  6. Implement for growth and continuous improvement
  7. Get expert help

1. Recognize Data as a Valued Enterprise Asset

Your enterprise data is not a byproduct of doing business—it’s a rich resource that helps build business. That’s the mindset that should drive evaluations of which data to collect, how to standardize and manage it, and how to best deliver insights to users. When teams understand what your enterprise data customers want from the data, they can manage it better to deliver it as an asset. That leads to better data quality, consistency, and currentness… but it also means your users will get exactly the data they need to do their best work.

2. Start With a Coherent Data Strategy

Your enterprise data strategy should start by recognizing and understanding what digital data is most important to the enterprise and to individual users. You need to understand who needs the information, and how they use it—so you can devise and implement the right systems to make the data accessible and ready to use when and where any customer needs it, in real time. 

That means identifying where and how data is captured, ensuring that it’s standardized and protected throughout its lifecycle, and making it easy to access and put to work.

3. Make the Data Do the Work, Not Your Users

Your data customers shouldn’t have to waste time trying to make assets out of data—that data should be delivered as a ready-to-use resource, personalized for their needs and organized, so it’s precisely what they need, on demand. Understanding the processes, tools, applications, automations, and objectives of users should inform your data management approaches.

4. Ensure Compliance at Every Level and Location

Global enterprises can have complex and intersecting compliance requirements. Not only are industries’ regulatory requirements a concern, but local governments may have strict rules regulating storage of nationals’ and/or residents’ personal data, such as the EU’s General Data Protection Regulations and California’s Consumer Privacy Act. And it’s not just customer privacy that matters; website visitors’ and employees’ data also need to be protected. That means implementing proactive compliance and enforcement, and ensuring regular compliance updates.

5. Make the Most of Automation Opportunities

Manual processes aren’t just slow, they’re more error-prone and less likely to follow standard protocols. Using automation wherever possible helps standardize collection and ensure enterprise-wide consistency. Build data collection standards into every implementation and deployment of any new application, device, or other data source. IT is responsible for monitoring data sources to prevent unanticipated changes that can have far-reaching consequences for users across the enterprise. 

6. Implement to Grow and Improve

A data-as-enterprise-asset strategy means every new application, device, integration, and process is evaluated with consideration for its potential effects on data. It also requires a consistently forward-looking approach with an eye on technologies and innovative applications. IT’s job is to help ensure that the enterprise has the flexibility and agility to stay competitive well into the future.

7. Get Expert Help from a Trusted Partner

Here’s a fact: tech innovation and the rise of data are outpacing the growth of IT departments, and it rarely makes fiscal sense to field a full-time, in-house team of top-tier experts in every aspect of data management and integration. Yet at the same time, you need interdisciplinary expertise and significant industry experience. That’s exactly how a trusted partner like Barcoding can make the difference in helping you meet your enterprise data objectives and protecting your budget.

Maximize Your Enterprise Data Assets

Legacy IT issues like silos, outdated management systems, and arbitrary data collection standards can create serious obstacles in your path toward data-driven IT operations. 

But with the right IT asset management and inventory tracking tools, you can embrace a holistic approach to data, and fully integrate your enterprise system of systems. Learn how you can unify enterprise data and put it to work by downloading our free guide, Achieving Data-Driven IT Operations Excellence. Just click the link below for your copy.

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