Moving your analytics applications to the cloud

0

Large organizations are seeing an increase in the number of applications that are migrating to the cloud. SAS is a global organization with travel expense accounting, personnel management and IT support as cloud applications. But when should analytics applications move to the cloud? Here are five reasons why you should consider the move now.

Access for everyone

A few years ago, only a few data specialists analysed company information. And gaining any meaningful insight from the data came long after the operational processes. Basic and detailed analyses were always provided - but not immediately requested. But in a world full of data, it's increasingly important that more than a handful of statisticians have access to analytics. Sales figures, customer analyses, quality reports - all belong in the hands of individuals within departments across the organisation. And that access should be on-site and in real time. The cloud provides a fast, low-maintenance way to roll out this information.

Hidden Insights: Moving your analytics applications to the cloud

Moving your analytics applications to the cloud

More data

With digitalization, the mountain of data grows daily. This naturally increases the need for summary, explanatory and value-adding analyses. During the Industry 3.0 era, individual machines filled individual data pots. But now, networked production produces exponentially more data. Internal IT departments have conventional architectures that cannot quickly meet data storage needs. Without cheap storage like Hadoop, no data lakes could have been built in recent years. Using the cloud allows these data lakes to be operational and always available.

Complex infrastructures

Compressing key sales figures in highly complex data warehouses is no trivial task. Handling these demands has driven a whole group of technologies, including SQL-based databases plus dashboarding and data editing tools. With newer requirements such as machine learning and machine data, these infrastructures are overtaxed. And IT also faces the problem of designing, building and operating additional architectures in parallel. Organizations that need know-how quickly in this area go to the cloud.

Diversity in the analytics area

Commercial software providers such as SAS recognize that open source software provides good options for analytics and machine learning capabilities. Open source software isn't viewed as an either-or solution; it's a complementary one. For example, Python and R programmers make it possible to build a larger analytics group with an organization. It also increases overall efficiency by working together instead of against each other. Existing, conventional architectures (including those from SAS) are inadequately prepared for this. SAS Viya delivers APIs for Python, R, Java, RESTful APIs, Jupyter Notebook integration, container technologies, etc. The cloud provides an ideal environment for building this new infrastructure.

Technical reasons

Machine learning is a computationally intensive process on as much hardware as possible. But its actual application can be performed on a much smaller hardware configuration that's highly scalable. At the end of the day, the hunger for hardware turns machine learning into a cost-intensive business. The famous Google algorithm, which beat the best Go player in the world, was trained on hardware worth about $20 million. The cloud allows you to cheaply rent hardware instead of buying it.

SAS Viya delivers APIs for Python, R, Java, RESTful APIs, Jupyter Notebook integration, container technologies, etc. The cloud provides an ideal environment for building this new infrastructure. Click To Tweet

A question for you

You've just read five reasons why it might make sense to migrate analytics to the cloud. SAS has a simple question for you: When will it make sense for your organization to migrate analytics to the cloud?

We'll help you figure that out in a brief, five-minute survey. Your reward: a (personalized, if desired) benchmark of your company compared to others. We already have more 1,000 answers, so the benchmark will be meaningful.

SAS conducted a survey on the topic and if you would like to receive an industry report with insights into how you industry compares to the market, please register here.

Share

About Author

Thomas Keil

Director Marketing

Dr. Thomas Keil is a specialist for the impact of technology on business models and on society in general. He covers topics like Digital transformation, Big Data, Artificial Intelligence & Ethics. Besides his work as Regional Marketing Director at SAS in Germany, Austria and Switzerland he regularly is invited to conferences, workshops and seminars. He serves as advisor to B2B marketing magazines and in program committees of AI-related conferences. Dr. Thomas Keil 2011 came to SAS. Previously, he worked for eight years for the software vendor zetVisions, most recently as Head of Marketing and Head of Channel Sales. Dr. Thomas Keil beschäftigt sich mit den Folgen des technologischen Wandels für Geschäftsmodelle und für gesellschaftliche Veränderungen. Dabei geht es ihm um Themen wie Digitale Transformation, Big Data, Künstliche Intellligenz und ethische Fragestellungen. Neben seiner Arbeit als Regional Marketing Director für SAS in Deutschland, Österreich und der Schweiz ist er regelmäßiger Gast auf Konferenzen, Workshops und Seminaren. Er ist Gutachter im Bereich Fachpublikationen im B2B-Marketing und agiert als Programm-Beirat für Konferenzen in seinem thematischen Umfeld. Dr. Thomas Keil kam 2011 zu SAS. Davor war er acht Jahre für den Softwarehersteller zetVisions tätig, zuletzt als Head of Marketing sowie Head of Channel Sales.

Leave A Reply

Back to Top