Growing Importance of Analytics As A Service
Growing Importance of Analytics As A Service
The convergence of cloud, big data, and AI has given rise to a new type of service that will provide businesses with a let up — Analytics-as-a-Service.
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As the world’s datasphere grows in stature and size, big data, artificial intelligence, and cloud computing are combining to provide enterprises the much-needed respite in the form of Analytics-as-a-service
Technology has become imperative — or has it?
Let’s take a brief look at what has happened since the year 2010.
The decade witnessed a number of technological advancements that have changed the way businesses interact, both with themselves and with their customers. While mobile telephony saw the pivotal leap to advanced smartphones (think iPhone), the social media world witnessed a powershift as Instagram took over from Facebook even as WhatsApp stands as the most ubiquitous personal interactive solution.
The decade also saw the proliferation of new-gen technologies – Big Data, Analytics, Internet-of-Things (IoT), Machine Learning (ML), and Artificial Intelligence (AI) — that have firmly entrenched themselves into the world of business. These new-gen technologies are powering businesses through useful insights and innovative solutions.
And then came the cloud.
Cloud or cloud computing began as the much-required solution to the compounding IT infrastructure and application challenges. The only major difference? It was off-premise and hence, the name.
The advent of cloud, thanks to AWS, Google, Azure, and Oracle, had completely changed the game for the world of IT infrastructure and IT services. Enterprises, big and small, are now fast embracing cloud technologies in some manner or the other even as we speak.
Big data, AI/ML, and Cloud opened doorways to another "X-as-a-service" category — Analytics-as-a-Service, or AaaS.
The Convergence of Data, AI and the Cloud
How would you rate the performance of a car with a Mercedes engine, Ferrari Design, and Michelin tires?Great, right?
Well, the convergence of Data, AI, and Cloud is breathtakingly great and more. This convergence is what leads to Analytics-as-a-service. AaaS helps organizations achieve a consistently great performance through higher levels of operational efficiency and productivity. All this, at affordable costs.
And yes, the IT infrastructure and applications environment is scalable, very scalable – that’s what cloud technology is all about.
It is an interesting world that we are living in today, a world full of data even as more data gets added every single minute. As the internet proliferates rapidly and computers and connected devices multiply, we are staring a completely different world altogether – a world of data, a really massive world.
Why AaaS In the First Place?
Organizations have always aspired to move away from the data center business by shifting the base from on-premise to cloud.
The complexity of managing a growing IT infrastructure, need for knowledgeable resources, and of course, the burgeoning costs associated with running on-premise prompted organizations to embrace the cloud. And embrace, they did.
It is true that more data remains idle than is used. This calls for ways to harness data comprehensively and analyze it. Data analytics brings out interesting and useful insights on business which when translated into strategic initiatives will bear fruit in the form of business growth.
But how do we capture, process, and analyze such humongous amounts of data?
Enter AI and ML.
Big data analytics is no longer a buzzword, and it has a great many use cases and is steadily transforming the way businesses operate and grow. AI and ML just about accelerate the process of big data analytics and bring in some level of automation. They help pull out some very interesting insights at a much faster pace and with as little human intervention as possible. Big data, in return, acts as the huge repository of data helping AI become much ‘smarter’ and efficient as it continuously provides humungous amounts of data for machine learning purposes.
Well, all this sounds great. But, increasing amounts of data will only burden enterprises with higher costs associated with IT infra and applications.
What then is the solution?
AI and Big Data make a great combination in helping enterprises reap benefits. But the rapid increase of voluminous data by the minute hangs as the ‘sword of Damocles’ on organizations. Increasing data demands higher storage and processing capacities which cannot be matched by traditional architectures making it very difficult for organizations to make comprehensive use of data.
Cloud technology solves all these issues making it very easy and affordable for organizations to avail the big data and analytics services leading to what we call – analytics-as-a-service or AaaS.
AaaS Use Cases
As AaaS gathers momentum and businesses begin to adopt it, the number of use cases will only grow. Let’s take a look at few use cases –
Retail is easily one of the major data-heavy industry segments. The amount of data generated through customer interactions, mobile POS, in-store walk-ins and purchases, product preferences, and visit timings – it's just too huge to handle. If a retail store needs to make the best use of this data, it needs to invest heavily in IT infrastructure, human resources, and software – a highly unprofitable proposition. AaaS is the perfect solution; it not only helps bring out great customer insights from data but also does it in an efficient and quick manner.
AaaS also brings in Agile business processes while reducing the cost of analytics by a huge margin.
Customizing Hospitality Solutions
The hospitality industry is undergoing a landmark shift in its business operations as we speak. Petabytes of data that have been sitting idle for years is now being utilized.
AaaS brings a definitive purpose to the tons of data being generated on a daily basis in the hospitality industry.
Data is proactively captured, mined, and analyzed to bring revealing insights on customer preferences, habits, and needs which is helping hotels target customers in an exclusive manner. From room preferences to services usage (Internet, complimentary breakfast), personal preferences (minibar, TV, welcome drink) and payment modes (credit card, debit card, cash) – a hotel will now have the luxury to study and map its customers to come up with innovative and customized initiatives and offers.
Develop Competitive Pricing
Ever seen Amazon change its pricing for the same product at different times? Well, that concept is here to stay. Backed by analytics and AI, companies such as Amazon, Flipkart, and Alibaba are using petabytes of customer (or user) data to develop dynamic and very competitive pricing. AaaS helps companies develop very specific and customized pricing strategies based on user browsing and shopping habits.
The biggest advantage AaaS provides is with respect to scale – for every single minute that users generate tons of data – it is captured, processed and analyzed quickly, and at low cost.
So, the next time you see an email or an alert with a change in the pricing of a product you window-shopped - AaaS is at play.
As we venture ahead, AaaS will adopt advanced analytics, which will take more prominence and become regular. Advanced analytics will change the game, not only for AaaS but for enterprises adopting it. Advanced analytics will shift gears when it comes to providing insights.
Equipped with AI and ML techniques, advanced analytics will make predictive analytics commonplace and give raise to foresight, instead of mere insights.
Imagine the kind of advantage an enterprise will have with a more robust and powerful AaaS solution in hand?
Businesses of all types will stand to gain massively through AaaS even as they will have a much more powerful and efficient way to shift through, harness and analyze data. The future for AaaS looks very great and its importance will only grow as enterprises create and consume data, every second.
Published at DZone with permission of Rajeev Ranjan . See the original article here.
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