What are the IT barriers that hinder organizations in maximizing the potential of analytics and AI, as seen through the perspective of IT leaders? How can they navigate the challenges and provide data and AI solutions without compromising governance? Let’s explore the answers in this article. 

1. Table of Contents

   An overwhelming 61% of IT leaders encounter difficulties when it comes to implementing and refining analytics and AI projects.

Achieving successful scalability in analytics and AI projects hinges on operationalization and continuous refinement, yet numerous IT leaders encounter challenges in this domain. One major contributing factor is the lack of synergy between IT and business units. Often, data experts collaborate with IT leaders for oversight to develop analytics and AI solutions that fail to align with the specific requirements of business users. This disconnect can result in low adoption rates and erode trust in the generated insights. 

From a methodological perspective, operationalization demands a streamlined, effective process encompassing integration, testing, deployment, impact measurement, and ongoing performance monitoring. Any inconsistencies in packaging and release can subtly degrade a model’s performance during the transition from development to production. 

Traditionally, the responsibility for refactoring data products to meet the requirements of target IT ecosystems, including performance and security standards, falls on the data engineering or IT team. However, facilitating a smooth handoff between the data team and IT or data engineering teams becomes significantly more manageable when both parties employ the same tools and share a common understanding of project objectives. Once again, effective communication, even within technical teams, emerges as a critical factor. 

2. A notable 55% of IT leaders face challenges related to either the availability of high-quality data or the ease of providing appropriate access to such data.

High-quality data serves as the cornerstone of prosperous analytics and AI initiatives. Regrettably, many organizations grapple with data-related obstacles that impede their scalability endeavors. These common issues encompass: 

• Data Silos: The presence of disparate data sources scattered across various departments can hinder IT leaders’ ability to access and integrate data efficiently. Data silos obstruct the attainment of a comprehensive organizational perspective, resulting in incomplete or inaccurate insights.
   
• Data Governance and Security: Subpar data governance and security practices can jeopardize data integrity and raise concerns regarding data privacy. Ensuring that data is managed in a compliant and secure manner is vital for fostering trust in analytics and AI projects.
   
• Data Accuracy and Completeness: Low-quality data, characterized by issues like duplicate records, missing values, or outdated information, can lead to erroneous conclusions and suboptimal decision-making. 

To address the challenge of inadequate data quality, IT leaders should concentrate on implementing robust data governance frameworks. This involves collaborating with data executives, such as Chief Data Officers (CDOs), to establish data policies, delineate data ownership, and set data quality standards. 

Additionally, the implementation of data cleansing and enrichment procedures should be prioritized to enhance the quality of existing data. Regular audits and the ongoing monitoring of data pipelines are indispensable to ensure data accuracy and currency. Data pipelines, in essence, serve as the lifeblood of analytics and AI projects. Even the most brilliant AI strategies will falter without the requisite data. 

Notably, our survey findings indicate that a significant majority (57%) of senior AI professionals within IT or information security departments acknowledge having designated owners responsible for data quality. Even if IT stakeholders aren’t directly responsible for data quality, they can orchestrate data quality during intricate data ingestion processes and delegate data quality tasks to the business departments, who possess an intimate understanding of the data, facilitating a decentralized approach that retains control. 

3. Nearly half, or 45%, of IT leaders grapple with a deficiency in the necessary data expertise or a workforce that possesses data literacy.

To bridge the talent gap and cultivate a workforce proficient in data, IT leaders must prioritize upskilling efforts and forge strong collaborations with business units. Drawing from insights provided by Capgemini, IT organizations can foster such collaboration and dismantle silos by: 

• Promoting Cross-Functional Collaboration: IT leaders should actively encourage interaction among data scientists, engineers, subject matter experts, and business stakeholders. This can be achieved by fostering regular communication, establishing interdisciplinary teams, and fostering a culture of knowledge sharing. Notably, our survey results align with this approach, as 76% of IT leaders affirm the interdisciplinary nature of their advanced analytics teams, exemplifying the synergy between business and data expertise.
   
• Implementing Agile Workflows: Agile methodologies, such as Scrum or Kanban, empower IT teams to break down projects into manageable segments, facilitating iterative development and quicker time-to-market. The adoption of agile workflows enables organizations to adapt swiftly to evolving requirements and deliver AI solutions that closely align with the ever-changing needs of the business.
   
• Investing in AIOps Skill Development: The upskilling of employees is paramount for the successful scaling of AI initiatives. IT organizations should proactively provide training programs and resources that equip their workforce with the requisite AIOps skills. This investment in skill development ensures that teams are well-prepared to make meaningful contributions to AI projects, thereby nurturing a culture of continuous learning and innovation that drives value delivery. 

From Adversity to Achievement: Overcoming IT Barriers

Scaling analytics and AI projects presents multifaceted challenges, including operationalization, data quality, and workforce readiness. IT leaders are central to surmounting these hurdles, ensuring the successful implementation and adoption of data-driven solutions. 

By fostering collaboration between IT and business units, embracing agile methodologies, and instilling a data-centric culture, organizations can bolster their capacity to operationalize and refine analytics and AI projects. Prioritizing robust data governance and implementing data cleansing procedures can effectively address data-related impediments. 

Ultimately, cultivating a data-savvy workforce through targeted upskilling and cross-functional cooperation empowers organizations to harness the full potential of analytics and AI, fostering innovation and gaining a competitive edge in the data-driven landscape of today.