Most businesses, globally are currently caught in a perfect storm of challenges and opportunities related to digital transformation, customer experience (CX) improvement, and exploiting emerging technologies – in particular artificial intelligence (AI) and smart automation.
From an IT service management (ITSM) perspective, it might all sound great. Especially since much of what’s currently used to optimize IT service delivery and support can also be employed to assist organizations with their digital transformation and customer experience strategies. Plus, of course, AI is a technology – and we all know that ITSM is about maximizing the business value and outcomes from technology investments and use.
But is all this really as straightforward as it sounds?
This article looks at the ITSM status quo and its ability to take advantage of the opportunities that these three “perfect storm clouds” bring – to help you better understand whether your IT organization is suitably equipped to deliver against the business requirements supported by the increased exploitation of technology and data.
Getting Digital Transformation Right
Digital transformation is one of those technology-based management buzz phrases that transcends the IT organization – with it a hot topic across multiple business functions and the organization.
Hopefully, the whole organization will be on the same page as to what digital transformation means (to both them and the organization) and what needs to be done to:
- Introduce new products and services (and revenue streams) based on both technology and data exploitation
- Improve customer engagement mechanisms – from “product investigation” touchpoints through customer conversion to retention, growth, and loyalty – again through technology and data exploitation.
- Improve back-office operations, particularly modernizing antiquated manual procedures, so they can successfully underpin the above two customer-facing elements.
There are many quoted pitfalls to avoid in delivering against a corporate digital transformation strategy, with a common one being the focus on new technologies at the expense of people – neglecting the fact that digital transformation is ultimately a business and people-related change.
However, there’s another important pitfall to avoid – hopefully clearer upon reading the above three bullets – that digital transformation relies not only on people and technology change but also on data and, importantly, the quality of data.
In particular, the quality of the service management data contained within your ITSM tool, for instance, ServiceNow.
Getting Your Customer Experience Right
There are many freely available definitions as to what CX is. For instance, that:
“A good customer experience means that the individual’s experience during all points of contact matches the individual’s expectations.”
Importantly, for service management pros, it’s not only a critical factor in winning, retaining, and growing external customers – it’s also a relevant driver of the service management strategies and policies related to employees (through the concept of employee experience management).
And to get CX and employee experience right, there’s a need to understand better what’s happening – in terms of service delivery and support – relative to the service-consumers’ expectations of what should be happening.
Thus, while organizations might invest in new technologies (that improve CX), more capable people, and understanding more about the customers they serve, there’s a potentially overlooked piece of the jigsaw – data and data quality.
Important, CX-related decisions will be made on data related to knowing your customers, and any decisions made on inaccurate data will likely result in suboptimal or potentially harmful decisions.
Optimizing AI and Automation Investments
As with the digital transformation realization – that a potential and common pitfall is the overlooking of people-change needs – the adoption of AI-enabled capabilities is currently being caveated with phrases such as: “You need to get your knowledge management capabilities, and then available knowledge, right to win with AI.”
I agree with this thinking – knowledge, information, and data fuel AI, especially machine learning. However, the issue is more extensive than insufficient knowledge management capabilities.
It just so happens that this focus on knowledge management capabilities is currently a hot topic thanks to the ITSM industry’s history of self-service investment failures. And we definitely can’t afford to focus on knowledge at the expense of data quality and information.
It should be obvious – AI and automation require accurate data, with data-quality issues guaranteed to affect the ability of AI, in particular, to serve its purpose and the ultimate outcomes of its use.
For instance, bots offer great potential to ITSM and broader business operations (potentially via enterprise service management). Still, they’ll only succeed if built on good-quality data.
Data Quality is An Important Foundation for This Perfect Storm
While each of the above three opportunities/challenges seeks to improve businesses across “better, faster, cheaper” although not necessarily against all three), it’s essential to recognize the criticality of data quality for all three digital transformations, CX improvement, and AI and smart automation adoption.
It should hopefully make sense – IT has long had the mantra of “bad data in, bad data out” – but poor-quality data does much more harm than this concept of “bad data out.” It will potentially derail an organization’s investment in digital transformation, CX, or AI (possibly all three).
What makes things worse is that poor data quality can be a hidden issue, such that decisions are being made without the knowledge that there are issues with the data.
Or, at best, decision makers feel that some data is incorrect, so they massage it to fit their opinions – when, again, it’s likely to deliver ill-informed decisions and suboptimal outcomes at best.
First part of this article series: We Blame “Bad” ITSM Tools but Is the Real Issue Hidden in Our Data?
Last part of this article series: The 7 Most-Common Data-Quality Issues in ITSM
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