Transform Your Data Strategy with AI Analytics

AI data analytics is becoming an integral part of modern data strategies.

As AI analytics capabilities become more advanced, organizations can extract more actionable insights from their data assets.

This article explores how AI can be leveraged within a comprehensive data strategy to enhance decision-making, improve operational efficiency, and create competitive advantages.

What Is AI Analytics?

AI analytics uses machine learning to discover insights, find new patterns, and discover relationships in the data.

AI analytics tools automate much of the work usually performed by data analysts. That’s not to say AI can replace human analysts, however. These machine learning algorithms help data analysts work faster, analyze more data, and monitor data with far greater granularity.

AI-augmented analytics help data analysts see patterns in data and discover more valuable insights.

Why Adopt AI Data Analytics

To stay competitive in the digital economy, a company’s internal processes and products need to be intelligent—and that intelligence comes from data and AI.

Digital-native companies have an advantage in adopting AI analytics, but established companies face challenges due to legacy infrastructure, a digitally immature workforce, and ingrained work practices.

AI adoption isn’t easy. Many organizations get stuck in a cycle of pilot projects without achieving large-scale transformation.

Overcoming this hurdle requires a strong commitment from leadership. They must recognize the importance of AI data analytics to all decision-making processes. Business leaders must be highly involved in and supportive of all aspects of executing data and AI analytics strategies and initiatives.

Setting the Vision

Setting and communicating the vision for a data strategy involves defining clear business goals and aligning AI priorities accordingly. Start with achievable use cases, such as process optimization, to demonstrate value and gain buy-in.

Consider both existing business optimization and new data-driven business opportunities. AI priorities are determined by business priorities. AI analytics will make different contributions depending on where it is used, so consider the business case for each area and AI’s impact on the case when assessing where to focus the AI data analytics effort.

Data Management and Governance

High-quality, accessible data is the foundation for successful AI. To deliver maximum value, data should be structured according to the European Commission’s Findable–Accessible–Interoperable–Reusable (FAIR) principles.

Assessing the data’s “FAIR-ness” is a practical way to start building a data asset. This assessment involves evaluating the current data to see what data exists, where it’s stored, and how it’s accessed. This review should also establish the data’s quality.

Other valuable questions to ask during this assessment are:

• Can the data be linked to other data?
• How easy is it to retrieve the data?
• Have we missed any obvious data sources?

Once the data’s current state has been assessed, the team can create a roadmap for its development.

When building the data asset, start with the data needed for the most important business cases. This might seem obvious, but often, data engineering teams don’t fully understand the business functions that require data-driven insights.

Solution Architecture and Technology

For existing, pre-digital businesses, legacy infrastructure may require modernization to support AI data analytics applications and ensure their seamless integration with operational systems.

New technology investments may be needed during the transition phase, and the transition from traditional IT systems to digital applications can be a lengthy process. Automation and AI will reduce costs in the long run, but in the near term, costs will likely increase as old and new solutions work in tandem.

Data Protection, Privacy, and Regulation

AI data analytics teams must proactively address data protection and privacy concerns and ensure that the solution they’re building is compliant with the laws concerning data handling and storage.

Regulatory oversight of private data is growing worldwide—for obvious reasons. Data breaches are becoming more costly to both individuals and businesses. The global average cost of a data breach in 2024 is nearly $5 million, according to a report by IBM. This represents a 10 percent increase over 2023.

When defining data usage policies, the development team should work closely with legal experts, business owners, and AI strategists. Ensure transparency and explainability of AI solutions, especially in high-risk areas.

Building the Right Team

When building the AI analytics team, aim for a diversity of expertise across business, data science, data management, and technology areas.

At the outset, hire a chief data and AI officer with experience in business, data, data science, and technology. This role oversees the hiring of all necessary talent and streamlines team setup.

Also, don’t neglect the role of the AI strategist. This role bridges the gap between business and data science. AI strategists help organizations develop and implement plans for using artificial intelligence to achieve their business goals. They combine technical expertise in AI with strategic business acumen to drive innovation and competitive advantage.

Adopting the Right Operating Model

In addition to hiring the right team, it’s crucial to create a Center of Excellence (CoE) to initially spearhead AI data analytics initiatives. An AI steering group will help prioritize use cases and guide the development of the model.

Centralize budgets to avoid fragmented efforts and ensure company-wide benefits. Align incentives across business and data teams to foster collaboration and drive business impact.

Managing AI and Data

AI algorithms are reusable assets. To use them effectively, establish code repositories and standards for efficiency, and implement maintenance processes for both the data and the algorithms. These practices help ensure ongoing value and scalability.

Additionally, focus on the AI models’ explainability and transparency. This can help address regulatory requirements and improve user trust. As more machine-learning algorithms are deployed, their explainability is getting greater scrutiny.

Common Pitfalls

Implementing an AI data analytics strategy can be a tricky endeavor. Here are some common pitfalls organizations encounter when implementing data and AI strategies.

Lack of a Clear Business Vision and Goals

Organizations often jump into data and AI initiatives without first defining their business goals and how data and AI can help achieve them. Data and AI are tools that can enhance decision-making, automate processes, and enable faster delivery, but they cannot replace a sound business vision and strategy.

Insufficient Focus on Data Management and Governance

High-quality data is fundamental to successful AI implementation. Organizations frequently struggle with data that is scattered across various systems, in different formats, or lacking key attributes. This makes it difficult to build a reusable data asset, leading to inefficient data science activities and an inability to scale AI solutions.

Neglecting Solution Architecture and Technology

Organizations often overlook the importance of a robust technical infrastructure to support their data and AI initiatives. Legacy systems and inadequate investment in new technologies can hinder the transition to a data-driven organization. A well-defined target architecture and a roadmap for its development are essential for success.

Underestimating the Importance of Human Skills

Implementing data and AI strategies requires new roles and skills within an organization. Often, companies solely focus on hiring data scientists while neglecting critical roles like data engineers, data architects, and AI strategists. The lack of these supporting roles can lead to frustration among data scientists and hinder the development of a scalable data foundation.

Ineffective Data and AI Organization and Operating Model

The organizational structure and operating model for data and AI initiatives must be carefully considered. A centralized Center of Excellence (CoE) can help initially, but its role should evolve as the organization matures. Establishing clear lines of communication, decision-making processes, and budget allocation are crucial for successful collaboration between business units and data experts.

Ignoring Data Protection, Privacy, and Regulation

Data privacy and compliance are paramount. Organizations must define their data protection policies in line with regulations like GDPR and consider the ethical implications of their AI solutions. Failure to do so can lead to legal issues, reputational damage, and a loss of customer trust.

Insufficient Algorithm Management and Explainability

Like data assets, algorithms should be treated as valuable assets with established maintenance processes, code repositories, and standards. Additionally, the explainability of AI algorithms is becoming increasingly important, particularly for “high-risk” applications, as stakeholders demand transparency and understanding of how AI systems make decisions.

Focusing Solely on Automation

The goal of data and AI strategies should not be to simply automate all decision-making. Instead, organizations should strive for a harmonious integration of human expertise and AI capabilities, where automation is used strategically to augment human intelligence and achieve optimal business outcomes.

The Path to Data and AI Maturity

Successful execution of an AI data analytics strategy involves a clear roadmap that aligns with your business strategy from the outset and continues to align as operations scale.

The ultimate goal isn’t complete automation but a data-driven culture in which all employees effectively utilize AI analytics tools. At peak AI maturity, the entire business moves in lockstep, silos are dissolved, and everyone uses AI and data as part of their daily routine.

By addressing the challenges and best practices we’ve outlined, organizations can unlock AI data analytics’ transformative potential for sustainable growth and competitive advantage.