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Back in 2019, a Deloitte report outlined how an impressive 70% of businesses had embraced the potency of HR analytics to enhance their overall performance.  This was in line with a 2018 CIPD-Workday study. It revealed that 65% of individuals from organizations that pursued a people analytics culture expressed that their companies exhibited superior performance compared to their competitors. Contrarily, only 32% of the employees from organizations with a weak analytics culture affirmed the same. Considering the rising prominence of data analytics solutions and the overall democratization of data analytics functions across enterprise departments, it won’t be an exaggeration to assume that this number would be even higher today. The growing significance of HR analytics can be attributed to companies’ need to become more data-driven in their decision-making processes. After all, the ability to critically analyze the data available across organizational functions and systems has become imperative for business success. Indeed, there seems to be a strong correlation between an efficient HR analytics function and overall employee performance and retention. HR Analytics – A Primer HR analytics, also known as people analytics or workforce analytics, works to apply data analysis to gather invaluable insights about human resources and subsequently make informed business decisions. At its core, HR analytics involves collecting and analyzing data related to employee performance, engagement, recruitment, retention, training, and other HR functions. Why? To gain a better understanding of people-facing processes To improve employee engagement To enhance the recruiting process To streamline workforce planning To improve measures targeted at talent retention All these efforts aim to keep the business competitive and successful by creating loyal, productive employees who add measurable value to the bottom line. How Does Data Science Power HR Analytics for Improved Employee Performance and Retention Utilizing data science means paving the way for a systematic approach that integrates data-driven insights into your organization’s HR strategies.  Once you follow a structured data science methodology, you can leverage its power to enhance employee performance, retention, and overall workforce management.  In that light, here’s a rundown of the six essential steps that outline how to effectively leverage data science-enabled HR analytics: 1. Define Your HR Analytics Objectives Take the time to clearly outline your HR analytics goals, ensuring they are in harmony with your organization’s overarching strategic objectives and individual employee performance plans.  Also, pinpoint the specific dimensions of employee performance and retention that you intend to enhance — whether it’s curbing turnover rates or amplifying productivity. Notably, this definition step plays a vital role in shaping how you harness the power of HR analytics to effectively pursue and accomplish the targeted goals. 2. Collect Relevant Data Initiate the process by aggregating data from a range of sources, including employee surveys, performance evaluations, and attendance records.  The wealth of data enables HR professionals to: Measure and analyze the impact of their human resource activities Measure specific traits of an individual, such as job satisfaction or turnover Monitor changes in the company’s workforce The resulting data analysis results in a structured, standardized report that provides information about employee demographics, compensation structure, training needs, and spending. 3. Analyze Trends and Patterns Engage in the process of reading patterns by executing rigorous statistical checks to unveil prevailing traits.  Upon completing this analytical phase, HR professionals can extract valuable insights. These insights then grow to be the bedrock upon which knowledgeable choices are made.  Such choices or decisions are instrumental in unraveling the intricate composition of employees and imparting a deep knowledge of the workforce’s dynamics, strengths, and regions for improvement. All in all, this analytical undertaking empowers HR professionals to navigate the area of human assets with heightened clarity and strategic acumen. Also Read: A CXO’s Guide to Collaboration Between Citizen Data Scientists and Data Science Teams 4. Conduct Predictive Analysis Leverage the prowess of predictive analytics to predict forthcoming scenarios rooted in historical data.  Utilize this predictive capacity to anticipate potential areas of concern, such as identifying employees who might be at risk of departure or pinpointing avenues for performance enhancement. This way, HRs can proactively address potential challenges on the horizon. With the foresight to anticipate future issues, the HR department is armed with the ability to swiftly provide strategic solutions, thus, fostering a more agile and adaptive work environment. 5. Implement Pertinent Strategies The formulation of targeted strategies, informed by the wealth of insights gleaned from data analysis, serves as a catalyst for optimizing the efficacy of the HR function within any organizational framework. For example, insights pertaining to development and skills training can help unearth the areas where training and development initiatives need to be implemented. This can play out particularly well for increasing employee retention.  Research has previously revealed that 92% of employees believe that employee training programs positively influence engagement. 45% even stay longer at companies when they see their organization investing in learning and development initiatives. At the end of the day, insights gleaned from analytics initiatives can help HRs acknowledge cultural sensitivities and tailor their approach to resonate more deeply with their workforce, thereby fostering a more inclusive and harmonious environment. The Employee Performance Front Now that HR professionals have a wealth of data at their disposal, they can use it to understand the performance of their employees. For example, they can: Measure employee performance in real-time and leverage it for greater accountability, more accurate performance management, and driving innovation opportunities. Use the data to provide valuable insight into the drivers of workforce productivity and be better positioned to attract, retain, and develop top talent. Identify employee trends, ranging from turnover rates and performance metrics to diversity and inclusion insights, to make actionable, data-driven decisions that lead to greater business results. Gain granular insights into the working patterns, benchmark performances, and make better decisions that drive long-term growth. What’s the Way Forward? While the notion of steering HR decisions through data might initially seem a bit foreign to enterprises that aren’t yet data-driven, embracing this analytical perspective can usher in a transformational era for talent management. By understanding the

  According to a BCG report, 72% of manufacturing companies are improving their productivity using advanced analytics. Today, the applications of data science have become pervasive.    Industries like finance, healthcare, and customer service are jumping onto the bandwagon, and rightly so. After all, data science gives them the ability to improve real-world use cases. For example, they can augment their industry knowledge, create a human-centered design framework, inform UX processes, and much more when designing products.   Customer-centric companies use data science to understand customer behavior, make real-time business decisions, reduce human error, and negate adverse business outcomes.   That said, here’s a look at 10 business areas most influenced by data science. 1. Customer Service Happy customers are the cornerstone of every business. Data science enables businesses to understand their customers better and provide exceptional service. For instance, data science tools can be leveraged to improve customer service by: Collecting and analyzing customer data for business insights Identifying customer trends and patterns Predicting customer behavior Developing customer self-servicing methods   Additionally, through regular customer feedback, companies can gauge customer satisfaction levels and rectify problems, if any. 2. Finance Data science is an integral part of the modern banking and finance industry. On its part, the finance industry generates massive volumes of data — most of which goes unutilized.    Data science tools in the financial domain are used for a variety of use cases like fraud detection and designing personalized services for individuals.   To that end, the top data science applications in the finance domain include: Data-based risk analytics to understand the scope of financial risks Real-time analytics on historical financial data of any company or entity Consumer analytics to personalize financial products for individual customers 3. Human Resource With the growing labor shortage, employers are struggling to attract and retain talent across industries. Data science-based HR analytics tools can help employers make improved workforce decisions and enhance work productivity.    For example, data science can help HR teams carry out employee sentiment analysis and accordingly redesign their HR policies.    Similarly, data science tools can measure the effectiveness of employee training programs and customize workforce development initiatives for a better future.    Predictive analytics in the recruitment process can further improve the talent acquisition process based on historical data. 4. IT Compliance Companies that collect customer data must adhere to regulations regarding data security and privacy. Without IT compliance, organizations are most likely to face violation-related penalties and fines.    Favorably, organizations can leverage data analytics to check if the collected data is compliant with industry regulations. For example, they can: Structure compliance-related data for high-quality and governance Understand IT compliance-related risks Improve internal compliance-related procedures and processes   With data-driven compliance, companies can now avoid a variety of non-compliance risks, such as internal fraud, ESG risks, and corruption. 5. Management With data-driven solutions, companies can also improvise their business management and achieve their business goals. Effective management analytics can support and influence business decisions with accurate insights.    At its core, a data-driven organizational culture enables efficient collaboration among multiple stakeholders, including product teams, project managers, and shareholders.   Further, data science skills enable potential business leaders to include data-driven problem-solving abilities in their daily work. It helps management teams arrive at smarter decisions based on the existing business data. 6. Marketing Data science in marketing is enabling companies to analyze their customer’s needs and provide them with personalized offerings. For example, companies like Spotify and Amazon can recommend content based on each individual’s previous interactions.   Besides providing recommendations, data science has multiple use cases in marketing, including: Performing sentiment analysis on customers’ posts and feedback on social media pages Developing a customer churn model to analyze the customer’s pain points and ways to retain users Segmenting customer profiles into various subgroups and targeting each group with personalized marketing Designing or improving products that can meet customer needs and solve their problems 7. Procurement Effective cost management is the top priority for procurement teams post the COVID-19 pandemic. The 2020 Deloitte survey concludes that procurement leaders must be resourceful about cash management while limiting supply-related disruptions.    As it stands, data science technology can streamline procurement activities and increase efficiency.   Data-based procurement analytics can analyze real-time procurement data for accurate business insights. Here are some of the benefits of data science in the procurement process: Improved budgeting and forecasting Improved risk mitigation and disruption management Effective contract management and discounting with approved vendors Improved benchmarking of procurement performance based on item category, quantity, and country 8. Quality Be it manufacturing or healthcare, high-quality products or services are creating new market opportunities for organizations. And data science has a significant role to play here.   Besides improving decision-making, quality-related data enables organizations to cut costs and improve their products or services.    For example, data-driven quality control can help companies detect issues at an early stage, thus avoiding the high costs of product recall or rework. Additionally, quality control enables companies to identify operational risks and improve their overall inspection process. 9. Sales Data science technology can improve customer satisfaction, which in turn, leads to higher sales. Besides, by gathering and analyzing customer information, data science provides sales teams with more cross-selling and up-selling opportunities.    McKinsey reports that 72% of the fastest-growing B2B companies plan their sales using data analytics as compared to 50% of the slow-growing companies.   Data science in sales enables organizations to: Maximize the customer lifetime value (CLV) Predict future sales Prevent or reduce customer churn Identify cross-sell and up-sell opportunities Optimize their product pricing 10. Supply Chain Every manufacturing or distribution company must optimize its supply chain to prevent delays or disruptions. At the same time, they need to manage their supply chain costs.    Typically, supply chain operations involve a host of operations, including: Procurement of raw materials Inbound and outbound logistics Inventory management Order fulfillment   With data science tools, companies have real-time visibility into their supply chains and the ability to simulate possible scenarios. Additionally, they can leverage data-driven analytics to achieve business sustainability and optimize their

With applications ranging from driving behavior analysis and driving assistance to safety management and predictive analytics, the potential of data science in the automotive sector goes well beyond what has been achieved with automation alone. Recent analysis shows that the value of the big data market in the automotive industry will reach $10 billion by 2027, up from $4216.8 million in 2021. In fact, McKinsey outlines how automotive companies are shifting away from “engineering-driven to data-driven development.” After all, the application of data and its timely usage paves the way for agile systems engineering, refined product development, revenue optimization, and more. But what does the future hold? What are the trends that’ll spell the value of data science for automotive innovation? Let’s explore. Advanced Driver Assistance Systems (ADAS) ADAS plays a significant role in making driving more secure and comfortable. These intelligent systems leverage data from sensors and cameras to help drivers with the knowledge of: The traffic in the area Any alternative routes to pursue to avoid congestion Road blockage due to various reasons (like construction) But they do more than just inform drivers. For example, ACC (Adaptive Cruise Control), a driver assistance system, automatically modifies the car’s speed while using information from radar reflection and cameras to maintain a safe distance from the vehicle in front. Data science works to optimize the ACC algorithms, taking into account variables like vehicle speed, distance, and traffic conditions. Likewise, LDW (Lane Departure Warning) warns drivers when they unintentionally drift out of their lane by using cameras to monitor lane markers. Predictive Maintenance :  The rise of predictive maintenance across manufacturing facilities can be attributed to the high availability of data pertaining to vibrations, pressure, equipment condition, etc. This data lends itself well to big data, machine learning, and deep learning techniques that can help with predicting failures before they escalate and cause disruption. Machine learning models trained on a substantial amount of data work well to predict failures with exceptionally high accuracy. They ward off the need for reactive or scheduled maintenance. The only downside to this is that data volume needs to be significant, which might not always be the case. Automotive manufacturers can also opt for digital twins for predictive maintenance, as they allow for more granular diagnosis. AI-Powered Driver Behavior Analysis About 94% of accidents stem from human errors. This can be drastically reduced with automakers utilizing the power of data science and artificial intelligence for AI-powered driver behavior analysis. We discussed ADAS above; these systems also use cameras and sensors to track the driver’s actions and facial expressions over time to gauge their level of focus. They can determine indicators of distraction or tiredness by examining factors like eye movement, head position, and blink rate. Moreover, these intelligent, AI-driven systems can evaluate the driver’s overall driving performance by continuously monitoring numerous data inputs, including vehicle speed, steering patterns, and lane-keeping behavior. As such, they can relay precautionary alerts. In extreme or rather challenging scenarios, they might even take control from the driver to effectively maneuver the vehicle to safety. Also Read: The Future of Data Science – Trends and Predictions Safety and Risk Assessment  Safety and risk assessment are vital components in developing autonomous vehicles — given the high stakes associated with deploying cars capable of making complicated driving decisions without human intervention. Data science is crucial in assessing and confirming the security and dependability of these cutting-edge systems. On the back of data science, a strong safety and risk assessment framework for autonomous vehicles is brought to light by combining simulation-based testing and real-world validation. Data scientists may test autonomous vehicles in a controlled and secure environment by simulating a variety of driving conditions. These simulations enable the investigation against various difficult circumstances, such as severe weather, backed-up traffic, or unexpected roadblocks. Next, real-world testing is still necessary to evaluate the performance of autonomous vehicles in dynamic and unpredictable conditions. It remains integral to gathering enormous volumes of data that can then be analyzed to determine the system’s cognitive capabilities. Predictive Navigation Traffic congestion for commuters poses a constant problem as urbanization proliferates. To enhance the driving experience, lessen traffic, and improve overall road safety, predictive navigation and traffic management are essential. Data scientists are now able to assess real-time traffic patterns and road conditions using data from linked automobiles, GPS devices, and traffic sensors. Predictive algorithms use this data to enable navigation systems to offer alternative routes that aren’t plagued by traffic jams, accidents, or construction zones. Also, data science enables the creation of predictive parking systems that estimate parking availability in specific areas using historical and real-time parking data. Through navigation apps, drivers may access this data, which directs them to locations with a higher likelihood of finding parking spaces. Additionally, in-car systems can connect to smart sensors or parking meters to enable real-time updates and streamline parking space reservations. Tap Into the Data Economy with Rubiscape We’ve seen how every automotive application discussed above is underpinned by a manufacturer’s ability to develop products that leverage data across every touchpoint for informed decision-making. But to realize success with the data science initiatives and foster a data-centric culture at all levels of development, automotive companies need to shift away from point, fragmented solutions. What they need is a unified and comprehensive data science platform like Rubiscape that can bring agility and quality to the mix and help manage the entire data science life cycle from one place. Explore Rubiscape’s capabilities here.

According to Gartner, the primary job function of citizen data scientists is predominantly outside the field of statistics and analytics. However, they create or generate models that leverage predictive or prescriptive analytics. The profile name “citizen data scientist” bears a close association with “citizen science,” where the findings from the general public contribute to the scientific research carried out by professional scientists.  National Geographic’s Citizen Science Projects are an excellent example of the same. In the business world, citizen data scientists today play a complementary function to professional data scientists. Traditional data scientists’ professional knowledge is frequently expensive and hard to come by. Citizen data scientists are effective assets to close the present skills gap.  This article serves as a guide to CXOs and covers how citizen data scientists and data science teams should be more collaborative than competitive.  The Need for Collaboration Between Citizen Data Scientists and Data Science Teams  Although the practice of citizen data science is growing in popularity, this collaboration is rarely considered or addressed. However, this needs to change to nurture more collaboration among individuals working to drill down into data and unearth actionable insights.  Here’s why the need for collaboration between citizen data scientists and data science teams is highly reasonable. Fostering a Collaborative Data Science Ecosystem Data science training for non-data scientists is an essential step in any company’s journey towards: Nurturing data science competency within the organization Acquiring a greater understanding of the current gaps in the offerings, market, workflows, etc. Increasing transparency and trust among people working on data Advancing the democratization of data science and facilitating equal opportunities for all stakeholders to contribute to decision-making Related Reading: The Democratization of AI and Machine Learning: Making Advanced Analytics Accessible Citizen data scientists need to work hand-in-hand with professional data scientists to ensure that relevant insights are churned out from the entire stack. Such an ecosystem would spawn more innovative solutions and processes while ensuring that quality data is quickly made available to the entire team.  In such an ecosystem, CXOs would be able to have a greater say in the formulation of goals, and they would be able to order more precisely the resources needed from vendors and other teams. For this, however, it’s critical that data science roles are clearly defined. In case of discrepancy, accountability Data scientists must be aware and open to collaboration with citizen data scientists at all levels of the organization. Reaping Maximum Benefits from Augmented Analytics Extending the capabilities of the analytics initiatives is something that CXOs aim for to accommodate a holistic and more profound view of the business functions, especially as the business scales and evolves.  With citizen data scientists on board, organizations can lay out a concrete augmented analytics roadmap that follows a phased approach to creating a holistic data science ecosystem.  For example, let’s assume that citizen data scientists carry out activities, including fundamental reporting, exploratory analysis, and data curation. The enterprise wants to augment these capabilities with functions that facilitate data storytelling, data querying, etc. Instead of opting for a big bang approach where they completely change the toolkit and processes to accommodate the next set of capabilities, it’s more lucrative for CXOs to implement a phased approach where citizen data scientists are equipped with enhanced resources to perform advanced analytics.  Driving Collaborative Model Development and Deployment Citizen data scientists can collect data and ensure that it is accurate and indicative of the target business environment. They can outline the pertinent characteristics, factors, and practical limitations affecting the problem domain. Data scientists can handle complex analytical problems and guarantee that the models follow the best standards. They can help with algorithm selection, model parameter adjustment, and the use of strict validation approaches like cross-validation and out-of-sample testing. This collaborative approach ensures that the predictive or prescriptive models are accurate, efficient, and in line with the unique needs and business objectives. This, in turn, results in better decision-making and more effective model deployment throughout the organization. Enhancing Business Resilience Often, businesses find themselves in the peculiar situation of not having enough expert data scientists and having to implement innovative solutions with limited resources.  Whether it’s because of talent shortage, lack of skillsets, or simply the fact that a data science team is still in its nascent stages of development, it’s common for enterprises to have a dearth of in-house data scientists to address critical business problems.  In such situations, it’s incumbent upon the CXOs to find a way in which they can utilize the expertise of their small data science teams — something that becomes even more critical in a scenario like the economic downturn of today.  This is where they can look toward skilled information analysts in their teams. These professionals might not have an out-and-out data science degree, but they have in-depth knowledge of statistical modeling and forecasting.  When equipped with AI, NLP, and ML tools, these analysts complement the work of the data science team.  Role of a CXO in Facilitating Collaboration Between Citizen Data Scientists and Data Science Teams CXOs should be aware of the difference between an information analyst and a data scientist. They should have a clear understanding of the right mix of talent needed for the business, as well as have a sense of how their team can best utilize the data science stack to further develop their capabilities. A clear vision and plan must be laid out by the CXOs for data-driven activities across the company. This includes: Underlining the significance of collaboration between citizen data scientists and data science teams Cultivating a culture that encourages cooperation Dismantling team silos and fostering information sharing Equipping citizen data scientists with the right resources and technology infrastructure Positioning citizen data scientists for greater accountability, responsibility, and authority Providing mentorship and training to expose them to cutting-edge tools and techniques It’s here that they can immensely benefit from a unified data science platform like Rubiscape.  Contact us to learn more.

Today, we live in a world where digital experiences are in the driving seat for nearly every aspect of our lives. From businesses to government services, people are increasingly reliant on digital channels to get what they need and make lives easier. This has resulted in a dramatic increase in the volume of data produced globally. Studies predict that, by 2025, nearly 463 exabytes of data will be generated daily. The biggest takeaway from this data generation scenario is that this very same data is leveraged for accurate decision-making by businesses as well as other entities. While analytics, artificial intelligence, machine learning, etc., are ways that drill deep into piles of data to uncover hidden insights and possibilities, the real mastermind behind the success of such large-scale data initiatives is data science. Data science is responsible for keeping any set of actionable data ready for analysis and processing. The role of data scientists has become a critical part of nearly every major business in all sectors. Together with other technology leaders, data scientists work to build new working models for the large gamut of data collected from across an organization’s operational domain. Data science has been around for quite some time now, and organizations are seeking ways to utilize data science as a key enabler of success and ROI in their business initiatives. So how does the future look for data science? Let us explore some of the top trends and predictions: The AI Takeover Over the last couple of years, and especially since generative AI technology like ChatGPT became a hot topic of interest, there has been widespread fear that machines will take over several jobs. While ethical implications and economic concerns associated with job losses are more likely to prevent or slow down a massive workforce transformation, it is certain that several job roles will undergo a series of transformations to amp up the skill quotient. Data scientists, too, will experience a part of this transitional ordeal as they may have to acquaint themselves with powerful AI-powered tools that can build data models faster and more efficiently than when done manually. Of course, the oversight needed is still a critical area where data scientists can exert their dominance, as most AI algorithms that work on data modeling are still in their learning stage. With industries like weather and banking being reliant on prediction models developed with core data science principles, the window for errors is non-existent. In that light, it might take a while before AI can significantly contribute to the core sophisticated functions. But it sure is invaluable in complementing data scientists’ workflow. Rise of Quantum Data Science In the next decade or so, we will surely witness a massive shift in the way computing power is considered for any kind of analytical initiatives courtesy of quantum computing becoming mainstream. Today, data scientists can build effective data models only through limited sequential matching of scenarios and data patterns. In other words, if a couple of inputs must be run across different scenarios for modeling, it should be done one by one. With quantum computing, however, it becomes possible to run them all parallelly without worrying about the performance of the underlying computing infrastructure. The key takeaway here is that, with near unlimited computing power, data scientists can build larger, more expensive, and more powerful models that can be leveraged to build state-of-the-art digital solutions powered by analytics run through these models. Build More from Models Traditionally, data scientists worked on translating complex business workflows and transactional processes into accurate data models that can be used for automation and analytics-driven decision-making. However, building data models is no longer the main ingredient of successful analytics initiatives. The quest is to operationalize these proven models across the business at the earliest opportunity to prevent any competitive loss in the market. Once these models are implemented, the next attempt will be to scale them. Leveraging of Tools Adding on to the previous point, the secret behind successful data science initiatives is the ability of data scientists to accurately identify which data needs to be where and how it should be processed. Today, all these activities are somewhat automated and handled through no-code and low-code applications. Even businesses with very less technical-focused employees can build extensive data models and scale them to meet the needs of modern problems. In fact, Gartner predicts that, by 2026, about 80% of low-code users will be developers working from outside the formal IT departments. This is a critical development for the data science space, as it’s directly associated democratization of resources and capabilities. Computing on the Cloud For data scientists, the future is highly dependent on how efficient their cloud provider is. From data migration to modern-era no-code and low-code tools, there is a rising dependence on SaaS applications and platforms that can cater to the needs of people hooked to their digital universe. Besides, the evolving capabilities of cloud platforms that can accommodate data engineering, ML engineering, data analytics, business intelligence, AI governance, etc., make way for enterprises to leverage the cloud for their rigorous data science initiatives. Wrapping Up As you can see, the future of data science is bright, as there are several areas where data science can be applied to bring about transformational changes. However, managing the entire transition to cloud and other emerging technologies is a critical task that necessitates maximum care and supervision. This is where Rubiscape can become the key game changer. Get in touch with us to know how we can help supercharge your data science programs.

In the realm of governance, data science has emerged as a catalyst for transformation. From shaping policies to evaluating performance, data-driven decision-making has become the cornerstone of effective governance in the public sector. By leveraging the power of data analytics, machine learning, and advanced algorithms, governments can unlock valuable insights, optimize resource allocation, and enhance policy outcomes. In this article, we aim to provide a primer on how different government domains are adapting and leveraging data science to advance policy and performance. Enhancing Governance Through Data Science: Applications in Government Data science and analytics are increasingly used by governments to facilitate evidence-based decisions. Here’s how the various public sector domains are infusing data science in their respective fields. Ensuring Cybersecurity CloudSEK’s data revealed a 95% surge in attacks on the government sector during the latter half of 2022 compared to the same period in 2021. Approximately 40% of these attacks targeted government entities in India, USA, Indonesia, and China. Indeed, cybersecurity is a major concern for governments in the digital era. Imagine the vast data held by governments. If it becomes accessible to the public or unauthorized individuals or disappears, the consequences can be severe. To address this, big data analytics and machine learning tools help: Monitor and analyze operations to identify patterns in valuable data Track network users and devices for flagging suspicious activity Data science, on the whole, enables predictive analytics, real-time threat detection, automation of security and risk management workflows, and better incident response. Such interventions can be used to detect and prevent cyberattacks, including malware, ransomware, phishing, and DDOS/IoT attacks. Detecting and Preventing Tax Evasion Tax evasion refers to the deliberate efforts made by individuals or organizations to hide their true financial status from tax authorities. One approach commonly employed for tax avoidance involves reducing taxable income. Manual analysis of the vast data required to identify tax evaders is impossible. Fortunately, modern analytical techniques like forensic toolkits, graph analyses, and predictive modeling solutions offer a way to combat tax evasion effectively. For instance, the US Department of the Treasury revealed how they were able to successfully identify a substantial surge in tax fraud by leveraging data analytics. In 2018 alone, they uncovered a staggering sum of approximately $10 billion in fraudulent activities. The analytical algorithms used in tax evasion investigations rely on a combination of financial data and information from social media platforms. By meticulously scrutinizing the data, these algorithms compare the spending habits of individuals to their reported incomes. Individuals who exhibit extravagant expenditures far beyond their reported earnings can be swiftly identified as potential tax evaders. Driving Evidence-Based Policymaking in Healthcare Healthcare data residing in government databases can be leveraged to improve health outcomes and reduce costs. For example, public healthcare repositories constitute a rich storehouse of healthcare data (about vaccinations, prescriptions, hospital visits, lab data, and more) that can be readily mined to extract various insights. Given the complex healthcare system encompassing numerous players (doctors, patients, hospitals, insurance providers, and government agencies) with myriad transactions, it is difficult to observe what actually transpired. In this case, data science can help reveal patterns in the healthcare system (for instance, the correlation between the number of vaccines administered and hospitalization rates). A fine example is that of the US Centers for Disease Control and Prevention, which uses real-time non-fatal suicidal data (such as data related to suicidal ideation) to proactive facilitate suicide prevention response across different states. Governments can also predict the outbreak of infectious diseases by using predictive analytics tools. Using data gathered from health records and large-scale epidemiology and surveillance databases, public sector organizations can achieve a greater understanding of the patterns of disease transmission. All such insights can help inform future decisions on healthcare policy and resource allocation. Enhancing Governance & Performance Evaluation in Education The education sector is a highly data-intensive area. Data science can help generate insights into the effectiveness of educational programs, including classroom practices, standards for teacher education, assessments, and curricula, in order to improve learning outcomes. For example, municipalities can use big data analytics to identify areas of weakness in their educational systems, like inadequate teacher quality and deficits in reading proficiency. Governments can also map demographic details, identify at-risk students, and plot a plan of action to help improve their learning outcomes. Educational data can also be leveraged to develop predictive models and assess the effectiveness of remedial measures in improving literacy rates. Overall, through advanced data analytics, governments can evaluate educational systems, ensuring effective governance and policy making. They can make evidence-based decisions, enhance educational outcomes, and provide transparent access to information. Identifying Criminal Hotspots Data science has the power to transform how law enforcement combats crime. By pinpointing areas prone to criminal activity and optimizing resource allocation, data science can help governments safeguard communities, ensuring their safety and security. Through data analysis and predictive modeling, law enforcement agencies can identify hot spots, enhance investigations, and proactively prevent crime. For example, the US Department of Homeland Security (DHS) leverages data science to predict potential threats by integrating and comparing data from multiple security agencies. Likewise, Palantir Technologies, funded by the CIA, fights terrorism by locating roadside bombs and combats cyber fraud by tracking fraudulent transactional patterns. The Way Forward The application of data science in government sectors, from policy design to sector-wise performance evaluation, is essential for driving effective governance. Rubiscape, a truly unified data science platform, empowers governments to leverage diverse data, accelerate decision-making, and transform ideas into tangible business value. To learn more about how Rubiscape can help unlock the full potential of data science in the government sector, get in touch with our experts.

Large enterprises have a lot working to their advantage – large budgets, a huge global workforce, global supply chains, and salesforce…the works. For a startup, these factors can look intimidating. But it pays to remember that every global giant was once a small fish in a big pond. It is by taking advantage of their strengths that they became the behemoths of today. The startup narrative has also undergone a sea of change over the years, with some very smart solutions coming up from this space. And large enterprises looking for solutions have noticed this. Here are some compelling reasons why some enterprises are taking a keen interest in working with startups to meet their needs. Agility Agility is perhaps one of the greatest advantages of a startup environment. Today businesses are evolving. Markets are changing and experiencing a state of constant disruption. Business models are in a state of constant evolution. Customer demands and expectations are becoming increasingly fluid. Technology changes and advancements are demanding organizations to come up with new solutions continuously. Given the smaller setup, startups gain an advantage here as it helps them remain more nimble and agile to change. What is becoming clear in today’s age is that success does not necessarily come from the position, scale, or first-order capabilities. Rather it comes from ‘second-order’ capabilities…capabilities that allow organizations to foster rapid adoption and the capacity to act on the signals of change. This is where startups score big as their internal structure allows them to become more responsive to change, come up with creative solutions to pressing problems faster and hence deliver what the customers want, when they want it. Greater risk-taking capabilities Startups have greater risk-taking capabilities than their larger counterparts. This is primarily because there is no bureaucratic red tape to navigate to implement change. Since the needs of the customer are at the heart of the startup culture, it is their needs that dictate the risks the startup needs to take. Initiating a change process, altering a roadmap, changing technology to meet the needs of a product, etc. are much easier and faster in a startup set-up because of the absence of slow-moving decision hierarchies. Access to the latest technologies and trends Technology startups usually work with the latest, and some of the most trending technologies. Their market positioning also demands them to stay updated on the latest technology trends. What to know which direction is UI and UX heading? Ask a startup. The resource pool of technology experts working in startups is also adept at coming up with creative solutions to pressing problems using the latest and the most relevant technology stack. Large organizations can gain access to qualified and trained professionals without incurring the cost and expending the time and the effort to locate and hire a trained resource by simply working with a startup. This becomes even more relevant as technologies such as AI and Machine Learning start becoming more mainstream, and accessing top talent becomes harder. Since most startups work in a niche area, they work with niche technologists to develop robust and relevant solutions to suit market demands. Rapid prototyping The early stages of designing technology solutions demand the capability to build a working prototype to go-to-market faster. Rapid prototyping is much easier in a startup environment because of a short feedback loop. Along with this, startups don’t have complicated, interconnected, and rigid tech stacks. With clearer communication between stakeholders, access to the latest technologies, low technical debt, and a willingness to come up with a compelling solution, startups become more adept at addressing stakeholder engagement, client demands, retrospectives, and build smart alignments that contribute to rapid prototyping. It is these same capabilities that make it easier for startups to make greater customizations for their customers. Feedback-driven When change is the only constant, it becomes imperative to be open to feedback and have the velocity to implement it. Changing product requirements are a given today – stakeholders can rethink the requirements and features. End-users might demand new features and functionalities. The technology choice might need an overhaul keeping business evolution in mind. New elements might need to be introduced to make the product more attractive and useful. Startups are adept at incorporating all the feedback owing to the absence of bureaucracy and structural flexibility because of smaller, tighter-knit teams. This helps them make group decisions based on feedback faster and implement change without impacting the velocity of development. Today, collaboration has moved from becoming a buzzword to a business imperative. It has become essential for innovation. Organizations that enable collaboration are successful. Those who don’t have to implement it eventually. A similar collaboration between large enterprises and technology startups can be the key to foster innovation across geographies and benefit both sides – the large enterprises to create and enter new markets and the startups to develop their solutions and to scale. It’s a win-win for both.

As of 2020, GitHub and Google Trends rank Python as the most popular programming language, surpassing longstanding Java and JavaScript in popularity. Python is a general-purpose and high-level dynamic programming language that focuses on code readability. After being founded in the year 1991 by Guido Van Rossum, Python has only soared in popularity. Its syntax allows programmers to write codes in fewer steps as compared to Java or C++. Some of the other reasons behind Python’s popularity include its versatility, effectiveness, ease of understanding, and robust libraries. Python’s high-level data structures and dynamic binding make it a popular choice for rapid application development. Data scientists usually prefer Python over other programming languages. But what exactly makes Python suitable for data science? Why do data scientists prefer working with Python? Let’s find out – The Benefits of Python A big reason why Python is widely preferred is because of the benefits it offers. Some of the major benefits of Python are – Ease of learning: Python has always been known as a simple programming language in terms of syntax. It focuses on readability and offers uncluttered simple-to-learn syntax. Moreover, the style guide for Python, PEP 8, provides a set of rules to facilitate code formatting. Availability of support libraries: Python offers extensive support for libraries including those for web development, game development, or machine learning. It also provides a large standard library that includes areas like web services tools, internet protocols, and string operations. Moreover, many high-use programming tasks are pre-scripted into the standard library. This significantly reduces the length of the code that needs to be written. Free and open-source: Python can be downloaded for free and one can then start writing code in a matter of minutes. It has an OSI-approved open-source license. This makes Python free to use and distribute. Being open-source, Python can also be used for commercial purposes. A vibrant community: Another benefit of being an open-source language is the availability of a vibrant community that keeps actively working on making the language more user-friendly and stable. Its community is one of the best in the world and contributes extensively to the support forums. Productivity – The object-oriented design of Python provides improved process control capabilities. This, along with strong integration and text processing capabilities, contribute to increased productivity and speed. Python can be a great option for developing complex multi-protocol network applications. Easy integration – Python makes it easy to develop web services by invoking COM or COBRA components, thanks to enterprise application integration. It possesses XML and other markup languages that make Python capable of running on all modern operating systems through the same byte code. The presence of third-party modules also makes Python capable of interacting with other languages and platforms. Characteristic features – Python has created a mark for itself because of some characteristic features. It is interactive, interpretable, modular, dynamic, object-oriented, portable, high-level, and extensible in C++ & C. Why Python is Ideal for Data Science Functions Data science is about extrapolating useful information from large datasets. These large datasets are unsorted and difficult to correlate unless one uses machine learning to make connections between different data points. The process requires serious computation and power to make sense of this data. Python can very well fulfill this need. Being a general programming language, it allows one to create CSV output for easy data interpretation in a spreadsheet. Python is not only multi-functional but also lightweight and efficient at executing code. It can support object-oriented, structural, and functional programming styles and thus can be used anywhere. Python also offers many libraries specific to data science, for example, the pandas library. So, irrespective of the application, data scientists can use Python for a variety of powerful functions including casual analytics and predictive analytics. Popular Data Science Libraries in Python As discussed above, a key reason for using Python for data science is because Python offers access to numerous data science libraries. Some popular data science libraries are – Pandas – It is one of the most popular Python libraries and is ideal for data manipulation and analysis. It provides useful functions for manipulating large volumes of structured data. Pandas is also a perfect tool for data wrangling. Series and DataFrame are two data structures in the Pandas library. NumPy – Numpy or Numerical Python is a Python library that offers mathematical functions to handle large dimension arrays. NumPy offers vectorization of mathematical operations on the NumPy array type. This makes it ideal for working with large multi-dimensional arrays and matrices. SciPy – It is also a popular Python library for data science and scientific computing. It provides great functionality to scientific mathematics and computing programming. It contains submodules for integration, linear algebra, optimization, special functions, etc. Matplotlib – Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python. It is a useful Python library for data visualization. Matplotib provides various ways of visualizing data in an effective way and enables quickly making line graphs, pie charts, and histograms, etc. scikit-learn – It is a Python library focused on machine learning. scikit-learn provides easy tools for data mining and data analysis. It provides common machine learning algorithms and helps to quickly implement popular algorithms on datasets and solve real-world problems. Conclusion Python is an important tool for data analysts. The reason for its huge popularity among data scientists is the slew of features it offers along with a wide range of data-science-specific libraries that it has. Moreover, Python is tailor-made for carrying out repetitive tasks and data manipulation. Anyone who has worked with a large amount of data would be aware of how often repetition happens. Python can be thus be used to quickly and easily automate the grunt work while freeing up the data scientists to work on more interesting parts of the job. 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Thankfully Blockchain technology has emerged out of the shadows of bitcoin and cryptocurrency. Today Blockchain is looked at as an emerging technology that promises to revolutionize the way organizations conduct business. As the bitcoin hangover wears off from Blockchain, we realize the immense potential this technology has – not only alone but in conjunction with other technology trends such as data science. The role and importance of data science in the enterprise landscape and how it powers business value has also now been established. While we might assume that Blockchain and data science are mutually exclusive, with each having its separate path and use cases, this would be a little off the mark. So how do these technologies complement each other? Let’s talk about Blockchain Blockchain essentially is a distributed, decentralized digital ledger that records every transaction and serves as a digital record of transactions. It employs a decentralized database managed by computers to a peer to peer (P2P) network. Owing to the decentralized nature, there is no one single authority that makes the database transactions free of manipulations and ensures that the transactions taking place in this ledger are tamper-proof. Altering one block means changing all the following blocks making sure that nothing, no change, goes unnoticed. The blocks in the Blockchain are versatile and can hold different kinds of information in a transparent, decentralized, and tamper-proof manner. Blockchain and Data Science – A Budding Relationship Data science, as we know it, is the science to extract valuable insights and information from both structured and unstructured data to solve real-world problems. The growth of data science can be credited to the meteoric rise of big data. Big data deals with humongous volumes of data that often cannot be managed by conventional data handling techniques. What becomes obvious here is that both Blockchain and data science have data at the center. The focus of Blockchain is to record and validate data, while data science focuses on deriving meaningful insights from data for problem-solving. However, sharing, securing, and ensuring data integrity has been a challenge for most data scientists. Since Blockchain manages to solve this core problem of data sourcing, it had managed to grab the attention of data scientists. So, how what makes Blockchain and data science a match made in heaven? Ensuring data traceability Since Blockchain employs peer-to-peer relationships, it makes the ledger’s channels transparent. It also shows the user which data is reliable to use, where it came from, how it was changed etc. Blockchain technology makes it possible to trace the entire data history on the distributed ledger from the point of entry to the point of exit. This data traceability makes sure that data scientists get access to the right data and curate the right data sets to enable more accurate decision making. Ensuring data integrity Today data is more available than ever before. However, the data that organizations want to leverage lie scattered and can take weeks and even months to sort out. It is hard to ignore the negative implications on the time, effort, and resource wastage here. Data integrity can also get impacted owing to human error, which eventually impacts the end analysis. We cannot eliminate the risk of data getting compromised, especially when it is stored in a centralized location. To deliver on its robust data analysis and predictive modeling capabilities, data science needs access to reliable and strong data sets. The decentralized nature of Blockchain makes it possible for data scientists to strengthen their capacity to manage data and also create a solid data infrastructure where they are sure of data integrity Blockchain possibilities for accurate real-time analytics What are amongst the most significant benefits of Blockchain? A decentralized framework, transaction transparency, and immutable recordkeeping. These factors also render Blockchain perfect for enabling real-time analytics as it allows organizations to detect anomalies in a database promptly. Data transparency increases greatly when we use Blockchain for data analytics. Organizations can monitor changes in data in real-time using Blockchain. This gives data scientists tremendous opportunities to design algorithms to leverage these real-time changes to design predictive models. They can enable better decision making and prevent malicious activities (such as fraud in banks and fintech). Better predictive analytics Data science is hailed for its predictive capabilities. However, the quality of the prediction relies wholly on the data in use. Data scientists can rest easy when it comes to this aspect if they start employing Blockchain data. Blockchain data, like any other data, can be used to derive valuable insights into behaviors and trends and can be employed to predict future outcomes more accurately. Blockchain capably provides access to huge volumes of structured data for data scientists to play with. Additionally, owing to the distributed nature of blocking and the computational power available, it gives data scientists even in small organizations the capability to undertake extensive tasks involving predictive analytics. Robust data security Today security is on everyone’s mind and ensuring data security and privacy become non-negotiable for organizations. Organizations can be assured about the security of information and data within the Blockchain because of its decentralized nature. Because of this decentralization, no one person holds control over it. It is also impossible to change, use or manipulate data without the approval of those involved. While this infuses data transparency into the system and gives data scientists more assurance about the data, it also helps to alleviate risks of fraudulent activities. Blockchains are ledgers that are maintained by nodes (a computer that contains a copy of the Blockchain data and stays up to date with any database changes). You have to either run your own node, pull from an existing node, or use hosted software that allows you to plug queries directly into the desired network to access the data. Owing to its system, Blockchain can ensure the security and privacy of data. Blockchain also facilitates improved and more secure data access. Organizations can identify the right users who should be a part of