Oracle introduced new data services that expand the number of services available on its cloud platform.

The marquee new service from the software giant is the Oracle Cloud Infrastructure Data Science offering — an evolved version of the DataScience.com platform that Oracle acquired in 2018.

The Oracle Data Science service provides an automated workflow for machine learning and data analysis. Oracle is also launching a new data catalog service that helps users organize data for analysis. Another new capability is the Cloud SQL service that enables users query cloud data stores, while the Data Flow service enables users to run Apache Spark big data analysis as a service.

Oracle is playing to its strength in data with the new services, unveiled Feb. 12, according to Nucleus Research analyst Daniel Elman.

“Oracle made its name on database technology and remains to this day a preeminent leader in the space,” Elman said. “With these services, it’s leveraging this expertise with data management and offering its thousands of database customers a natural route to enabling data science initiatives without having to migrate data or learn new specialized tools.”

Oracle Data Science positioned for ease of use

Oracle is marketing the Data Science service as a way for teams of data scientists to work together collaboratively to generate machine learning models and then apply them to production applications.

The data science service has a project environment that sets up all the infrastructure and the networking needed to access data assets, as well as providing the tools needed for data science, explained Greg Pavlik, senior vice president of product development, data and AI services at Oracle. Among the tools is an automated machine learning feature that provides these capabilities for common data science tasks such as algorithm selection.

Oracle getting into the data catalog market
Alongside the Oracle Data Science service, the vendor launched a new data catalog to help organizations track all the data sets that come into a cloud deployment.

“Say you’re setting up a data warehouse, we can introspect the data warehouse model, and allow users — it could be data scientists, it could be data stewards, it can be analysts — to find out what data is available, who owns it and what it’s meant to be used for,” Pavlik said.

The Oracle data catalog also provides tagging capabilities that enable administrators to define taxonomies and start to organize data sets hierarchically.

Data Flow service enables Apache Spark Big Data
The new Data Flow service also helps meet a different need, enabling users to run Apache Spark jobs as service in the Oracle cloud. One of the challenges some organization face with running Spark analytics jobs is that they are often running on top of Hadoop clusters, which introduces additional complexity, Pavlik noted.

All that’s needed to run a big data workload in the Data Flow service is to upload the script, click on an application that is sort of the pointer to the script, and then specify how many CPUs the job should run on, Pavlik explained.

“We will synthesize the job on the fly in a totally serverless architecture, executing in tens of seconds,” he said. “We really think about this as a big generational leapfrog in terms of how to how to make big data workloads consumable by the enterprise.”

Oracle is also expanding the ability of users to query data in the cloud with the new

Oracle Cloud SQL offering. Users can use the SQL capability to query against cloud-based object stores.

“So you can reach out into a cloud-based data lake and apply the full semantic richness of the Oracle database,” Pavlik said.

Data integration service is coming

In addition to the Oracle Data Science services, the vendor has more data services in the works, among them a data integration service. Pavlik said that an upcoming data integration service will provide data preparation and ETL capabilities.

“It figures out where’s the most cost-effective way to run elements of the flow so that it’s filtering data and minimizing data movement,” Pavlik said. “It’s also filled with a data immersive view, so you can really drill down, understand your datasets and manipulate the data.”

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Before we get to the working of artificial intelligence with big data services, we will first go through the brief introduction of both of them and then understand how these are related.

Big Data

Big data, as the name suggests, is a massive amount of data to be stored, analyzed, and worked upon to obtain fruitful results that will help in the overall growth of an enterprise. Big data is popularly known as the three V’s, which are- Volume, Variety, and Velocity. Volume refers to the amount of data. Variety refers to the different types of data that too in a wide variety, in different formats, and lastly ,velocity refers to the speed with which new data is generated regularly or on a daily basis.

Now the big data analytics solutions are far more advanced. This is because the size of big data is way too big for the traditional software to work upon. And also, the big data faces many challenges too, some of which are capturing the data, sharing it, and analyzing it, etc. Also, there are types of big data which are namely- structured, unstructured and Semi-structured. The first one has a definite format unlike the second one, and the third is a mix of the former two. Big data consulting services are in demand because of the benefits that have brought into the market. Now let’s understand what is AI or artificial intelligence.

Artificial Intelligence

AI or Artificial Intelligence as the name suggests, or synthetic or man-made intelligence. AI is one of the many branches of computer science. These are machines that perform a number of tasks that require human intelligence. Advancement in artificial intelligence goes hand in hand with the development of deep learning and machine learning. Growth in these two brings about massive changes in the technical field and particularly, in artificial intelligence. It is also referred to as machine intelligence sometimes.

It was first started in 1956 and since then, a lot of funding has been invested in it. Huge Amounts were wasted but compared to the development that the world has achieved with AI, it is nothing. It is also mostly known as robotics because it is how human intelligence is portrayed through machines in the best way. But it does not revolve around that only.

The challenges or problems that artificial intelligence comes across are reasoning, perception, representation among many others. It uses a number of tools and applications to work and is also made up of several software and programs written in various computing languages using a number of operating systems, frameworks, and platforms.

The relation between Artificial intelligence and Big Data

Big data services companies were not in much use in the earlier times but now they are and this is because of the development of artificial intelligence and machine learning. Earlier, having big data was a problem as there were no methods to interpret such a large amount of data that too, in a wide variety. But this has changed now. Artificial intelligence and machine learning help a lot in interpreting all different types of data, be it in textual form or pictures, videos, etc. With the help of artificial intelligence and machine learning, enterprises are able to unleash a whole lot of different uses from the big data they have and acquire continuously.

Let’s understand a bit more. The type of relationship that exists between artificial intelligence and big data or big data analytics solutions is of the reciprocative type. Artificial intelligence is somehow meaningless without big data. If artificial intelligence does not have any data, how or on what will it work? One can say that it is mainly made for this purpose, to tackle large amounts of data and extract out meaningful insights from it. People benefitting from this are saying that, the more data they put into it, the better it gets.

How AI is able to give better insights from big data?

Here, we will see what are the ways in which artificial intelligence is able to provide enterprises with better insights than ever before:

1. AI brings new methods for big data analytics solutions
   Earlier, people used queries and MySQL for analyzing data but now, artificial intelligence and machine learning have brought new and advanced methods of analyzing or rather, they are the new methods. In fact, the statistical methods that were used before have now merged with computer science and are known as machine learning and artificial intelligence.
2. The intensity of labor reduces with AI
   As machines do most of the work, the human capital is now able to devote their time to better and more meaningful tasks. Also, AI is able to do these tasks in much less time than compared to humans.

Parting Words

There are many other reasons and ways in which AI works together with big data to give desired insights and help businesses prosper, some of which were mentioned above. There is no doubt in the fact that AI and Big data are bringing a new bright future for the professional world.

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McKinsey’s Global AI survey has found that the use of artificial intelligence (AI) in automating business processes has increased by 25% year on year, yet a number of sectors are struggling.

In a global survey of 2,360 businesses, McKinsey found that 58% of organisations have embedded at least one AI capability into a process or product in at least one function or business unit. This is up from 47% in 2018, which means AI adoption is becoming more mainstream, it suggested.

According to McKinsey, the top quarter of businesses – the 54 “AI high performers” that took part in the study, where AI is being used in five or more business activities – reported seeing an average revenue increase of at least 5% from AI adoption in the business units where AI is used. The study also found that these businesses saw an average cost decrease of 5% or more from AI adoption in the business units where AI is used.

McKinsey’s research found that these high performers in AI are nearly three times more likely than those from lower-performing companies to report revenue gains of more than 10%.  It also found that businesses are most likely to report revenue growth from AI when it is deployed in marketing and sales, product and service development, and supply-chain management.

Overall, almost two-thirds of the organisations that took part in the study said they saw revenue increases from AI adoption in the business units where they use AI. 

About one-third of respondents said they expect AI adoption to lead to a decrease in their workforce in the next three years, while one-fifth expect an increase. Those organisation identified by McKinsey as AI high performers tend to do more retraining.

According to McKinsey’s data, the sectors with the most high performers are high tech, telecoms and financial services. McKinsey said there were no respondents in electrical power and natural gas that met the criteria to be classified as AI high performers.

The research showed that infrastructure, professional services and the pharmaceutical sector are the sectors that generally score lower for adoption of various AI-related technologies, such as machine learning and robotic process automation.

According to Jacomo Corbo, co-founder and chief scientist at QuantumBlack, which was acquired by McKiney in 2015, infrastructure companies are generally slower with enterprise software implementations, which means IT systems and data are more functionally siloed. “Both business-side and IT department resources generally have fewer resources adept at configuring and building new data services and workflows, including those that embed AI and machine learning,” he said.

Looking at the professional services sector, McKinsey said it, too, had similar issues to the infrastructure sector and was at a disadvantage because of the lower level of sophistication of the enterprise IT systems deployed. This leads to challenges with integration and data.

Also, said McKinsey, AI in professional services is further complicated by the nature of their work, which tends to be very bespoke and non-repeatable. “Many professional services firms may not have access or licence to a lot of the data they generate,” said Corbo.

Asked about the pharmaceutical sector’s lower score in AI adoption, Corbo said: “We see a lot of variation within this sector. Some companies are definitely adopting AI and machine learning at scale across functions, but on the whole there is still relatively little penetration at scale in the sector.

“Much of that is to do with a combination of factors: established practices around IT outsourcing that is now starting to reverse itself as companies build capabilities around AI; a complex data landscape including legacy systems, made more complex by regulatory oversight and different requirements by function and different regulators in different geographies.”

Significantly, across all sectors, irrespective of the level of AI maturity, McKinsey’s study showed that less than half of respondents (41%) said their organisations comprehensively identify and prioritise their AI risks. Corbo said: “This reflects that the majority of companies, including those that are adopting AI, recognise that they are not being systematic nor comprehensive about how they are identifying and mitigating risks associated with the design, development and operational deployment of AI.”

As Computer Weekly has previously reported, organisations are starting to realise that to deploy AI, they need to give careful consideration to fairness and bias in the training datasets used for machine learning and AI, and AI algorithms need to be accountable and transparent.

Corbo said enterprises should also assess data model drift and degradation, and the potential for adversarial attacks, where a hacker deliberately feeds the algorithm corrupted data.

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