Building A Strong Data Foundation: The Key To Successful Ai Integration In Business

Richa Arora, Senior Director Of Data Governance, Cbre

Richa Arora, Senior Director Of Data Governance, Cbre

Salesforce's recent acquisition of Informatica highlights a significant trend in the business landscape: the need for a strong data foundation to support the development of AI. As organizations increasingly adopt AI to drive growth and profitability, it is evident that the success of these AI initiatives hinges on the quality and management of the underlying data.

Before implementing AI, organizations must ensure they have a robust data foundation in place.

The Five Pillars of Data Foundation

1. Data Quality: Data is often referred to as the "fuel" that drives AI. Data quality represents the fuel’s grade. Organizations should adopt a strategy that focuses on monitoring, cleansing and enriching data to instil confidence and reliability in the related insights.

2. Data Governance: Effective data governance provides the guard rails to maintain data quality and is imperative to build stakeholder trust. This involves establishing clear roles, responsibilities and policies that guide employees in managing data effectively. Without these measures in place, organizations risk falling into data chaos. A wellstructured data governance framework, bolstered by trustworthy metadata, ensures compliance and nurtures ethical AI practices.

3. Data Accessibility and Integration: The ability to integrate diverse data sources is a must for achieving a comprehensive view of organizational data. A strategy that breaks down silos and encourages data integration creates an ecosystem that allows information to flow freely. AI thrives on interconnected datasets. In other words, fragmented datasets impede holistic growth.

4. Data Security and Privacy:Compliance with privacy and data protection regulatory requirements is key to building lasting client trust. Addressing ethical considerations from the outset and striving for explainable AI (XAI) are essential steps in safeguarding client information and ensuring responsible AI practices.

5. Data Literacy: Data literacy is the ability to explore, understand, and communicate with data in a meaningful way. Encouraging data literacy across all departments marks the cultural shift needed so most employees can interpret and effectively leverage data insights. If your workforce doesn’t understand your data, then it is illequipped to make informed decisions.

CBRE’s Success Story

As a global leader in commercial real estate for over 30 years, CBRE exemplifies how a well-integrated AI strategy can deliver superior client value. AI is a core component of our service offerings. By integrating AI into our workflows, we streamline operations, improve decision-making and deliver tangible benefits to our clients.

CBRE boasts the largest commercial real estate dataset globally, with trillions of data points ingested from more than 300 different sources. This fuels our AI capabilities and enables us to combine artificial intelligence with human expertise and generate powerful insights. However, this data also presents challenges, like historical data quality, variety, and data disparity.

Encouraging Data Literacy Across All Departments Marks The Cultural Shift Needed So Most Employees Can Interpret And Effectively Leverage Data Insights

At CBRE, we treat data as a product designed to serve our AI applications. We strive for data governance excellence through a global enterprise program. Our commitment extends beyond our own data; we advocate for high data quality across our clients' datasets as well.

Pragmatism and adherence to safety requirements guide CBRE’s AI initiatives. We recognize AI's limitations and honour the trust our clients place in us to manage their data. Cross-functional collaboration is a cornerstone of our approach, bringing together business leaders, data scientists, data governance specialists, and AI engineers from ideation through implementation.

With access to the largest trove of data in the industry, our AI platform, Ellis AI empowers us to drive transformation, efficiency, and productivity for both clients and our workforce, all through conversational AI interfaces. These interfaces enable users to quickly analyse vast amounts of data, automate routine tasks, extract key information, and work smarter by summarizing text, translating languages, and generating insights.

Measuring Success

The ultimate measure of alignment between data and AI strategies is not just internal efficiency; it is the impact felt by our clients. We monitor key metrics such as operational costs, customer satisfaction (CSAT), and client engagement to optimize our AI solutions continually. Continuous client feedback creates a positive reinforcement cycle to fine tune our AI offerings.

The future of competitive advantage depends on the synergy between data and AI strategies. Adopting a holistic approach to data management from the beginning leads to intelligent insights, which empower organizations to enhance client satisfaction. By focusing on The Five Pillars of Data Foundation, businesses can increase client loyalty and drive sustainable growth.

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Building A Strong Data Foundation: The Key To Successful Ai Integration In Business

AI

Data Management

Data Security

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