How To Choose Data Science Consultants (Buyer’s Guide)

The advancements in emerging technologies like AI, ML, and IoT are increasingly driving companies to adopt different platforms and software. And with technology, the demand for data is also growing. Companies are using data for enhancing business operations and revenue. Data science has also become imperative for companies in their quest for competitive advantage. According to reports, “The Data Science Platform market size is projected to grow from USD 95.3 billion in 2021 to 322.9 USD billion in 2026, at a Compound Annual Growth Rate (CAGR) of 27.7%”. What is Data Science Consulting? Data science is considered a means of enhancing a company’s ability to identify patterns and trends in its data. To do this, companies hire a data scientist or a data science consulting firm that uses different techniques, including statistics, machine learning, and artificial intelligence to optimize brand performance with the most promising results.  Data science is integral to modern-day business, and its importance cannot be understated. As companies and individuals grapple with the ever-growing demand for data, the need for data consultants is also growing. These consultants help organizations collect, process and use data to improve decision-making and operations. They can also help create new insights and models to facilitate the best business processes.  How Does Data Science Work? Companies are beginning to realize the importance of incorporating data scientists into their organizations. Not only are technology companies utilize data science, but even industries like healthcare, retail, finance, transport, and many others are engaging customers with data science insights. For example, the application of data science in the healthcare industry is being used to improve patient care and develop new treatments.  Banks are utilizing data science to prevent fraud and detect money laundering. Retailers use it for a better understanding of customer behavior and to create targeted marketing campaigns. Even the hospitality or eCommerce industry uses data science to target customers with their campaigns, offer discounts, and much more. Hence, data science is being used extensively by different companies for various purposes. Some companies use data science to improve their product or service, while others use it to gain a competitive edge in the market. Data science is a versatile tool used for a variety of purposes. But, before you start to apply data science for company growth, companies must consider a few things. The first step is choosing an experienced data consultant or data science consulting company. But, are you wondering how you can choose the best data consultants for your enterprise? Let’s have a look below: How To Choose Data Science Consultants? Choosing the right data science consultant can be an overwhelming task. Multiple qualities and skills must be factored in when selecting a consultant to determine who is best suited for the job. Some of them include: Experience and Background Data science consultants work with businesses to help them understand and organize data. They can come from different backgrounds, but they all share a love of data and analytics. Yet, they have an experience that can vary depending on the consultancy.  Some data science consultants have a background in statistics, machine learning, or data engineering. Others may have worked in a specific industry and know how to apply data analysis techniques to that domain. So, it is critical to find a consultant who has the right skills for your project and who can comprehend your business needs. A consultant’s experience will also affect the type of services they offer. For example, a consultant may provide strategy services, such as helping you define your goals and identify the best data sources, or they may cater to implementation services, such as building models or pipelines to extract insights from your data.  Thus, before appointing, you must understand their experience and how it can benefit your business. Skills Companies look for consultants who help them make the most of their data. So, companies must look for a skilled data science expert who: Analytical Knowledge A data science consultant can help organizations use data to make informed decisions. And with that comes a need for consultants who can help businesses use analytics to their advantage. Data analytics is crucial for an Organization to understand customer behavior, trends, and preferences; optimize marketing campaigns; determine where to allocate resources; identify opportunities and challenges. In addition, data analytics can play a significant role in developing new products and services. Therefore, enterprises must look for a data science consultant with a strong understanding of analytics principles and tools. It includes knowing how to gather, analyze and interpret data. The consultant should be someone who can understand how data flows within a company and has the know-how to put together complex analysis plans. They must further be able to recommend solutions that help the organization meet its goals. Technical Abilities Choosing the right data science consultant is critical to successful data analysis and inference. The data scientist must have technical knowledge including but not limited to data modeling, data warehousing, data visualization, and database design. The data scientist must have technical proficiency in areas such as: Furthermore, the consultant should also be able to build predictive models to help the organization make informed decisions quickly. Data scientists must have an excellent problem-solving capability and strong programming familiarity with at least one of the following- SQL, R, SAS, Tableau, or other software. Thus, when selecting a consultant, also choose someone who will be able to recommend the appropriate technologies to meet your business requirements.  Big Data While looking for consultants, companies must look for data scientists who have an experience in big data. Why? Because big data will help consultants manage and process large amounts of data in your company. This technology can help data scientists find patterns and insights that they would not be able to find otherwise. Big data technology can help data scientists to automate particular tasks, which can speed up the process in your company. Also, prevent fraud and improve the accuracy of their predictions. Therefore, these are some

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Data Science for Risk Management

While 57% of senior-level executives rank “compliance and risk” as the top two risk categories that must be managed, only 36% of organizations have a formal risk management plan, and 69% of executives are not even confident about their current risk management policies.  The global enterprise risk landscape is highly volatile and teeming with new and critical challenges now and then, and one such example is the ongoing COVID-19 pandemic.  Despite being a significant organizational process, risk management processes and practices across the globe continue to be a challenging business aspect to manage.  One of the primary reasons behind this is the consistent technological and market evolution and industry disruptions.  While having a 100% sure and fool-proof risk management strategy is a utopia, getting data-driven insights from historical data and predictive analytics, in short, Data Analytics, can give you the key to a lot of many concerns.  How to know whether your business is running at maximum efficiency, and what is your exact risk profile? Do you understand your risk profile in its entirety, and is your exposure to business risks, accidents, and disruptions minimum? Here, we explore how data analytics can help you resolve these and many other doubts by taking all the guesswork out of your risk management practices.  Let us begin by understanding the term in the modern digital business landscape.  What Is Risk Management and What Activities Does It Involve? Gartner defines enterprise risk management (ERM) as the process of identifying, analyzing, and treating the enterprise’s exposure as visualized by the executive management.  It includes looking at the various exposures, such as frauds, credit, finance, strategic and operational matters. ERM is a top-down strategy to identify, assess, and prepare for the potential dangers, losses, and hazards that might interfere with the operations and objectives of a firm in multiple ways.  Hence, instead of being a siloed analysis of different processes, departments, or operations, it is a holistic and highly consolidated analysis of all the business units and segments.  The following visual shows the various activities involved in risk management: 1. Align ERM Processes to Business Goals Senior management and enterprise leaders work together to establish the organizational and business goals and educate the staff about the importance of ERM. 2. Identify Enterprise Risks Compile a list of various risks that can affect the enterprise and impede it from achieving its goals. 3. Risk Assessment Risk examination, keeping the likelihood and impact of risk on the organization. 4. Select Risk Response Selecting an appropriate risk response based on the impact and risk appetite, such as risk acceptance, avoidance, reduction, transfer, sharing, etc. 5. Risk Monitoring Monitor the ways risks are changing and how your responses are faring against them. 6. Communicate or Report Risks You have to communicate the risk events, their results, and steps you took to counter them, to the stakeholders and upper management, via reports.  Traditional Risk Management vs. Modern Risk Business: The Paradigm Shift Traditional risk management approaches are highly subjective and are based on individual perceptions. They also tend to be non-optimal in dealing with the emerging risk landscape.  Today, enterprises generate limitless data, the technology is getting smarter with every passing day, and business dynamics are becoming highly challenging. Hence, having an effective risk management process is a must-have to steer your business towards growth.  And so is having a data-based approach towards risk management! As businesses continue to digitize the operations and automate the processes, the risk profiles continue to assume a more critical, diverse, and complicated stance.  Why? – You might ask. Well, this is because of many reasons, such as: Hence, to survive in this new digital era, businesses should identify the early indicators of risk events and have to act proactively to mitigate those risks before they become disruptions. Further, it is important to understand your risk portfolio and requirements for advanced technologies to minimize your risk exposure.  Below, we discuss some key considerations that you must consider while applying data analytics for risk management. Applying Data Analytics to Risk Management Process: 5 Key Considerations 1. How to Measure and Quantify Risk? While there is no exact science or mathematical formula to measure risk, using analytics, you can easily create measurement parameters that can, in turn, help you in establishing and examining the likely risk scenarios.  Once this is done, understanding the potential impact of any risk becomes easier. Now, you can start planning around it and gradually establish a baseline of data using analytics to measure risk across the entire organization. Hence, your end goal should be to use data analytics for the entire organization in a holistic manner.  2. Understanding “What Is New” in Analytics One of the most common doubts people have about analytics is that they have been using it for years, so what’s all this hype about?  While we have been using data analytics in some form for years, there is a world of difference between that tradition and the current version. The modern data analytics platforms are highly sophisticated, and modern risk analytics is more focused on data exploration, statistical clustering, data segmentation, predictive analysis, scenario analysis, event simulation, etc. So, even if you have been using numbers for risk analysis and management for years now, modern risk analytics leverages various advanced technologies and is undoubtedly more effective and reliable. 3. Using ERM Solutions With in-Built Analytics While having an ERM department with an entire team is a huge asset for every business, it comes at a higher cost, and most organizations have a standalone ERM function.  Hence, it is essential to opt for professional risk analytics services that can tap into the business-wide structured and unstructured data blocks and understand the potential impacts of a wide range of risks. Further, as risk management gradually becomes more complex, an ERM with in-built risk analytics becomes a huge overhead.  4. Using Analytics for Financial Statements and Reporting Analytics and financial reporting have a lot of natural overlapping and are mutually fulfilling. Analytics can offer data

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17 Most Important Data Science Trends of 2023

There’s nothing constant in our lives but change. Over the years, we’ve seen how businesses have become more modern, adopting the latest technology to boost productivity and increase the return on investment. Data analytics, big data, artificial intelligence, and data science are the trending keywords in the current scenario. Enterprises want to adopt data-driven models to streamline their business processes and make better decisions based on data analytical insights. With the pandemic disrupting industries around the world, SMEs and large enterprises had no option but to adapt to the changes in less time. This led to increasing investments in data analytics and data science. Data has become the center point for almost every organization. As businesses rely on data analytics to avoid and overcome several challenges, we see new trends emerging in the industries. AI trends 2023 by Gartner are an example of development. The trends have been divided into three major heads- accelerating change, operationalizing business value, and distribution of everything (data and insights). In this blog, we’ll look at the most important data science trends in 2023 and understand how big data and data analytics are becoming an inherent part of every enterprise, irrespective of the industry. Top Data Science Trends of 2023 1. Big Data on the Cloud  Data is already being generated in abundance. The problem lies with collecting, tagging, cleaning, structuring, formatting, and analyzing this huge volume of data in one place. How to collect data? Where to store and process it? How should we share the insights with others? Data science models and artificial intelligence come to the rescue. However, storage of data is still a concern. It has been found that around 45% of enterprises have moved their big data to cloud platforms. Businesses are increasingly turning towards cloud services for data storage, processing, and distribution. One of the major data management trends in 2023 is the use of public and private cloud services for big data and data analytics. 2. Emphasis on Actionable Data  What use is data in its raw, unstructured, and complex format if you don’t know what to do with it? The emphasis is on actionable data that brings together big data and business processes to help you make the right decisions. Investing in expensive data software will not give any results unless the data is analyzed to derive actionable insights. It is these insights that help you in understanding the current position of your business, the trends in the market, the challenges and opportunities, etc. Actionable data empowers you to become a better decision-maker and do what’s right for the business. From arranging activities/ jobs in the enterprise, streamlining the workflows, and distributing projects between teams, insights from actionable data help you increase the overall efficiency of the business. 3. Data as a Service- Data Exchange in Marketplaces  Data is now being offered as a service as well. How is that possible? You must have seen websites embedding Covid-19 data to show the number of cases in a region or the number of deaths, etc. This data is provided by other companies that offer data as a service. This data can be used by enterprises as a part of their business processes. Since it might lead to data privacy issues and complications, companies are coming with procedures that minimize the data risk of a data breach or attract a lawsuit. Data can be moved from the vendor’s platform to the buyer’s platforms with little or no disturbance and data breach of any kind. Data exchange in marketplaces for analytics and insights is one of the prominent data analytics trends in 2023. It is referred to as DaaS in short. 4. Use of Augmented Analytics  What is augmented analytics? AA is a concept of data analytics that uses AI, machine learning, and natural language processing to automate the analysis of massive data. What is normally handled by a data scientist is now being automated in delivering insights in real-time. It takes less time for enterprises to process the data and derives insights from it. The result is also more accurate, thus leading to better decisions. From assisting with data preparation to data processing, analytics, and visualization, AI, ML, and NLP help experts explore data and generate in-depth reports and predictions. Data from within the enterprise and outside the enterprise can be combined through augmented analytics. Actionable Advice for Data-Driven Leaders Struggling to reap the right kind of insights from your business data? Get expert tips, latest trends, insights, case studies, recommendations and more in your inbox. 5. Cloud Automation and Hybrid Cloud Services The automation of cloud computing services for public and private clouds is achieved using artificial intelligence and machine learning. AIOps is artificial intelligence for IT operations. This is bringing a change in the way enterprises look at big data and cloud services by offering more data security, scalability, centralized database and governance system, and ownership of data at low cost. One of the big data predictions for 2023 is the increase in the use of hybrid cloud services. A hybrid cloud is an amalgamation of a public cloud and a private cloud platform. Public clouds are cost-effective but do not provide high data security. A private cloud is more secure but expensive and not a feasible option for all SMEs. The feasible solution is a combination of both where cost and security are balanced to offer more agility. A hybrid cloud helps optimize the resources and performance of the enterprise. 6. Focus on Edge Intelligence  Gartner and Forrester have predicted that edge computing will become a mainstream process in 2023. Edge computing or edge intelligence is where data analysis and data aggregation are done close to the network. Industries wish to take advantage of the internet of things (IoT) and data transformation services to incorporate edge computing into business systems. This results in greater flexibility, scalability, and reliability, leading to a better performance of the enterprise. It also reduces latency and increases the processing speed. When

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Importance of Building AI and Data Science Capability For Strategic Growth

Businesses across the globe have started adopting artificial intelligence and data analytics to streamline their operations. Investing in the latest technology helps enterprises optimize resources, increase productivity, improve quality and get a faster return on investment.  From following the traditional approach of decision-making, SMEs are slowly turning into data-driven businesses. Big data is being stored, processed, and analyzed to gain actionable insights. These insights are gathered from historical and real-time data to help the management make faster and better decisions.  However, doubts and confusion about AI, ML, and data science still persist. The concepts overlap and coexist. That makes things a little hard to understand. Artificial intelligence consulting companies have been helping SMEs understand how AI, ML, and data science are different from each other.  The companies provide continuous assistance to revamp the business processes and build an efficient operational system in the enterprise. The aim is to strengthen the business in all aspects to achieve its long-term goals.  What is Artificial Intelligence?  One thing most of us do know is the generic definition of artificial intelligence. Minsky and McCarthy, in the 1950s, defined AI as a task performed by machines that would usually be handled by humans.  But artificial intelligence is much more than a machine taking over the tasks of a human. AI is a collection of algorithms that help computers understand the relationship between various entities, gather meaningful insights, or plan a course of action for the future. AI is all about actions. Machine learning and deep learning are a part of artificial intelligence.  The machine-learning algorithm can learn and improve itself without the intervention of humans. Have you read about AI solutions like Chatbots and virtual assistants providing personalized assistance to employees depending on their responses to the questions?  It means that the machine learning algorithms use data in the system and the response of the employees. The algorithms learn from the feedback and deliver better accurate services.   Types of AI Artificial intelligence is classified into different types based on functionality and technology.  Based on Functionality:  Reactive Machines These are the oldest AI type of AI systems with limited capabilities. They do not execute memory-based functions but are used to respond quickly to typical input datasets.  Theory of Mind These AI systems are supposed to understand the human mind, emotions, feelings, thoughts, etc., and identify the factors that influence the human thinking process.  Limited Memory These AI systems learn from previous data and are the ones we commonly see in today’s world. A vast amount of data is fed to train these systems.  Self-Aware These AI systems are yet to be fully used in the market. Some of these are still in the developing stages. These systems are meant to have the self-awareness and consciousness of a normal human being.  Based on Technology:  Artificial Narrow Intelligence (ANI) Also known as weak AI, this is the common type of AI technology we see in the industry. The systems use a predefined set of constraints to process data and deliver results.  Artificial General Intelligence (AGI) This technology is connected to the Theory of Mind and has been fully developed as yet. The aim of this technology is to develop machines that create independent connections across various domains.  Artificial Super Intelligence (ASI) This technology is still in the early stages of development and can be linked to Self-aware AI systems.  Why Use AI for Future Growth According to Fortune Business Insights, the global AI market value is expected to touch $267 billion by 2027. AI-powered robots and machines are going to become an integral part of most industries, be it the manufacturing or service and hospitality sectors.  Investing in AI at this stage will ensure that the organization is ready to adapt to the changes in the future market and grab more opportunities.  How Do You Develop an AI Strategy? (AI Strategy Framework) Developing an AI strategy is a task, right? Why not take the help of our AI services for easy implementation and customized solution for your business problems? Our AI developers have the skill and expertise required to transform your organization into a dominant force in your niche. What is Data Science?  Data science is a broad field of science that deals with collecting, cleaning, storing, processing, and analyzing data to derive meaningful insights. The techniques of mathematics and statistics are used along with advanced technology and tools to derive useful information and knowledge from vast amounts of data.  Data analytics, data mining, and data visualization are a part of data science. AI and ML are used in the data science field to produce insights that help in better decision-making. Hiring the leading data analytics company in India provides the necessary skills and technology required to gather raw data and process it to detect hidden patterns, identify market trends, and predict future outcomes for the enterprise.  Data scientists and analysts use AI and ML tools to work on chunks of data (historical and real-time) to draw inferences and generate reports to explain the insights in a simple and easy-to-understand format.  How Do You Develop a Data Science Strategy? (Data Science Strategy Framework) Similar to the AI strategy framework, the data science framework also needs to follow a systematic process to yield the expected results. Relationship Between Data Science and Artificial Intelligence Human intervention is required in data science to process and analyze huge amounts of data and derive actionable insights. Data scientists build ML models that can be implemented in the business to make the most of the data that’s available in the enterprise to forecast business. The following are the two popular ways in which consulting companies use data science in business development.  Predictive Analytics: Data from the past and present are used to analyze trends and patterns to predict the future outcomes for the business and in the market. This helps enterprises be ready to grab new opportunities in the market and make the most of the latest trends. Organizations can also be better prepared to deal with market fluctuations. Predictive analytics is

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10 Things to Consider While Building your Right Data Science Team

Enterprises these days no longer follow outdated business processes. The focus is more on adopting the latest technology and relying on new software and tools to increase productivity and ROI. But working with advanced systems means hiring experts who have experience in the said field. Data is a major part of every business. Be it artificial intelligence, machine learning, natural language processing, or business intelligence, these technologies work with vast amounts of data. Organizations need employees who can work with data and the latest software to derive insights and generate reports. Ensuring data quality helps an organization make better decisions. Many of you would have already heard of data scientists. A core data scientist is supposed to know everything and manage it all. But that is hardly possible. Even though the same person should have a varied skill set, a data scientist alone will not be enough. What enterprises need is a fully equipped data science team to work on analysis. What is Data Science? Before we talk in detail about why and how you should build a successful data science team, let us first see what it actually is. The simplest definition is- In computer science, data analytics is the process of analyzing raw data to make conclusions about it. A data science company offers multiple services related to data analytics, data infrastructure, data strategy, big data, deep learning, machine learning, artificial intelligence, and much more. The technologies and services are interlinked with the teams working on different aspects of data science and advanced analytics. Not every business has the suitable infrastructure to build a data science team. While some find it easy, others have to make a lot of effort. A global digital framework can introduce governance, the social environment, business, and technology. In such instances, hiring the services of a data science company is a better option. How Data Scientists are different from Data Architects? A data architect has an evolving role, so there is no industry-standard certification program. As data engineers, data scientist experts, or solutions architects, individuals typically gain experience in data design, data management, and data storage work as they work their way up to the role of a data architect. The Importance of Building the Right Data Science Team What if you want to build a team for data science projects? At what stage should you introduce data science into your enterprise? Mostly, the decision has the highest impact at early stages only. How important is it to build the right team? Introducing data science roles into your business processes requires a lot of planning. You will need to be sure that you have enough budget to invest in systems, people, and processes. You also need to be assured that your existing employees will welcome the changes and embrace them. If your employees do not value the insights offered by data scientists, the purpose will be lost. Many companies expect data analysts to be able to convert alienating numbers in order to provide tangible insights. The following are some reasons you should invest in building data science for your business. Empower the employees and management Recommending future actions based on insights derived from past and real-time data Identifying growth opportunities in the market Helping in better decision making based on data-driven reports Assisting the employees to adopt industry best practices Analyzing and evaluating the decisions made by the management Identifying the target audiences Identifying customers’ issues and finding solutions Helping recruit the right talent for the business Data Science Team Roles A data science team has multiple experts, each dealing with different aspects of the field. The roles and responsibilities of the team members depend on their experience in domain expertise, technical knowledge, and quantitative skills. Team Leader- Chief Analytics Officer or Chief Data Officer  Data Strategist Data Scientist  Data Engineers and Architects Data Analysts Machine Learning Engineer Business Analyst  Data Journalists Data Visualization Engineer The actual team positions might differ, depending on the types of data science teams an enterprise wants to build and how much it can invest into it. How to Structure the Data Science Team? The data science team structure can further be classified. You will first need to decide the type of team you want to build in your organization and then hire the right kind of experts who require data analysts with market-tested skills. Decentralized: Works the best for short-term, initial data science integration activities and SMEs that don’t want to become a full-fledged data-driven business. It totally depends on the business objectives to opt for a decentralized model. Centralized: These are data science teams that work on multiple projects scattered in different departments throughout the enterprise. This structure works well for enterprises focusing on long-term growth and development. Functional: One team works with one department like the marketing or the logistics. The focus area is limited to that department. This structure is best suited for startups where there is no need to analyze every single piece of information. Center of Excellence (CoE): This is similar to the centralized structure but with a separate unit for data scientists. It is known as one of the most balanced structures since there is a higher level of coordination between the teams. Consulting: This is similar to having a data analytics company within the enterprise. The data engineering team can be hired by different departments to work on specific projects. This structure works from SMEs where the management cannot allocate many external resources to the teams. Democratic: This allows you to combine and integrate the simple or specialized data science model with other systems in the enterprise. Employees have access to data science systems and can make changes to them. This works when businesses focus on building data science infrastructure for the enterprise. Federated: This is similar to employing a SWOT team in the organization. The federated structure is a combination of decentralized and CoE types. So does this make you wonder who should the data science team report

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Impact Of COVID-19 On Data Science  Industry You Should Be Aware Of!

Modern business applications use machine learning (ML) and deep learning (DL) models for analyzing real and large-scale data, predicting or reacting to events intelligently. Unlike research data analysis, the models deployed in production have to manage data on a scale, often in real-time and produce reliable results and forecasts for end users. Often these models must be agile enough in production to handle massive streams of real-time data on an ongoing basis. At times, however, such data streams change due to environmental factors that have changed, such as changes in consumer preferences, technological innovations, catastrophic events, etc. These changes result in continuously shifting data trends — which eventually degrade the predictive capacity of designed, educated, and validated models based on data trends that are suddenly no longer important. This change in the meaning of an incoming data stream is referred to as “concept drift” and what they predict is nothing new. Although idea drift has always been a matter for data science, its effect has rapidly escalated and reached unparalleled rates due to the COVID-19 pandemic. And this is likely to happen again as the world continues to plan for COVID rehabilitation and more changes in human behavior. Concept drift exists because of the significant changes in human behavior and economic activity resulting from social distancing, self-isolation, lockdown, and other pandemic responses. Nothing lasts forever — not even carefully built models trained with well-labeled mountains of data. Concept drift leads to limits of decision divergence for new data from those of models developed from earlier data. Its effect on predictive models developed across industries for different applications is becoming widespread, with far-reaching implications. For example, in-store shopping has experienced a dramatic decline and an unparalleled rise in the number of items purchased online. Additionally, the type of things customers buy online has changed — from clothing to furniture, furniture, and other essential products. ML models designed for retail companies now offer no longer the right predictions. Because companies no longer have precise predictions to guide operational decisions, they cannot optimize supply chain activities adequately. Concept drift also impacts models designed to predict fraud across various industries. For example, models were previously trained to see buying one-way flight tickets as a reliable indicator of airline fraud. That is not the case anymore. A lot of fliers have bought one-way tickets with the advent and spread of the Coronavirus.  It will possibly take some time to be a reliable predictor of fraud before this returns. Insurance is not being left out. Until this pandemic period, predictive models were used to evaluate various factors to determine customers’ risk profiles and thus arrive at pricing for different insurance policies. As a result of self-isolation and movement limitation, along with a demographic-related shift in risk, many of these variables are no longer the predictors they used to be. Also, a previously unknown range of data is added, requiring new categories and labels. Primarily, data scientists can no longer rely on historical data alone to train models in real-world scenarios and then deploy them. The pandemic’s ripple effect tells us that we need to be more agile, flexible, and use better approaches to keep deployed models responsive and ensure they provide the value they were designed to provide. How Have ML Models Shifted During COVID-19? AI and ML models need to train raw data on mountains before implementing or operationalizing data science into real-world scenarios. There’s a catch, though — once these models are deployed, while they continue to learn and adapt, they’re always based on the same concept they were initially designed on. Development models don’t compensate for factors and don’t react to patterns emerging in the real world. As a result, model predictions appear to deteriorate over time, and their purpose is no longer served. Models trained to predict human behavior are particularly vulnerable to such deterioration, especially in acute circumstances such as the current pandemic, which has changed the way people spend their time, what they buy, and how they spend their time altogether. Drift detection and adaptation mechanisms are crucial under these changing conditions. The continuous method is to track models to detect drift and adapt accordingly. Mechanisms must be in place to monitor errors on an ongoing basis and allow predictive models to be adjusted to rapidly evolving conditions while preserving accuracy. Otherwise, these models may become outdated and generate results that are no longer reliable or efficient for the organization. Feasible And Fast New Situations There is more to projects in data science than creating and deploying ML models. Monitoring and preserving model output is an ongoing process that’s made simpler with MLOps being embraced. While you can re-label data and retrain models on an ongoing basis, this is an extremely expensive, cumbersome, and time-consuming approach. To identify, understand, and reduce the effect of design drift on production models and automate as much of the process as possible, data scientists need to exploit MLOps automation. Given DevOps’ track record of enabling the fast design and delivery of high-visibility and quality applications, it makes sense for data science teams to leverage MLOps to manage the development, deployment, and management of ML models. MLOps allows data science  teams to either leverage change management strategies continuously update models upon receiving new data instances or update models upon detection of a concept or data drift With this, new data can be obtained to retrain and adjust models, even if the original data set is considerably smaller. Teams should build and construct new data, where possible, in a way that accounts for missing data. Most notably, MLOps automation allows teams to implement these change management techniques in rapid iterations, as long-term implementation is no longer time-bound. The lifecycle of data science needs to be carried out in much shorter periods, and this can only be done by automation. Those Who Adapt Will Survive Data science needs to respond rapidly to the rapid changes taking place across the globe. Many companies are currently in a

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Impact Of COVID-19 On Business & Relationship With Data

No matter where you are or what you do, the current situation is favourable for none and has pushed us all to get comfortable out of our routine. Be it a startup or well established MNC, tech or any other industry, the COVID-19 has touched every little part of the planet, brought us all to a pause. There is a strong impact of COVID-19 on business, be it small or large. But the least impacted industry would be tech, who quickly shifted to remote working and went upscale digital. We can, with confidence say, that we will come out of this situation, as altogether different. While the hunt for COVID-19 is on, small businesses are the most hard-hit and are going to suffer heavily even after everything reopens. For larger businesses, it’ll be important to protect their people while establishing an effective way of working, and moving towards the need to recover. While many businesses are trying to reinvent themselves to get along with the situation, many can see exciting new opportunities. We are sure to witness a Global Digital Transformation post COVID-19. Importance of understanding the data with business perspective is being realized. The data that was at a time deleted as unwanted user data, is been implemented for better understanding of customers and a way to provide improved services to the customer. How Data Is Helping Us Fight The Virus Though we couldn’t successfully predict the outcome of COVID-19, there are many ways, the data has been employed for betterment. Many companies are trying to understand the virus, it’s symptoms, its impact on various kinds of people, how and where it is spreading, and may such questions are been answered using the data and it is helping. Allocating the medical resources to the areas and communities that might be hit next. Trying to look out for various ways to understand the virus, look out for possible symptoms, perform tests and all these operations are been helpful and made possible by properly understanding the already existing data. The output of multiple vaccines trails are been implemented to understand what would work miracles. Following are some observations made out of the current situation and a look into what a normal working day in future will feel like. Normal Times Post COVID-19 Soon, we might understand that offices are not as necessary as we thought. The focus is supposed to be on work done, anyway. Everything will be stored over the cloud instead of an HDD. Mask, Sanitizers, No Handshake policy, these things will be normal. Cloud Will Be New Basic Infrastructure Major sector of tech companies still rely on traditional infrastructure which is within the company but COVID-19 situations as forced us to implement cloud and make a shift. Seeing this major change, it is uncertain that these companies will roll back to traditional ways and Cloud services will boom. Automation Will Be Largely Involved Automation can be involved in most parts of SDLC and results are as good as expected. We were aware of automating tasks but COVID pushed us to implement it and for the coming times, automation will help in almost every major aspect. We are experiencing how automation is helping manage necessary medical requirements during the COVID situation. Backups Will Be Prepared COVID situation has helped us understand the need and importance of Backups. Not preparing proper backups will be estimated to be costlier in future and will lead us to have proper backups. These are the steps implemented by the businesses but the need to understand the customer will rise, again, differently. We had seen many Machine Learning models implemented to understand the customer and provide customer-centric services. But the COVID-19 situation has altered the habits of customers and we will have to begin from scratch. But it is not just customer that will require readjustments, the businesses and the way they used work must be reinvented, the businesses will have to lead to a proper restructuring. We all at a point thought, that with enough data we will predict the spread of disease and yet we failed to see into the future. But now we are at a point into this epidemic where we have an enormous amount of data, from various sources relating to various stages, with added features like geographical advantages, results of various vaccines implemented. Right now, we might be in a position to understand the data and make a move towards better tomorrow. My take out of all this and an attempt to connect the data leads me to two conclusions. The businesses that we knew shifted from tradition decision-making habits to relying on data might shift back to traditional ways and follow their instincts. The data that they relied on earlier has been changed, a whole new unexpected chapter has been added and is not compatible anymore to process. While the thought that many businesses will rise out of this COVID-19 situation with an attempt to seize new opportunities still floats. The ones capable of delivering immediate attention and action, understanding new formats of data and the newly added features. TO WRAP IT UP We must have an open mind for what is yet to come. Many businesses have seen worst and many new opportunities are been recognized. These are the times where we have to stand up and face the challenges, for what lies beyond the challenge is a brand new day. Every business will have its chances to accept the day and reenter the market, while many will stand up to provide ways to help others. Schedule a call with our business experts to know how any business can survive & flourish amidst this pandemic by taking business decisions based on data trends.

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What is Data Science? How Do Data Scientists Help Businesses

Ever wondered what is data science? Do you know what a data scientist does? Here is something to help you. Data science is advancing as one of the most motivating and after sought vocation ways for all the gifted experts. Today, effective data experts comprehend that they should progress past the conventional aptitudes of breaking down a lot of data, data mining, and programming abilities. To reveal helpful knowledge for their associations, data researchers must ace the full range of the data science life cycle and have a degree of adaptability and comprehension to amplify returns at each period of the procedure. Data science or data-driven science empowers better dynamic, prescient examination, and example disclosure. It lets you: Locate the primary source of an issue by posing the correct inquiries Perform exploratory investigation on the data Model the data utilizing different calculations Convey and picture the outcomes using charts, dashboards, and so forth. By and by, data science is as of now helping the carrier business foresee disturbances in the movement to lighten the torment for the two aircraft and travelers. With the assistance of data science, carriers can streamline activities from numerous points of view, including: Plan courses and conclude whether to plan direct or corresponding flights Fabricate prescient investigation models to estimate flight delays Offer limited customized time offers dependent on clients booking designs Choose which class of planes to buy for better generally speaking execution In another model, suppose you need to purchase new furniture for your office. When looking on the web for the best choice and arrangement, you should address some necessary inquiries before settling on your choice. What is Data Science? (Understanding Data Science Before Becoming Data Scientist) In the previous decade, data researchers have become vital resources and are available in practically all associations. These experts are balanced, data-driven people with significant level specialized abilities who are fit for building complex quantitative calculations to sort out and incorporate a lot of data used to respond to questions and drive methodology in their association. It is combined with the involvement with correspondence and administration expected to convey substantial outcomes to different partners over an association or business. Data researchers should be interested and result-arranged, with extraordinary industry-explicit information and relational abilities that permit them to disclose profoundly specific outcomes to their non-specialized partners. They have a solid quantitative foundation in measurements and straight variable-based math just as programming information with centers in data warehousing, mining, and displaying to assemble and dissect calculations. Why Become a Data Researcher?  As expanding measures of data become increasingly available, large tech organizations are never again the main ones needing data researchers. The developing interest for data science experts across businesses, of all shapes and sizes, is being tested by a deficiency of qualified applicants accessible to fill the open positions. The requirement for data researchers does not indicate easing back down in the coming years. LinkedIn recorded data researchers as one of the most encouraging occupations, alongside various data-science-related abilities as the most sought after by organizations. How Data Scientist At Big Companies Use Data Science? IT associations need to address their complex and extending data conditions to distinguish new worth sources, misuse openings, and develop or improve themselves, productively. Here, the integral factor for an association is ‘the thing that esteem they extricate from their data store utilizing investigation and how well they present it.’ Beneath, we show the absolute greatest and best organizations that are enlisting Data Scientists at first-rate pay rates. Google is by a long shot, the most significant organization that is on an enlisting binge for prepared Data Scientists. Since Google is generally determined by Data Science, Artificial Intelligence, and Machine Learning nowadays, it offers perhaps the best data science compensation to its representatives. Amazon is a worldwide online business and distributed computing monster that is procuring Data Scientists on a significant scale. They need Data Scientists to discover client outlook and improve the topographical reach of both online business and cloud areas, among different business-driven objectives. Data Science Life Cycle Data Revelation  The primary stage in the Data Science life cycle is data revelation for any Data Science issue. It incorporates approaches to finding data from different sources, which could be in an unstructured configuration like recordings or pictures or an organized arrangement like in content documents, or it could be from social database frameworks. Associations are likewise peeping into client web-based life data, and so forth, to comprehend client attitude better. Right now, as Data Scientists, our goal is to help the deals of Mr. X’s retail location. Here, factors influencing the deals could be: Store area Staff Working hours Advancements Item position Item valuing Contenders’ area and advancements, etc Remembering these components, we would create clearness on the data and get this data for our examination. Toward the finish of this stage, we would gather all data that relate to the components recorded previously. Data Preparation When the data revelation stage is finished, the following step is the data arrangement. It incorporates changing over divergent data into a typical configuration to work with it consistently. This procedure includes gathering clean data subsets and embedding appropriate defaults, and it can likewise include increasingly complex strategies like recognizing missing qualities by displaying, etc. The following stage is to coordinate. And further, make an end from the dataset for examination when the data cleaning is done. It includes the coordination of data which incorporates blending at least two tables of similar items, yet putting away extraordinary data, or condensing fields in a table utilizing accumulation. Mathematical Models Do you know, all Data Science ventures have specific numerical models driving them. These models are arranged. They are further worked by the Data Scientists to suit the particular need of the business association. It may include different zones of the digital space, including measurements, strategic and direct relapse, differential and indispensable analytics, and so forth. Different instruments and mechanical assembly utilized right now are

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25 Best Data Mining Tools in 2023

In this article, you are going to learn what data mining is, what are its benefits and what are the best data mining tools of 2023 that you must know about. So, if you are looking for the data mining tools, we hope you get the answer after reading our piece. What is Data Mining? Data mining is a method that businesses use to turn raw data into useful information. Businesses may understand more about their clients by using algorithms to scan trends in large batches of data, and create more effective marketing campaigns, raise revenue, and decrease costs. Data mining relies on efficient data collection, storage, as well as computer processing. To collect concrete patterns and trends, data mining includes investigating and evaluating large blocks of knowledge. It can be used in a variety of ways, such as selling the site, handling credit risk, identifying theft, screening spam messages, or even discerning consumer preferences or views. The method of collecting data breaks down into five phases. Secondly, companies are collecting data and putting it into their data stores. Next, they store and maintain the records, either in-house or cloud servers. Business analysts, management teams, and IT experts access the data to decide whether they want it to be structured. Data Mining applications evaluate data interactions as well as the trends depending on what consumers are looking at. For instance, a company may use data mining software to create knowledge groups. For example, consider a restaurant wanting to use data mining to assess when specific specials should be served. It looks at the knowledge it has obtained, then generates classes based on when and what clients are doing. In other instances, data miners may consider knowledge clusters based on logical connections or will look through correlations and temporal patterns to conclude consumer behavior trends. Benefits of Using Data Mining Tools Data Mining Tools Help to Identify Shopping Pattern Most of the time, one might encounter some form of unexpected problems when designing some shopping patterns. And it can be useful to resolve so find out the real purpose behind the data mining. One of the techniques of data mining is to learn all the knowledge about those buying habits. This method of data mining creates a space that decides all of the unforeseen buying habits. Such data mining can, therefore, be useful when detecting shopping habits. Website Optimization Can Be Done With The Help of Data Mining Tools It allows us to learn all sorts of information about the hidden components according to the purpose and interpretation of data mining. Then contributing to that data mining allows refining the platform further. Similarly, as most main website optimization considerations deal with information and analysis, this mining offers such details that data mining techniques can be used to improve website optimization.  Companies Use Data Mining Tools For Marketing Campaigns More notably, all data mining aspects are concerned with the exploration of knowledge and also in the way it is summarised. It is also useful for marketing campaigns, as it helps define the reaction of the consumer over certain items available in the market. Thus, through the marketing campaign, all the operating structure of these data mining processes recognizes the consumer reaction, which can execute benefits for the growth of the company. Data Mining Tools Help In Determining Customer Groups As explained earlier, data mining frameworks help to provide marketing campaign answers for customers. And it also includes information assistance when assessing classes of consumers. Through some kind of surveys, these new customer segments can be introduced, and these surveys are one of the ways of mining where various types of knowledge regarding unknown products and services are collected with the aid of data mining. Measure Profitability Factors The data mining system provides all manner of consumer answer details and customer category determinations. It can, therefore, be useful when calculating all of the profitable business considerations. As these forms of data mining operating conditions, one can better understand the actual calculation of the company’s productivity. Moreover, these methods in data mining discern critical factors between the market components’ profit and loss. Data mining is finding secret, real, and all conceivable useful correlations in data sets of large sizes. Data Mining is a technique that helps you find unsuspected relationships among the company, which gains the data. Data Mining Tools There are many useful Data Mining tools available.  The following is a compiled collection of top handpicked Data Mining tools with their prominent features. The reference list includes both open source and commercial resources. 1. SAS Data Mining Tools The program of Statistical Analysis is a result of SAS. It was created for data management and analytics. It offers non-technical consumers with a streamlined UI. Features:  2. Teradata Teradata is a massively parallel distributed processing system developed to create large scale systems for data warehousing. Teradata can operate on Database server Unix / Linux / Windows. Features of this data mining tool: 3. R-programming | The Most Famous Data Mining Tools  R is a Mathematical Computer and Graphics language. It is also used for processing big data. It offers a wide array of statistical tests. Features: 4. BOARD The panel is a Toolkit for Handling Intelligence. It blends business intelligence and corporate performance management functions. Business intelligence and business analytics are provided in a single package. Features:  5. Dundas Data Mining Tool | Know All About It! Dundas is a data mining platform designed for the business that can be used to create and display virtual dashboards, reports, etc. Dundas BI can be installed as the organization’s primary data repository. Features:  6. Ineytsoft | Features & More! Intelligence type Data Mining technology from Inetsoft is a powerful forum for data mining and intelligence. It enables data to be processed quickly and flexibly from various sources. Features:  7. H2O, Data Mining Tools H2O is another outstanding Data Mining method for open-source software. It is used by cloud computing technology frameworks to do data analysis

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Search Engine Optimization Using Data Mining Approach | Become A Smarter Digital Marketer!

You have probably heard, used, and perhaps even overused those buzz words as you persuade consumers how to take their business to the next stage. The terms are more than just the effective aspect of your sales pitch. However, their apparent popularity is an indication that in pursuing digital marketing and search engine optimization, we have entered a new age. In this article, we are going to dive in to unpack the words, research their importance to SEO, and go over some best practices to say a data-driven SEO story. Defining Data Mining And Its Place In Business Decisions Big Data and data mining have become, to some degree, umbrella words that sum up a modern reality: now, all digital activities are both data moving and data induced behavior. Data mining activity is focused on evaluating vast knowledge sets to discover trends and values that can then be leveraged to generate improved efficiencies or new opportunities within an enterprise. Google Mapping, your path, uploading on Twitter, buying from Seamless, watching your Netflix favorites – all these behaviors cause new data sources. Further, these systems collect, interpret, and use to forecast your next breath, so the internet will anticipate your next appetite for sushi better than you can. Once the prerogative of computer scientists, quants, or model-risk researchers, the methods of data mining now get used by almost every sector or occupation that has access to large data sets. As a gold digger during the Klondike Gold Rush, the task is to wade across knowledge sources in pursuit of a little nugget of evidence that really can benefit you. Amazon transformed the way businesses incorporate big data storage and processing into their processes and DNA, providing automated data warehousing tools, clickstream analytics, fraud detection, recommendation engines, event-driven reporting. Their innovation paved the way for companies to audit their data access levels, as well as the marketing possibilities that access offers. Consumers of today are not only comfortable with having their online behavior recorded, but they want the organizations with which they communicate to automate such experiences by data mining. For many consumer-facing organizations, this predictive ability is now the Holy Grail, with the quality of their data analysis being a key component of maintaining a competitive edge in their industry. With so much information available to the companies, there’s no reason to focus on assumptions or reflex judgments. Internal stakeholders now have to band together not only to unravel patterns of data but also to advocate their path through bureaucratic hold-ups and into actionable status. Organizations need to harness their enhanced consumer understanding to drive customer service, product satisfaction, successful marketing, and ultimate growth. Optimizing The Relation Between SEO And Data Mining Search engines are the most consumer-oriented entity, with consumers having driven the development of the business model since the web search first launched. Google’s goal, Bing, and their equivalents are there to provide meaningful answers to their customers. It is because, like any other company, they need to sustain a competitive model to keep traffic running, which in their case depends on driving traffic to the most relevant information at the exact moment when consumers need it to make a decision. Google dubs the zero moments of truth (ZMOT) on this point. Unsurprisingly, this business model has a sudden impact on how we treat search engine optimization as digital marketers, as well as also on how we interpret the data from analytics platforms. Data Mining SEO operation can be described as reviewing large data sets to identify new traffic patterns and to uncover possibilities for niches. These niche trends then get leveraged to market a service or product to a user segment in a better way. Abnormalities that you want to look for include traffic sources, simple and long-tail keywords that drive people to your site, and trends in traffic over time. For example, growth year-over-year, seasonality, and how all these factors relate to the traffic sources. Having revealed overarching patterns from large data sets, you need to adjust your SEO approach to say the true story based on the findings. Quality data mining can open up a wealth of possibilities for storytelling, but it’s not always a good thing to have all those options. To further set yourself up for success, make sure that you have set up key performance indicators (KPIs) to benchmark your performance against goals that matter to your customers and that remain relevant to the organic acquisition realm. Then make sure you monitor your progress as well as revising strategy consistently when it does not seem to be measuring up. While researching and posting on Google Analytics, stay away from bi-weekly duration data, or even month-over-month analysis. Unless you want to calculate the short-term effect of an on-page shift or determine that seasonality is at risk, you should always look at the larger picture–and, therefore, the more significant timeline. That is when the data gets large enough to be useful as well as actionable. How Can You Get Help From Data Mining With SEO (Search Engine Optimization)? That applies most in Big Data mining is what follows after in SEO and business analytics: to increase ROI by using smart data.  If you have been thinking about how to achieve that goal but have not yet found a satisfactory answer, then it is high time to get in contact with experienced data miners. One of the Search Engine Optimization strategies that have proved to work well in the past has been allowing other websites to connect to the material of another website. It indicated that Google had a better ranking of the site whose advertising was linked to high-quality content. Google recently appears to be using fear to combat this technique. They want to make sure, according to Google, that websites with poor content, but use spam links to rank high in the SERP, no longer rank highly. High-quality link building techniques, such as guest blogging, should be used. You may want to consider

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