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WHAT’S NEXT FOR ORACLE’S CLOUD STRATEGY?

– Richard Phillips

 

In the rapidly evolving world of cloud technology, the question of which service to adopt is generally seen as a choice between Microsoft Azure and AWS with Google Cloud computing services bringing up the rear and IBM and Oracle some way behind [1].  However, for large-scale online transaction processing, Oracle still leads the way as the world’s most popular database management system [2] and Oracle’s cloud based offering deserves serious attention.

 

Richard Phillips

If we consider a typical enterprise there is likely to be a sizable existing investment in Oracle database technology.  When moving services to the cloud, however, AWS or Azure have quickly become the go-to providers.  Substantial investment in Oracle is effectively written off in favour of spend on new capability.  To combat this rush to AWS and Azure, Oracle launched their Autonomous Database in 2018 but it’s still not seen as the first choice for many enterprises; even those with large existing Oracle investment.  The reasoning behind this may be a perception that Oracle is expensive, complicated and arduous to deal with.  Oracle’s introduction of its autonomous database on its own cloud is an attempt to address this perception and has gone quite some way to bringing a challenger to AWS and Azure.

 

The Autonomous Database quite simply  runs itself without downtime: it does all the administration, optimisation, patching, memory management and, to some extent, scaling, automatically.  This is an incredibly significant move for cloud providers. Cloud databases have evolved significantly but, for the most part, you still need technical teams to administer and keep them running at their optimal level.  Microsoft has managed some of this with their Azure SQL database but elements such as optimisation of indexes are still down to the administrator and the database is not as full featured as Redshift or Oracle.  Oracle has removed the burden completely by using AI to supplement their in-house teams keeping the system running as part of the service.  As an example of how much they have simplified things, the admin manual for standard Oracle 19c database runs to a total of 1690 pages (about the same as the first two Game of Thrones books) and that’s just the admin section – this is almost entirely taken care of in the autonomous version of the same database. With most of the admin taken away, the team of expensive Oracle DBAs are free to do something else (maybe retrain in Amazon Redshift which still has a high demand for admins).

 

The next thing to consider is pricing.  As is the case for most cloud database platforms the price is based around compute and storage with discounts based on committed usage.  Following this model the price comes out around 10% more expensive than the equivalent service using AWS.  But if we then factor in the lower administrative burden, the total cost of ownership comes out strongly in favour of Oracle.  We need to remember, however, that many enterprises have already made a strong financial commitment to their on premise Oracle licences and it is here that the biggest advantage becomes clear.  The licences are fully portable to the autonomous version and if you follow this “bring your own” licence model the cost for the service is going to be around 1/3 of the cost of an equivalent AWS.

 

As far as performance goes the database wins hands down in the published benchmark reports against AWS Redshift and even the same version of Oracle running within the AWS RDS platform.  Performance is between 4 and 14 times as fast than the equivalent workloads [3].  If however more power is needed then scaling the database is done online with the ability to auto scale up and down with zero downtime.  This extra compute can be used to deploy “just in time” extra resource when needed automatically and scale back again when not to save costs.  Unlike other platforms there is no admin overhead as the system manages scaling based on the query demand and can automatically scale up to three times the original compute levels.  Importantly you only pay when the extra compute is being used.

 

The final point to make is around availability.  We normally talk here of 99.9% or 99.95% guaranteed availability, but Oracle have gone quite a bit further here offering 99.995% availability.  The most important part of this guarantee is what it means for updates.  When standard maintenance is your responsibility, then the downtime associated with this is not included in the availability – you must take the hit yourself.  For Redshift on AWS you need to schedule in a 30 minute maintenance window every week.  With SQL Azure there is no window so your connection will drop at the time of update and you just need to make sure your code allows for this.  As the admin is included in the Oracle offering, the uptime includes all the updates and they happen automatically with no outage.  For mission critical services this may well be the most important factor.

 

The database is of course just one part of your cloud strategy and it’s quite widely accepted that the rest of Oracle’s cloud offering is still evolving.  For now an all-in approach with Oracle is probably not a sensible approach but many would argue that a multi cloud approach spanning more than one provider is a more sensible approach for many enterprises.  The risk is spread and you retain leverage over the providers when it comes to renegotiation of contracts.  As it stands, for transactional and analytical needs the Oracle Autonomous Database should almost certainly form a large part of any serious multi cloud strategy.

[1] Source RightScale 2019 State of the cloud report from Flexera

[2] Source Gartner June 2019

[3] Source Oracle OpenWorld tests October 2018

 

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Technology

DISPELLING BIOMETRIC MYTHS AND MISCONCEPTIONS

By Lina Andolf-Orup, Head of Marketing at Fingerprints

Gangsters cutting off enemies’ fingers to access secret locations and spies lifting fingerprints from martini glasses – the imagination of the entertainment world has been running wild ever since biometrics entered the scene.

Couple that with the limitations of some early biometric solutions from fifteen years ago, still anchored in the minds of many consumers, and you have the perfect recipe for an apprehensive and uncertain public.

 

Thawing lukewarm attitudes with a biometric touch

The biometrics industry has made great strides in the last few years – something particularly true for smartphones. Fingerprint authentication has replaced PINs and passwords as the most popular way to authenticate on mobile, with 70% of shipped smartphones now featuring biometrics.

And it doesn’t end there. Many adjacent markets are now eager to benefit from the secure and convenient authentication solutions that biometrics offer. Take the payments industry, for example, where biometrics payment cards are currently gathering real momentum.

However, some consumers are still uneasy about accepting biometrics. A recent study found that 56% of US and EU consumers are concerned about the switch to biometrics as it’s not enough understood to be trusted.

Although attitudes are shifting for the better, stats like this demonstrate there is still some work to do to disprove common biometric myths and showcase just how smart today’s solutions really are.

 

Lina Andolf-Orup

Dispel, adopt, repeat

The evolution in consumer biometrics in the last two decades has been phenomenal. And today’s solutions are far more advanced and safe than many may think.

To help bring an end to the myths, let’s expose some of the most common misconceptions around biometrics.

Myth: Biometric data is stored as images in easy-to-hack databases.

A leading myth about biometrics is that when a fingerprint is registered to a device, it is stored as an image of the actual fingerprint. This image can then be stolen and used across applications. In reality, the biometric data is stored as a template in binary code – put simply, encrypted 0s and 1s. Storing a mathematical representation rather than an image makes hacking considerably more challenging. In most consumer applications, this template is also not stored in a cloud-based location, its securely hosted in hardware on the device itself for example in the smartphone, in the payment card. Thus, it stays privately with its owner.

Myth: Fingerprints can be easily replicated to ‘trick’ devices.

The internet is full of articles and videos that claim it is possible to use materials from cello tape to gummy bears to craft fingerprint spoofs and access biometric systems. Although there may have been a time where gummy bear spoofing was the go-to party trick, todays’ consumer biometric authentication solutions have too many technological defences, such as improved image quality and matching algorithms, to simply ‘trick’ devices. Plus, on top this, the criminal needs to have access to the person’s device where this fingerprint is enrolled e.g. smartphone, payment card, before he/she notices and blocks it. This is not scalable nor common, in comparison to gaining access to someone’s PIN code or skimming a contactless card.

Myth: Physical change will prohibit access to my device.

Although our irises don’t change as we age, our fingerprints can and our faces will. Does that mean we have to update our biometric devices every few months to capture these changes? Not quite! Unless there are drastic, sudden changes, the ‘self-learning’ algorithms in modern-day biometric systems are able to keep up with our developing looks.

 

Who you gonna call? Mythbusters!

These are just some of the common biometric myths and misunderstandings perpetuating in consumer mindsets. Thankfully, though, while we’re working hard to rid the world of the myths, belief in the value of biometrics is only expected to grow. But as solutions expand and diversify, the myth-busting fight will continue.

Fingerprints has been a leader of innovation in biometrics for the last two decades. We’re proud of the expertise and R&D we’ve been able to pour into our biometrics solutions to deliver stronger security and a better user-experience. To learn more about the most common biometric misconceptions and the modern-day technology that allows us to dispel them, download our eBook here.

 

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Technology

WHAT EVOLUTIONARY AI MEANS FOR FINANCIAL SERVICES

FINANCIAL SERVICES

by Babak Hodjat, VP of Evolutionary AI at Cognizant

 

Many banks and other financial services institutions (FIs) are beginning to recognise the benefits of AI-driven solutions as a way to get ahead in the market and challenge the competition. Amongst many other benefits, the technology enables organisations to offer hyper-personalised customer experience,  dramatically improve internal decision making, and drive operational efficiency. However, many businesses are struggling to move beyond the experimental phase and reach actual AI deployment. It is those organisations that are at risk of being left behind.

The financial world has already been transformed by AI, and this transformation is continuous. A new breed of AI, known as ‘evolutionary AI’ has begun to further accelerate innovation. It is capable of automatically designing itself with little need for explicit programming by humans – innovatively creating complex AI models, and optimising decisions considering multiple scenarios.

This technology is revolutionary for industries across the world, but in particular it is set to transform the financial services sector. Enabling businesses to spot novel strategies that would never have been identified by human data scientists, and, in turn, allowing companies to take full advantage of today’s massive data sets – evolutionary AI will soon be a vital tool in all FIs’ arsenals.

 

The nuts and bolts of evolutionary AI

Emerging technologies that enable AI algorithms to design themselves are allowing organisations to transcend human limitations. Evolutionary AI operates iteratively. Firstly, it randomly generates a set of potential solutions to form an initial population and assigns a score to each solution based on how well it performs relative to other solutions. In the second round, it retains the solutions that performed best, perhaps only 5% of the total, and recombines their components, sometimes “mutating” them to create a new population. This new population is then tested, and the process begins again. Over multiple generations, the appropriate components of the more successful solutions become increasingly prevalent in the population, and eventually a solution is discovered that yields the best outcomes.

 

Advantages and use cases

Compared to human design, evolutionary AI can be deployed far more quickly, avoids biases and preconceptions, and typically performs better. Furthermore, the chosen model will evolve and improve over time based on new data.

Evolutionary AI can be applied in a wide variety of areas at FIs. Some examples include designing quantitative trading strategies to maximise returns while minimising risk and loan underwriting. Rather than relying on human analysis, evolutionary AI solutions can quickly analyse all the combinations of relevant variables to create models that more accurately assess the risk of default by a potential borrower.

 

A recipe for success

In order to reap the benefits of the technology, FIs should focus on the following:

  • Responsible AI – Behave in ways that make customers and employees comfortable, i.e. not making decisions that are unethical or exhibit bias. Companies need to monitor them to ensure they continue to act appropriately, as they learn and evolve.
  • Viewing AI through a business lens – Having AI projects managed by cross-functional teams with business executives in the lead is a good place to start. Companies also need to look across their organisations to identify opportunities to generate concrete business value from AI — not only in reduced costs but also in boosting revenues by delivering enhanced customer experiences and through improved decision-making.
  • Enhance data management – AI applications depend on access to timely and accurate data, which is a challenge for many FIs that have fragmented data architectures with multiple legacy systems. Companies need to identify which types of data are required for each AI project and ensure they can be captured in an appropriate format.
  • Approach with speed and caution – AI projects need to be rolled out quickly, while at the same time be rigorously measured, so failures are terminated promptly while successes are moved into production.

The sophistication of AI technology is set to significantly improve over the coming years as it continues to design and test itself. As a result, it will become more critical to the productivity of FIs, and soon businesses will recognise it as a vital tool for consulting on important business decisions. It will not be long before humans and AI are working alongside each other, with robots handling routine tasks, enabling employees to focus on more complex and sensitive activities. Delivering more value together than either could on their own.

 

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