Author name: webhero

Amidst the COVID 19 pandemic, when jobs were lost on a scale of millions worldwide, there was one news from India on that front which stood out. There were close to one lakh vacant data science jobs in the country as of August 2020. India is one of the world’s top destination for technology skills. From Fortune 100 companies to the largest global startups, India continues to be an attractive destination for enterprises to set up their technology development hubs. While in the past, lower cost of operations has been a deciding factor, today the IT industry in India is booming not because of the cost advantage, but also the tremendous quality of resources having technical knowledge. It has become a hotbed for innovation and, every year, hundreds of thousands of emerging technology jobs are created in the country by enterprises across the globe. An indication of a trending technology skillset being in high demand in India is direct proof of the global affinity for the same technology. On that note, the close to one lakh vacant jobs in Data Science in India throws light on the rising importance of data science and the role data scientists play in companies that successfully navigate their business journey with the help of digital solutions. Nearly 65% of business owners agree that big data is a key tool that can help them be more competitive in the market. So how exactly can data science bring about radical changes in businesses? Let us examine a few use cases Transform existing business models Data scientists can create innovative solutions for existing businesses that will transform the way they did business and bring in more efficiency, cost savings, and ultimately drive more profits – especially in the post-pandemic era. One of the finest examples in this regard is the transformation witnessed at one of the world’s largest courier and logistics company UPS when they rolled out a data analytics-based routing solution for optimizing delivery. The system called ORION applies data science on information such as shipping points, customer requirements, delivery timelines, and traffic information to calculate the most efficient route for its delivery drivers. The initiative aims to reduce distance driven and improve the speed of service and at the same time optimize routes for more service being handled efficiently with fewer delivery trucks. Considering that every single mile saved by UPS drivers every day can result in an annual savings of over USD 50 Million, the transformation in operations brought about by data science is huge. Problem Discovery Businesses of all sizes and from different sectors may face situations where their core operational models do not yield the expected results. A simple check on the business health may not help in identifying the root cause of the slump or unexpected deviations. However, data science can make lives easier for decision-makers in this sense by helping them accurately identify the root cause of issues through simulations and pattern discovery mechanisms. This facet covers the exploratory prowess of data science which, when put in the right perspective, can uncover problems that may have never been tracked by business leaders. An example would be identifying patterns in cart abandonment and return of purchased goods in the case of online retail. By applying data science logic, it becomes easier for sellers to identify the root cause of what made their product less consumer friendly. For example, inefficiency in parcel handling by a particular delivery partner may be the reason for an unexplained number of returns from shoppers who are located in areas where the said partner delivers. Through data science, such patterns can be uncovered, and businesses can further investigate the insights and make amendments to help solve the problem at the earliest. Integrate Artificial Intelligence and Machine Learning Faster Data Science is the foundation of AI and ML and folks with specialized skills in data science can create algorithms and data models that can easily be adopted within your business’s digital channels. In addition to creating the most efficient roadmap for AI integration, data scientists can continuously monitor your data models and introduce alterations to algorithms to incorporate the latest market trends. They will be responsible for ensuring optimal performance from all AI initiatives deployed within a business. Improving Predictive Capabilities Data science plays an important role in enabling businesses to forecast outcomes from historic data. For example, by developing data models that simulate sales of items from past seasons, combined with market trends such as economic situation, weather, brand sentiments, etc., it becomes easier for retailers to stock inventory for events like the holiday shopping season. The COVID-19 pandemic has been yet another great revelation in this regard. Across the globe, leading pharma companies and scientists are using advanced data models to predict the behavior of the virus to chemical and biological environments to develop an effective vaccine. Data Science will be one of the hottest skillsets in the 21st century as more digital transformation initiatives take center stage across industries and consumer domains. Businesses and organizations, public and private, of all sizes will rely on data scientists to help drive better results across their operations.  With more data generated every day, the need for skilled professionals who can build models to analyze and realize value from these data sets will keep on increasing. The meteoric rise of job vacancies in data science in nations like India is a testimonial to the value created by data science across sectors.

With a new regulatory alert being issued every 7 minutes, growing compliance regulations are challenging banking institutions in a variety of ways. Changing customer behavior, and the constant evolution of technology is compelling them to change how compliance is approached. Ensuring compliance with a rising number of government and industry regulations can be hard-hitting and put a strain on the already drained resources. While traditional compliance models were effective for an era where simple enforcement was sufficient, today, they offer a limited understanding of business operations and underlying risk exposures. With the risk of regulatory sanction, reputation and financial loss, due to a failure to observe compliance obligations becoming extremely far-reaching, those who adapt best are the ones to enjoy a distinct competitive advantage. As each new industry regulation and its associated deadline causes a massive influx of new data that has to be stored and analyzed, garnering insights rapidly becomes vital for optimizing processes and pinpointing any potential problems areas. With compliance costing businesses $5.47 million annually and non-compliance $14 million, analytics is enabling organizations to keep pace and avoid the risk of costly non-compliance. It is helping banking organizations to stay ahead of compliance requirements, and better anticipate and respond to change. Here’s how analytics can help with banking compliance: Unearth reporting insights: Institutional banking clients, as well as regulatory auditors, constantly demand banks to reveal risk and possible exposure scenarios. Real-time analytics is a critical aspect here that allows banks to handle high volumes of data and unearth insights that meet the growing compliance needs. Using analytics, organizations can collect and distribute necessary compliance data to deliver reporting insights that are required throughout the enterprise, and meet regulatory requirements with ease. Improve risk control: Since non-compliance can result in substantial losses, analytics can help scale up the computational power of risk management. Decision-makers can ask more complex questions and get more accurate answers faster while developing new business strategies. Analytics-aided techniques can produce more accurate regulatory reports and deliver them more quickly. Since the need to pre-aggregate data is eliminated, risk managers are in a better position to understand the nuances in data, reduce fraud losses, and improve risk control across the enterprise. Enhance productivity: As banks need to be always ready to provide regulators with a quick response to regulatory stress tests, analytics plays a big role in making processes faster and more effective. Using advanced analytics, organizations can achieve faster and more accurate responses to regulatory requests and give teams analytics-driven decision support. Banks can use analytics to understand compliance levels across the enterprise, identify avenues that fare poorly, and take measures to enhance productivity and save money. Drive agility: With thousands of new regulatory requirements being ushered in every year, manually managing compliance activities is a fruitless undertaking. Manual compliance efforts are not only cumbersome and tedious, but they are also extremely prone to error. This increases the degree of risk and limits a company’s ability to meet growing regulatory requirements. Analytics allows organizations to better manage risk and compliance obligations; by aggregating data that’s needed from across the business, analytics paves the way for greater reporting accuracy and efficiency. Using analytics, organizations can respond quickly to the evolving regulatory landscape, and drive agility. Lower costs: With massive legacy and personnel costs going towards regulatory and financial reconciliation, firms have a pressing need to comply at a lower total cost of ownership. Since regulations and the market environment greatly hamper banks’ abilities to just throw money at the problem, analytics helps drive improved metrics and reporting through automation. Banks can transform raw data for cognitive and analytic processing, meet regulatory needs at a fraction of the costs, and drive higher efficiency. Effectively manage compliance Banking and other financial services companies have to contend with a variety of industry regulations and compliance requirements. As the time and cost of regulatory compliance and reporting vastly increases with every new regulation, keeping up is a great cause for additional stress – especially at a time when new competition and increasing customer demands is creeping from the sides. Advanced analytics is enabling the banking industry to become smarter in managing the myriad challenges it faces – by offering compliance officers enterprise-wide intelligence, analytics can help avoid financial non-compliance and stay a step ahead. Analytics-backed solutions are enabling banks to not only manage the increasing cost of compliance, but also the risk of non-compliance – both monetary and reputational.

Innovation is the big buzzword with most of the software development organisations. At Inteliment, we try to put a lot of emphasis and focus to drive this concept and make it an integral part of the company’s day to day activities and value structure. Innovation is an idea or concept that can be transformed into reality and applied for practical use, which can eventually contribute to the success and growth of the organisation. It is an important catalyst for an organisation to adapt to the ever changing and dynamic market. Being innovative does not always mean the need to come up with new inventions but it can also mean changing the existing strategies, mode of operations, business model, etc. to deliver better products or services. Inteliment follows a culture which fosters innovation. To achieve this, we follow these basic set of guidelines within the organisation. This blog is an attempt to share with you all our learnings in the process. Innovation Workshops At regular intervals, we organise innovation workshops for our teams. The idea of having a dedicated workshop ensures that we all get time, apart from our day to day tasks to collaborate, brainstorm, and work towards our innovative ideas. As a part of the workshops, specific teams are created with employees from different teams and departments. This not only helps to build a camaraderie within the organisation but also generates ideas which are out of the box, build trust, and prevents conflicts or disagreements among peers. Also, the workshops are conducted as a contest where the top innovations are selected and brought into practice. To encourage participation, rewards and company wide recognition are published for the winning team. At times, some of these workshops are conducted offsite to create a more relaxed environment for the teams and help their minds to wander in search of the ultimate idea. Making it a part of everyday activity Although Inteliment focuses on dedicated workshops it does not limit the creativity of its employees to occasions like the above. Employees are continuously encouraged to share their innovations with each other as a part of their day to day work, meetings, and also coffee breaks.  Simple targets are set to come up with one idea per week for each team. The brightest ideas are then selected, nurtured, and taken to the next step for implementation. At an individual level, we set personal goals for each employee to think creatively and differently and help them achieve those. Accepting Failure We do not believe in building a culture which inhibits people from taking risks or making mistakes while coming up with innovative ideas. Failure is inevitable and if criticised, it often brings down the morale of the employees. Not a single great idea or innovation is done right the first time. Learning from mistakes paves the way for more innovate thoughts and brighter ideas for the next time. If an innovative idea is not feasible then the best option is to understand the root cause and develop alternate approaches. Inteliment’s belief in nurturing innovation also stresses on having an open culture where employees can regularly freely interact with their supervisors or the management team irrespective of their designations or positions. Training It is not rational for an organisation to consider each and every employee to be sharp and inclined towards innovation. As a part of the on-boarding program at Inteliment, specific training related to innovations is provided to the employees. In the long run, the Return on Investment is high, both for the organisation as well as the employee at a personal level. Ideas Bank We have a well-defined process in place to ensure that innovate ideas are captured and thoroughly documented; kept at a centralised location which is accessible to every employee. This helps us to check how a similar issue or problem was dealt with in the past or what approach was suggested or implemented in order to fix it. Also, by having a repository, it acts as a source of reference for the employees. They get a broader view and a better perspective to study the innovations in detail which have been already documented. Innovation can help organisations to deliver better and produce exciting products and services. In today’s highly dynamic and volatile market, there is no future for run-of-the-mill products. There is no option but to keep evolving by using innovation to ignite the spark.

Digital Transformation has emerged as one of the top priorities of the enterprise as a whole. While 2018 has been more focused on optimizing existing processes and operations, 2019 aims to leverage technology to create new business models that will take the enterprises’ digital transformation journey to fruition. Some of the main focus areas promise to be security and an increased focus to improve customer experience by leveraging technology. However, if 2018 has taught us anything it is that while Digital Transformation is technology-focused it is more about an organizational change that includes business and people as much as the tech at play. As we are entering 2019, here’s a look at some technology trends that I feel will drive digital transformation in the coming year. Blockchain Deloitte predicts that Blockchain will soon overtake other technologies such as cloud computing, IoT and data analytics in the VC investment race. Given that digital transformation focuses heavily on customer experience, technologies like Blockchain significantly impact the speed at which elevated customer experiences can be delivered. Blockchain will mature considerably in the coming year owing to the data preservation, security and networking capabilities of this technology. With Blockchain, enterprises can circumvent traditional cybersecurity barriers and support the information sharing needs of the new digital enterprise of today. Blockchain gives enterprises the structure that they need to overcome and mitigate the threats that arise with the digitally-charged enterprise that is more connected and integrated. Marco Lansiti and Karim R. Lakhani in a Harvard Business Review article aptly state that Blockchain “has the potential to become the system of record for all transactions. If that happens, the economy will once again undergo a radical shift, as new, blockchain-based sources of influence and control emerge.” IoT IoT will be one of the trends spearheading business transformation in 2019. It is expected that IoT is heading for wider adoption in the year ahead owing to the strong gains in business efficiency and charged up innovation and business profitability. Enterprises are jumping on the IoT train to create smart workplaces for greater productivity and efficiency. Research shows that 78% of businesses say that IoT introduction has improved their IT team efficiency. The industrial sector has shown a great affinity towards this technology with six out of ten respondents already having implemented IoT. Next-gen IoT platforms are giving enterprises the capability to merge new data sources with traditional ones, provide more precise data inputs, examine data in real-time and consequently help businesses with the capability to gather new data correlations, analyze important data and question institutional thinking. Artificial Intelligence and Machine Learning Digital Transformation thrives on data. And while data analytics has proved its merit to the enterprise in their digital transformation journey, it’s time to supercharge it. As data becomes more strategic to organizations to make intelligent decisions about services, products, employees etc. the need for better, faster and smarter analytics is pushing the enterprise towards AI and Machine Learning. AI and Machine learning will gain a stronghold in 2019 as the technologies that hold the key to solve pressing business problems, drive data-driven decisions, enhance customer experience, optimize and automate processes. The transition to an API-based economy will also be driven with AI as analytics become more pervasive in the enterprise ecosystem in their digital transformation journey. In the ensuing year, we can expect enterprises to progressively weave AI all through their innovation stacks to weed out a portion of the experimentation that CIO’s feel compelled to undertake in their digital transformation initiatives. The Rise and Rise of Real-Time Edge Analytics As the connected ecosystem proliferates in 2019 as organizations continue on their digital transformation journey, the spending on real-time analytics is only going to rise. In the coming year, we can expect to see wider adoption of real-time edge analytics to find co-relations within internal and external data. We can expect a greater push to move from batch to stream data processing to get real-time actionable insights. This also becomes more relevant in the digital transformation context as the consumer lies in the heart of all digital transformation initiatives. We can also expect to see an increased AI-cloud interdependency with most leading cloud giants pursuing an AI-lock in approach by providing open source AI-related services to reduce complexity and the burden on IT departments. While 2019 looks promising for digital transformation, enterprises need to ensure that they contain their technical debt, and strategically organize the posture of enterprise data so that the data is not inconsistent, fragmented, duplicated, and siloed. We need to focus on loosening parochial data ownership and adopt disruptive emerging technology to support digital transformation efforts to usher in the enterprise of tomorrow.

Today, every business is a digital business. The evolution of digital businesses demands business leaders to make a leap towards a newer view of data and analytics. As the world moves deeper into the age of technologies like AI, ML, Blockchain, IoT, etc. what data science and analytics trends should businesses be most aware of? Given that data science and analytics are projected to become critical to any business strategy, what does this mean for businesses looking for growth in 2021? Here are 7 data science and analytics trends that will be dominant in 2021. Data Mesh A data mesh is an innovative architectural paradigm. It embraces the ubiquity of data in the organization by utilizing a domain-oriented, self-serve design. As against conventional monolithic data infrastructures that handle the usage, storage, transformation, and output of data in a single central data lake, a data mesh facilitates distributed domain-specific data consumers and takes ‘data-as-a-product,’ with every domain taking care of their own data pipelines. Data mesh will take the industry by storm in 2021 because it provides a solution to the shortcomings of data lakes by enabling greater autonomy and flexibility for data owners, allowing greater data experimentation and innovation while reducing the burden on data teams to field the requirements of every data consumer through one pipeline. Increasing Value of Data & Analytics with Data Marketplaces and Exchanges According to Accenture, by 2030, over 1 million businesses will monetize their data assets, and over 12 exabytes of data will be transacted each day. Additionally, the data marketplace will unlock more than USD 3.6 Trillion in value. In the coming years, large organizations will either become sellers or buyers of data through formal data marketplaces. Data marketplaces and exchanges are surfacing as both products and platforms across private and public sectors. They enable the contribution of and access to critical data assets powering a wide array of global data for initiatives such as climate change, wildlife protection, or other public health, social issues. Today, individuals and IoT powered devices generate exponentially more data than ever before. If leveraged appropriately, this will revolutionize the impact of data and analytics and spur completely new data-based innovations. It will also generate new sources of value and revenue via data monetization for businesses that wouldn’t otherwise have a chance to contribute or access unique datasets. Data Democratization Data democratization means that everyone in the organization has access to data, and there are no gatekeepers that could create a bottleneck at the gateway to the data. The objective is to have everyone utilize data at any time to make insightful decisions with no constraints to access or understanding. The capability to instantly access and comprehend data translates to quicker decision-making, which further translates into more agile teams and business model innovations. These teams will have a competitive edge over slower data-stingy organizations. When businesses allow data access to everyone across all levels, it empowers individuals with ownership and responsibility to leverage data in their decision-making. Bye Bye Dashboards. Hello Data Storytelling Modern data analytics platforms fail most of the frontline workforce – because insights are not contextualized, easily consumable, or actionable. Businesspeople are still clueless to know which insights to act upon. Businesses expect everyone to be data-driven, not just the analysts or data scientists working in the company. But the tools that work exceptionally well for data analysts and scientists are not extendable. These are too complex for salespeople, customer success people, and almost every other non-technical employee. Consequently, automated data stories with additional consumerized experiences are foreseen to replace visual, point-and-click authoring, and exploration. The transition to in-context data stories will transform how and where users interact with analytic insight, and the most relevant insights will stream to users based on their context, role, or use. Continuous Intelligence Today, the digital revolution demands speed, regardless of data complexity. Businesses want to see all the data immediately and continuously. They do not want to get trapped with an IT-established dashboard with rigid drill paths that restrict their capability to instantaneously answer critical questions. Businesses that are driven by revenue growth and capitalizing on the digital revolution recognize that today’s analytics cannot tolerate a punctuated analytic pipeline. There have always been multiple ways to carry out analytics fast by employing various tools and tricks. However, analytics was always disconnected by separate modules, separate tasks, and independent teams with dedicated skills. But this robs time from what matters most today – timely nonstop actionable information from all the data. Continuous intelligence is about frictionless cycle time to draw constant business value from all data. It’s an innovative machine-driven approach to analytics that enables businesses to access all the data quickly and accelerate the analysis businesses require, regardless of how off the beaten track it is, irrespective of how many data sources there are, or how enormous the volumes are. DataOps DataOps is a new system for data management. It incorporates development, DevOps, and statistical process controls and employs it in Data Analytics. It fosters collaboration, automation, and continuous innovation of data in a data-powered environment. DataOps has been mainly aimed at advanced data models. It plays an indispensable role in building best practices throughout a function. Leveraging automation and agile approaches, DataOps builds best practices that allow businesses to deliver value to a range of stakeholders via continuous production. DataOps enables automation and brings speed and agility to the data pipeline process. Before the data is implemented, data scientists must create data pipelines, test them, and change them. By implementing DataOps best practices, businesses can have a continuous stream of data flowing in the pipeline. This unlocks one of the most critical benefits of DataOps, that is, the potential to gain real-time insights. Gaining real-time insights from the data shortens the time it takes to transform raw data into actionable business information. Additionally, DataOps helps enhance data quality via version control, continuous development, and continuous integration. Practical Blockchain for Data and Analytics The promise of blockchain

With 80% of enterprises smartly investing in Artificial Intelligence (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 2019: 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. 2019 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 2019, 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 2019 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 2019.

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 Inteliment’s decade long journey and the people who helped us in this journey of building this high-performance organisation. At Inteliment, 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 Inteliment employee shares – They are Innovative Innovation is a strong part of the personality of Inteliment 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 Inteliment, every employee is enthusiastic to ride the wave of change and accept the challenges around it. Excellence Drives Them Everyone at Inteliment 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. Intelimentians 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 Intelimentians. 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 Inteliment 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. Intelimentians are empowered to take risks, try new possibilities, alternate options thereby fuelling an innovate culture. Integrity is Important for Everyone Employees at Inteliment 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 Intelimentians 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 Intelimentians exhibit much more than just these seven characteristics synonymous with Inteliment. Drop me a line if you feel that you would like to be part of such an intelligent, proactive, and innovative team!

Today’s digitally-savvy and modern enterprises, across a wide range of industries, have become data-driven. From businesses across healthcare or retail to technology, data is the new oil. Only those organizations which leverage the power of the right data can create a competitive advantage for themselves. With new government resolutions and regulatory regimes, businesses are forced to pay more attention to risk and compliance analytics to prevent unethical business decisions and practices. Compliance and risk are the integral parts of any organization and the absence of either can result in distrust, loss of potential customers, and reputational damage to the organization. In light of this, businesses have started putting together an organized plan to leverage the structured and unstructured data and harness its power to effectively monitor threats, frauds and comply with the rules. The use of data and technological advances in the analytics solution space help organizations reduce the chances of violating government compliance norms. Apart from this, a risk-aware culture in the organization works as the backbone for the proper functioning of the departments. According to a survey by OCEG, more than 84% of the companies agree to the fact that using analytics in GRC would benefit their company in the long run. Some of the key benefits of Risk and Compliance Analytics include – Effective Risk Management – Monitoring of the complete risk lifecycle through visualizations for risk-based pricing, fraud detection, line-assignment, credit-risk modeling, loss forecasting, foreclosure prediction, risk-based pricing, and event modeling Compliance management – Maintenance of compliance and effective business processes while reducing risks in areas such as environment, green technology, and International Trade Compliance Fraud Management – Uncovering of new trends, fraudulent schemes, and scenarios Audit Management – Managing the internal audits more effectively by aligning them closer to the business Analytics, undoubtedly, is the future of risk and compliance. Some of the advanced analytics techniques that are widely used to adhere to the regulatory norms include – Early detection of new fraud and risk associated with the organization In-depth analysis of the text to detect problems in the written documents Visual analytics to display the right information in front of the different stakeholders and regulatory bodies Monitoring to keep track and mitigate the damage due to known compliance and fraud risk Raising of risk-based alerts for taking better business decisions   The compliance teams within organizations need to constantly navigate the challenges of complexity and norm changes around compliance. The changing regulatory pressures over a wide variety of subject areas and the changing regularity environment keep the compliance teams on their toes. Technology has been the backbone of helping organizations with their risk and compliance management. There is no one-size-fits-all compliance management solution for all the organizations – the selection of the solution depends completely on the compliance needs of the organization, the budgets, and the available skills. Having said that, here are a few things which businesses must consider while selecting the solution- Functional Coverage Checking the functional coverage of the solution is important to understand whether the solution matches the requirements and business goals of the organization. Also, organizations need to check whether the solution covers some of the specific functions essential for compliance and risk management of various departments of the particular business. Integrations The technological solution should provide a holistic approach to data gathering and analysis. It should provide a centralized ecosystem that gathers the data from various source systems and offers a one-stop solution for the analysis of that data. Flexibility and Adaptability Considering that the regulatory norms keep changing frequently, the technological solution should be flexible enough to adapt to such constant changes. The solution should be able to quickly capture the new changes without any impact on the legacy systems. Reusability The data gathered for risk and compliance is very valuable and can be useful for the overall integrated analytics. The solution should be able to reuse this data so that minimal incremental time and effort are spent on data acquisition regularly. Ease of Use Another key aspect before selecting the compliance analytics solution is the degree of user-friendliness. It’s important to know the predictive text input, the total number of entries required to operate, and the level of customization of reports. Customization of reports is an essential feature to look for because it helps you alter the analytics depending upon the variables in the picture. Compliance and risk analytics helps in preventing corporate scandals, fraud, and even civil and criminal liability of the company. It also enhances a company’s image in the public eye as a self-policing company that is responsible and worthy of shareholders’ and debt-holders capital. Today, every organization needs to take risk and compliance analytics seriously because it is the only possible way of identifying and addressing issues, which allows the company to avoid potential fraud, scandal, and even criminal behavior.

Data Science helps businesses gain actionable insights from various sources of structured and unstructured data by applying scientific methods, processes, and systems. It requires a proper understanding of the different techniques used for preparing the data and knowledge about various data models that may be used to finally measure the outcome from the full process. In this entire cycle, there may be numerous factors that may be overlooked even by the most seasoned data scientists. Through this article, we share some of our insights on some of the most common mistakes made by data scientists.   Growing demand for data scientists and their role in the information age According to a recent survey made by KMPG on C-Level executives, 99% of them affirmed that big data would be a core part of their company’s strategy in the coming year. It’s believed that enterprise data will exceed by around 240 exabytes per day in 2020, which will create a greater demand for data scientists with key skills for extracting actionable insights from data. A striking example is that of the popular social networking site LinkedIn where data scientists have played a vital role in boosting business intelligence for the company. LinkedIn relies mainly on the data that is transferred by its 3,80,000 users who have built connections with each other. LinkedIn is utilizing the skills of such professionals to explore the world of Big data. Apart from LinkedIn, other big names such as Google and Facebook are utilizing the role of data scientists to give a better structure to large quantities of formless data to help them establish the significance of its value and bring a standard relationship between the variables. Most of the data architects extract information through large volumes of data and use SQL queries and data analytics for slicing these datasets. On the other hand, data scientists have a larger role to play as they need to have advanced knowledge of machine learning and software engineering to manipulate the data on their own to provide deeper insights. They mainly use advanced statistics along with complex data modeling techniques to come up with their future predictions.   What are the common mistakes made by data scientists? Failure to address the real questions The entire process of data science revolves around addressing the business questions and in most cases, it is the most neglected issue due to lack of communication between the sponsors, end-users, and the data science team. To get the most benefits from the data science initiatives, all the stakeholders need to stay connected and share information and knowledge which can help in defining the real business issues. Beginning with excessive data In many companies, the team members are involved in working on a huge chunk of data which is a waste of valuable time and effort. Instead, it can be more worthwhile for them to choose a subset of specific data to make the process much easier. For example – Is it possible to focus on just a single region or look for data from the last three months? To start with the prototype, random sampling may be taken into consideration. When the initial exploration, cleaning, and preparation are done, a bigger data set may be included in the process. Trying to complicate things Sometimes, even if the current project requires a simpler solution, data scientists often make the mistake of complicating matters by introducing more complicated models into the process. This can jeopardize the chances of completing the project on schedule and make it more difficult to achieve the main purpose. Not validating results The models that are created by the team of data scientists should enable the business to take suitable action. And once an action has been taken, it’s necessary to measure its effectiveness for which the team needs to have a validation plan ready, even before the actual implementation. Only this can help in making the process more efficient and give more meaningful results. More focus on tools than on business problems The major function of any data-driven role is to focus on solving problems through data extraction, but sometimes, the data scientists get overwhelmed and obsessed with using new tools than solving the real issues at hand. They need to understand the problem first and find out the requirements for finding the solution and finally decide on the best tools that may be used to solve the problem. Lack of proper communication There is plenty of communication involved in assessing the business problem and providing constant feedback to the stakeholders. The greatest risk comes when the data scientists do not ask enough questions and make their assumptions, which actually can result in providing a different solution than what is required.   The Key Ingredients of Data Science Data science requires knowledge of Statistics and Applied Maths Data science requires actual application of Statistics along with Applied Maths, which can provide guidance regarding uncertainty in data and allow companies to gather valuable insights from it Data Science involves solid communication A data scientist needs to be an effective team player who helps to initiate, iterate and drive some core decisions in the company. The role of a data scientist involves working along with product managers and the other team members to influence them to take vital business and product-related decisions. Data Science is about using creativity and dealing with people Data scientists need to have a creative approach as they need to understand the needs of the users in the system and convey their findings to the other core members of the team. At the same time, they need to be creative enough to derive insights from the system that generated the data in the first place. In this age of Big data, the biggest challenge will be on collecting data and extracting value from it which will get more demanding in the coming years. Data Scientists will have a key role to play in shaping the economy of the future by