“Data is the new oil.”
My colleague, Dr. Srinivas Mukkamala, recently made this statement, and I have repeated it ad infinitum – with good reason. Dr. Mukkamala made the wise point that those who collect, own and control data have enormous influence on our world. Data determines everything from how much funding schools receive to selecting the right life-saving treatment for a critical patient.
You’ve probably heard the expression “garbage in, garbage out.” It goes without saying. If your input is flawed, your output will also be flawed.
SVP Product Management for Secure Unified Endpoint Management at Ivanti.
But what if you don’t know your input is flawed?
Data bias is an epidemic. As much as we are increasingly entering an AI-powered world, humans are still the core of the source material. Spoiler alert: people are flawed. People are biased. Therefore, the data is flawed and biased. Data models are flawed and biased. The results are flawed and biased.
Depending on the context, the consequences can be catastrophic. In some cases, these implications are literally life or death. At the enterprise level, poor data tends to create a vicious cycle: bad data forms the basis for a model; the output of that model is used to inform future models; bad data metastasizes. To repeat. Anyone who has seen The Last of Us can support this story with a visualization of nefarious, out-of-control growth. (Sorry about that. But at least I have your attention!).
Long story short: data accuracy is everything. Without complete, accurate data, you can only be reactive; you can’t be proactive.
From streamlining operations to driving strategic decisions, organizations rely on accurate and accessible data to stay competitive and meet changing demands. The problem: Ensuring data is accurate and accessible can be a huge challenge, and that challenge weighs heavily on those trying to navigate competitive asset management and services.
Beyond all the fear-mongering I’ve done above, inaccurate data can lead to slower ticket resolution, increased security risks, and higher costs, hindering your ability to deliver market-competitive, efficient services.
The good news is that you don’t have to go through all the shenanigans from The Last of Us to meet the challenge of data accuracy and accessibility. To meet these challenges, smart organizations are discovering new and innovative solutions and best practices that strengthen governance, improve data alignment, and make the most of AI-powered insights. Here’s an introduction for you:
Are your insights actually insightful?
Data reconciliation is at the heart of effective asset management and service delivery. It involves comparing and aligning data from multiple sources to identify discrepancies and ensure consistency. By aligning data from different systems and sources, organizations can create a single source of truth, enabling informed decision-making and actionable insights.
Data reconciliation is critical to ensuring the accuracy and reliability of AI-driven recommendations, which rely heavily on high-quality data for training and operation.
Eliminating blind spots and data silos
Lack of visibility is bad data’s best friend. Bad data likes to sneak through every available blind spot. The opposite is also true. To effectively deploy AI and advanced analytics, companies must first eliminate blind spots and break down data silos. Blind spots, or gaps in data visibility, can lead to significant overspending and inefficiencies.
Improving asset visibility and monitoring pays big dividends by highlighting hidden costs and proactively reducing costs. Likewise, consolidating data silos – disparate repositories of information – facilitates a holistic view of business assets and operations. By combining asset inventory, management software, service maps and other data sources, enterprises can gain a 360-degree view of assets and ultimately optimize performance.
Setting up data governance and privacy frameworks
So far we’ve discussed data reconciliation and eliminating blind spots. But that’s only half the battle. Robust data management is critical to ensuring data integrity, and that includes ensuring privacy. Your input may be top-level, but if there’s a risk of breaches or tampering (malicious or accidental) along the way, you’re out of luck unless you have a data governance and privacy framework in place.
At its simplest level, data management includes the policies, procedures, and controls that govern data use and ensure the quality, integrity, and security of data. Data governance and privacy frameworks help enforce data standards, ensure compliance, protect consumer trust, improve data quality, and mitigate the risks associated with data misuse or unauthorized access.
AI and automation in service management
As companies embrace AI and automation, the role of IT teams is evolving. AI-powered Enterprise Service Management (ESM) solutions revolutionize workflows, throughput and flexibility, helping organizations deliver efficient and responsive services.
Leveraging AI and automation makes it easier than ever to streamline IT operations, reduce manual tasks and improve the digital employee experience (DEX). From automating ticket resolution to equipping helpdesk specialists with AI-driven insights, AI and automation can take the heavy burden of routine tasks and free up IT specialists to focus on strategic initiatives.
Of course, that comes with a caveat: remember “garbage in, garbage out”? AI and automation only positively improve operations when they are based on platforms/solutions and models that are rooted in accurate, secure data. That means deploying a trusted, proven solution – ideally a streamlined set of solutions that can eliminate gaps and simplify management.
If you’ve made it this far, congratulations: you get the summary of the takeaways. If you remember nothing else, remember this:
- Accurate data is essential for proactive, efficient service.
- At best, inaccurate data leads to slower ticket resolution, increased security risks and higher costs.
- Data reconciliation is crucial for accurate, reliable AI recommendations.
- To leverage AI in service management, ensure you eliminate blind spots, remove data silos, and establish data governance/privacy frameworks.
Data will only become more important. This means that the accuracy of data is only becoming more important. Now is the time to get ahead of this by putting in place the infrastructure needed to make data a source of power, not a burden.
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This article was produced as part of Ny BreakingPro’s Expert Insights channel, where we profile the best and brightest minds in today’s technology industry. The views expressed here are those of the author and are not necessarily those of Ny BreakingPro or Future plc. If you are interested in contributing, you can read more here: https://www.techradar.com/news/submit-your-story-to-techradar-pro