Talent Intelligence: Unlocking The Power Of Multidimensional Data

Talent Intelligence: Unlocking The Power Of Multidimensional Data

Joanna Riley, CEO & cofounder of Censia. Striving for a more just and efficient global economy through better talent data and technology.

getty

In the age of data-driven decision-making, the significance of structured, multidimensional data cannot be overstated. This data type forms the backbone of advanced AI systems, facilitating nuanced insights and predictive analytics essential for modern business strategies, including talent management.

Data-Driven Recruiting And Talent Management

Companies that harness data in talent management have seen remarkable improvements in both efficiency and insights into workforce dynamics. For instance, incorporating data-driven strategies in recruiting has been shown to significantly enhance the quality of the workforce, increase productivity, and support unbiased hiring decisions. Workable’s comprehensive guide on data-driven recruiting emphasizes the importance of choosing the right data and metrics, collecting data efficiently, and acting on this data to improve hiring processes.

The Rise Of Multidimensional Data in HR

Multidimensional data isn’t a new concept. Most business intelligence (finance, consumer behavior, etc.) relies on structured data across multiple dimensions to gain profound insight and determine future strategy. HR has always been the big exception because HR has traditionally been one-dimensional (a linear resume of qualifications matched to a linear list of job requirements), often inaccurate or messy (outdated profiles, multiple job titles meaning similar things, etc.), and limited only the candidate and the role—not, for instance, other important factors such as the industry or the organization.

By cleaning and structuring this talent data and introducing new layers of information (company size, revenue levels, company events, industry and more), the insight that AI can derive from this data grows immensely.

The deployment of structured, multidimensional data in talent management allows organizations to gain a deep and holistic understanding of their workforce, encompassing diverse dimensions such as people, jobs, skills, and industries. This comprehensive dataset supports AI-driven solutions, enabling organizations to navigate complexities with precision and agility. MyHRfuture highlights how data-driven HR impacts recruitment and talent management by measuring key talent acquisition data points for greater impact, supporting workforce planning, and facilitating training and development.

Inferring Skills And Bridging Gaps

The capacity of structured data to infer skills is invaluable in talent acquisition and workforce planning, where accurate skill assessment is crucial. Analyzing patterns and contextual information allows AI-powered systems to uncover latent skills, providing a broader understanding of individuals’ capabilities.

Transformative Impact Of AI In Talent Management

The convergence of structured data and AI heralds a new era in talent management, with organizations increasingly turning to AI-driven solutions to streamline processes and decision-making. Korn Ferry’s insights into the telecom sector, for example, reveal the critical role of strategy and talent in organizational success, emphasizing the importance of learning agility as a predictor of long-term leadership potential.

To further underscore the transformative power of data in talent management, it’s essential to recognize the nuanced ways in which organizations leverage this data to foster a culture of continuous learning and adaptability. A data-driven approach streamlines recruitment and talent development and underpins strategic workforce planning and development initiatives. According to research by Deloitte, integrating data analytics into HR practices enables organizations to forecast talent needs, identify skill gaps, and optimize resource allocation more effectively. This strategic alignment ensures that talent management efforts are about filling positions and building a resilient, skilled workforce capable of driving long-term business growth.

Moreover, adopting AI and data analytics in talent management extends beyond operational efficiency to enhancing the employee experience. Organizations can address individual career aspirations and skill development needs by personalizing learning and development opportunities, thereby increasing engagement and retention. This personalized approach, grounded in data, signifies a shift from traditional, one-size-fits-all HR practices to more dynamic, responsive strategies that value and cultivate individual talent.

In this rapidly evolving landscape, harnessing and interpreting multidimensional data becomes a critical competitive advantage, enabling organizations to navigate the complexities of the modern workforce with agility and insight.

Common Pitfalls When Adopting Talent Intelligence

Several common pitfalls occur when organizations adopt this type of technology. The first is that system implementations are prone to failure. According to a Deloitte study, 70% of digital transformation efforts are considered less than successful, and organizations require three years to start competing in digital markets. Working with APIs or native integrations to upgrade current systems is one way to avoid the financial, time and engagement loss caused by this.

The other big pitfall is not understanding the data well enough. It is essential to ask how the data is collected, cleaned and structured. If a provider runs new algorithms on old data or limited source data, you’ll get biased and unreliable results.

And finally, you’ll want to understand the legal restrictions you might face in your field. Several regions have already restricted the use of AI in HR decision-making, so you’ll want to make sure that you deploy it in a way that assists, not replaces, your team and their decisions.

Embracing A Data-Driven Future

As organizations undergo digital transformation, the importance of leveraging structured, multidimensional data and AI will continue to grow. McKinsey’s guide on building a data-driven strategy highlights the need for an integrated approach to data sourcing, model building, and organizational transformation tailored to the company’s desired business impact.

In conclusion, structured, multidimensional data is the cornerstone of AI-driven talent management, enabling organizations to unlock insights, optimize processes, and drive strategic outcomes. By leveraging advanced analytics and AI technologies, organizations can chart a course toward a data-driven future where talent becomes a true differentiator in driving business success.

Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?

Transforming Your Organization with the Power of Business Intelligence

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With the ability to drive strategic initiatives, make educated decisions and extract valuable insights from raw data, business intelligence (BI) has become a key enabler for organizational success. BI has revolutionized the way businesses operate and plan for the future in this age of intense competition and rapid technological advancement. One of the most powerful business intelligence tools available, Intellicus has provided tailored solutions to over 17,000 small and large businesses, enabling them to make data-driven decisions.

Understanding Business Intelligence

Business Intelligence (BI) is a technology-driven process that analyzes business data to provide actionable information that informs strategic and operational decisions. It involves collecting, storing, analyzing, and visualizing data to uncover patterns, trends, and insights that drive business performance. It combines internal and external data sources into a logical framework that helps produce insights that can be put into action. Business intelligence is a valuable resource that facilitates managers, executives and stakeholders to make informed decisions.

Business Intelligence (BI) is a technology-driven process that analyzes business data to provide actionable information that informs strategic and operational decisions. It involves collecting, storing, analyzing, and visualizing data to uncover patterns, trends, and insights that drive business performance. Essentially, BI transforms raw data into meaningful information that empowers organizations to make data-driven decisions.

The Four Pillars of Business Intelligence

Data Collection and Integration: Business Intelligence starts with the compilation of information from various sources, such as social media interactions, market trends, consumer profiles and sales statistics. This data must be integrated into a single platform in order to provide a comprehensive view and analysis.

Data Visualization and Analysis: BI tools analyze trends, patterns, and connections using sophisticated analytics. Visualization tools like dashboards, charts, and graphs transform complex data into understandable insights for efficient decision-making.

Predictive Analytics: BI doesn’t merely focus on the present; it anticipates the future. To predict future trends, predictive analytics uses machine learning models and statistical algorithms. This makes it possible for companies to plan ahead and adapt proactively.

Actionable Insights: BI aims to generate actionable insights, not just reports. These insights help optimize operations, identify new opportunities, and enhance overall performance.

The Transformative Impact of Business Intelligence Improved Decision Making

BI tools give businesses access to real-time data, which is essential for making timely, well-informed decisions. This eliminates the need for guesswork and intuition-based decisions in favor of data-driven ones. Additionally, BI tools, like dashboards and visualizations, present data in an understandable format, making it easier for decision-makers to quickly comprehend complex information and react to changes that may have an impact on the business.

Operational Efficiency

The operational efficiency of an organization can be greatly improved using business intelligence. By automating repetitive operations, BI solutions allow employees to concentrate on more important facets of the company. Furthermore, by highlighting areas in the business process that require improvement, BI tools enable firms to get rid of bottlenecks, simplify procedures and cut expenses. Customer satisfaction and service delivery are enhanced as a result.

Improved Bottom Line

Business Intelligence can assist companies in increasing revenue and sales by offering insightful information about consumer behavior. With the use of BI technologies, marketers can better focus their campaigns and boost sales by analyzing customer data to find trends, buying patterns and preferences. Additionally, organizations can use business intelligence to pinpoint successful consumer categories and concentrate their marketing efforts on them. Revenue growth and improved conversion rates are possible outcomes of this focused strategy.

Competitive Advantage

Business Intelligence provides a competitive edge in today’s data-driven world. Through the utilization of BI tools, companies can acquire comprehensive insights into the tactics, advantages and disadvantages of their rivals and utilize this data to formulate strategic plans. Similarly, it facilitates prompt market adaptation and can assist companies in recognizing customer behavior shifts and market trends.

Ethical Considerations in BI

The Future of Business Intelligence

Advancing technology promises a bright future for BI. AI and machine learning will enhance predictive analytics, enabling businesses to foresee trends and adapt quickly. Integrating BI with emerging technologies like IoT and blockchain will unlock new dimensions of data analysis and decision-making.

Conclusion

Business Intelligence has evolved from being a mere buzzword to a transformative force shaping the future of businesses. It empowers organizations to navigate complexities, capitalize on opportunities, and drive growth in an increasingly data-centric world. Embracing BI isn’t just an option; it’s a necessity for businesses aspiring to thrive in today’s competitive landscape.

repeating the info from intro [SD1]

This doesn’t fit right here… please change the placement [SD2]

Talent Intelligence: Unlocking The Power Of Multidimensional Data

Joanna Riley, CEO & cofounder of Censia. Striving for a more just and efficient global economy through better talent data and technology.

getty

In the age of data-driven decision-making, the significance of structured, multidimensional data cannot be overstated. This data type forms the backbone of advanced AI systems, facilitating nuanced insights and predictive analytics essential for modern business strategies, including talent management.

Data-Driven Recruiting And Talent Management

Companies that harness data in talent management have seen remarkable improvements in both efficiency and insights into workforce dynamics. For instance, incorporating data-driven strategies in recruiting has been shown to significantly enhance the quality of the workforce, increase productivity, and support unbiased hiring decisions. Workable’s comprehensive guide on data-driven recruiting emphasizes the importance of choosing the right data and metrics, collecting data efficiently, and acting on this data to improve hiring processes.

The Rise Of Multidimensional Data in HR

Multidimensional data isn’t a new concept. Most business intelligence (finance, consumer behavior, etc.) relies on structured data across multiple dimensions to gain profound insight and determine future strategy. HR has always been the big exception because HR has traditionally been one-dimensional (a linear resume of qualifications matched to a linear list of job requirements), often inaccurate or messy (outdated profiles, multiple job titles meaning similar things, etc.), and limited only the candidate and the role—not, for instance, other important factors such as the industry or the organization.

By cleaning and structuring this talent data and introducing new layers of information (company size, revenue levels, company events, industry and more), the insight that AI can derive from this data grows immensely.

The deployment of structured, multidimensional data in talent management allows organizations to gain a deep and holistic understanding of their workforce, encompassing diverse dimensions such as people, jobs, skills, and industries. This comprehensive dataset supports AI-driven solutions, enabling organizations to navigate complexities with precision and agility. MyHRfuture highlights how data-driven HR impacts recruitment and talent management by measuring key talent acquisition data points for greater impact, supporting workforce planning, and facilitating training and development.

Inferring Skills And Bridging Gaps

The capacity of structured data to infer skills is invaluable in talent acquisition and workforce planning, where accurate skill assessment is crucial. Analyzing patterns and contextual information allows AI-powered systems to uncover latent skills, providing a broader understanding of individuals’ capabilities.

Transformative Impact Of AI In Talent Management

The convergence of structured data and AI heralds a new era in talent management, with organizations increasingly turning to AI-driven solutions to streamline processes and decision-making. Korn Ferry’s insights into the telecom sector, for example, reveal the critical role of strategy and talent in organizational success, emphasizing the importance of learning agility as a predictor of long-term leadership potential.

To further underscore the transformative power of data in talent management, it’s essential to recognize the nuanced ways in which organizations leverage this data to foster a culture of continuous learning and adaptability. A data-driven approach streamlines recruitment and talent development and underpins strategic workforce planning and development initiatives. According to research by Deloitte, integrating data analytics into HR practices enables organizations to forecast talent needs, identify skill gaps, and optimize resource allocation more effectively. This strategic alignment ensures that talent management efforts are about filling positions and building a resilient, skilled workforce capable of driving long-term business growth.

Moreover, adopting AI and data analytics in talent management extends beyond operational efficiency to enhancing the employee experience. Organizations can address individual career aspirations and skill development needs by personalizing learning and development opportunities, thereby increasing engagement and retention. This personalized approach, grounded in data, signifies a shift from traditional, one-size-fits-all HR practices to more dynamic, responsive strategies that value and cultivate individual talent.

In this rapidly evolving landscape, harnessing and interpreting multidimensional data becomes a critical competitive advantage, enabling organizations to navigate the complexities of the modern workforce with agility and insight.

Common Pitfalls When Adopting Talent Intelligence

Several common pitfalls occur when organizations adopt this type of technology. The first is that system implementations are prone to failure. According to a Deloitte study, 70% of digital transformation efforts are considered less than successful, and organizations require three years to start competing in digital markets. Working with APIs or native integrations to upgrade current systems is one way to avoid the financial, time and engagement loss caused by this.

The other big pitfall is not understanding the data well enough. It is essential to ask how the data is collected, cleaned and structured. If a provider runs new algorithms on old data or limited source data, you’ll get biased and unreliable results.

And finally, you’ll want to understand the legal restrictions you might face in your field. Several regions have already restricted the use of AI in HR decision-making, so you’ll want to make sure that you deploy it in a way that assists, not replaces, your team and their decisions.

Embracing A Data-Driven Future

As organizations undergo digital transformation, the importance of leveraging structured, multidimensional data and AI will continue to grow. McKinsey’s guide on building a data-driven strategy highlights the need for an integrated approach to data sourcing, model building, and organizational transformation tailored to the company’s desired business impact.

In conclusion, structured, multidimensional data is the cornerstone of AI-driven talent management, enabling organizations to unlock insights, optimize processes, and drive strategic outcomes. By leveraging advanced analytics and AI technologies, organizations can chart a course toward a data-driven future where talent becomes a true differentiator in driving business success.

Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?

Transforming Your Organization with the Power of Business Intelligence

Share

 

Share

 

Share

 

Email

 

With the ability to drive strategic initiatives, make educated decisions and extract valuable insights from raw data, business intelligence (BI) has become a key enabler for organizational success. BI has revolutionized the way businesses operate and plan for the future in this age of intense competition and rapid technological advancement. One of the most powerful business intelligence tools available, Intellicus has provided tailored solutions to over 17,000 small and large businesses, enabling them to make data-driven decisions.

Understanding Business Intelligence

Business Intelligence (BI) is a technology-driven process that analyzes business data to provide actionable information that informs strategic and operational decisions. It involves collecting, storing, analyzing, and visualizing data to uncover patterns, trends, and insights that drive business performance. It combines internal and external data sources into a logical framework that helps produce insights that can be put into action. Business intelligence is a valuable resource that facilitates managers, executives and stakeholders to make informed decisions.

Business Intelligence (BI) is a technology-driven process that analyzes business data to provide actionable information that informs strategic and operational decisions. It involves collecting, storing, analyzing, and visualizing data to uncover patterns, trends, and insights that drive business performance. Essentially, BI transforms raw data into meaningful information that empowers organizations to make data-driven decisions.

The Four Pillars of Business Intelligence

Data Collection and Integration: Business Intelligence starts with the compilation of information from various sources, such as social media interactions, market trends, consumer profiles and sales statistics. This data must be integrated into a single platform in order to provide a comprehensive view and analysis.

Data Visualization and Analysis: BI tools analyze trends, patterns, and connections using sophisticated analytics. Visualization tools like dashboards, charts, and graphs transform complex data into understandable insights for efficient decision-making.

Predictive Analytics: BI doesn’t merely focus on the present; it anticipates the future. To predict future trends, predictive analytics uses machine learning models and statistical algorithms. This makes it possible for companies to plan ahead and adapt proactively.

Actionable Insights: BI aims to generate actionable insights, not just reports. These insights help optimize operations, identify new opportunities, and enhance overall performance.

The Transformative Impact of Business Intelligence Improved Decision Making

BI tools give businesses access to real-time data, which is essential for making timely, well-informed decisions. This eliminates the need for guesswork and intuition-based decisions in favor of data-driven ones. Additionally, BI tools, like dashboards and visualizations, present data in an understandable format, making it easier for decision-makers to quickly comprehend complex information and react to changes that may have an impact on the business.

Operational Efficiency

The operational efficiency of an organization can be greatly improved using business intelligence. By automating repetitive operations, BI solutions allow employees to concentrate on more important facets of the company. Furthermore, by highlighting areas in the business process that require improvement, BI tools enable firms to get rid of bottlenecks, simplify procedures and cut expenses. Customer satisfaction and service delivery are enhanced as a result.

Improved Bottom Line

Business Intelligence can assist companies in increasing revenue and sales by offering insightful information about consumer behavior. With the use of BI technologies, marketers can better focus their campaigns and boost sales by analyzing customer data to find trends, buying patterns and preferences. Additionally, organizations can use business intelligence to pinpoint successful consumer categories and concentrate their marketing efforts on them. Revenue growth and improved conversion rates are possible outcomes of this focused strategy.

Competitive Advantage

Business Intelligence provides a competitive edge in today’s data-driven world. Through the utilization of BI tools, companies can acquire comprehensive insights into the tactics, advantages and disadvantages of their rivals and utilize this data to formulate strategic plans. Similarly, it facilitates prompt market adaptation and can assist companies in recognizing customer behavior shifts and market trends.

Ethical Considerations in BI

The Future of Business Intelligence

Advancing technology promises a bright future for BI. AI and machine learning will enhance predictive analytics, enabling businesses to foresee trends and adapt quickly. Integrating BI with emerging technologies like IoT and blockchain will unlock new dimensions of data analysis and decision-making.

Conclusion

Business Intelligence has evolved from being a mere buzzword to a transformative force shaping the future of businesses. It empowers organizations to navigate complexities, capitalize on opportunities, and drive growth in an increasingly data-centric world. Embracing BI isn’t just an option; it’s a necessity for businesses aspiring to thrive in today’s competitive landscape.

repeating the info from intro [SD1]

This doesn’t fit right here… please change the placement [SD2]

Talent Intelligence: Unlocking The Power Of Multidimensional Data

Joanna Riley, CEO & cofounder of Censia. Striving for a more just and efficient global economy through better talent data and technology.

getty

In the age of data-driven decision-making, the significance of structured, multidimensional data cannot be overstated. This data type forms the backbone of advanced AI systems, facilitating nuanced insights and predictive analytics essential for modern business strategies, including talent management.

Data-Driven Recruiting And Talent Management

Companies that harness data in talent management have seen remarkable improvements in both efficiency and insights into workforce dynamics. For instance, incorporating data-driven strategies in recruiting has been shown to significantly enhance the quality of the workforce, increase productivity, and support unbiased hiring decisions. Workable’s comprehensive guide on data-driven recruiting emphasizes the importance of choosing the right data and metrics, collecting data efficiently, and acting on this data to improve hiring processes.

The Rise Of Multidimensional Data in HR

Multidimensional data isn’t a new concept. Most business intelligence (finance, consumer behavior, etc.) relies on structured data across multiple dimensions to gain profound insight and determine future strategy. HR has always been the big exception because HR has traditionally been one-dimensional (a linear resume of qualifications matched to a linear list of job requirements), often inaccurate or messy (outdated profiles, multiple job titles meaning similar things, etc.), and limited only the candidate and the role—not, for instance, other important factors such as the industry or the organization.

By cleaning and structuring this talent data and introducing new layers of information (company size, revenue levels, company events, industry and more), the insight that AI can derive from this data grows immensely.

The deployment of structured, multidimensional data in talent management allows organizations to gain a deep and holistic understanding of their workforce, encompassing diverse dimensions such as people, jobs, skills, and industries. This comprehensive dataset supports AI-driven solutions, enabling organizations to navigate complexities with precision and agility. MyHRfuture highlights how data-driven HR impacts recruitment and talent management by measuring key talent acquisition data points for greater impact, supporting workforce planning, and facilitating training and development.

Inferring Skills And Bridging Gaps

The capacity of structured data to infer skills is invaluable in talent acquisition and workforce planning, where accurate skill assessment is crucial. Analyzing patterns and contextual information allows AI-powered systems to uncover latent skills, providing a broader understanding of individuals’ capabilities.

Transformative Impact Of AI In Talent Management

The convergence of structured data and AI heralds a new era in talent management, with organizations increasingly turning to AI-driven solutions to streamline processes and decision-making. Korn Ferry’s insights into the telecom sector, for example, reveal the critical role of strategy and talent in organizational success, emphasizing the importance of learning agility as a predictor of long-term leadership potential.

To further underscore the transformative power of data in talent management, it’s essential to recognize the nuanced ways in which organizations leverage this data to foster a culture of continuous learning and adaptability. A data-driven approach streamlines recruitment and talent development and underpins strategic workforce planning and development initiatives. According to research by Deloitte, integrating data analytics into HR practices enables organizations to forecast talent needs, identify skill gaps, and optimize resource allocation more effectively. This strategic alignment ensures that talent management efforts are about filling positions and building a resilient, skilled workforce capable of driving long-term business growth.

Moreover, adopting AI and data analytics in talent management extends beyond operational efficiency to enhancing the employee experience. Organizations can address individual career aspirations and skill development needs by personalizing learning and development opportunities, thereby increasing engagement and retention. This personalized approach, grounded in data, signifies a shift from traditional, one-size-fits-all HR practices to more dynamic, responsive strategies that value and cultivate individual talent.

In this rapidly evolving landscape, harnessing and interpreting multidimensional data becomes a critical competitive advantage, enabling organizations to navigate the complexities of the modern workforce with agility and insight.

Common Pitfalls When Adopting Talent Intelligence

Several common pitfalls occur when organizations adopt this type of technology. The first is that system implementations are prone to failure. According to a Deloitte study, 70% of digital transformation efforts are considered less than successful, and organizations require three years to start competing in digital markets. Working with APIs or native integrations to upgrade current systems is one way to avoid the financial, time and engagement loss caused by this.

The other big pitfall is not understanding the data well enough. It is essential to ask how the data is collected, cleaned and structured. If a provider runs new algorithms on old data or limited source data, you’ll get biased and unreliable results.

And finally, you’ll want to understand the legal restrictions you might face in your field. Several regions have already restricted the use of AI in HR decision-making, so you’ll want to make sure that you deploy it in a way that assists, not replaces, your team and their decisions.

Embracing A Data-Driven Future

As organizations undergo digital transformation, the importance of leveraging structured, multidimensional data and AI will continue to grow. McKinsey’s guide on building a data-driven strategy highlights the need for an integrated approach to data sourcing, model building, and organizational transformation tailored to the company’s desired business impact.

In conclusion, structured, multidimensional data is the cornerstone of AI-driven talent management, enabling organizations to unlock insights, optimize processes, and drive strategic outcomes. By leveraging advanced analytics and AI technologies, organizations can chart a course toward a data-driven future where talent becomes a true differentiator in driving business success.

Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?

De-siloing the enterprise: The challenges and opportunities in data consolidation

A hot topic in the data management space is the argument against data silos. Factors such as generative artificial intelligence and decentralized data infrastructures have intensified the need to embrace the data consolidation ethos.

RelationalAI’s Molham Aref discusses modern data platforms.

What is the imperative to de-silo data operations? And what challenges and opportunities exist as companies move toward interconnected data infrastructures?

“You’re building intelligent applications, and by definition, these applications have to look at data,” said Molham Aref (pictured), chief executive officer of RelationalAI Inc. “The old architecture is where you’re moving a few records of data over to some Java to do something, to book you an airline seat, that doesn’t work anymore. You’re now doing things that require you to predict and you need to look at lots of data. And so, the new architectures will move the semantics and the business logic to the data. And my view is that business logic is going to have to change to fit in the modern data stack.”

Aref spoke with theCUBE Research’s Dave Vellante and George Gilber at the Supercloud 7: Get Ready for the Next Data Platform event, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed the future of data consolidation as one that integrates and harmonizes data with business logic, creating a seamless and intelligent ecosystem.

Data consolidation: Key players and battles

The last decade has seen significant developments in the data stack. Snowflake Inc. emerged as a dominant force in the database engine space, while Databricks Inc. took the lead in managing data pipelines. The field of data formats is another competition frontier, one that has seen the emergence of Iceberg as a dominant player, according to Aref.

“I think the next layer is about semantics,” he said. “Where do you put application logic and semantics in this new world? And where do you put it in a world where people are asking to rethink applications in terms of intelligent applications?”

One inalienable theme in the various contests across the data stack is openness. The next frontier is the catalog wars, where the aim is to unbundle catalogs from the respective technologies, allowing for greater flexibility and interoperability. This unbundling is a critical step toward the next layer of evolution: semantics.

“Let’s say, I have information about people in my database,” Aref said. “And let’s say there’s an entry for Dave, an entry for George, an entry for Molham. I might want to compute our ages from our birthdays. Now, I can just add an extra column and key in our ages, but that’s not good, because as time passes, I’ll have to keep updating our ages as we all get older. Instead of just storing the raw data, I might put in a bit of business logic that says, ‘A person’s age is equal to today minus their date of birth.’ And anytime someone needs to look up any of our ages, okay, I will just apply the semantics, the business logic.”

In traditional systems, application logic was often written in languages such as Java or COBOL, detached from the data itself. The modern approach expresses business logic relationally, ensuring it coexists seamlessly with the data. This transition to data consolidation is essential for applications that are capable of making predictions and optimizations based on vast amounts of data, according to Aref.

The legacy challenge and path forward

The current state of enterprise data management is a patchwork of thousands of applications, each with its data model and logic. This fragmented architecture is a product of technological constraints and business needs from decades past. However, with advancements in scalability, algorithms and data structures, there is an opportunity to revisit and overhaul these systems, according to Aref.

“Because the relational databases at the time were not powerful enough and scalable enough and their pricing models were such that they were too expensive to run the business logic, as a workaround, we pulled that logic out, we implemented by hand procedurally, because we didn’t have the relational language that could express it declaratively,” he said. “We had to live with this two-brain architecture, where the database had no idea what the application logic was doing. And the application logic didn’t understand databases.”

The goal is to move toward a common model where data and business logic are integrated into a unified framework. This approach simplifies the architecture, reduces redundancy and facilitates better decision-making. Subsequently, a gradual refactoring of legacy applications will occur, bringing forth a modern, data-centric framework, without the need for a disruptive big bang rewrite, Aref added.

Stay tuned for the complete video interview, part of SiliconANGLE’s and theCUBE Research’s coverage of the Supercloud 7: Get Ready for the Next Data Platform event.

Photo: SiliconANGLE Your vote of support is important to us and it helps us keep the content FREE. One click below supports our mission to provide free, deep, and relevant content.   Join our community on YouTube Join the community that includes more than 15,000 #CubeAlumni experts, including Amazon.com CEO Andy Jassy, Dell Technologies founder and CEO Michael Dell, Intel CEO Pat Gelsinger, and many more luminaries and experts.

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Talent Intelligence: Unlocking The Power Of Multidimensional Data

Joanna Riley, CEO & cofounder of Censia. Striving for a more just and efficient global economy through better talent data and technology.

getty

In the age of data-driven decision-making, the significance of structured, multidimensional data cannot be overstated. This data type forms the backbone of advanced AI systems, facilitating nuanced insights and predictive analytics essential for modern business strategies, including talent management.

Data-Driven Recruiting And Talent Management

Companies that harness data in talent management have seen remarkable improvements in both efficiency and insights into workforce dynamics. For instance, incorporating data-driven strategies in recruiting has been shown to significantly enhance the quality of the workforce, increase productivity, and support unbiased hiring decisions. Workable’s comprehensive guide on data-driven recruiting emphasizes the importance of choosing the right data and metrics, collecting data efficiently, and acting on this data to improve hiring processes.

The Rise Of Multidimensional Data in HR

Multidimensional data isn’t a new concept. Most business intelligence (finance, consumer behavior, etc.) relies on structured data across multiple dimensions to gain profound insight and determine future strategy. HR has always been the big exception because HR has traditionally been one-dimensional (a linear resume of qualifications matched to a linear list of job requirements), often inaccurate or messy (outdated profiles, multiple job titles meaning similar things, etc.), and limited only the candidate and the role—not, for instance, other important factors such as the industry or the organization.

By cleaning and structuring this talent data and introducing new layers of information (company size, revenue levels, company events, industry and more), the insight that AI can derive from this data grows immensely.

The deployment of structured, multidimensional data in talent management allows organizations to gain a deep and holistic understanding of their workforce, encompassing diverse dimensions such as people, jobs, skills, and industries. This comprehensive dataset supports AI-driven solutions, enabling organizations to navigate complexities with precision and agility. MyHRfuture highlights how data-driven HR impacts recruitment and talent management by measuring key talent acquisition data points for greater impact, supporting workforce planning, and facilitating training and development.

Inferring Skills And Bridging Gaps

The capacity of structured data to infer skills is invaluable in talent acquisition and workforce planning, where accurate skill assessment is crucial. Analyzing patterns and contextual information allows AI-powered systems to uncover latent skills, providing a broader understanding of individuals’ capabilities.

Transformative Impact Of AI In Talent Management

The convergence of structured data and AI heralds a new era in talent management, with organizations increasingly turning to AI-driven solutions to streamline processes and decision-making. Korn Ferry’s insights into the telecom sector, for example, reveal the critical role of strategy and talent in organizational success, emphasizing the importance of learning agility as a predictor of long-term leadership potential.

To further underscore the transformative power of data in talent management, it’s essential to recognize the nuanced ways in which organizations leverage this data to foster a culture of continuous learning and adaptability. A data-driven approach streamlines recruitment and talent development and underpins strategic workforce planning and development initiatives. According to research by Deloitte, integrating data analytics into HR practices enables organizations to forecast talent needs, identify skill gaps, and optimize resource allocation more effectively. This strategic alignment ensures that talent management efforts are about filling positions and building a resilient, skilled workforce capable of driving long-term business growth.

Moreover, adopting AI and data analytics in talent management extends beyond operational efficiency to enhancing the employee experience. Organizations can address individual career aspirations and skill development needs by personalizing learning and development opportunities, thereby increasing engagement and retention. This personalized approach, grounded in data, signifies a shift from traditional, one-size-fits-all HR practices to more dynamic, responsive strategies that value and cultivate individual talent.

In this rapidly evolving landscape, harnessing and interpreting multidimensional data becomes a critical competitive advantage, enabling organizations to navigate the complexities of the modern workforce with agility and insight.

Common Pitfalls When Adopting Talent Intelligence

Several common pitfalls occur when organizations adopt this type of technology. The first is that system implementations are prone to failure. According to a Deloitte study, 70% of digital transformation efforts are considered less than successful, and organizations require three years to start competing in digital markets. Working with APIs or native integrations to upgrade current systems is one way to avoid the financial, time and engagement loss caused by this.

The other big pitfall is not understanding the data well enough. It is essential to ask how the data is collected, cleaned and structured. If a provider runs new algorithms on old data or limited source data, you’ll get biased and unreliable results.

And finally, you’ll want to understand the legal restrictions you might face in your field. Several regions have already restricted the use of AI in HR decision-making, so you’ll want to make sure that you deploy it in a way that assists, not replaces, your team and their decisions.

Embracing A Data-Driven Future

As organizations undergo digital transformation, the importance of leveraging structured, multidimensional data and AI will continue to grow. McKinsey’s guide on building a data-driven strategy highlights the need for an integrated approach to data sourcing, model building, and organizational transformation tailored to the company’s desired business impact.

In conclusion, structured, multidimensional data is the cornerstone of AI-driven talent management, enabling organizations to unlock insights, optimize processes, and drive strategic outcomes. By leveraging advanced analytics and AI technologies, organizations can chart a course toward a data-driven future where talent becomes a true differentiator in driving business success.

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Innovative Data Strategies: Leveraging AI For Smarter Enterprise Operations

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