The Analytics Lifecycle Allows an Iterative Approach to Customer Analytics

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In un recente seminario web, Christine Richards, direttore dei servizi di conoscenza presso l'Istituto di Analytics Utility condiviso tre raccomandazioni per utilità guardando verso implementando customer analytics.

 Contatore dati sono solo una fonte. Mentre Richards ha convenuto che il contatore dati sono importanti, ha anche esortato utilità di considerare altre fonti di dati e sistemi che supportano l'analisi operazioni dei clienti da tutta l'azienda.Automatizzare per efficienza. A causa del volume di dati, è fondamentale per determinare la modalità di filtro che informazioni ha bisogno di intervento umano e la cui analisi possono essere automatizzato per ridurre l'impegno di tempo, liberando ai dipendenti di concentrarsi su questioni più complesse di cliente.Creare un team interfunzionale. Mettere la squadra giusta nel luogo ora e l'assegnazione di risorse dedicate servirà utilities cos? come gli sforzi di customer operations analytics espandere.

Infine, Richards ha esortato utilità per visualizzare Google analytics come uno strumento per raggiungere un obiettivo strategico, non l'obiettivo di per sé. Noi non potevamo più d'accordo. Il valore in Google analytics è la capacità di acquisire conoscenze che consentono di utilità migliorare affidabilità, servizi e relazioni con i clienti attraverso la catena del valore energetico.

Brian Jore, direttore di Utility Business Consultant presso Teradata seguito presentazione di Richard per discutere un approccio iterativo di customer analytics le operazioni che lo rendono molto più facile per utilities iniziare. Jore inizia dalla definizione dei dati integrati Google analytics come, 'che unisce e correlazione di dati disparati per scoprire nuove intuizioni di business e ottimizzare i processi.

I motivo utilities necessità integrato dati per eseguire analisi sono quello di superare la necessità di acrobazie di dati. Jore citati esempi di processi manuali, fogli di calcolo, skunk works e disparate applicazioni che lo rendono difficile da tirare insieme tutti i dati e utilizzarlo per spostare l'ago su operazioni aziendali.

Per combattere questo status quo, Jore introdotto l'analisi del ciclo di vita.

Applicando questo costrutto customer analytics, un'utilità inizia con dati di contatori intelligenti e lo circonda con altri dati del cliente per costruire il repository dei dati integrati su cui possono essere applicati segmentazione. Questo approccio di segmentazione consente rapidamente il targeting dei clienti che si adattano al profilo per i servizi che si sta creando. E lo dimostra anche il valore dei dati integrati per guadagnare buy-in da leader del programma di utilità per ampliare la portata del vostro programma di Google analytics.

Questo approccio dimostra come le interazioni con i dati possono funzionare. Ad esempio, dati integrati rende facile legare in un approccio di marketing multicanale e semplificare i processi perché riutilizza le regole di business stesso, consistente nel data warehouse per identificare questi clienti consentendo lo sviluppo di messaggi di marketing personalizzati per l'uso in call center.

Il ciclo di vita di Analytics dà utilità un grande ritorno sui loro investimenti creando un quadro che è un fondamento per un processo di business creare nuove intuizioni che consentono agli utenti aziendali di interporre se stessi quando un processo non funziona come previsto. Questo è molto diverso — e più penetranti — che un insieme di cruscotti e report a fine mese.

La ruota di three-step intende continuamente il cerchio in maniera cronometrica. Ogni passo fornisce valore, ma il centro è il concetto di dati integrati che continuamente si evolve come il ciclo si ripete nel tempo. Il quadro di Analytics rimuove efficacemente l'aspetto manuale della raccolta e assemblaggio dei dati per ciascuna analisi.

Passo 1: Analizzare & esplorare:
Questo passaggio non è sulla generazione di un report. Molti programmi di utilità non so qual è il requisito di uscita quando iniziano. In altre parole, essi lo saprete quando lo vedono. Utilità di bisogno di un ambiente che consente loro di accedere ai dati, applicare diverse ipotesi e segmentazione dinamica che conduce alla scoperta.

Sostenitori di Jore sfruttando tutti toccano punti con un cliente per capire dove si trovano le opportunità. Questo processo aiuta a facilitare approfondimenti per gli utenti business regolari, ma anche per gli analisti più avanzati in termini di fornitura di correlazioni e analisi del percorso; i passi principali che portano a un determinato comportamento del cliente. Una volta scoperto, l'utilità pu? monitorare tale comportamento attivamente e con ogni iterazione meglio prevedere che cosa potrebbe causare il comportamento del cliente.

Passo 2: Allineare & ottimizzare:
Prendere le intuizioni e i segmenti di clientela individuati nella prima fase di lavorare verso scoprendo la combinazione ideale di prodotti e servizi per ogni segmento. L'utilità pu? anche imparare in quale misura tali profili sono stati penetrati e ingrandita.

Inoltre, marketing di utilità pu? cominciare a determinare l'efficacia di canale marketing individuando come clienti rispondono alle offerte e comunicazioni collocato in diversi canali, come ad esempio web, call center, e-mail e customer portal.

Con queste intuizioni in mano, canali possono ora essere ottimizzati per approfittare delle opportunità. Gli esempi includono la capacità di aumentare l'adozione di programmi di risposta richiesta per i servizi regolamentati o a scala lead generation per i fornitori di energia al dettaglio.

Passo 3: Produzione & Tracking:
Attraverso il lavoro svolto nei primi due passaggi, utilities svilupperanno una serie di regole di business. Questi servono come parametri che possono essere utilizzati coerentemente attraverso canali per produrre l'output che stai cercando. Con questo processo automatizzato, Utility inizierà a non dover interporre e manualmente kick off relazioni. Invece, sarà sufficiente eseguire.

Con segnalazione automatica, gli utenti aziendali possono iniziare a prendere azione, capire le tendenze, conoscere nuove opportunità e identificare le aree dove comportamento del cliente non è in movimento in una direzione che vuole che l'utilità — problemi di credito e raccolte, per esempio.

Una volta che sono stati identificati i comportamenti specifici, possono sempre essere apportate modifiche per raffinatezza. Ma ti consigliamo anche di tenere traccia di questi parametri per capire le diverse transizioni. Questo conduce al passo 1: analizzare & esplorare per continuare l'evoluzione delle intuizioni si sta guadagnando da integrato analisi di dati.

Essenzialmente, il ciclo di vita di Google Analytics permette di utilities agire sui dati, piuttosto che spendere tutto il loro tempo di ricreare i dati e le analisi.

Per ulteriori informazioni, Guarda il webcast on-demand.

5 steps to making BI more intelligent in big data Analytics

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In recent months, I met with the Business Intelligence (BI) teams in different countries to discuss Big Data Analytics. What transpired from the meetings is clear lack of awareness of what Big Data Analytics can do for the BI team and how Big Data Analytics fit within the enterprise data warehousing (EDW). As ambassadors to their business community, BI teams have the opportunity to be at the forefront of new technology trends and be able to articulate the value of Big Data Analytics to business stakeholders.

The Big Data trend has been here for a while and there is no shortage of publically available resources on the subject. However, many of these sources do not seem to allow the audience “to see the wood for the trees”! Also, storage vendors such as Dell and EMC are not helping the situation either by confusing the BI teams with low cost storage aspects in preference over business value of Big Data Analytics. I believe that paying attention to business value of Big Data Analytics will make the BI team not only look smarter in front of the business stakeholders but also make it easier to get funding for Big Data Analytics projects which many of the BI teams are considering as an opportunity to advance their career ambition.  

In the next few paragraphs below I have described in a few steps some essentials of Big Data Analytics in technical terms and how they fit into the enterprise data warehousing ecosystem as unified data architecture (UDA) that supports the next era of analytics and business insights. Many of these examples are related to the airline industry but the principles equally apply  to any industry.   

Step 1: Getting to know the essentials of Big Data

First step to Big Data Analytics is to understand the new technology capabilities such as Map Reduce, Hadoop, SQL-Map Reduce (SQL-MR) and how they fit within the enterprise ecosystem. It is also important to understand the differences in approaches between traditional EDW and Big Data Analytics design, development and implementation processes.

For instance, if you are in the airline industry, you would have designed the enterprise data warehouse for transactional reporting and analysis with structured stable schema and normalised data model.

You probably stored unstructured data such as ticket image, recorded audio conversations with customer service agent and ticketing / fare rules in the database as BLOB (Binary Long Object). Furthermore, you may have found it difficult to write in declarative SQL language the complex business rules such as financial settlements of inter-line agreement from code share arrangements, open jaw fare rules, say between Zone 1 and Zone 3, and business rules for fuel optimisation; so, you may have resorted to procedural languages such as user defined functions (UDF).

But UDFs have numerous limitations that MapReduce, more specifically, SQL-MapReduce (SQL-MR) makes it easy to solve while allowing for high performance parallel processing.

- What if you are able to use MapReduce API (Application Programming Interface) through which you can implement a UDF in the language of your choice?
- What if this approach allows maximum flexibility through polymorphism by dynamically allowing determination of input and output schema at query plan-time based on available information?  
- What if it increases reusability by enabling inputs with many different schemas or with different user-specified parameters?
- Further, what if, SQL-MR functions can be leveraged by any BI tools that you are familiar with?

As you can guess, SQL-MapReduce (SQL-MR) overcomes the limitations of UDF by leveraging the power of SQL to enable Big Data Analytics by performing relational operations efficiently while leaving non-relational tasks to procedural MapReduce functions.

You will see some examples of this later but, first and foremost, what is MapReduce? MapReduce is a parallel programming framework invented by Google and popularised by Yahoo!.MapReduce enables parallelism for non-relational data. By making parallel programming easier, MapReduce creates a new category of tools that allows BI teams to tackle Big Data problems that were previously challenging to implement. It should be noted that unlike the core competency for parallelism of the Teradata’s relational database technology over the last 30 years, MapReduce is not a database technology. Instead, MapReduce relies on file system called Hadoop Distributed File System (HDFS). Both MapReduce and HDFS are the open source versions of the Big Data technologies.

Step 2: “Hello World” welcomes you to the world of MapReduce with “Word Count”

Let’s take look at how Hadoop MapReduce works! When you wrote your first program you may have tested it to make sure “Hello World” works by printing / displaying the words correctly. With MapReduce, you will most likely to be testing Word Counts in your MapReduce program.

A MapReduce (MR) program essentially performs a group-by-aggregation in parallel over a cluster of machines. A programmer provides a map function that dictates how the grouping is performed, and a reduce function that performs the aggregation.

Let’s say that you want to create a Book Index from Big Data Analytics for Dummies. When writing your MR program, you will provide a map function that dictates how the grouping is performed on paragraphs containing words, and a reduce function that performs the aggregation of the words to produce the book index. The MapReduce framework will assume responsibility to distribute the Map program to the cluster nodes where parts of the book is located, processed, and output to intermediate files.  The output of the map processing phase is a collection of key-value pairs written to intermediate flat files. The output of the reduce phase is a collection of smaller files containing summarized data. The key-value pairs of words above are reduced to aggregates that produce the book index.

Because the MR program runs in parallel you will notice tremendous increase in reading (e.g. grouping of paragraphs from Big Data Analytics for Dummies) and processing speed (e.g. summarising and aggregation of key-value pairs) that would impress even Johnny 5

Creating an index list of words and counts from Big Data Analytics for Dummies may not be terribly interesting or useful for you but, the capability of such key-value pair generation from any multi-structured data sources can be put to analytical use by creating a set of useful dimensions and measures that the BI teams are familiar with that can be integrated with data in the EDW. Perhaps, instead of creating the Book Index, you may choose to create an index of all flight numbers, origins and destinations from the booklet of an airline time table which you may find more useful in the airline business.

Step 3: Putting MapReduce to solve business problems

Long gone are the days of GSA’s (General Sales Agents) enjoying hefty sales commissions from the airlines! The market is highly competitive and organisations are looking for best decision possible from analytics. With ubiquitous availability and convenience offered by broadband connections, customers’ attitudes and behaviours are rapidly changing. Now customers are looking for best travel and holiday packages online. They are also listening to the opinions of their friends and public remarks on social network forums. Interestingly, this is also instrumental in rapid rate at which huge volumes of data is generated, opening up the need for Big Data technologies.

What if we could utilise the multi-structured data from click streams, Facebook, Twitter data for improving business performance? What if we are able to extract the IP Address from the click stream data and correlate with the profile of the customer from EDW along with best fare for the Round The World Travel deal that the customer is looking for? What if we are able to extract the sentiment of the customer’s travel experience from Twitter and Facebook data and use the positive / negative experience to provide the Next Best Offer during the customer’s next inbound call to the agent or online visit?

Step 4: Integrating unstructured and structured data for Big Data Analytics

Here we consider how the integration of multi-structured data in MapReduce and structured data in EDW can be used for improving business outcome. You will see that instead of the MapReduce program for Word Count that you wrote previously, you will write a new MapReduce program to extract the key-value pairs for IP Address, flight deals and any other relevant information from the Apache Weblog files where the customer’s online interaction is recorded. In a later paragraph I will describe how the MapReduce program you wrote is invoked in SQL by means of SQL-MR or better still how you can leverage several pre-built functions (without having to write your own MapReduce program) using SQL-MR. For now, let’s assume the extracted data from MapReduce is created as a table in the EDW. The extracted IP Address can then be joined with Master Reference in the EDW to identify the User ID which is then used to match the frequency of online visits and lifetime value of the customer etc.

Step 5: Flying high with SQL-MR (SQL-MapReduce)!

While MapReduce is good for solving Big Data problems it can cause a number of bottlenecks, including the requirements to write software for answering new business questions. Trying to exploit data from HDFS through Apache Hive is another story; let’s not even go there! SQL-MapReduce (SQL-MR) on the other hand helps to reduce the bottleneck of MapReduce by allowing maximum flexibility through polymorphism (by dynamically allowing determination of input and output schema at query plan-time based on available information). It allows reusability by enabling inputs with many different schemas or with different user-specified parameters. More importantly, you can exploit all types of Big Data using the BI tools that you and your business analysts are familiar with.

Here you will see examples of how you may use the SQL-MR function text_parser (with just a few lines of code) to solve the word count problem / creation of a Book Index for Big Data Analytics for Dummies / extraction of IP Addresses from online clickstream data. You will notice reusability of the SQL-MR function that enables inputs with many different schemas and with different user-specified parameters to create output schema at query time.

You will find that SQL-MapReduce (SQL-MR) provides excellent framework for jump starting Big Data Analytics projects with substantial benefits, viz. 3 times faster in development efficiencies, 5 times faster in discovery and 35 times faster with analytics. My colleague, Ross Farrelly, demonstrates with an example of how to reduce the pain of MapReduce ,which will be of interest to you as well. You can see how SQL-MR provides an excellent framework for customising / developing SQL-MR functions easily with an Integrated Development Environment (IDE).

Exploring and discovering value from Big Data is how you will divide and conquer the volume, velocity, variety and complexity characteristics of Big Data. You will also gain great benefits from seamless integration of the different Big Data technologies as a Unified Data Architecture (UDA) to provide advanced analytics.

Here is another business use case that the SQL-MR functions nPath and GraphGen solve elegantly and efficiently compared to either SQL or MapReduce. Try writing this in SQL or MapReduce and notice the difference! The business problem that we are trying to solve is related to identifying the more frequent customer activities or sequence of events that lead to disloyalty.

You can see from the chart below that of all the different channels that customers use to buy airline tickets, the online channel leads to unsuccessful ticket sale. By visualising the sequence of all customer events you will notice that the Online Payment page is where abandonment occurs (i.e. noticeable from the thick purple curved line that indicates the strength of the path segment) which provides insights about the issues with the online channel. By taking corrective actions ahead of the online payment event step you will create customer loyalty and growth in sales.      

Here is the SQL-MR code for the above visualisation of ticket purchase path analysis:

If you are all set and ready to go on your first class journey with Big Data Analytics then, check-in here .While ‘inflight’, treat yourself with ‘cocktail’ of analytical functions from a wide ranging selection of 70+ pre-built SQL-MR functions .

Travel smart, impress your accompanying business stakeholder, double your rewards from analytical outcomes and enjoy your journey with Big Data Analytics! By the way, don’t forget to drop me a note, if you found this useful! Bon voyage!

Sundara Raman is a Senior Communications Industry Consultant at Teradata ANZ. He has 30 years of experience in the telecommunications industry that spans fixed line, mobile, broadband and Pay TV sectors. At Teradata, Sundara specialises in Business Value Consulting and business intelligence solutions for communication service providers.

Volare Airlines superiore con Google Analytics

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Airlines, plagued with sluggish profits and frustrated customers, realize the growing importance of data analytics as customers vent their frustration across the social media universe. Dr. Nawal Taneja, an aviation industry expert and author, says carriers are facing both a challenge and an opportunity to create new customer experiences and increase profitability. In response, airlines are working ever harder to not only to better understand what makes happy and loyal passengers but also which ones are the most profitable.

The solution is better use of data and technology, such as data warehousing and analytics, that allows companies to transition from being service providers to providing solutions; to shift from a fee-based to a value-based business model. Now when they look toward the end of the runway, industry leaders are getting a glimpse of a future in which they'll be able to choose passengers, rather than the other way around.

Brett Martin
Senior Editor
Teradata Magazine

La realtà di grandi quantità di dati Analytics

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"We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run," said Roy Amara, former president of the California-based Institute for the Future. How right he was. Think about the old days of the World Wide Web. During the 1990s it was widely believed that the Internet would revolutionize our economy suddenly. And related technological advance was expected to boost profits in the near future. This "irrational exuberance" (Alan Greenspan) then led to the dot-com bubble at the turn of the century. Meanwhile, the Web has changed business dramatically and those Internet-based corporations that managed to survive the turmoil began to see profits or even rose to become industry-dominating enterprises.

The entire story is what Gartner calls a hype cycle. The consulting company has been characterizing what typically happens to new technologies since 1995. And as I recently mentioned, the phenomenon of big data – according to Gartner's Report for 2012 – is about to reach its top of the hype-graph. If that turned out to be true, big date would soon slip towards the trough of disillusionment. Bang! You wouldn't hear much about big data technologies for a while. They would only slowly recover and would finally level at a normal stage, embedded within many other established technologies.

Newsflash! A recent report published by the Business Application Research Center (BARC) in Würzburg, Germany, shows you big date is already transforming companies. The Big Data survey was conducted in major European software markets and answered by 274 decision makers in IT and other departments. The answers show that – beyond all the talk about it – big date has become a reality within European companies, helping managers to gain deep insights into markets and customer needs.

While 14 percent of the companies have already developed a detailed big data strategy, 75 percent are aware of the new possibilities arising from big data technologies. However, most companies still face serious challenges in monetizing big date. Lack of expertise is one of the main obstacles, but you can clearly see the trend: Big date has long since moved into the slope of enlightenment. We are there to help a broad range of users benefit from big data technologies. And I feel we are getting very close to the final stage.

How to super-power your efforts: learn from Heroes of Analytics

In Hollywood, summer means blockbuster movie ... and with blockbusters like explosions, Car Crash, daredevil stunts and of course superheroes. Summer 2013 is no exception, with titles like the man of steel and The Lone Ranger, the movie audience is for lots of action-packed adventures in the coming months.

Of course, not everyone needs to go to the theater to get their fill of superhero Action. Some of us are fortunate to work in offices where every day there shoulder to shoulder with the superhero – super heroes of data analysis, that is.

These heroes transform large amounts of data from an insurmountable challenge business to actionable, acquired and processed for the revenues of units. As described on the website of heroes of Teradata and SAS Analytics:

"One by one, they emerge from the darkness, snatching the value from the clutches of ambiguity, exposing fraud in a sea of chaos and innovate in the face of statistical impossibility ..."

As you can tell, here at Teradata, we had some fun with the metaphor of the superhero altogether. But that's not to diminish, in any way, the true that Analytics professionals today are committed every day trailblazing work (and a lot of nights, work too).

For example, meet Megavox – otherwise known as Frank Caputo.

As a member of the marketing team at Medibank, Frank used solutions in the Teradata database and SAS to improve the speed of creating campaign from hours to minutes, optimize marketing investments, minimize dependence on external suppliers and more – that led to enormous improvements in the effectiveness of the campaign, together with significant reductions in costs and the substantial increase in revenue.

There is an analytic superhero at your company? Maybe you are one yourself? If so, please consider applying to our Analytics Heroes program. Is your chance to receive hero's fame and respect Analytics, get your own action figure and also be immortalized by a famous comic artist.

Talking about fame and respect, also we are now accepting nominations for awards 2013 Teradata Epic, which recognize our customers and partners for their leadership in implementing data solutions and data analysis.

If your team has created a solution based on a Teradata platform that delivered the bottom-line business value to your company, this is an opportunity to win the recognition they deserve. The application deadline is July 19, 2013, and award winners will be announced and celebrated at the awards ceremony of Teradata EPIC held during Teradata Partners Conference, User Group Expo & this October in Dallas.

How does the superpower google analytics? Apply to Analytics heroes Teradata program and/or an epic Prize for sharing your story ... and who knows, maybe you'll be on your way to saving the world, a campaign based on data at a time.

-Darryl