data visualisation

6月 252015
 

After doing some recent research with IDC®, I got to thinking again about the reasons that organizations of all sizes in all industries are so slow at adopting analytics as part of their ‘business as usual’ operations.

While I have no hard statistics on who is and who isn’t adopting analytics, the research shows that organizations that do leverage analytics are more successful on average than those that don’t. What we need is a new analytics experience, an experience where organizations can:

  • Make confident decisions
  • Analyze all their data where it exists
  • Seize new opportunities with analytics
  • Remove restrictions for data scientists

IDC states that “50.6% of Asia Pacific enterprises want to monetize their data in the next 18 months”. Are you one of them or are you going to let your competition get the jump on you?

Big data (or more specifically how to actually gain some sort of competitive advantage from it) is top of mind for forward-looking businesses.

Our research with IDC gives us a few clues on where to head when it comes to the monetization discussion.

In the recent Monetizing Your Data infographic (PDF) created by IDC and SAS, three key approaches to monetizing big data emerged:

  1. Data decisioning, where insights derived from big data can be used to enhance business processes;
  2. Data products, where new innovative data products can be created and sold;
  3. Data partnerships, where organizations sell or share core analytics capabilities with partners.

Organizations that adopt and combine all three key approaches to leverage analytics are twice as likely to outperform their peers1.

If you’re looking to truly create value from the stores of data you have then you need to look at deploying analytics.

                                 monetizing-your-data-info-pdf-button                 big-data-resource-ctr-button

1 IDC APEJ Big Data MaturityScape Benchmark Survey 2014 (n=1255) IDC APEJ Big Data Pulse 2014 (n = 854)

tags: analytics, big data, business analytics, business intelligence, Data, data management, data quality, data visualisation, high performance analytics, visual analytics, visualisation, visualization

Are you missing out when it comes to data monetization? was published on Left of the Date Line.

6月 092015
 

customer-intelligenceYou've probably heard many times about the fantastic untapped potential of combining online and offline customer data. But relax, I’m going to cut out the fluff and address this matter in a way that makes the idea plausible and its objectives achievable. The reality is that while much has been written about the benefits of online customer intelligence, it far outweighs what’s happening in most organisations today. In fact, considering how beneficial tapping the data can be, I don’t think enough has been written about what types of online customer behaviours should be tracked and how they could be used to create a better customer experience across all touch points.

So where do you begin? 

It all starts with what you have decided are the objectives for your digital presence – are they to register, to make a transaction, sign up for a newsletter, interact with a certain content object such as internal or third party? Those are generally the key objectives I see organisations having in order to understand the customer journey leading up to these events, as well as tracking and ‘remembering’ when the customer interacts with all the organisation’s available channels to the market. A key aspect is to monitor and understand how external campaigns, in-site promotions and search contribute towards those goals and how this breaks down into behavioural segments/profiles.

Recognising a customer

The next important consideration is – how do we recognise visitors/customers we should know from previous interactions even if they haven’t identified themselves on this occasion? Identification doesn’t have to be dependent on a log-in. It could be through an email address we can match with a satisfactory level of confidence, or it could be a tracking code coming from another digital channel where customers had earlier identified themselves. It’s of much greater value if we can match their behaviour as unknown visitors when the identify themselves and not have to start building our knowledge from scratch at the time of identification.

This leads to the point where we need to explore our options for weaving a visitors’ online behaviours into our offline knowledge about them and how – at the enterprise level – we can best exploit the capabilities of our broader data-driven marketing eco-system. We should ask ourselves, is it valuable to us to be able to send a follow up email to the ones that abandoned a specific form? Can our call centre colleagues enrich their conversations by knowing which customers downloaded particular content? How important is it to us as an organisation to be able to analyse text from in-site searches and combine it with insights driven of complaint data from our CRM system? What are the attributes of the various parts of the journey leading up to completing an objective?

Perhaps you wonder what I mean by the capabilities of the ‘broader data-driven marketing eco-system’. Well, my point is that it that it puzzles me that most organisations today can’t integrate/report/visualise online customer intelligence in the systems that already comprise the backbone of their information infrastructure. They don’t utilise their existing campaign management systems to make decisions on what’s relevant for the individual and drive online personalisation which increase the online conversion rates, but at the same time can be used across channels. Organisations rarely take ownership of online customer data or use their advanced analytical engines and existing analytical skills to drive next level insights.

Not taking full advantage of campaign management systems already in place is opportunity missed because the deliverables of integrated online and offline customer intelligence are very real. We should be looking for them every day.

This post first appeared on marketingmag.com.au.

Take the Customer Intelligence Assessment

tags: business intelligence, CRM, customer experience, customer intelligence, Data, data management, data visualisation, marketing

All customer intelligence must be woven into CRM programs – online and offline was published on Left of the Date Line.

12月 162014
 

Flexibility and nimbleness are terms synonymous with small and mid-sized businesses. They have the ability to react quickly to changing market conditions as they are made aware of them. Traditionally these businesses have lived in the world of spreadsheets - and why not? They are easy to use, very affordable and readily available to all staff across the business. However increasingly, they are realising there are a wealth of insights hidden within their data that once uncovered, can offer them a first-mover advantage and the opportunity to capitalise and stay ahead of the game.

IT departments of one

Most organisations of this size run a very lean IT team which requires finding those rare expertly-general skilled professionals to run everything from setting up computers to managing networks and dealing with internet security issues. Often these small teams do not have the bandwidth or desire to also become analytics experts. With our new generation of reporting and analytics tools your IT team does not need any analytics or programming skills as the creation of reports, dashboards and analytics are kept within the hands of the analyser.

IMG_5694 copyFootball NSW is a not-for-profit organisation that looks after 208,000 registered football players across the state. It employs 57 staff, one of which forms the entirety of its IT department – focusing primarily on desktop support. ‘Analytics’ was a meaningless term for them a year ago, before they introduced SAS® Visual Analytics to replace their spreadsheets. Their reason for turning to a full reporting and analytics tool was clear from the start, they had one question to answer:

“How do we use our data to better engage to our stakeholders – whether it’s councils, government, sponsors or member clubs.”

With that goal front and centre their small organisation is now using powerful visualisations to attract and retain participants focusing on the three F’s – football, facilities and finance. Answering new questions with their data such as:

  1. What do future numbers of football players look like and will councils have the facilities to cater for them?
  2. How do we provide our sponsors with information that provides value to them so they stay with us?
  3. What is the profile of our typical referee and how do we educate and retain them?

You might be thinking, yeah but how does a Football club relate to me and my organisation? The principle behind all of this remains the same; analytics is not just for the big guys, in fact small and mid-sized organisations can easily use analytics to discover insights without the need for specialist skills. In fact you don’t even need to purchase extra hardware as we enter the age of Cloud Analytics.

Start by dodging the Buzz-word bingo

Business is buzzing with terms such as ‘big data’ ‘industrial internet’ and ‘advanced analytics’. Companies are talking about needing to hire ‘data scientists’ and having ‘machine to machine’ conversations, but for most organisations the question of where to start does not involve any of these terms.

The best starting point for most businesses embarking on an analytics journey is to get back to basics by better understanding their internal data… For the average business, data is all over the place. It can be found in different applications (finance, HR etc) some of which may be sitting in the cloud, or in dusty places such as archives, storage devices or spreadsheets that have been buried deep within your filing systems. Identifying and bringing all of this data together in a ‘single version of the truth’ is the foundation for gaining deeper insights, more accurate reporting and improved confidence in your data. It’s critical when you’re faced with this environment to ensure you seek a solution that not only consolidates and standardises data to build an integrated data view but then allows you to tell a story that looks both to the past and helps hypothesise about the future.

You do need to start with a clear attainable goal in mind, and it doesn’t need to include ‘saving the world’ at step one. Ensure your objective will enable you to either show value quickly (payback value) or achieve something which will have widespread visibility within the business (an issue that no one has been able to solve, or a way of using data to look at a falling market in a different way for example).

The world is rapidly changing. The value of managing data as an asset is now becoming a topic for most boardroom conversations. SAS Visual Analytics for the Cloud gives small to mid-market businesses the ability not only to have those exact same conversations but to act on them immediately. Analytics is no longer just for the large banks or government departments, it’s an option everyone can now capitalise on, and those who are flexible and nimble have the most to gain.

tags: analytics, Australia, business analytics, business intelligence, Cloud Analytics, Data, data visualisation, Football NSW, SMB, visual analytics
5月 222014
 

824009.TIFNext generation business intelligence and visualisation tools such as SAS Visual Analytics, are revolutionising insight discovery by offering a truly self service platform powered by sophisticated visualisations and embedded analytics. It has never been easier to get hold of vast amounts of data, visualise that data, uncover valuable insights and make important business decisions, all in a single day’s work.

On the flip side, the speed and ease of getting access to data, and then uncovering and delivering insights via powerful charts and graphs have also exasperated the issue around data quality. It is all well and good when the data being used by analysts is clean and pristine. More often than not, when the data being visualisation is of poor quality, the output and results can be telling and dramatic, but in a bad way.

Let me give you an example from a recent customer discussion to illustrate the point (I have, of course synthesised the data here to protect the innocent!).

Our business analyst in ACME bank has been tasked with the job of analysing customer deposits to identify geographically oriented patterns, as well as identifying the top 20 customers in terms of total deposit amount. These are simple but classic questions that are perfectly suited for a data visualisation tool such as SAS Visual Analytics.

We will start with a simple cross-tab visualisation to display the aggregated deposit amount across the different Australian states:

when visulisation image 1

Oops, the problem around non-standardised state values means that this simple crosstab view is basically unusable. The fact that New South Wales (a state in Australia) is represented nine different ways in our STATE field presents a major problem whenever the state field is used for the purpose of aggregating a measure.

In addition, the fact that the source data only contain a full address field (FULL_ADDR) means that we are also unable to build the next level of geographical aggregation using city as it is embedded into the FULL_ADDR free form text field.

when visulisation image 2

It would be ideal if the FULL_ADDR was parsed out and street number, street name and city are all individual, standardised fields that can used as additional fields in a visualisation.

How about our top 20 customers list table?

when visulisation image 3

Whilst a list table sorted by deposit amount should easily give us what we need, a closer inspection of the list table reveals troubling signs that we have duplicated customers (with names and addresses typed slightly differently) in our customer table. A major problem that will prevent us from building a true top 20 customers list table unless we can match up all the duplicated customers confidently and work out what their true total deposits are with the bank.

All in all, you probably don’t want to share these visualisations with key executives using the dataset you were given by IT. The scariest thing is that these are the data quality issues that are very obvious to the analyst. Without a thorough data profiling process, other surprises may just be around the corner.

One of two things typically happens from here on. Organisations might find it too difficult and give up on the dataset, the report or the data visualisation tool all together. The second option typically involves investing significant cost and effort in hiring an army of programmers and data analysts in order to code their way out of their data quality problems. Something that is often done without detailed understanding of the true cost involved in building a scalable and maintainable data quality process.

There is however, a third and better way. In contrast to other niche visualisation vendors, SAS has always believed in the importance of high quality data in analytics and data visualisation. SAS offers mature and integrated Data Quality solutions within its comprehensive Data Management portfolio that can automate data cleansing routines, minimise the costs involved in delivering quality data, and ultimately unleash the true power of visualised data.

There is however, a third and better way.

Whilst incredibly powerful and flexible, our Data Quality Solution is also extremely easy to pick up by business users with minimum training and detailed knowledge around data cleansing techniques. Without the need to code or program, powerful data cleansing routines can be built and deployed in minutes.

I built a simple data quality process using our solution to illustrate how easy it is to identify and resolve data quality issues described in this example.

Here is the basic data quality routine I built using the SAS Data Management Studio. The data cleansing routine essentially involves a series of data quality nodes that resolve each of the data quality issues we identified above via pre-built data quality rules and a simple drag and drop user interface.

when visulisation image 4

For example, here is the configuration for the "Address standardisation" data quality node. All I had to do was define which locale to use (English Australia in this case), which input fields I want to standard (STATE, DQ_City) ), which data quality definitions to use (City - City/State and City) and what the output fields should be called (DQ_State_Std and DQ_City_Std). The other nodes take a similar approach to automatically parse the full address field, and match similar customers using their name and address to create a new cluster ID field called DQ_CL_ID (we’ll get to this in a minute)

when visulisation image 5

I then loaded the newly cleansed data into SAS Visual Analytics to try tackle the questions that I was tasked to answer in the first place.

The cross-tab now looks much better and I now know (for sure), the best performing state from a deposit amount point of view is New South Wales (now standardised as NSW), followed by Victoria and Queensland.

when visulisation image 6

As a bonus for getting clean, high quality address data, I am also now able to easily visualise the geo based measures on a map, down to the city level since we now have access to the parsed out, standardised city field! Interestingly, our customers are spread out quite evenly across the state of NSW, something I wasn’t expecting in the first place.

when visulisation image 7

As for the top 20 customer list table, I can now use the newly created cluster field called DQ_CL_ID to group similar customers together and add their total deposit to work out who my top 20 customers really are. As it turns out, a number of our customers have multiple deposit accounts with us and go straight to the top of the list when their various accounts are combined.

when visulisation image 8

I can now clearly see that Mr. Alan Davies is our number one customer with a combined deposit amount of $1,621,768, followed Mr. Philip McBride, both of which will get the special treatment they deserve whenever they are targeted for marketing campaigns.

All in all, I can now comfortably share my insights and visualisations with business stakeholders with the knowledge that any decision made are using sound, high quality data. And I was able to do all this with minimum support and all in a single day’s work!

Is your poor quality data holding you back in data visualisation projects? Interested in finding out more about SAS Data Quality solutions? Come join us at the Data Quality hands on workshop and discover how you can easily tame your data and unleash its true potential.

tags: data management, data quality, data visualisation, visual analytics
4月 032014
 

Demand for analytics is at an all-time high. Monster.com has rated SAS as the number one skill to have to increase your salary and Harvard Business Review continues to highlight why the data scientist is the sexiest job of the 21st century.  It is clear that if you want to be sexy and rich we are in the right profession! Jokes aside I have spent the past five weeks travelling around Australia, Singapore and New Zealand discussing the need to modernise analytical platforms to help meet the sharp increase in demand for analytics to support better business and social outcomes.

While there are many aspects to modernisation, the most prolific discussion during the roadshow was around Hadoop. About 20% of the 150 plus companies were already up and running with their Hadoop play pen. Questions had moved beyond “What is Hadoop?” to “How do I leverage Hadoop as part of my analytical process?”. Within the region we have live customers using Hadoop in various ways:

  • Exploring new text based data sets like customer surveys and feedback.
  • Replicating core transaction system data to perform adhoc queries faster. Removing the need to grab extra data not currently supported in the EDW.
  • The establishment of an analytical sandpit to explore relationships that can have an impact on marketing, risk, fraud and operations by looking at new data sets and combining them with traditional data sets.

The key challenge discussed was unanimous. While Hadoop provided a low cost way to store and retrieve data, it was still a cost without an obvious business outcome. Customers were looking at how to plug Hadoop into their existing analytical processes, and quickly discovering that Hadoop comes with a complex zoo of capabilities and consequentially, skills gaps.

The SAS /Hadoop Ecosystem

The SAS /Hadoop Ecosystem

Be assured that this was and is a top priority in our research and development labs. In response to our customers' concerns, our focus has been to reduce the skills needed to integrate Hadoop into the decision-making value chain. SAS offers a set of technologies that enable users to bring the full power of business analytics functionality to Hadoop. Users can prepare and explore data, develop analytical models with the full depth and breadth of techniques, as well as execute the analytical model in Hadoop. It can be best explained using the four key areas of the data‐to‐decision lifecycle process:

  • Managing data – there are a couple of gaps to address in this area. Firstly, if you need to connect to Hadoop, read and write file data or execute a map reduce job; using Base SAS you can use the FILENAME statement to read and write file data to and from Hadoop. This can be done from your existing SAS environment. Using PROC HADOOP, users can submit HDFS commands and Pig Scripts, as well as upload and execute a map reduce tasks.
    SAS 9.4 is able to use Hadoop to store SAS data through the SAS Scalable Performance Data (SPD) Engine within Base SAS. With SAS/ACCESS to Hadoop, you can connect, read and write data to and from Hadoop as if it were any other source that SAS can connect to. From any SAS client, a connection to Hadoop can be made and users can analyse data with their favourite SAS Procedures and Data Step. SAS/ACCESS to Hadoop supports explicit Hive QL calls. This means that rather than extracting the data into SAS for processing SAS translates these procedures into the appropriate Hive‐QL which resolves the results on Hadoop and only returns the results back to SAS. SAS/ACCESS to Hadoop allows the SAS user to leverage Hadoop just like they do with an RDBMS today.
  • Exploring and visualising insight - With SAS Visual Analytics, users can quickly and easily explore and visualise large amounts of data stored in the Hadoop distributed file system based on SAS LASR Analytics server.  This is an extremely scalable, in‐memory processing engine that is optimised for interactive and iterative analytics. This engine addresses the gaps in MapReduce based analysis, by persisting data in‐memory and taking full advantage of computing resources. Multiple users can interact with data in real‐time because there is no re‐lifting data into memory for each analysis or request, there is no serial sequence of jobs, and computational resources available can be fully exploited.
  • Building modelsSAS High Performance Analytics (HPA) products (Statistics, Data Mining, Text Mining, Econometrics, Forecasting and Optimisation) provide a highly scalable in‐memory infrastructure that supports Hadoop. Enabling you to apply domain‐specific analytics to large data on Hadoop, it effectively eliminates the data movement between the SAS server and Hadoop. SAS provides a set of procedures that enable users to manipulate, transform, explore, model and score data all within Hadoop. In addition, SAS In‐Memory Statistics for Hadoop is an interactive programing environment for data preparation, exploration, modelling and deployment in Hadoop with an extremely fast, multi‐user environment leveraging SAS Enterprise Guide to connect and interact with LASR or take advantage of SAS’ new modern web‐editor, SAS Studio.
  • Deploying and executing models - conventional model scoring requires the transfer of data from one system to SAS where it is scored and then written back. In Hadoop the movement of data from the cluster to SAS can be prohibitively expensive. Instead, you want to keep data in place and integrate SAS Scoring processes on Hadoop. The SAS Scoring Accelerator for Hadoop enables analytic models created with Enterprise Miner or with core SAS/STAT procedures to be processed in Hadoop via MapReduce. This requires no data movement and is performed on the cluster in parallel, just like SAS does with other in‐database accelerators.

To be ahead of competitors we need to act now to leverage the power of Hadoop. SAS has embraced Hadoop and provided a flexible architecture to support deployment with other data warehouse technologies.  SAS now enables you to analyse large, diverse and complex data sets in Hadoop within a single environment – instead of using a mix of languages and products from different vendors.

 Click here to find out how SAS can help you innovate with Hadoop.

tags: big data, data visualisation, data warehouse, Hadoop, Harvard business review, HDFS, Hive, In-Memory, in-memory analytics, MapReduce, modernization, Pig, visual analytics
8月 152013
 
At SAS Forum Sydney 2013, Dr Jim Goodnight SAS CEO opens the session

Dr Jim Goodnight SAS CEO at SAS Forum Sydney 2013

This week Sydney played host to two conferences that for me, underpinned the theme of analytics everywhere that my colleague Vincent Cotte noted about SAS Global Forum. First on my agenda was SAS Forum Sydney 2013, where over 1,000 people gathered to hear from leaders in the analytics space, including SAS customers and staff. You can recap the agenda over there, or download the presentations over here.

First up, I'll cover off some of the more salient takeaways from SAS Forum for marketers. If you’re in marketing, you can’t have missed the change from colouring in to data-driven marketing. Do you have an Emergent CMO? One who understands the new art and science of marketing? Or are you still looking to IT to solve your problems?

Michael Pascoe, journalist, opened the event with the bold statement “Australia does not have a budget problem” and that set the tone for the day – spend! Use what you know to make it count, be more creative and use your intelligence. More telling, and also what became a theme for the day was the data story.  David Bowie, the Managing Director of SAS Australia, really brought this home with his statement that it’s not enough to just think about your customers, you need to think like your customers - what story are they telling you through their data? And are you listening?

At SAS, we know analytics is the key to actually doing this and we pay more than lip service to the idea of customer-centricity. We recently released this story of how we are drinking our own champagne and using the SAS customer intelligence suite  to build a full picture of the customer interaction leading to dramatic increases in response rates.

Back to SAS Forum ... it was  a packed day of hearing just how our customers think like their customers – eye-opening to be sure. For instance, as a result of better customer insights achieved through its data analytics initiative, KnowMe, Westpac Bank is achieving a 40 per cent uptake on customer offers. Later at the ABO panel, David Mortimer went on to tell us that the customer is actually the one in power now. If you want to unlock that power, it’s in your data. But the challenge is to turn your data into actual information ... that you can act on. WA Police has a fascinating data story - business intelligence is being used to build a picture of crime over time, and put in place measures to reduce societal impact (read more here). 

Under the analytics covers, of course is the data. SAS CMO Jim Davis told us that an organisation’s critical asset is its data and more people need access. Actually, a great insight from our CEO Jim Goodnight was that the SAS Visual Analytics software is a first step to handling your big data, and driving the power of fact-based decision-making. Big Data is not the size (can you imagine some of the tweets after that comment from Jim Davis?) it’s data that has exceeded the processing capacity of conventional database management solutions. With SAS you can process billions of rows of data in a couple of seconds, but analytics is about more than just pretty graphs, and Jim (Davis) talked us through where SAS sits in the analytics quadrant – “Quadrants aren’t just for Forrester” he said to much laughter.

So after a full day of analytics and data, I was ready for what I thought might be some nice colouring in style sessions at ADMA (I am of course, kidding!). The ADMA line up this year was quite different and reflects the organisation’s change in direction from direct mail to data-driven marketing. I couldn’t wait to hear Rayid Ghani, Chief Scientist from the Obama campaign and I was not disappointed. Analytics was critical to Obama’s election - from fundraising to getting the voters out the door to getting the vote. The constant test and optimization process that he talked about was a lesson for all marketers. The campaign didn’t want to spend any more than was absolutely necessary to win 51% of the votes in each state (because of the election/voting system – it would never work in Australia that way). We talk about the 'segment of one' and from what I was hearing, it was almost a 'campaign of one'.

We then heard from Dr Nicola Millard, futurologist with British Telecom (BT) and her ideas and research projects on the tech-savvy customers which was later borne out by research projects from Australia's largest telco, Telstra. Liz Moore, Head of Customer Insights & Analytics gave a compelling presentation on how five megatrends are informing Telstra’s infrastructure and platform development. My personal favourite was 'taking care of myself', and the whole movement around ‘life tracking’, using the data that we generate via devices like the Fitbit or Nike Fuel band, to make positive changes on our lifestyle. One day I’ll tell you about how I’ve used analytics to reduce insomnia episodes by analysing my life data.

There was much more to both of these days than I can possibly cover off here, and I invite you to download the presentations from SAS Forum for even more nuggets of gold.

Learn more - Download presentations from SAS Forum 2013.

tags: customer intelligence, Data, data visualisation, marketing
8月 152013
 
At SAS Forum Sydney 2013, Dr Jim Goodnight SAS CEO opens the session

Dr Jim Goodnight SAS CEO at SAS Forum Sydney 2013

This week Sydney played host to two conferences that for me, underpinned the theme of analytics everywhere that my colleague Vincent Cotte noted about SAS Global Forum. First on my agenda was SAS Forum Sydney 2013, where over 1,000 people gathered to hear from leaders in the analytics space, including SAS customers and staff. You can recap the agenda over there, or download the presentations over here.

First up, I'll cover off some of the more salient takeaways from SAS Forum for marketers. If you’re in marketing, you can’t have missed the change from colouring in to data-driven marketing. Do you have an Emergent CMO? One who understands the new art and science of marketing? Or are you still looking to IT to solve your problems?

Michael Pascoe, journalist, opened the event with the bold statement “Australia does not have a budget problem” and that set the tone for the day – spend! Use what you know to make it count, be more creative and use your intelligence. More telling, and also what became a theme for the day was the data story.  David Bowie, the Managing Director of SAS Australia, really brought this home with his statement that it’s not enough to just think about your customers, you need to think like your customers - what story are they telling you through their data? And are you listening?

At SAS, we know analytics is the key to actually doing this and we pay more than lip service to the idea of customer-centricity. We recently released this story of how we are drinking our own champagne and using the SAS customer intelligence suite  to build a full picture of the customer interaction leading to dramatic increases in response rates.

Back to SAS Forum ... it was  a packed day of hearing just how our customers think like their customers – eye-opening to be sure. For instance, as a result of better customer insights achieved through its data analytics initiative, KnowMe, Westpac Bank is achieving a 40 per cent uptake on customer offers. Later at the ABO panel, David Mortimer went on to tell us that the customer is actually the one in power now. If you want to unlock that power, it’s in your data. But the challenge is to turn your data into actual information ... that you can act on. WA Police has a fascinating data story - business intelligence is being used to build a picture of crime over time, and put in place measures to reduce societal impact (read more here). 

Under the analytics covers, of course is the data. SAS CMO Jim Davis told us that an organisation’s critical asset is its data and more people need access. Actually, a great insight from our CEO Jim Goodnight was that the SAS Visual Analytics software is a first step to handling your big data, and driving the power of fact-based decision-making. Big Data is not the size (can you imagine some of the tweets after that comment from Jim Davis?) it’s data that has exceeded the processing capacity of conventional database management solutions. With SAS you can process billions of rows of data in a couple of seconds, but analytics is about more than just pretty graphs, and Jim (Davis) talked us through where SAS sits in the analytics quadrant – “Quadrants aren’t just for Forrester” he said to much laughter.

So after a full day of analytics and data, I was ready for what I thought might be some nice colouring in style sessions at ADMA (I am of course, kidding!). The ADMA line up this year was quite different and reflects the organisation’s change in direction from direct mail to data-driven marketing. I couldn’t wait to hear Rayid Ghani, Chief Scientist from the Obama campaign and I was not disappointed. Analytics was critical to Obama’s election - from fundraising to getting the voters out the door to getting the vote. The constant test and optimization process that he talked about was a lesson for all marketers. The campaign didn’t want to spend any more than was absolutely necessary to win 51% of the votes in each state (because of the election/voting system – it would never work in Australia that way). We talk about the 'segment of one' and from what I was hearing, it was almost a 'campaign of one'.

We then heard from Dr Nicola Millard, futurologist with British Telecom (BT) and her ideas and research projects on the tech-savvy customers which was later borne out by research projects from Australia's largest telco, Telstra. Liz Moore, Head of Customer Insights & Analytics gave a compelling presentation on how five megatrends are informing Telstra’s infrastructure and platform development. My personal favourite was 'taking care of myself', and the whole movement around ‘life tracking’, using the data that we generate via devices like the Fitbit or Nike Fuel band, to make positive changes on our lifestyle. One day I’ll tell you about how I’ve used analytics to reduce insomnia episodes by analysing my life data.

There was much more to both of these days than I can possibly cover off here, and I invite you to download the presentations from SAS Forum for even more nuggets of gold.

Learn more - Download presentations from SAS Forum 2013.

tags: customer intelligence, Data, data visualisation, marketing
5月 292013
 

At SAS Global Forum 2013, one of the key announcements was that a new platform SAS release, 9.4 will be available from June 2013.  While 9.4 underpins some of the headlines around high performance analytics and visual analytics and the cloud initiative,  it will be of great interest for SAS users in its own right.

While SAS 9.3 has been our current release for the past two years, 9.4 offers a huge number of enhancements, 
adding lots of new goodies that people will find useful.  SAS 9.4 can be viewed both as an incremental release, as well a platform for exciting and totally new features.

 SAS 9.4 will offer  a straightforward migration path, using the same approaches proven in the migration to 9.2 and 9.3.

Vincent covered the main highlights but I'm going to concentrate on the new SAS foundation features that will benefit SAS analysts and programmers:

  • In the area of analytics, we are seeing some of the new algorithms pioneered in our High Performance Analytics products implemented in an SMP (single server multiple cpu) environment in the “standard” editions of SAS/Stat and other analytic products.
  • New languages such as DS2 to allow code to be submitted from Base SAS sessions to run in-database to perform advanced data manipulation without moving the data out of the database taking advantage of parallelisation.  Furthermore extending implementations of existing languages such as the ANSI 1999 compliant FEDSQL.
  • Support for the latest operating system versions and other third party product versions, such as Microsoft Office 2013.
  • New trigonometric functions such as COT, CSC and SEC.
  • One of the areas that will be really exciting for many people will be the long awaited production availability of  ODS Layout and ODS Report Writing Interface.  For old timers who remember the DATA _NULL_ report writing with PUT statements, these new features allow unlimited flexibility in creating highly customized PDF and HTML documents.  Read this SGF paper for more information.
  • ODS Graphics is now in its third generation and has many new features that give you more flexibility and control.   This combines with the LAYOUT to create publication ready content.
  • The ODS EPUB destination  creates output optimised for eReaders and tablets.
  • In SAS/Access a new pipeline implementation can improve performance especially when data is being streamed from a database into a complex SAS data step.
  • One useful little goodie is the ZIP Filename engine; this allows reading and writing of files inside a zipped archive.

So when 9.4 is released, investigate the “What’s New in 9.4” documentation on the SAS Support site to find out details of these and many other new features.  I’m sure you will find some that will give you real value – and for each person they may be different.  I'll be very interested to hear from you as to what you found valuable so we can share them!

Stay tuned to this blog where I will discuss new features for SAS Administrators in a later post.

Want to find out more about the exciting announcements, you can attend SAS Forum Sydney on August 8th.

 Click here to register

tags: analytics, big data, cloud, data visualisation, high performance analytics, SAS 9.4
5月 272013
 

SAS Global Forum 2013 provided a platform for SAS to share its vision.  If you couldn't make it to San Francisco, no problem, I have interviewed our key thought leaders to bring you their thoughts in a game of buzzword bingo.

First up is Paul Kent, SAS' Vice President of Big Data.  Paul discusses how SAS is parallelising our algorithms and math, fusing this together with advances in in-memory hardware to take advantage of what's happening in the community around Hadoop and cloud.

 

Keith Collins, SAS Chief Technology Officer introduces his thoughts around his new tagline "your cloud, their cloud or my cloud".  Keith discusses how SAS 9.4 will deliver cloud friendly infrastructure to all.

 

Tapan Patel, SAS Global Product Marketing Manager provides a rundown on how SAS is innovating to work with and within Hadoop; "After all big data does not equal Hadoop".

 

Wilson Raj, SAS Global Director Customer Intelligence discusses how the new edition of SAS Customer Intelligence can help bridge marketing effectiveness (customer impact and deep customer insights) and marketing efficiency (time-to-market, operational cost and  ROI outcomes) for your marketing department.

Subscribe to our blog to read Bill Gibson’s in depth breakdown of the key announcements from Global Forum 2013 and why its important to you.

BINGO!  If you missed the action or want to hear more about the exciting announcements, you can attend your local forums in the following countries.

 Click here to check them out

tags: cloud, customer intelligence, data visualisation, Hadoop, high performance analytics, marketing, Mobile BI
5月 062013
 

Better data visualisation. Easy analytics. Discover a new world of possibilities.

Recently on this blog we’ve been discussing data visualisation and how organisations, irrespective of size and industry, have data that can deliver insights. We’ve covered the gamut of topics starting with the business case that you can present, through to case studies from leading companies in South Asia, and on to more technical ‘how to’ posts. Finally we finished up with some invaluable tips on how to get started on your visualisation journey.

I thought it would be helpful to recap the coverage in one place – or if you want to see them all online, here’s the collection.

Ease your growing pains with data visualisation
An overview of the excitement around visualisation and what it will mean to you.

  1. Providing information in real-time and on-the-go
  2. See smarter insights on large sets of data
  3. Delivering insights with pure speed

Make better business decisions with Visual Analytics
In this post, Greg Wood explains how visualisation aids in the process of discovery and should form a key part of the decision making process and the overall analytics lifecycle.

Reducing decision bias with Visualisation
Using SAS® Visual Analytics, we see how novel insights can be found amongst the generalisations, and indeed overgeneralisations. Using a single data point, Minh Lam explores a retail dataset.

Overcoming the Top Four Challenges to More Effective Decision Making With Data Visualisation
The top four reasons to accelerate pace and increase accuracy of decision-making are:

  1. Inability to quickly identify trends and patterns.
  2. Lack of business self-serviceability.
  3. The need to make informed decisions when and where it counts.
  4. Data must be reliable.

BIG DATA: Communication is key!
Going beyond the big data hype, Des Viranna talks about how to ensure you are focusing effort on big data analytics activities that relate to your business objectives, and considers these from a perspective foles/skills, organizational structure, management processes and technology.

Look Before You Leap with Data Visualisation
In order to be a more effective decision maker we need to have the ability to spot patterns, identify opportunities for further analysis and convey results quickly. To achieve this in tabular reports alone is difficult as people tend to more naturally interpret a visual. Data visualisation is key in order to make better business decisions

Case study: Transport and logistics empowers managers to drive competitive advantage
The transportation and logistics industry is faced with the complex challenge of driving profits with different dynamics going against them, like new low-cost players coming into the market, rising fuel costs and consumers pushing for faster deadlines. These pressures drove one particular Australian trucking transport company to investigate a better way to empower their managers to maintain a competitive advantage and profitable business in a price sensitive industry. Data Visualisation was key.

The journey to building a useful model with data visualisation
As we undertake our journey through the Analytic Lifecycle towards building a model and beyond, being time efficient and accurate is key. Using the freely available SAS Visual Analytics Full Demo Michelle Homes looks at customer satisfaction and sales representative ratings with the toy company data.

Data Visualisation + Advanced Analytics = faster smarter decisions
Typically the benefits of leveraging analytics have been reserved for companies employing rocket scientists – abstract and nerdy.  However recent innovations in data visualisation, hardware, analytics and mobility are providing this same insight to anyone, anywhere and anytime.

Monkeys, visualisation and decision making
Don’t rely on chance with your decision making, when it comes to making better business decisions, we all need to be able to see what’s happening in your organisations, share what you see, and understand what will happen.

How to get started with data visualisation
If you’ve beein thinking about big data and how to get started with data visualisation, it’s time to ask yourself, do you know the value of your data? And if you don’t, how do you figure out what the value is? Excellent insights into beginning your journey.

Where are you in your data visualisation journey? Do tell us in the comments below.

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Start your own journey - try it for yourself for free right here.

tags: analytics, big data, data visualisation, visual analytics, visualisation, visualization