HBR: Aproveche al máximo el escaso talento Data-Mining
Make the Most of Scarce Data-Mining Talent
The immense promise of big data to reveal new opportunities and deliver practical business results has so far been focused on technologies and models, and less on the human challenges of staffing roles and processes to take advantage of big data’s promise. The technology may be abundant, but developing, recruiting and hiring the people to use it is becoming an acute challenge for Fortune 1000 companies. Defining the roles, recruiting talented practitioners, setting up center of competence structures, establishing data governance across business units, and tying advanced data and analytics (AD&A) to the results of those businesses is lagging the deployment of tools and the collection of the data.
The Talent Gap in Big Data
By 2018, the United States alone could face a shortage of as many as 190,000 people with deep analytical skills according to a study by the McKinsey Global Institute. The study also found a looming need for over 1.5 million managers and analysts who understand big data and how to apply it to business operations. More than 70% of the Fortune 1000 companies surveyed by NewVantage Partners said it was “very difficult” to source analytical skills, with more than a third of the respondents saying their current level of AD&A skills are less than adequate.
To some extent, higher education is stepping into the gap. North Carolina State University worked with locally-based SAS — a leader in business analytic software — to offer a Master of Science in Analytics (MSA). Other universities are establishing similar programs, but until the supply of qualified candidates catches up with demand, most organizations are focusing on the internal development of big data skills by creating data literacy programs to establish a baseline level of knowledge for all their employees and setting professional tracks to build data skills and nurture career paths to retain their existing specialists.
Human resources departments are also looking globally at traditionally analytically-intensive sectors ranging from meteorology to medicine and finance to find talented candidates. This talent isn’t cheap: a top data scientist can command a $300,000 salary in the current market.
The Nature of the Roles in an AD&A Organization
There are clear categories of staff roles required to drive a successful advanced data and analytics agenda. Companies that are far along in advanced data and analytics have adopted a roughly similar model organized around a center of excellence with three types of talent:
1. Technical and Data Specialists: These positions range from data quality managers who ensure the collected data are clean and accurate to business solution architects who assemble the data and organize it so it is ready for analysis.
2. Analysts and Data Scientists: This includes the foot soldiers of the function who sift through mountains of data seeking insights and the “ninja” scientists who create sophisticated models to predict customer behavior and allow advanced customer segmentation and pricing optimization.
3. Business Analytics and Solutions specialists: These people are aligned by domain and sometimes sit within the business units they serve. This category would include insights analysts who turn models into actions and are the primary interface between the center of excellence and the business units.
Form a Center of Excellence to Extend Scarce Resources
Advanced organizations are making the most of their scarce resources by centralizing their analytics organizations into centers of analytic excellence (COEs) to act as a hub to serve business units and departments. Success in companies that have adopted a COE model depends on strong leadership by a leader who can break down silos and foster a strong culture of customer service for internal customers who may lack confidence in the value and trustworthiness of data models. The best COEs measure performance not on volume or speed but by their impact on business success. COEs need clear governance on how advanced data and analytical decisions are made, laying out how impact is measured and constantly reviewing the COE’s agenda to ensure the business units are using the team in a way that leads to practical results.
The lessons we have learned from our work in big data is that success comes when companies make strategic hires from hotbeds of data-driven cultures such as Silicon Valley to identify and recruit the new breed of leaders who understand the technology, science and data behind advanced data and analytics. Successful big data practitioners leverage strategic partnerships to obtain scarce talent across geographies while sourcing globally with the understanding that the analyst and scientist roles are especially dispersed. Finally, big data success requires a change in culture to be driven from the top to retain the new breed of data-driven managers while convincing the rest of the organization to be more oriented and aligned around big data and its potential.
Achieving Impact by Changing the Business Culture
Most important, COE leaders are acting as change leaders to drive data-driven approaches into the business, and shift the culture from art to science. Some leaders are achieving this by focusing on the business units that can become reference cases across the entire corporation. In addition, leaders are praising and rewarding the individuals and groups in their company who have made the transition to advanced analytics and big data. With techniques like these, COE leaders are creating a wave of front line change.
The immense promise of big data to reveal new opportunities and deliver practical business results has so far been focused on technologies and models, and less on the human challenges of staffing roles and processes to take advantage of big data’s promise. The technology may be abundant, but developing, recruiting and hiring the people to use it is becoming an acute challenge for Fortune 1000 companies. Defining the roles, recruiting talented practitioners, setting up center of competence structures, establishing data governance across business units, and tying advanced data and analytics (AD&A) to the results of those businesses is lagging the deployment of tools and the collection of the data.
The Talent Gap in Big Data
By 2018, the United States alone could face a shortage of as many as 190,000 people with deep analytical skills according to a study by the McKinsey Global Institute. The study also found a looming need for over 1.5 million managers and analysts who understand big data and how to apply it to business operations. More than 70% of the Fortune 1000 companies surveyed by NewVantage Partners said it was “very difficult” to source analytical skills, with more than a third of the respondents saying their current level of AD&A skills are less than adequate.
To some extent, higher education is stepping into the gap. North Carolina State University worked with locally-based SAS — a leader in business analytic software — to offer a Master of Science in Analytics (MSA). Other universities are establishing similar programs, but until the supply of qualified candidates catches up with demand, most organizations are focusing on the internal development of big data skills by creating data literacy programs to establish a baseline level of knowledge for all their employees and setting professional tracks to build data skills and nurture career paths to retain their existing specialists.
Human resources departments are also looking globally at traditionally analytically-intensive sectors ranging from meteorology to medicine and finance to find talented candidates. This talent isn’t cheap: a top data scientist can command a $300,000 salary in the current market.
The Nature of the Roles in an AD&A Organization
There are clear categories of staff roles required to drive a successful advanced data and analytics agenda. Companies that are far along in advanced data and analytics have adopted a roughly similar model organized around a center of excellence with three types of talent:
1. Technical and Data Specialists: These positions range from data quality managers who ensure the collected data are clean and accurate to business solution architects who assemble the data and organize it so it is ready for analysis.
2. Analysts and Data Scientists: This includes the foot soldiers of the function who sift through mountains of data seeking insights and the “ninja” scientists who create sophisticated models to predict customer behavior and allow advanced customer segmentation and pricing optimization.
3. Business Analytics and Solutions specialists: These people are aligned by domain and sometimes sit within the business units they serve. This category would include insights analysts who turn models into actions and are the primary interface between the center of excellence and the business units.
Form a Center of Excellence to Extend Scarce Resources
Advanced organizations are making the most of their scarce resources by centralizing their analytics organizations into centers of analytic excellence (COEs) to act as a hub to serve business units and departments. Success in companies that have adopted a COE model depends on strong leadership by a leader who can break down silos and foster a strong culture of customer service for internal customers who may lack confidence in the value and trustworthiness of data models. The best COEs measure performance not on volume or speed but by their impact on business success. COEs need clear governance on how advanced data and analytical decisions are made, laying out how impact is measured and constantly reviewing the COE’s agenda to ensure the business units are using the team in a way that leads to practical results.
The lessons we have learned from our work in big data is that success comes when companies make strategic hires from hotbeds of data-driven cultures such as Silicon Valley to identify and recruit the new breed of leaders who understand the technology, science and data behind advanced data and analytics. Successful big data practitioners leverage strategic partnerships to obtain scarce talent across geographies while sourcing globally with the understanding that the analyst and scientist roles are especially dispersed. Finally, big data success requires a change in culture to be driven from the top to retain the new breed of data-driven managers while convincing the rest of the organization to be more oriented and aligned around big data and its potential.
Achieving Impact by Changing the Business Culture
Most important, COE leaders are acting as change leaders to drive data-driven approaches into the business, and shift the culture from art to science. Some leaders are achieving this by focusing on the business units that can become reference cases across the entire corporation. In addition, leaders are praising and rewarding the individuals and groups in their company who have made the transition to advanced analytics and big data. With techniques like these, COE leaders are creating a wave of front line change.
por Brad Brown y Brian Henstorf | 10 a.m. 17 de enero 2014
La inmensa promesa de grandes volúmenes de datos para revelar nuevas oportunidades y ofrecer resultados prácticos de negocios hasta el momento se ha centrado en tecnologías y modelos, y menos en los desafíos humanos de los roles y procesos de dotación de personal para aprovechar la promesa de grandes datos. La tecnología puede ser abundante, pero desarrollar, reclutar y contratar a las personas que lo utilizan se está convirtiendo en un desafío grave para las compañías de Fortune 1000. Definir las funciones, la contratación de profesionales con talento, la creación de centro de las estructuras de competencia, el establecimiento de la gobernabilidad de datos a través de unidades de negocio, y la vinculación de datos avanzadas y análisis (AD & A) a los resultados de esas empresas se está retrasando el despliegue de herramientas y la recolección de los datos.
La brecha de talento en Big Data
En 2018, sólo en los Estados Unidos podría enfrentar una escasez de nada menos que 190.000 personas con capacidad de análisis profundos de acuerdo con un estudio realizado por el Instituto Global McKinsey. El estudio también encontró una necesidad inminente de más de 1,5 millones de gestores y analistas que entienden los grandes datos y la forma de aplicarlo a las operaciones de negocio. Más del 70% de las compañías Fortune 1000 encuestados por NewVantage Partners dijo que era "muy difícil" a la fuente de la capacidad de análisis, con más de un tercio de los encuestados diciendo que su nivel actual de habilidades AD & A son menos que adecuadas.
Hasta cierto punto, la educación superior es entrar en la brecha. Universidad del Estado de Carolina del Norte trabajó con SAS de base local - un líder en software de análisis de negocio - para ofrecer una Maestría en Ciencias en Analytics (MSA). Otras universidades están estableciendo programas similares, pero hasta que la oferta de candidatos calificados pone al día con la demanda, la mayoría de las organizaciones se están centrando en el desarrollo interno de las habilidades de grandes datos mediante la creación de programas de alfabetización de datos para establecer un nivel básico de conocimientos para todos sus empleados y el establecimiento profesional pistas para desarrollar habilidades de datos y fomentar las carreras de conservar sus especialistas existentes.
Los departamentos de recursos humanos también están buscando a nivel mundial en sectores tradicionalmente analíticamente-intensivos que van desde la meteorología a la medicina y las finanzas para encontrar candidatos cualificados. Este talento no es barata: un científico de datos superior puede exigir un salario de 300.000 dólares en el mercado actual.
La naturaleza de los roles en un AD & A Organización
Hay categorías claras de las funciones del personal necesarios para conducir un programa avanzado de datos y análisis acertado. Las empresas que están más avanzadas en los datos avanzados y análisis han adoptado un modelo más o menos similar organizado en torno a un centro de excelencia con tres tipos de talento:
1. Especialistas de Datos Técnicos y: Estas posiciones van desde gestores de calidad de datos que aseguran los datos recogidos son limpios y precisos a arquitectos de soluciones de negocios que se reúnen los datos y organizarlos para que esté listo para su análisis.
2. Los analistas y científicos de datos: Esto incluye los soldados de infantería de la función que tamizar a través de montañas de datos en busca de puntos de vista y los científicos "ninja" que crean sofisticados modelos para predecir el comportamiento del cliente y permitir la segmentación de clientes avanzados y optimización de precios.
3. Business Analytics y especialistas Soluciones: Estas personas están alineados por dominio y, a veces se sientan dentro de las unidades de negocio a las que sirven. Esta categoría incluiría insights analistas que recurren los modelos en acciones y que son la principal interfaz entre el centro de excelencia y las unidades de negocio.
Formar un Centro de Excelencia para extender Recursos Escasos
Organizaciones avanzadas están haciendo la mayor parte de sus escasos recursos mediante la centralización de sus organizaciones de análisis en los centros de excelencia analítica (COE) para que actúe como un centro para servir a las unidades de negocio y departamentos. El éxito en las empresas que han adoptado un modelo COE depende de un liderazgo fuerte por un líder que puede derribar los silos y fomentar una sólida cultura de servicio al cliente para los clientes internos que pueden carecer de confianza en el valor y la fiabilidad de los modelos de datos. El mejor rendimiento de medida COE no en volumen o la velocidad, sino por su impacto en el éxito del negocio. COE necesitan gobernanza clara sobre cómo se toman los datos avanzados y decisiones analíticas, por el que se cómo se mide el impacto y la revisión constante de la agenda del Consejo de Europa para asegurar las unidades de negocios están utilizando el equipo de una manera que conduce a resultados prácticos.
Las lecciones que hemos aprendido de nuestro trabajo en los grandes datos es que el éxito viene cuando las empresas hacen contrataciones estratégicas de focos de culturas basadas en datos tales como el Silicon Valley de identificar y reclutar a la nueva generación de líderes que entienden la tecnología, la ciencia y los datos detrás de avanzada datos y análisis. Practicantes de grandes datos El éxito de aprovechar las alianzas estratégicas para obtener el escaso talento a través de geografías, mientras que el abastecimiento a nivel mundial con el entendimiento de que los roles de analistas y científicos están especialmente dispersos. Por último, el éxito grande de datos requiere un cambio de cultura para la conducción de la parte superior para retener la nueva generación de directores por datos mientras que convencer al resto de la organización a ser más orientados y alineados en torno a los grandes datos y su potencial.
Lograr Impacto por Cambio de la Cultura Empresarial
Lo más importante, los líderes del COE están actuando como líderes del cambio para impulsar enfoques basados en datos en el negocio, y cambiar la cultura del arte a la ciencia. Algunos líderes están logrando esto, centrándose en las unidades de negocio que pueden convertirse en casos de referencia en toda la corporación. Además, los líderes están alabando y recompensar a los individuos y grupos en su empresa que han hecho la transición a la analítica avanzada y datos grandes. Con técnicas como éstas, los líderes del COE están creando una ola de cambios en primera línea.
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