Big data is nothing but the confidential information storage center, which needs the support of AI to manage the huge volumes and function seamlessly.

In most organizations, big data storage is spread across different computers, on-prem, or on the cloud. The analysis of such an incredible and useful source of data, is best done with AI based tools and applications.

The insights derived by AI tools from both structured and unstructured data, effectively have the power to change the course of a company’s business growth. The World Economic Forum in 2016 has estimated an increase of stupendous US$100 trillion in social value and global business by 2030. Most of this growth will be driven by the power of data collated today!

A report suggests that, on average, every day, humans create about 2.5 quintillion bytes of data. If all these data are well-utilized, they will surely allow humans to get an improved view of the future. In business, this could be the difference between survival, or liquidation- or explosive growth.  To be precise, data is valued to assets, which allows companies to get a hint of the future through advanced analysis.

AI technologies will probably be one of the biggets drivers for this growth, if studies from  PwC and McKinsey are anything to go by. They estimate the increase in AI tools business to touch US$15.7 trillion, and be around US$13 trillion of the annual GDP by 2030. However, there are a few significant challenges that companies face concerning big data, which could be easily resolved by adopting technological changes:

Diversity in IT source system

Storing data is always a complicated process, and securing, maintaining/managing it is even more difficult. The average Fortune 500 Company has thousands of enterprise IT systems across diverse formats, with mismatched references across data sources and duplication errors. Such diversity only complicates the situation and creates chaos.

High-frequency data management

Data flow is real-time, so critical issues like censoring of data still stay unspoken. So, high-frequency data management not only complicates the process but multiplies the vulnerabilities and risks. AI can help sort and censor the data as and when they flow in.

Organizing data content from diverse sources

Since big data is gathered from varying and wide varieties of sources, their formats are different, and most of it is unstructured.  So, even to structure the data into analyze-able bites is challenging and involves a lot of tools. This is merely to differentiate them and put them across diverse channels before conducting the in-depth analysis. One more added issue is data clarity, as some files don’t even comply with the set minimum clarity bar.

Looking at all these issues, AI can be the superhero sorting them all. Enterprise analytics and IT team need to provide advanced tools to empower employees with diverse levels of data science proficiency to function effectively with large data sets, and perform smart predictive analytics using a unified vision.

 Resolving the big data issues

Data Scientists could potentially be the magicians that will derive insights form the humongous amounts of Big Data in the market today. The predictions and insights could be the deciding factor for nay industry. Many CIOs feel ML algorithms are of great use as it facilitates the necessity to receive new data, generate outcomes, and have some decisions or actions to be made based on the outputs. Multiple algorithm libraries are widely available to data scientists today, and they can use these to create the highest quality actionable insights.

Big data issues like unstructured data, bad clarity content, data lakes, etc. are problems that could be demystified with the help of ML, data science applications, and with efficient application of AI tools.

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The imminent demise of the benchmark Libor interest rates is one of the most important developments in the history of financial markets and one that will pose a challenge for all financial institutions. Fortunately, recent technology innovations are available to assist in the monumental task of defining issues, managing data and creating new processes and protocols for progress. In particular, the challenges posed by Libor can be well served by the application of machine learning.

In our white paper, Libor as a Template for Digital Document Transformation in Financial Services, we lead an exploration of the ways in which machine learning can be utilized to answer the challenges posed by Libor, including pitfalls and important lessons to be learned. In this blog post, we address some of the key ways that the project should be structured to ensure maximum success. 

Key Elements of a Libor Change Initiative

Libor has been used for most contracts and agreements in derivatives, bonds, mortgages, commercial and retail loans. In documentation, the term is used trillions of times in hundreds of millions of contracts and agreements. Successfully managing the transition to a post-Libor financial world is a large and sensitive task and therefore requires a comprehensive approach that marries data science with change management. Some of the most important functions include:

Setting the table: Just as a house rests on its foundation, a successful change effort is based upon the work put in on problem definition and setting success metrics. The more that the current state is analyzed and comprehensive and realistic goals are set upfront, the more that success flows down the road.

Data is fundamental to success: Data is also foundational to the process. In order for the effort to pay off, care needs to be taken from the beginning or outcomes will be compromised. The issues are complex and important steps include access to appropriate and necessary data, take time for data labeling, and to perform rigorous exploratory analysis.

Utilize appropriate tools and processes: You’ll need to use the right tools for each step and the number of different skills needed for such a comprehensive undertaking is broad. Two examples are optical character recognition (OCR) and multiple, additional ML models to separate documents into categories. From there, other steps include business model grooming, feature engineering, and error analysis.

Make the results work for you: Too often, there is a failure to fully follow through and map results to success metrics and follow up to strategize and create a roadmap forward. Candor and objective analysis are necessary to achieve optimal results and commitment from stakeholders is an essential ingredient as well.

Successfully Applying Machine Learning to the Libor Data Challenge

The end of the Libor era is one of the most important and far-reaching events in the history of financial markets and making the most of the transition to a new regime can be viewed as an opportunity to accomplish a broad transformation for a large number of important areas within the enterprise. The task isn’t simple but the potential rewards are large. Making the most of them will help determine which firms do well and which fall behind. Tackling the challenges with the application of new technologies such as machine learning as detailed in Libor as a Template for Digital Document Transformation in Financial Services is critically important.

Maven Wave helps drive the future of financial services with innovative business outcomes, fueled by cloud, with risk top of mind. To help organizations maximize economic outcomes and advancements, Maven Wave brings a rich blend of industry-specific technological expertise, agile-integrated design, and best practices for transformation. 

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The use of artificial intelligence (AI) and machine learning (ML) is fundamentally changing the way we think about DevOps. Most notably, it is delivering a new form of DevOps that recognizes the need to have systems that are intelligent by design and underpinned by comprehensive security (DevSecOps). For many, this will be the crucial next step if DevOps is to shorten the software development lifecycle for all connected intelligent systems, ensuring the continuous delivery of secure high-quality software.

By now, most organizations understand DevOps is a substantial discipline that they must adopt – according to Deloitte, organizations adopting DevOps see an 18%-21% reduction in time to market. By breaking down the silos between business and IT operations, DevOps can ensure consistent levels of productivity, efficiency and service delivery, all of which hold weight in these times of heightened uncertainty.

To put it simply, DevOps can help businesses compete in already congested marketplaces. Through a foundation of continuous integration (CI) and continuous delivery (CD), organizations can ensure the customer receives the product they demand in the fastest time possible, while mitigating any elongated frustrations experienced from a lack of harmony between systems engineers and operations teams. Incorporating AI and ML into that DevOps strategy will take things to the next level.

AI and ML Are the Next Evolutionary Step for DevOps

Today’s companies are data-driven and being built as digital platforms. However, just 28% of organizations are currently succeeding in their digital transformation journeys. Increasingly, corporations are looking at what AI and ML can do to help them realize their transformative ambitions.

Both the AI and ML markets are expected to experience huge growth over the next three to four years. Analyst firm IDC, predicts worldwide spending on AI systems will reach $97.9B in 2023, while the market for ML is forecast to have grown at a Compound Annual Growth Rate (CAGR) of 42.8% by 2024.

What AI and ML reveals for these companies, and the increasing value of what Al, ML and devices can do together, are changing the way organizations view the world as they digitally transform. Coupling AI and ML with DevOps will lead to a significant shift in what DevOps gets involved with. 

Primarily, it sets DevOps front and center of an organization’s wider digital transformation ambitions. If digital companies run on living data, then the development of these intelligent systems is an enticing environment for DevOps to prove its wider value to the organization like never before.

Security Takes the Center Stage

According to a TechTarget survey of key IT decision makers, cybersecurity and risk management were found to be the number one area for spending this year, with more than 53% of respondents saying they saw budgets increase in this area. And security is a crucial element that any organization employing a DevOps strategy should be considering. 

DevSecOps is the simple premise that everyone involved in the software development cycle is responsible for security. In fact, the saying “security is everyone’s responsibility,” is a mantra that has been repeated often, and just as often ignored. Embedding security within every part of the development process, ensures security and compliance monitoring tools keep pace with the speed and agility that DevOps offers. It is a common understanding that security is the biggest block to the rapid and seamless development and deployment of systems, as security solutions have not traditionally been built to test and code at the speed DevOps requires. 

Ultimately though, more automation from the start, reducing the need for manual configuration from security architect is where DevSecOps shines. This reduces the chance of misadministration and occurring faults, which can lead to downtime, and potential breaches or attacks. Quite often, the best ways to secure your organization is through simple hygiene factors, such as regular patching and software updates. 

The reality is that every organization employing DevOps should really be approaching it as DevSecOps. Security is of ever growing importance to each and every organization and, through empowering it with AI and ML processes, it can be enhanced too, simplifying the processing of data to easily identify threats or potential vulnerabilities in the security makeup. 

Reaching Beyond Human Capability

The future will be made up of connected intelligent systems that span the intelligent edge, all the way to the cloud. The expectations of these systems are based on the lifecycle of a typical mobile application with constant feature enhancements. But, how do you know what to deliver next, especially when things are moving so quickly, and as developers are inundated with information and data from a number of different sources? This data deluge can be powerful, but it can also be overwhelming.

Crucially, this is where the power of AI and ML comes into play. AI and ML will aid developers in making sense of the information housed across various data warehouses. In DevSecOps, there has always been an approach to automate everything, and AI and ML will be instrumental in automating the analysis and processing of data – a task that is now far beyond the capabilities of humans.

Incorporating AI and ML will enable developers to better understand and use the data at hand. This will see developers understand not only the error, or the occurrence of a fault, but the detail of what happened in the run up to the fault – vastly reducing the chances of incurring that fault again. AI and ML will also be responsible for enabling the transformation from diagnostics to prognostics across any and all tools and systems, making it easier for developers to anticipate, identify and resolve faults or errors. 

Increasingly, we’ll see organizations benefit and drive value from real-time insights. They’ll do this through AI and ML frameworks deployed on active systems to deliver optimizations based on real-time development, validation and operational data. This level of real-time actionable insight bolsters DevSecOps’ ability to achieve CI and CD, through improved, reduced mean time to resolution, easing the impact on operations and, ultimately, enhancing the end-user experience. It remains to be seen if incorporating AI and ML into the DevSecOps cycle will achieve the delivery of self-healing systems that can detect and resolve issues without the need for human intervention. With true AI, however, this might well be possible.

The benefits and potential use cases of AI and ML are widely acclaimed, and while we are still some way from seeing their full potential, the capabilities they currently possess are revolutionizing many sectors. The fact is, if DevSecOps is to continue to keep pace with the vastly growing digital data economy, then it will become reliant on advanced technologies such as AI and ML. Data is powerful if used correctly, but with so much of it to process and analyze, that can be a challenge.

A move toward secure automated processes is the progressive step that many organizations need to consider if they are to realize their digital transformation ambitions. Organizations cannot continue to do things the traditional way and expect to get the results that the new world is looking for.

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