The Big Data Roadmap To A Winning Big Data Strategy. Knowing what big data is, is one; knowing what a big data strategy is two; knowing how to implement that big data strategy is even more difficult. At least, that is how a lot of organisations perceive it. And it must be said, in large process-directed organisations, what most of the large corporates are, it can be difficult. Convincing the board and defining were to start could be a daunting task. While in fact the steps that need to be taken are clear and straightforward.
This roadmap can help you in defining and implementing the right big data strategy. First of all, organisations need to understand what big data is to begin with. Knowing what big data is can help you to get management buy-in at your organisation. When senior management or the board approves the decision to move forward, it is important to get together a multi-disciplinary team from all different departments within the organisation. Involving all departments within the organisation has a major advantage. Big Data y analítica avanzada para un mayor conocimiento del cliente. Del mismo modo que en el entorno social un cliente no se conforma exclusivamente con la información que le proporciona una marca y recopila información de diversas fuentes, cualquier empresa que se precie debe tener el mejor conocimiento posible de sus clientes.
Adaptar y convertir rápidamente la información en conocimiento constituye un mecanismo muy valioso para satisfacer, atraer y retener a los clientes. Las compañías líderes están combinando los recursos tradicionales y sistemas Big Data para ejecutar programas de analítica avanzada con los que descubrir tendencias, patrones y otros insights relativos a nuestros clientes; alentar la generación y consolidación de nuevos productos y servicios; y guiarnos hacia operativas más eficaces y eficientes.
El Big Data no es exclusivo de grandes organizaciones. Comprender el comportamiento de los clientes y no-clientes en los diferentes canales (tiendas físicas, call centers, mobile, e-commerce, medios sociales). A Fascinating Look Inside Those 1.1 Million Open-Internet Comments : All Tech Considered. When the Federal Communications Commission asked for public comments about the issue of keeping the Internet free and open, the response was huge. So huge, in fact, that the FCC's platform for receiving comments twice got knocked offline because of high traffic, and the deadline was extended because of technical problems.
So what's in those nearly 1.1 million public comments? A lot of mentions of the F word, according to a TechCrunch analysis. But now, we have a fuller picture. The San Francisco data analysis firm Quid looked beyond keywords to find the sentiment and arguments in those public comments. Quid, as commissioned by the media and innovation funder Knight Foundation, parsed hundreds of thousands of comments, tweets and news coverage on the issue since January. The firm looked at where the comments came from and what common arguments emerged from them. Check out the cluster map that visualizes the emergent themes: How To Read This Cluster Map Templates are not unusual. Gamification Is The Friendly Scout Of Big Data. In the coming years, big data will change the way organisations and societies are operated and managed.
Big data however, is not the only trend that will impact significantly how organisations operate. Another major trend at the moment is gamification. Gamification will change the way organisations connect with consumers and it will provide extremely valuable big data that can be turned into insights. Let’s first look at what gamification exactly is. Gamification is the use of game elements in non-game contexts. It can be used externally, to interact with customers and improve marketing efforts that lead to increased sales. Gamification is also used internally, where gamification can lead to increased employee productivity as well as internal crowd sourcing activities. The game elements that are often used in gamification are points, challenges, awards, leader boards, levels, avatars and badges. Gamification has the potential to become more and more integrated with our lives. Can Big Data Predict The Future? – Video. Understanding Descriptive, Predictive and Prescriptive Analytics.
Companies have long been involved in the analysis of how a company performed over time. As the history of big data shows, already for many years we try to understand how the organisations or the world around us behaves by analysing the available data. In the past this used to be merely descriptive analytics. This answers the question “what happened in the past with the business?”
With the availability of big data we entered the new area of predictive analytics, which focuses on answering the question: “what is probably going to happen in the future?” However, the real advantage of analytics comes with the final stage of analytics: prescriptive analytics. This type of analytics tries to answer the question: “Now what?” First of all, these three types of analytics should co-exist. Descriptive analytics is about the past Descriptive analytics helps organisations understand what happened in the past. Predictive analytics is about the future Image: WWTOnline Copyright Big Data Startups 2013. Using External Data in MDM Systems | InfoTrellis blog. June 10, 2014 by Jan D. Svensson Let me start by saying that this is not an article about big data. While the source of big data is external to your organization, it is a topic of its own. Many of the concepts and approaches discussed will definitely apply to your big data initiatives, but that won’t be the focus of this article.
External data is information that is sourced from outside of your organization. This could be information you purchase from a marketing or service organization, a government agency, the post office, or a business partner. External data can be used for various purposes in your MDM implementation. There are many approaches that can be used to integrate external data with your MDM implementation.
When integrating information from external sources, you may face the issue of which source to trust over another. Using External Data for Enrichment Data enrichment is the process of augmenting your MDM data with external information. External Data as Reference Data. Social Media, Big Data and Visualization - Hootsuite Social Media Management. Recently I participated in a panel discussion on social media, big data and visualization at the Vancouver Enterprise Forum, along with Tommy Levi, Senior Data Scientist at PlentyofFish.com, Stephen Ufford, Founder & CEO of Trulioo, Bruno Aziza, VP, Worldwide Marketing at SiSense.
Big data is one of the most promising – and hyped – trends in technology today. While notable companies like Facebook, Google and Netflix get most of the big data headlines, it’s quietly transforming entire industries behind the scenes, including retail, insurance and medical research. Most exciting of all, big data has the potential to improve our everyday lives by giving us insight into our social relationships, our habits, and the things we care about. That’s where big data collides with two other exciting fields: social media and data visualization. Here are some of the key takeaways from our conversation. What is big data? First off, let’s try to define what big data really is. Big Data, tres casos de éxito: T- Mobile, Unilever y MoneyBall.
Como hemos comentado en anteriores ocasiones, el Big Data es ya hoy en día una gran oportunidad en el ámbito del marketing. Los profesionales del campo deben entender bien su funcionamiento y las ventajas que presenta a la hora de diseñar y ejecutar campañas de marketing. En esta nueva entrega de nuestro especial os presentamos tres casos de éxito que muestran cómo aplicando estrategias y técnicas del Big Data podemos conseguir alcanzar nuestros objetivos (T-Mobile), importantes ventajas competitivas frente a nuestros rivales (Moneyball) o conocer con mayor precisión el comportamiento y las necesidades de nuestros consumidores (Unilever).
Cómo el Big Data ayudó a T-Mobile a reducir a la mitad el número de portabilidades T-mobile consiguió reducir a la mitad el número de portabilidades (de 100.000 el primer trimestre de 2011 a 50.000 en el segundo trimestre) gracias a la aplicación de técnicas sobre Big Data. Moneyball: el Big Data aplicado al baseball En BlogginZenith | Especial Big Data. Big Data Analytics: el futuro de la analítica digital vía @tristanelosegui. En el anterior post vimos los pasos previos al Big Data: basic y small data. Ahora nos toca adentrarnos en el mundo del Big Data. Es un tema que da para decenas de post, así que lo mejor es que empecemos por el principio. ¿Qué es Big Data? “Es el término que se refiere a un conjunto de datos tan grande y complejo, que resulta difícil de procesar usando los sistemas de gestión de bases de datos disponibles o las aplicaciones tradicionales de procesamiento de datos” Definición de Big Data de Wikipedia ¿Cuál es el origen del Big Data?
La realidad es que la gestión de grandes volúmenes de datos no es algo nuevo. Según IBM, se generan más de 2,5 quintillones de bytes al día. Los beneficios que ofrece el análisis de estos grandes volúmenes de información para las empresas e instituciones son inmensos. Tipos de datos en Big Data Smart Data Son todos los datos referentes al negocio (tanto online, como offline). Identity Data Open data Big Data Analytics Analizando:
Crowd Control Management in the Twente Region. The combined use of data can help companies achieve more information and make better business decisions, but big data will have also a major impact on they way public services like the police, health organizations or the fire brigade operate. In The Netherlands, a remarkable, and for The Netherlands unique, initiative took place in December 2012. During the week before Christmas, a Dutch radio station called 3FM organizes Serious Request, an annual benefit project that collects money for charity. For nine years this event is organized now and every year it is in a different location.
In 2012 the event took place in Enschede, in the Twente region. What did they do? They used three different tools to monitor what was going on in real-time in the centre of Enschede: Twitcident: Developed in conjunction with the Delft University of Technology, Twitcident is a tool that can sift through massive amounts of local tweets to find information about emergencies happening. Crowd Control Room. Doing Big Data Right. It’s really easy to say your company needs to use big data to be better. It’s another thing entirely to actually put that idea into practice and get results. Big data may be one of the hottest buzzwords going around right now, but that doesn’t make it any easier to implement within a company. It requires a firm commitment, a clear vision, and significant resources to make big data adoption a reality. Luckily, businesses small and large alike have a number of examples to look to of companies that have made the big data leap and have been wildly successful with it.
Here are just a handful of companies that may provide some inspiration. 1. Amazon Perhaps Amazon’s incredible success with big data shouldn’t come as a big surprise. 2. The airline industry can be very competitive, so it’s important for a company to stand out from the rest. 3. EBay has largely always been customer driven, but some of the site’s latest developments in utilizing big data are taking that concept even further. 4. Audience Modeling & Customer Lifetime Value 101. Customer lifetime value (CLV) is at the core of all of our advertising efforts. Being able to distinguish between good customers and bad customers (and all the grey in between) is what enables marketers to build scalable programs without being limited to a direct response. A good CLV model highlights the good and the bad, embracing variance in user behaviors. A bad model assumes a homogenous user — blurring the lines of actions and events to create an average response, crippling the marketer from ever maximizing opportunity.
The foundation of CLV modeling uses a RFM framework. RFM stands for Recency, Frequency, and Monetary Value. How recently the user interacted or purchasedHow often they interact or purchaseHow much they purchase The logic is difficult to argue with. Represented mathematically, a simplified CLV model might look like this: Where revenue is the gross contribution within a given time frame, r is retention rate, and d is the discount rate. (Source: Fader, Peter S., Bruce G. 10 Big Data Facts That You Should Know.
What are the top 10 Big Data facts that you need to know about Big Data? What are the most important aspects of the Big Data hype that your organisation should be aware of when developing a Big Data strategy? BigData-Startups has the answer for you and has created a list of the most important Big Data facts that you cannot ignore in the coming years (in any order). If your organisation requires help in developing that Big Data strategy, do not hesitate to contact us. 1) Big data requires a different culture In order to truly take advantage of Big Data, it is important to turn your organisation into an information-centric company. 2) Hadoop is not the Holy Grail Data in a Hadoop cluster is broken down into smaller pieces (called blocks) and distributed throughout the cluster.
However, Hadoop is not the Holy Grail. However, there are also quite some substantial disadvantages of Hadoop. 3) The real driver behind Big Data is the people within the organisation. Big Data and Little Data Can Give You a Competitive Advantage. Part One: Big Data Much has been written about Big Data. For the past several years, it’s been a hot subject, thanks to new technologies that allow us to obtain large amounts of information about our customers’ buying patterns, behaviors, etc. Used correctly, it will help you spot trends and make big-picture decisions. The definition of “Big Data,” according to Wikipedia, is a collection of data so large and complex it becomes difficult to process.
However, many companies embrace a different concept of Big Data. What data do you want? The bottom line is that you need to know what information is important to have. Part Two: Little Data “Little Data” refers to collecting information about individual customers or smaller groups of customers. For example, a car rental company knows from a customer’s past rentals that he likes a large SUV – even a specific color. Sometimes data is collected using very unscientific methods, like good, old-fashioned recognition. Two questions to ask: Conclusion: