June 19, 2024

Executive Q&A: Data Quality, Trust, and AI

Data observability is the process of interrogating data as it flows through a marketing stack -- including data used to drive an AI process. Data observability provides crucial visibility that helps users both interrogate data quality and understand the level of data quality prior to building an audience or executing a campaign. Data observability is traditionally done through visual tools such as charts, graphs, and Venn diagrams, but is itself becoming AI-driven, with some marketers using natural language processing and LLMs to directly interrogate the data used to fuel AI processes. ... In a way, data silos are as much a source of great distress to AI as they are to the customer experience itself. A marketer might, for example, use a LLM to help generate amazing email subject lines, but if AI generates those subject lines knowing only what is happening in that one channel, it is limited by not having a 360-degree view of the customer. Each system might have its own concept of a customer’s identity by virtue of collecting, storing, and using different customer signals. When siloed data is updated on different cycles, marketers lose the ability to engage with a customer in the precise cadence of the customer because the silos are out of synch with a customer journey.

Only 10% of Organizations are Doing Full Observability. Can Generative AI Move the Needle?

The potential applications of Generative AI in observability are vast. Engineers could start their week by querying their AI assistant about the weekend’s system performance, receiving a concise report that highlights only the most pertinent information. This assistant could provide real-time updates on system latency or deliver insights into user engagement for a gaming company, segmented by geography and time. Imagine being able to enjoy your weekend and arrive at work with a calm and optimistic outlook on Monday morning, and essentially saying to your AI assistant: “Good morning! How did things go this weekend?” or “What’s my latency doing right now, as opposed to before the version release?” or “Can you tell me if there have been any changes in my audience, region by region, for the past 24 hours?” These interactions exemplify how Generative AI can facilitate a more conversational and intuitive approach to managing development infrastructure. It’s about shifting from sifting through data to engaging in meaningful dialogue with data, where follow-up questions and deeper insights are just a query away.

The Ultimate Roadmap to Modernizing Legacy Applications

IT leaders say they plan to spend 42 percent more on average on application modernization because it is seen as a solution to technical debt and a way for businesses to reach their digital transformation goals, according to the 2023 Gartner CIO Agenda. But even with that budget allocated, businesses still face significant challenges, such as cost constraints, a shortage of staff with appropriate technical expertise, and insufficient change management policies to unite people, processes and culture around new software. To successfully navigate the path forward, IT leaders need a strategic roadmap for application modernization. The plan should include prioritizing which apps to upgrade, aligning the effort with business objectives, getting stakeholder buy-in, mapping dependencies, creating data migration checklists and working with trusted partners to get the job done. ... “Even a minor change to the functionality of a core system can have major downstream effects, and failing to account for any dependencies on legacy apps slated for modernization can lead to system outages and business interruptions,” Hitachi Solutions notes in a post.

Is it time to split the CISO role?

In one possible arrangement, a CISO reports to the CEO and a chief security technology officer (CSTO), or technology-oriented security person, reports to the CIO. At a functional level, putting the CSTO within IT gives the CIO a chance to do more integration and collaboration and unites observability and security monitoring. At the executive level, there’s a need to understand security vulnerabilities and the CISO could assist with strategic business risk considerations, according to Oltsik. “This kind of split could bring better security oversight and more established security cultures in large organizations.” ... To successfully change focus, CISOs would need to get a handle on things like the financials and company strategy and articulate cyber controls in this framework, instead of showing up every quarter with reports and warnings. “CISOs will need to incorporate their risk taxonomy into the overall enterprise risk taxonomy,” Joshi says. In this arrangement, however, the budget could arise as a point of contention. CIO budgets tend to be very cyber heavy these days, Joshi explains, and it could be difficult to create the situation where both the CISO and CIO are peers without impacting this allocation of funds.

Empowering IIoT Transformation through Leadership Support

To gain project acceptance and ultimately ensure project success will rely heavily on identifying all key stakeholders, nurturing an on-going level of mutual trust and maintaining a strong focus on targeted end results. This involves a full disclosure of desired outcomes and a willingness to adapt to individual departmental nuances. Begin with a cross-department kickoff/planning meeting to identify interested parties, open projects, and available resources. Invite participation through a discovery meeting, focusing on establishing the core team, primary department, cross-department dependencies, and consolidating open projects or shareable resources. ... Identifying all digital data blind spots at the outset highlights the scale of the problem. While many companies have Artificial Intelligence (AI) and Business Intelligence (BI) initiatives, their success depends on the quality of the source data. Consolidating these initiatives to address digital data blind spots strengthens the data-driven business case. Once a critical mass of baselines is established, projecting Return On Investment (ROI) from both a quantification and qualification perspective becomes possible.?

Will more AI mean more cyberattacks?

Organisations are also potentially exposing themselves to cyber threats through their own use of AI. According to research by law firm Hogan Lovells, 56 per cent of compliance leaders and C-suite executives believe misuse of generative AI within their organisation is a top technology-associated risk that could impact their organisation over the next few years. Despite this, over three-quarters (78 per cent) of leaders say their organisation allows employees to use generative AI in their daily work. One of the biggest threats here is so-called ‘shadow AI’, where criminals or other actors make use of, or manipulate, AI-based programmes to cause harm. “One of the key risks lies in the potential for adversaries to manipulate the underlying code and data used to develop these AI systems, leading to the production of incorrect, biased or even offensive outcomes,” says Isa Goksu, UK and Ireland chief technology officer at Globant. “A prime example of this is the danger of prompt injection attacks. Adversaries can carefully craft input prompts designed to bypass the model’s intended functionality and trigger the generation of harmful or undesirable content.” Jow believes organisations need to wake up to the risk of such activities.

Read more here ...