“In god we trust, all others bring me data”

Deming, an American statistician, has made the sentence rather famous. From Erich Schimdt (former Google´s CEO) to Barry Beracha (former Sara Lee Bakery Group´s CEO), they all have used it.

At the heart of the matter lies the importance of analytics today. Its role has shifted to the heart of the decision making process. Decisions are based on data.

Indeed, in some sectors (consumer products, finance, retail, travel or entertainment) analytics was the key to success many years ago (in firms such as Marriot, Amazon, Harrah´s, the Boston Red Sox, Capital One, UPS, Barclays Bank, Procter and Gamble) so that nowdays reluctant firms have been obliged by the market to be more analytical ( ). However, not all the companies have been succesful in implementing and adopting analytics.

CHARACTERISTICS

These companies have three differential attitudes:

  • Widespread use of modeling and optimization. Companies look for a comprehensive understanding of their customers, prices, and products through predictive modelling (instead of descriptive statistics).
  • An enterprise approach. Data needs to be consolidated in just one source (instead of spread in different departments). It is also important to consider having a centralised human resources group dealing with the data and the visibility of  analytics within the company.
  • Senior executive advocates.  However important everyone agrees analytics to be, it needs a “big push” from top level executives.

PRACTICES

  • The right focus: different leading points
  • The right culture: achieve a companywide respect for analytics: measuring, testing and evaluating. For instance, base decisiones on hard facts
  • The right people: analytical talented, business oriented and relationship skilled
  • The right technology: competing analytics require a technology which tales into account or includes a data strategy, business intelligence software and high capacity computing hardware

WHICH ARE THESE COMPETITIVE ADVANTAGES?

COSTIn terms of efficiency, and being activity based.

PRECISION

REPEATABILITY AND COMPLIANCE

AGIL

 

HOW TO IMPLEMENT THEM?

Investment in technology

Storing and builidng a data management strategy

Data culture

Timing/Long term perspective: even if a company decides to implement an analytics strategy,  people and technology will need to be adjusted, that is to say: the company needs to give training to its existing employees, hire new “fresh” ones and preserve managers. “On the technical side”, as has been previously said, data management (from integration to quality control and tests)  requires large periods of time.

 

WHERE DO YOU FIND ANALYTICS IN A FIRM?

SUPPLY CHAIN

PRICING

HUMAN CAPITAL

PRODUCT & SERVICE QUALITY

FINANCIAL PERFORMANCE

R & D

CUSTOMER (Selection, Royalty and Service)

Source:

Big Data

Big data es un concepto referido a la posibilidad de utilización de grandes cantidades de información, que puede provenir tanto de la actividad de una empresa como de los particulares, también de los ciudadanos en su relación con la Administración pública, de la actividad en redes sociales, de los estaciones meteorológicas, los cajeros automáticos, la geolocalización de fotos enviadas a la nube o los sensores de tráfico de un ayuntamiento. Todos aquellos datos que los sistemas tradicionales no pueden procesar ni almacenar y mucho menos analizar son los Big Data.  Se definen por sus tres V: gran volumen de datos, velocidad de estos datos y variedad del origen de los mismos.

El valor del Big data radica, por tanto, en el análisis que sea capaz de realizar cada empresa, gobierno, universidades o individuos de los datos disponibles; y en las aplicaciones múltiples que puedan llevarse a cabo en el campo comunicativo y cultural, en el ámbito sociopolítico y el económico.

LÍNEAS DE ESTUDIO
– Enfoques prácticos sobre recogida de grandes masas de datos y sus aplicaciones comunicativas, culturales y sociales.
– Problemas planteados a los derechos individuales:privacidad, intimidad, secreto, olvido.
– Sistemas de inclusión de Big Data en métodos y herramientas en universidades y empresas.
– Utilización en Internet y Redes sociales.
– La Administración Pública, empresas y organismos ante el Big data: transparencia y participación democrática.
– Nuevos aspectos jurídicos provocados por Big data.
– Innovación social: Smart Cities, sanidad, telecomunicaciones y energía ante el Big data.
– Innovación económica: nuevos negocios, nuevas profesiones y nichos de trabajo.