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I'm sure I'm not the only one who has read and contributed to threads on the internet about all the different languages used for data mining. But one aspect that's been left out of most of these comparisons is that SAS is more than a 4th generation programming language (4GL). It's always been and always will be more than a language because SAS has been engineered to be an analytic platform -- or to put it another way, an analytic processing environment designed to support the entire analytics life cycle. 

What does this mean? It means that our software engineers have developed the environment to take advantage of underlying hardware such as CPUs, memory, etc., so that SAS users don't have to concern themselves with the details of leveraging the hardware efficiently. The SAS environment does it for them. 

A good programmer may be able to use another language to create code that assists with efficiency, but that's complex coding that takes time, and not all programmers have that level of ability. In the long run, that can lead to inconsistencies in running and maintaining your business processes. SAS provides this type of efficiency either automatically or with a simple option setting. 

It's like the choice between a potluck dinner at a friend's house versus eating out at a nice restaurant. Some of your friends will prepare better food than others, but few will provide the level of service and quality that a trained restaurant chef offers. In addition, good restaurants stand behind their food and service.
Why doesn't this topic come up more often? Probably because it's related to back-end architecture and not as easy to show off as a nice dashboard with the end results of the processing.
SAS provides either a Service Oriented Application (SOA) based architecture or a more modern cloud friendly micro-services based architecture (SAS Viya), or a combination of both, all engineered to make it easier for users to manage data, analyze it and deploy results in a consistent, governed and highly efficient manner -- regardless of the size of the data involved. If you're ready to learn more, make plans to attend Analytics Experience 2017, September 18-19 in Washington, D.C. 

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SAS Visual Analytics (VA) is web-based environment that supports several applications. It allows you to create beautiful, interactive dashboards or reports that are immediately available on the web or a mobile device.
Benefits of Using SAS Visual Analytics:
Using SAS Visual Analytics, users can enhance the analytic power of their data, explore new data sources, investigate them, and create visualizations to uncover relevant patterns. Users can then easily share those visualizations in reports. In traditional reporting, the resulting output is well-defined up-front. That is, you know what you are looking at and what you need to convey. However, data discovery invites you to plumb the data, its characteristics, and its relationships. Then, when useful visualizations are created, you can incorporate those visualizations into reports that are available on a mobile device or in the viewer.
SAS Visual Analytics provides users with the following benefits:

• enables users to apply the power of SAS analytics to massive amounts of data
• empowers users to visually explore data, based on any variety of measures, at amazingly fast speeds
• enables users to share insights with anyone, anywhere, via the web or a mobile device

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The widening spectrum of Data Science Roles

The development and use of self-service analytics has brought with it a new role in many organizations: the citizen data scientist. But is this genuinely a new role, or is it just a new name for a business analyst?

​Is this a definition thing?
​Business analysis is broadly defined as analyzing the business, including processes or systems, and putting forward solutions. I would say that all those were part of the citizen data scientist’s role, which suggests that it may be the term alone that is new. But I think there is one key difference: citizen data scientists have to do all these things, but these functions are not at the core of that person’s role.

In other words, citizen data scientists are not actually data scientists. Instead, they are business users who understand and can do analytics. They are able to use data but that is not their primary role in the business. Uniquely, they bring together knowledge of the business, and some understanding of analysis. The best citizen data scientists know the business, and are not afraid to get their hands dirty with data. They use the right tools to generate insights that can bring value to the business. It is, however, up to the business to evaluate the insights.

​The role itself has arisen as we all become more data-driven. At the same time, a shortage of dedicated data scientists has meant that there is a lack of capacity to crunch numbers and produce insights. In other words, citizen data scientists have developed out of necessity. It is, perhaps, best described as a hybrid role, but is certainly filling a gap in many organizations.

The enablement journey But as citizen data scientists are not dedicated data scientists, they need more support to enable them to perform their role successfully.
First of all, they need suitable self-service analytics tools to enable them to manipulate data and achieve information and insights. These tools need to be simple to use, but with a wide range of analytical options available. Citizen data scientists may also need training and support in using these tools, at least initially.

The next requirement is suitable data. This means from multiple sources, because insights seldom come from rehashing a single source, and the data also need to be clean and high quality. Finally, but perhaps most importantly, citizen data scientists need sponsorship within the organisation. In practice, that means permission to experiment and try things out, just as dedicated data scientists do. They should not be limited by organisational culture.

​Indeed, it is entirely possible that embracing the role of citizen data scientist within the organisation could bring about some very positive organisational change. The type of people who tend to gravitate towards these roles are experimenters and innovators, and by definition, they are not very good at ‘business as usual’. They are therefore often disruptive, in a good way, changing the culture and driving positive organisational ‘adjustments’.

Substitution or complementarity? 
Citizen data scientists have been suggested as the answer to the shortage in data science skills. In my view, however, they cannot entirely replace data scientists. It even seems likely that the rise of citizen data scientists will lead to new roles for data scientists: providing support, but also enabling them to spend time on more complex analysis, adding significantly more value to the organisation. It makes sense that some of your most expensive and highly skilled resources should not be wasted on basic analysis, but instead, used to add real value that only they can add.
As cars became simpler, chauffeurs ceased to be necessary for most of us. But that did not make us all Formula 1 drivers. In the same way, the rise of citizen data scientists does not mean that there is no role for those with significantly stronger skills and greater knowledge and ability in data science.

A win-win situation In other words, the roles are complementary, rather than a substitute. Business users need support to enable them to explore data. But self-service analytics offers the opportunity for business users to explore data without being limited by a shortage of analytical and data science skills within the organisation.
At the same time, the existence of self-service analytics removes the chore of basic analytics from skilled data scientists. It frees up their time and capacity to enable them to get on with what interests them: highly complex analysis that will add significant value for the organisation.
Here you can find a fact sheet about SAS Visual Analytics, our self-service tool for the citizen data scientist.

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INTERESTING CAREERS TO EXPLORE IN Learn SAS® BIG DATA with Epoch Research Institute India PVT. LTD.(www.epoch.co.in)

Big data is a term that describes the large volume of data – both structured and unstructured – that inundates a business on a day-to-day basis. But it's not the amount of data that's important. It's what organizations do with the data that matters.

DATA SCIENTIST:

These people use their analytical and technical capabilities to extract meaningful insights from data.

DATA ENGINEER:

They ensure uninterrupted flow of data between servers and applications and are also responsible for data architecture.

BIG DATA ENGINEER:

Big Data Engineers build the designs created by solutions architects. They  develop, maintain, test and evaluate big data solutions within organizations.

MACHINE LEARNING SCIENTIST:

They work in the research and development of algorithms that are used in adaptive systems. They build methods for predicting product suggestions and demand forecasting, and explore Big Data to automatically extract patterns. 

BUSINESS ANALYTICS SPECIALIST:

A Business Analytics specialist supports various development initiatives, assists in testing activities and in the development of test scripts, performing research in order to understand business issues, and developing practical cost-effective solutions to problems.  

DATA VISUALIZATION DEVELOPER:

They design, develop and provide production support of interactive data visualizations used across the enterprise. They  possess an artistic mind that conceptualizes, design and develop reusable graphic/data visualizations and uses strong  technical knowledge for implementing these  visualizations using the latest technologies.

​BUSINESS INTELLIGENCE (BI) ENGINEER:

They have data analysis expertise and the experience of setting up reporting tools, querying and maintaining  data warehouses. They are hands-on with big data and take a data driven approach to solving complex problems.

BI SOLUTION ARCHITECT:

They come up with solutions quickly to help businesses in making time sensitive decisions, have strong communication & analytical skills, passion for data visualization, and a drive for excellence and self motivation.

BI SPECIALIST:

They are responsible for supporting an enterprise wide business intelligence framework. This positions requires critical thinking, attention to detail, and effective communication skills.

ANALYTICS MANAGER:

An analytics manager is responsible for configuration, design, implementation  and support of data analysis solution or BI tool. They are specifically required to analyze huge quantities of information gathered through transactional activity. 

MACHINE LEARNING ENGINEER:

Machine Learning engineer's final "output " is the working software, and  their "audience" for this output consists of other software components hat run autonomously with minimum human supervision. The decisions are  made by machines and they affect how a product or services behaves.

STATISTICIAN:

They gather numerical data and then display it, and help companies to make sense of quantitative data and to spot trends and make predictions.

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Learn Advance SAS® Batch on Live Web Training by Epoch Research Institute India Pvt. Ltd. (www.epoch.co.in)

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Data without analytics is data not yet realised (interview)

Dr Jim Goodnight, co-founder and chief executive of SAS, has been at the helm for the past 41 years. Under his guidance and some very astute management philosophies, the company has become the largest privately owned, pure-play company in the big data and analytics space.
And according to industry analysts’ reports, it’s also very much a leader in predictive analytics and machine learning solutions.
Throughout the Forum Dr Goodnight's name was used in revered tones. As Oliver Schabenberger, executive vice-president and chief technology officer said, “It may be SAS.com but it is really Jim.Goodnight.com – he has had such an amazing influence on the company”.
Goodnight admits SAS is now a little too big for him to totally oversee and he has selected some excellent VPs. How long will he continue? “As long as I am having fun and can make a difference,” he responded.
He is currently the head of an education task force with the chief executives of the Business Roundtable. There, he is focusing on helping children learn to read.
He was interviewed during the recent SAS Global Forum.

What are some of the biggest changes you have seen over the past 40 years?
After leaving the university environment all we hoped was to make it through the next year! That SAS has thrived and grown each year is a testament to the foresight, the corporate philosophy, and great people. In 2016, SAS made US$3.2 billion, had over 83,000 customers and over 14,000 employees.
SAS was doing analytics before it was cool. It can do so much more to effect change for good.
We are in the age of Analytics 4.0 and over 40 years we have had to reinvent ourselves on average every ten years. We started in 1969 writing in PL/1 for IBM 360 mainframes. Ten years later SAS needed to run on Vax/DG minicomputers. Ten years after that – 1989 - was the beginnings of democratisation of computing with the 16-bit IBM PC so we went there too. Now it is all about the cloud.
That meant a fundamental rewrite using C language and that was to become a real strength as it supported all platforms. One version of SAS and portability to different operating systems was way ahead of its time. It also marked the move of analytics to the data – we were able to use massively parallel PCs to give us the memory and processing capacity.
The next big change was the coming of the cloud and Viya is a cloud extension of SAS 9.4. It is for big tasks – high-performance analytics. It is both a superset and subset of SAS 9.4 – both platforms will continue to develop and interoperate but Viya allows you to use the power of the cloud – you can get up to 3 billion instructions per second and work on much larger data sets.
The next evolution is definitely about the rise of the machines. Analytics is not standing still – Machine learning (ML), Deep learning, Artificial Intelligence (AI), advances in insights, algorithmic intelligence, automation, and more.
Classic ML is data driven. Modern ML is where the algorithm acquires new skills by itself and that is where we are heading.
Oliver Schabenberger said we need to be careful not to label ML as AI. Is that your corporate direction?
Yes, our version of ML is about algorithms acquiring new skills. There is no computer powerful enough today to build the neural networks and approach AI status. Oliver called it algorithmic intelligence and that is more accurate.
As computer hardware increases in power and storage, what does that mean to SAS?
SAS has always had more capability than the machines it runs on so we welcome technology advances. Bigger, faster, more powerful hardware is good for SAS. It means we can solve bigger tougher problems. SAS capabilities still exceed even the biggest, fastest hardware of today.
The cloud is interesting as it potentially can link every computer together so bigger problems can be solved – That is Viya’s direction.
Tell me more about SAS visualisation techniques?
In the past analytical uses of data tended to be relatively flat – crosstabs and graphs could represent it.
Today data is multi-dimensional – you add your data to other sources and it is simply beyond human comprehension to think multi-dimensionally. Visualisation takes you so far but we need ML to reveal those hidden linkages and reveal insights.
Over the past 40 years we have seen visualisation go from standard X, Y charts to dashboards and scorecards but frankly these do not work for dimensional data – its big data in overdrive.
SAS uses a bucketing system to compress thousands of data points into perhaps 50 important ones that we can understand. We think that democratising visualisation techniques is where we can make the most difference to uncovering new insights – opening it up to everyone – not just data scientists.
You demonstrated on stage accessing SAS via Alexa. Is that the next logical step in analytics?
Anything that helps humans interact with data and analytics is a good step. By the way, SAS works with Siri, Cortana, Google Assistant and Bixby too. But these are nothing more than digital assistants and we think the “miss” rate is way too high for analytical use.
We plan to have our own analytics focused voice engine that will understand data, analytics and SAS.
Is SAS’s commitment to education a big overhead?
It has never been regarded as such. I think most CEOs of large companies have that responsibility. It takes the CEO to at least approve doing something like this.
We need to get people talking about analytics – data at rest is data not realised. Let’s just say that some 63 Universities now offer Masters Degrees in Advanced Analytics and 144 certifications. Between SAS University Edition and SAS OnDemand for Academics, there have been more than 1 million registrations and downloads of free SAS software for teaching, learning and research.
SAS training in Australia is the equal of any offered in the world and its certification program is recognised globally. Our commitment means that SAS is synonymous with analytics and people get well-paying jobs.
Where is SAS heading?
If analytics is the engine of change, data is the fuel. The opportunity is enormous and we need to bring analytics everywhere.
SAS has grown so much from my original vision, not in the technology sense but its uses. We can help analyse sports performance, reduce credit and financial fraud, address cyber security, solve marketing issues, reduce child abuse/neglect, use crowdsourcing (Gather IQ) to solve humanitarian issues, and one new area is Results as a Service to enable everyone to access SAS and pay for results.
The next decade of development is about machine learning, algorithmic intelligence, IoT and the masses of new data we will see generated by things like autonomous cars. I think there will be a move from using analytics in a reactive mode to a proactive mode – real-time.
Meanwhile, we will be looking 20 years ahead to begin to address emerging issues.
The writer attended the SAS Global Forum in Orlando, Florida, as a guest of the company.

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