Author name: Rubiscape Team

With 80% of enterprises smartly investing in Artificial Inteligence  (AI), the technology is transforming every possible business function, and software development is no exception. It is projected that AI-enabled tools alone will generate $2.9 trillion in business value by 2021. Traditionally, software development required developers to specify, in advance, exactly what they wanted the system to do and then manually develop all the said features. However, in the AI age, all developers need to do is feed data into machine learning algorithms; the model will automatically deduce what features and patterns are important – without needing a human developer to explicitly carry out coding. In the coming year, AI is not only poised to accelerate the traditional software development lifecycle; rather, it is expected to present a completely new paradigm in software development. Let’s see how AI is set to transform how we develop software in 2025: Improved time-to-market: Over time, software systems have become incredibly complex, requiring multiple dependencies and integration as well as layers upon layers of functionality and interfaces. All these components have, until now, been manually managed and updated by humans, leading to discrepancies and unresolvable bugs. In contrast, AI models can extrapolate important features and patterns in data and reduce the time taken to develop high-quality, complex software – thus improving time-to-market. Rapid prototyping: The process of turning business requirements into technology products has typically been long and cumbersome. Getting the idea to a prototype level has been another daunting step which needs massive budgets and resources. 2025 will see AI shortening this process to a few lines of code; by using pre-defined natural language or visual interfaces, AI will speed up the prototyping process and enable technical domain experts to quickly develop quality solutions of tomorrow. Intelligent programming assistants: In software development, a lot of time is spent on going through important documentation and debugging code. Enter intelligent programming assistants, and the debugging process can be accelerated. In 2025, smart AI assistants will become extremely popular; through deep learning, they will offer just-in-time support and provide recommendations including relevant documentation, best practices, and code examples to developers, and help them speed up the development process. Automatic error handling. AI algorithms can also learn from experience to identify common errors automatically during the development phase. AI’s deep learning algorithm can help flag known errors and learn how to fix each of them – with enhanced accuracy. It can do this by analyzing system logs and proactively identifying and rectifying errors even after the software solution has been deployed. In the coming year, it would also be possible for software to change dynamically in response to errors without human intervention. Accurate estimates: Software development projects are notoriously known to miss timelines and go over budget. Reliable estimation requires developers to learn from past situations regarding delivery times, and common pitfalls. AI models can train on data from past projects including data about features, bugs, average development time, resource allocation, testing times, user reviews etc. and predict effort and budget far more accurately. By learning about individual habits, team performance, and possible obstacles, AI can create personalized work schedules that take into account the work patterns of each member, for maximum efficiency. Automatic code refactoring. Clean code is critical in software development as it can make or break a project. Contrary to the belief that programmers spend a lot of time writing code, they actually spend a lot of time reading code, documenting it, debugging it and figuring out what to do next. AI can analyze code and automatically optimize it for interpretability and performance; by mutating a piece of software hundreds of times, it can determine which of those versions are better, and then mutate those, until the end result is the best possible version of the code. Quality development: A substantial portion of development time is often spent debating which features to prioritize and which to eliminate. An AI solution that is trained on past development projects can assess the actual performance of existing features and help both business leaders and development teams identify efforts that would maximize revenue and minimize risk. By analyzing customer reviews and product features, AI can create a list best features to have and improve the quality and success rate of the software under development. Automated testing: Another major impact AI will have on software development in 2025 is in the area of automated testing and bug detection. Traditional testing involved creating a comprehensive list of most probable test cases, as well as some extreme ones that could affect the performance of the program. AI can automate the testing process by looking at past logs and automatically generating a list of test cases. What’s more, it can also predict outcomes of testing without performing the actual tests and only focus on cases that have a higher likelihood. Enhance software development accuracy In the AI era, programming is no longer about developing code and wondering if the features will meet the said requirements. It is more about choosing the right data to train AI algorithms which will satisfy needs to the T – all without human intervention. The coming year will see the human-driven era of software development, which involved writing rule-based code to solve deterministic problems using logic, paving the way to AI that will forever change the way we develop software. With the ability to improve time-to-market, increase prototyping speed and efficiency, automatically handle error and refactor code, accurately estimate budget and time, and automate testing, AI has massive potential for speeding up and improving the accuracy of the software development process in 2025.

I was recently reading an interesting article which mentioned that Google receives close to 3 Million job applications in a year and it hires only 4000 people. In the article, Laszlo Bock, who is the Head of People Operations at Google, shared a few traits which Google looks for while hiring the top 4000. Yes, each company looks for specific traits in its potential employees – the traits, skills, qualities – whatever you call those, are specific to that organisation. Those resonate strongly with the company culture. The article made me think of Rubiscape’s decade long journey and the people who helped us in this journey of building this high-performance organisation. At Rubiscape , while I haven’t conducted any formal study to identify the traits of the ace team members, when I look back, I know that they all share common traits. Now we are working on our plans of becoming the global leader in IoT, data science and analytics technology. We are looking for the best minds in the industry to work with us. I thought of taking this opportunity to pen down the key qualities which every Rubiscape employee shares – They are Innovative Innovation is a strong part of the personality of Rubiscape employees. They are persistent, they are willing to change, they have innate abilities to solve the toughest problems, and they have an intrinsic interest in their work. They are curious and diligent.  Everyone always tries to constantly keep evolving and adapting to the latest industry trends. Everyone is encouraged to generate, evaluate, and implement better and creative ideas. Everyone is open to embrace and learn the latest technologies in the market. Innovative ideas are rewarded from time to time. They are Willing to Learn Change is inevitable in any industry or business but in the field of technology, changes happen relatively quickly. Even a technology which is a ‘hot cake’ can be replaced by better or newer technology. This is the age of a technological revolution where business and dynamics change quite often. Without adapting to newer technologies or methodologies, the organization is at a risk of becoming obsolete and becoming non-competent. At Rubiscape , every employee is enthusiastic to ride the wave of change and accept the challenges around it. Excellence Drives Them Everyone at Rubiscape is passionate about the work we do. Passion is rewarding and keeps us enthusiastic and happy. Everyone is committed to go the extra mile for the betterment of the organisation and providing value to the end customers and partners. We have very high standards for quality. For us, commitment means a lot and we are willing to do anything in support of the commitment. Rubiscape are willing to take extra tasks and go above and beyond their expected roles and responsibilities. Authenticity is Crucial for Them Authenticity is one of the most crucial traits of Rubiscapians. They are driven by inner passion rather than external triggers. They are not afraid to express their opinions even if those are different than the opinions of the majority. Everyone here recognises that each person is unique and has different values. Everyone is open for feedback. We have an open communication channel and open door policy where everyone can discuss issues, concerns, or have a productive one-on-one discussion with their every other team member. They all Display Strong Leadership Qualities Irrespective of the titles and the roles, all the employees at Rubiscape possess leadership skills. For us, leadership means proactive problem-solving. It means practicing patience and looking for the interests of the company before one’s own interests. Rubiscapians are empowered to take risks, try new possibilities, alternate options thereby fuelling an innovate culture. Integrity is Important for Everyone Employees at Rubiscape follow the basic principles of integrity like trustworthiness, honesty, and decency. There is a culture of being respectable and professional within the entire organisation. They are Customer-Centric Rubiscapians have a passion for providing the best service to our customers. They have a positive ‘Can Do’ attitude and are always ready to put in the extra effort in order to resolve customer issues. They are appreciative of the customer responses and learn from the past mistakes. They are gifted with the ability to anticipate customer expectations well in advance. They also ensure that in case of any problems reported the customer the matter is handled on priority thereby making the customer feel more valued, eventually building trust with the customer. We take pride in the fact that Rubiscapians exhibit much more than just these seven characteristics synonymous with Rubiscape. Drop me a line if you feel that you would like to be part of such an intelligent, proactive, and innovative team!

The capacity to innovate, make better decisions, and improve profitability are just a few examples of the data science advantage. These are perhaps some of the compelling reasons why organizations across the globe are looking towards data science to extract information from the vast sea of data at their disposal. Businesses today are looking to operationalize the data that they have faster. For that, they not only need ace data scientists but along with this, they need data science platforms that can help them design and test out the algorithms that matter. A data science platform should democratize data science as data becomes the lifeblood of every business department. With an extremely easy interface, it should help business users become more data-driven in their decision-making process. However, it is also true that not all data science platforms are created equal. It can be easy to get distracted by fancy features but make sure that the data science platform that you choose helps you achieve the following: Model development and workflow designs Selecting and creating the right data model is an important factor that contributes to the success of any data science initiative. This involves identifying the right data, curating the right data sets, and selecting the right and appropriate algorithms to create a model that suits the business purpose. The data science platform has to assist this activity and help with data extraction, cleaning, and then apply the right algorithms to help them reach their business objectives. The platform also has to assist in creating workflow designs to help business users understand how they can go from a business problem to business value. The platform should also be intelligent to allow business users to apply the right data models to the right data and gain predictive insights – quickly and easily. Work the data We will all drown in the sea of data unless we can work it. Automating the data model and data refresh are actions that any data science platform should enable. These tasks can be time-consuming, error-prone, and just add to the cognitive load of the data scientist when done manually. Given the surging volumes of data, data scientists also need the data science platform to capably automate these models and enable data refresh. Additionally, the data science platform also has to have robust data integration capabilities and should be able to integrate big data, legacy data, and textual data with ease. The platform thus should enable scheduling of Model Runs, EDGE, APIs, and ETL. Predictive intelligence The speed of change is accelerating and thereby increasing the need for business users to become proficient in fine-tuning their predictive capabilities. A data science platform has to be democratic enough to enable business users to use predictive intelligence by allowing them to create, deploy, and maintain predictive models easily. Using these models, business users can then capably anticipate business trends, take pre-emptive steps to minimize risks, and improve decision quality. Segmentation & Recommendation Engine Organizations today need the capability to categorize and segment their customer demographic better to drive business results. Clear segmentation capabilities are perhaps one of the greatest advantages that data science brings to the table. A data science platform should enable both data scientists and business users to develop their segmentation and recommendation engine needs without the effort of stringing together voluminous and effort-intensive lines of code. This can be achieved using Regression, Classification & Clustering, and makes it faster for business users to target the correct market segment. Forecasting and optimization Data science has become as huge as it has because it helps organizations to take a peek into the future. It helps greatly to improve demand and price forecasting capabilities and removes the guesswork from this crucial exercise. Price movements, demand forecasting, price, and revenue optimization, and insights are capabilities that a good data science platform must-have. It should also be simple enough for business users. By employing forecasting and time series techniques, a data science platform can make forecasting and optimization easier and can help business users also leverage the data advantage. Sentiment analysis and social listening The volumes of data are growing exponentially owing to the proliferation of the internet, smartphones, and subsequently social channels. This data is a treasure trove of information that organizations want to use for several purposes…whether it is to conduct sentiment analysis or to listen actively to what their customers want. For this, data science platforms should have robust text analytics capabilities. It should also have a pre-built set of Linguistic, Statistical, NLP, and Machine Learning techniques to Model & Structure textual data for analysis, visualization, and collaboration and help the business user employ these to drive business decisions. Playing with the data – Data Storyboards, Data Exploration, Impact Analysis, and Dashboarding Data Storyboards, Data Exploration, Impact Analysis, and Dashboarding are key responsibilities of data science. Just having data is not enough. Having the capability to work the data, explore it for insights, create data storyboards, and dashboarding for better understanding and generating impact analysis are essential capabilities of a data science platform. The business users should be able to easily conduct ‘What-if’ Analysis and create rich Data Visualizations by integrating complex datasets across various business and analytical areas. The ultimate aim of a data science platform should be to democratize data science and help business users become citizen data scientists. It should give organizations the capability to glean intelligent insights from data to optimize their operations and maximize business value across the employee chain. A robust data science platform with the above-mentioned capabilities will not only come to the aid of the data scientists but will also enable organizations to convert their regular employees into citizen data scientists – that is when we will be able to unlock the real value of data.

Big data analytics is taking the business world by storm, adding a whole new level of business value. By analyzing the humongous amount of data that gets generated by applications every single day, big data analytics is helping businesses make data-driven decisions across verticals. Now imagine if big data analytics is coupled with blockchain – the possibilities of data integrity are incredible. As blockchain provides a platform for safe and secure transactions, it can act as an effective framework for dealing with the complexities of big data. The Blockchain Revolution Although big data analytics is enabling enterprises across the world to drive substantial business value, some challenges can be overcome through blockchain: Immutability of the data is crucial to big data analytical models. Blockchain extends the ability to turn insights into immutable assets, allowing superior control of data usage. Powering big data analytics with blockchain can minimize the cost implications of data storage to a large extent; reports suggest that blockchain can help enterprises achieve up to 90% savings. The transformation of big data by blockchain will enable isolated data silos to move to blockchain shared data layers, making the process of data access and insights more streamlined. Data will no longer be stored in company databases; rather the owner will shift to individuals and will be represented by secure tokens. Impact on Industries Blockchain has been touted as a technology with the potential to disrupt every industry by improving business efficiency and bringing transparency in how enterprises manage business data. Financial: Billions of transactions worth trillions of dollars happen across the global financial system every day. And although big data provides data-driven insights into these transactions, it is incapable of curbing fraud and theft; according to reports, 45% of financial intermediaries suffer from economic crime every year. With blockchain in place, vast amounts of transactional data can be protected from several hurdles, thereby providing the much-needed reliability, transparency, traceability. Do you know? The Australian Securities Exchange (ASX) is set to be the first mainstream financial market to have a blockchain-based stock exchange. Manufacturing: Over the past decade, manufacturers have been able to optimize processes, improve product quality and yield and reduce waste using big data. Move over to 2018, and manufacturers can now take advantage of blockchain and ensure optimum security of this data and bring in transparency and accountability in their production processes. By having an increased level of visibility, manufacturers can ensure transparent monitoring of transactions, reduced production delays, and assured product authenticity. Do you know? IOT Group Australia has signed an agreement with Hunter Energy, NSW to build a blockchain center inside the Redbank coal-fired power station to provide cheap electricity for blockchain applications. Healthcare: The amount of medical data that gets generated from millions of healthcare devices is unimaginable. Although big data analytics has helped the sector gain insights into this data for improved diagnosis, research, and treatment, blockchain can enable healthcare organizations to break down data access hierarchies and provide every individual with equal access to relevant healthcare data, while maintaining patient privacy. Do you know? screamed, an Australian healthcare start-up, has created a blockchain prescription solution that enables patients to receive prescriptions directly from the clinician to their mobile phones. E-commerce: The e-commerce industry has been leveraging the benefits of big data in predicting trends, understanding buying behavior, forecasting demand, and optimizing prices and customer service. However, e-commerce transactions have always been susceptible to fraud. With blockchain, merchants can ensure the integrity of each transaction by allowing both buyers and sellers to protect themselves by verifying transactions. Do you know? Chinese e-commerce giant JD.com is partnering with Australian beef exporter InterAgri to launch a blockchain-enabled traceability system that offers greater transparency in tracking the production and delivery of meat from farms in Australia. IoT: The explosive growth in the number of devices connected to the Internet of Things (IoT) has resulted in an exponential rise in the amount of data that is being generated. According to Gartner, the revenue generated from IoT-enabled services and products will exceed $300 billion by 2020. Although big data is facilitating the consumption of this enormous data, concerns over data security have been widespread. Blockchain will play a positive role in IoT by ensuring data is exchanged only between authenticated devices. Do you know? Perth-based engineering services company LVX Group and Melbourne-based cybersecurity company VeroGuard Systems have partnered together to secure IoT devices using blockchain technology. Immense Potential Blockchain’s potential is irrefutable, and as more and more industries embrace the technology, its overall impact on various aspects of modern life will grow manifold. Although big data will continue to provide detailed insights into data, blockchain will offer a gamut of benefits such as shared control of data, easy auditing, and secure data exchange. Since owning data – especially critical financial data – is always surrounded by controversies, moving data from private data centers onto public blockchains can help enterprises drive innovation by developing the best big data analytical models for open blockchain data layers.

The world today is connected aggressively. Networks, devices, systems, processes, and humans- everything is bound by data. And as the quote goes, “Data is a precious thing and will last longer than the systems themselves”, today’s hyper-connected world is fueled thoroughly with data and has an impact on every single action and decision that humans take across the globe. No longer confined to any particular tech or industry, data has helped revamp global operations across diverse industries and has significantly transformed the ease of living and day-to-day living. But how is this new and connected world of tomorrow powered by data? What implications will it have on the way people live, and businesses operate? Will there be unforeseen challenges that can convulse the data-driven world, or are we ready to take on the hurdles? Let’s discuss… All roads lead to…data Data collection has been one of the attributes of modern civilizations and has been existent for a long time, but never has been as important as it is today. What started with simple data markers that were not interconnected, Big Data evolved as an all-knowing, all-encompassing umbrella that holds enormous amounts of data that is precious for several industries and sectors. Manufacturing With Industry 4.0, the importance of data has been aggrandized and data is essentially the driver of the key components – from risk assessment, supply chain, maintenance to sales. From Big Data to AI-powered tools, from cloud manufacturing to smart factories, data is flown through and collected via several diverse elements of the manufacturing industry where digital transformation is deeply penetrated. Big Data brings in operational transparency, helps in strategic decision making, helps in offering bespoke solutions tailor-made to suit the customer requirement based on previous purchase preferences, and significantly helps in improving the overall product quality. Naturally, this further leads to better after-sales service, operational efficiency with detailed tracking of employee and machine productivity, close monitoring of logistics, and predictive maintenance and support. Automotive The automotive sector has undergone a huge revamp in recent years, primarily to suffice the industry’s growing needs and balance the fossil energy deficit. The automotive industry is largely moving towards electric cars or autonomous/self-driven cars that are most certainly the future of automotive. Modern cars are heavily connected to other interconnected systems, such as home automation and IoT devices powered by data. The large amounts of data collected from IoT based sensors in cars are important to enhance the driving experience, offer the latest traffic and weather stats, streamline insurance in case of incidence, provide predictive maintenance to avoid on-road failure, and reduce accidents and improve the safety standards of the car. Healthcare The Healthcare industry is immensely data-driven. Data in healthcare has been incremental in maintaining a 360-degree view of patient data, offering predictive modeling and analysis, extending preventive healthcare, and using the data to treat large subsets of the population in a particular geography. Connecting patient data with employee data can also help organizations in the healthcare sector achieve their business goals, align necessary care with optimized resource planning, and further improve the patient experience. Banking The banking sector, although highly organized across the globe, has been hesitant when it comes to a complete digital overhaul. While the financial sector has been welcoming to the change caused by digital transformation, the conventional banking sector has taken slow and steady steps into implementing data-powered tools while ironically, they have a vast amount of precious customer data that is documented to perfection. Banks that are making wide use of this data can create a more immersive customer experience powered by Artificial Intelligence and Machine learning. Cognitive banking involves the use of robotic and automotive AI to execute, process, and implement data to improve the overall digital banking experience. From customer communication, selling and upselling of financial products, to building a loyal customer base, data can do wonders to the banking sector, when tapped into with precision. Energy Reducing energy consumption, optimization of non-fuel resources for energy generation, refining energy efficiency, and offering consistent energy with minimum power outages are the key benefits of Big Data, IoT-powered sensors, and other data-driven technologies used in the energy sector. The growing demand for smart cities further highlights the importance of data in the energy sector. India alone has deployed more than 1500,000 smart meters. The rate of adaptation of smart metering and smart grid systems is also increasing across China – two countries with the largest populations in the world. Big Data Analytics has been immensely powerful in smart metering, which helps forecast the energy consumption to manage the demand against the supply and reduce energy wastage. The world of tomorrow is truly expected to be connected with data breathing life into it. However, this interconnected world of data and the companies that drive this data-powered innovation are met with several challenges, which, if not mitigated, can cause more harm than good. Let’s discuss… Challenges of the data-powered world and the role of data science platform Data quality We all agree that data is gold, but when collected in copious amounts, there’s no denying that there are tons (virtual) of junk, unusable, and uninterpretable data. Dirty data or data with substandard quality, which can be inaccurate, incorrect, duplicate, inconsistent, incomplete, or data that has violated rules and compliance, can create complexities for the systems that are data-dependent. Maintaining data quality based on user requirements or organizational framework is a real challenge and can only be mitigated with data profiling, data annotation, and analysis to continuously make the data readable and usable. A data science platform can be critical in data cleaning, quality investigation, and maintenance and is crucial for the data to work the wonders it can. Data integration and transformation Inconsistency in data collection can cause challenges related to data integration when companies depend on more than one source for data collection. Raw data needs to be normalized before it can be accessed by data-powered systems and be meaningful enough to drive any insights.