6 Business Benefits of Outsourcing Your AI Projects

The biggest dilemma for a business is to choose between building an in-house team and outsourcing the AI project to an offshore expert. We’ll discuss the benefits of outsourcing in detail to understand why it is the preferred choice for most enterprises.  Artificial intelligence has been a game-changer for many small, medium, and large enterprises. It has made digital transformation possible by helping businesses use data to derive actionable insights and make better decisions. AI can be used in different verticals in the organization. For example, AI applications help the sales team with demand forecasting and market trend predictions. Artificial intelligence is used by the human resource department for recruitment and tracking employee performance. AI has a role in supply chain management, finance, transportation, and customer service.  According to Gartner, organizations using AI for customer service are expected to see a 25% growth in customer satisfaction by 2023. More than 90% of the leading business organizations are investing in AI. In fact, 86% of the CEOs considered AI the mainstream technology in their offices as of 2021.  This raises the question of whether you need to hire an in-house team for AI projects or outsource the responsibilities to offshore AI developers in India. Outsourcing has its benefits and is a preferred choice for many businesses.  Small and medium-sized businesses outsource to avoid infrastructure and maintenance costs. Large enterprises outsource to increase employee productivity. Around 70% of the projects are outsourced to reduce the budget. Flexibility is the second next reason for outsourcing. Read on to understand how artificial intelligence can help your business and why outsourcing AI projects is beneficial. Importance of Artificial Intelligence  Artificial intelligence is becoming an imperative part of several businesses because of the range of advantages it offers. How you use AI within the business determines the results. Here are a few important areas where AI is prominently used by leading organizations.  Automation  Automation is the process of using technology to complete repetitive tasks that are otherwise handled by employees. It reduced workload to save time and energy. It reduces the risk of human error and delivers more consistent results in less time. Machine learning algorithms are used for automation in various industries like healthcare, manufacturing, retail, entertainment, etc.  Market and Customer Research  The sales team has a lot of responsibility. It has to predict the market trends, understand customer behavior, identify the right time for the promotional campaigns, and align everything with the business goals. AI application will simplify their job while empowering them to use the insights to plan marketing strategies more effectively.  Logistics  Inventory management, warehouse management, and aligning the movement of stock from the warehouse to the stores or transporting it to the distributors require constant coordination with internal and external teams. A small miscommunication at any stage of the supply chain can lead to delays and losses. Artificial intelligence prevents this by predicting delays in advance and sending automated alerts to supervisors.  Unlock the Power of Data  Data is available in abundance in today’s world. Enterprises can gather data from several external sources. But how you use the data determines the success of your business. You need the right tools to clean, structure, process, and analyze data to get accurate insights. AI streamlines this process and also provides insights in real-time. You can use the latest data from the market to make decisions instead of relying on outdated information.  Competitive Advantage  Using AI tools will give you an edge over other businesses in the same industry. It helps small and medium-sized businesses establish themselves in the market despite the strong presence of global enterprises. Artificial intelligence is ‘the key’ you need to survive competition, capture markets, and expand your customer base.  Disadvantages of In-House AI Team for AI Projects  Having learned how AI can help your business, you should decide between building an in-house team and outsourcing the projects to AI consulting companies in the market. It might appear tempting to start from scratch and develop an in-house team so that you will always have someone working on the AI tools.   However, you should consider the following aspects before going ahead with a permanent team within the enterprise.  Shortage of Skilled Professionals  Despite the increasing demand for AI and ML engineers, data scientists, etc., there aren’t enough professionals to bridge the gap between demand and supply. In such instances, hiring talented and experienced professionals in a short time is almost impossible unless you are a leading global organization. Furthermore, there’s no guarantee that the professionals will stay with your business. They can move to another company if they get a better offer. This means you have to start the recruitment process once again. It will lead to more delays and cause a waste of valuable resources.  Cost Intensive  The cost of having an in-house team doesn’t end with recruitment. You have to pay salaries and provide the necessary infrastructure and resources for the team to work on the AI projects. You also have to deal with the cost inflations due to additional requirements/purchases, pay hikes, and losses if the project is not successful.  An in-house team is expensive for a business unless you have the budget to sustain an R&D department and invest millions into it regularly. Machine learning outsourcing will cost less and deliver faster results.  Involvement from Different Departments The in-house cannot work in isolation. It needs input from other departments and should have team members for the same. However, not every business has enough manpower to spare employees from one department to work in another. Asking the employee to handle everything will lead to stress and decreased productivity.  Access to Quality Data for Training Models  Quality data is the key to accurate AI models. The algorithm will deliver results based on the input data you provide. But where does quality data come from? Data available online cannot be directly used to train models. It needs to be cleaned, formatted, structured, and processed before being fed into the model. The in-house team will spend most of the

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Business Intelligence Consultants: How to Choose the Right One

In recent years, Business intelligence has become an integral part of many businesses. By analyzing data and trends, businesses are able to make more informed decisions that can lead to increased profits and efficiency. Business intelligence can be used to track customer behavior, understand how products are selling, and predict future needs. The Business Intelligence market size globally is expected to grow to USD 33.3 billion by 2025, which is at 7.6% CAGR. Rising investment in analytics, rising demand for dashboards for data visualization increased focus on digital transformation, and growing adoption of cloud and data generation are all reasons that are expected to contribute to the growth of the business intelligence market. So why is BI so important? Because it can give companies a competitive edge. By providing managers and decision-makers with timely, accurate information about their customers, operations, and financial performance, BI can help them make better-informed decisions that improve overall results. How Does BI or Business Intelligence Work? Business intelligence (BI) is a process that uses technology to convert data into actionable insights. These systems are used to identify trends, spot business problems, and track performance. They can also be used to predict future events and trends. BI tools include data visualization, dashboards, and reporting. This method of presentation is usually through simple and easy-to-read reports, as well as pictographs and infographics for understanding in a single glance.  It is the easiest way to gain insight into business performance and collect all relevant information quickly regarding any queries or questions. Some BI software also offers the option of a selective display so that the viewer only sees the information pertinent to their needs. Trend analysis becomes a breeze with interactive BI reports. Since these trends become easy to recognize and understand, businesses can focus on increasing revenues and finding more business expansion opportunities. It even assists in making wise business decisions and is an overall sound investment for any business, big or small.   Business Intelligence Tools Business intelligence tools are used to collect, store, and analyze data to help businesses make better decisions. These tools can help businesses track and understand their customers, competitors, and markets. BI tools can also help businesses improve their operations and strategies. There are leading BI tools and their benefits that a company can achieve by utilizing the software –  Why You Must Consider BI Consultant or Consulting Services If you are considering Business Intelligence (BI) for your company, you should strongly consider using a BI consultant or consulting service. Here are four compelling reasons why:  1. Expertise and Objectivity A good BI consultant will have a deep understanding of the BI landscape and be able to objectively evaluate your company’s specific needs. They can help you select the best BI platform and tools for your business. 2. Streamlined Implementation A consultant can help you plan, implement and integrate a BI system into your business. A good consultant will also be able to provide training to your IT teams on how to use the BI tools. 3. 360-degree View A consultant can help you get a 360-degree view of your company, including how to optimize business processes, how to measure the efficiency of key business operations, and how to identify new opportunities for growth. 4. Value Add A consultant can help you identify the potential value of BI in your business. They will also help you to design and implement a pilot project, which will help you measure success and ROI. 5. Strategic Thinking A BI consultant will help you to set a strategic direction for your company. This is crucial for success. For example, if you want to grow in the Asian markets, then a BI consultant can help you determine the best way to do so. They will also help you know the feasibility of such markets. 6. Data Quality A BI consultant can help you clean, integrate and analyze data from various sources in your company. What Are the Responsibilities of BI Consultants? BI consultants are responsible for helping businesses to collect, store, and analyze their data. They work with businesses to identify their specific needs and then craft a solution that will help them to achieve their goals. Like, in the case of a novice business that has not yet implemented Business Intelligence, a consultant will help you choose the right BI tools and technology depending on the specific business requirements. They help the company determine a BI roadmap and set some BI goals and work with the management to achieve them.  On the other hand, if businesses already have BI software set in place, the work of a BI consultant is to review the current structures and strategies, along with coordinating with the technical team to identify trends and predict significant events for better business decision-making. They set up systems to consistently monitor progress and change parameters, thus helping organizations grow.  What Are the Qualities of a BI Consultant? BI Consultants have a set of niche skills that include but are not limited to extraction, analysis, and debate. These consultants are flexible, creative, and detail-oriented while foreseeing the future and overseeing the big picture.  What Are the Required Technical Skills for a BI Consultant? They are adept at business strategy and computer technology. They must have an aptitude for Data Analysis, Problem Solving, Communication, Business Intelligence, Basic Knowledge of SQL, and intricate knowledge of the sphere of the business itself, such as healthcare, food industry, etc.  The Data Science Skills Required Include There Are Several Business Skills Involved As Well What Are Some of the Responsibilities of a Business Intelligence Consultant? What Are Some Mistakes To Avoid While Partnering With a BI Consultant? Since the job of a BI consultant is critically fundamental, choosing the right company or partner for your requirement is necessary. Before doing so, here are a few factors to keep in mind- Top Business Intelligence Consulting Firms While there are many business intelligence consulting firms, it is better to entrust your company

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HireLakeAI vs. Affinda vs. Sovren: Find the Best Resume Parsing Software

Parsing resumes using online AI-based software saves time, money, and resources for your business. Here, we’ll discuss the factors to consider when selecting an AI tool for recruiters and compare the features of HireLakeAI, Affinda, and Sovren resuming parsing software applications. Recruiters always have their hands full with tons of resumes. Accessing talent has become easy thanks to several online job postings and professional networking sites. However, the actual task lies in filtering the resumes to identify the best fit for the job. This takes hours and days as the recruiters go through hundreds and thousands of resumes. Close to 70% of recruiters agree that new recruiting technology is necessary to improve the performance of the hiring process. Another report shows that the top candidates are available in the open job market for just ten days. If you don’t reach out to them during this window, chances are high that you lose a potentially good candidate for the job. So how do you further speed up the process? Online resume parsing is the solution. Fortunately, there are more than 50 resume parsing software applications in the market. All tools promise to reduce workload and speed up the process. But unfortunately, not every resume parsing software is efficient or does what it claims. In this article, we compare the top three resume parsing tools- HireLakeAI, Affinda, and Sovren in order to assist you in choosing the best recruiting software for your company. To begin with, let’s take a closer look at the definition of resume parsing and the factors to consider when investing in a resume parser for your business. 📄 What Is Resume Parsing? Resume parsing is the process of using software to extract data from resumes and store it in a structured format. This format is usually in the form of tables and is easily understood by the HR team. It helps recruiters use keywords to search for resumes that match the job description and shortlist the best fit. It speeds up the initial screening process by using automated software. There are many free resume parsing software tools, while the premium ones are available for free and on a subscription basis. Resume parsing is mainly of three types- Statistical Parsing: This method uses numerical phrases like addresses, experience, work timelines, etc., for sorting is considered highly accurate. Grammar-Based Parsing: This method uses grammar rules to identify resumes with certain core characteristics. It may not be accurate as candidates use different writing styles. Keyword-Based Parsing: This is the most common and simplest method, where resumes are sorted based on specific keywords. Recruiters use skills and core competencies as keywords in this method. 💡 What to Look for in a Resume Parser? Resume parsing software has to be fast, accurate, and efficient. But what features sets the best parser at the top of the table? Here’s what you should consider when choosing resume parsing software. Multiple Format Support Resumes are posted in different formats like MS Word, PDF, HTML, RTF, JPEG, etc. Recruiters collect resume datas in CSV sheets or download video resumes from emails or directly from third-party platforms. The software should be compatible with a range of formats and accept input data with different extensions. Make sure to double-check that the resume parser can read the formats used by your HR team. Artificial Intelligence Keyword-based and grammar identification resume parsing methods are not enough. Today’s resume parsing software has to have a human-like understanding to deliver efficient results. Artificial intelligence and NLP (natural language processing) algorithms are fine-tuned to read resumes with greater accuracy and precision. You need software that uses advanced algorithms to deliver the results. Third-Party Integration There’s no point in using a resume parser if it doesn’t work with your existing systems. The software has to be integrated with your HRMS and ATS without issues. The best resume parsing tool is the one that is compatible with integration with all major HRMS. Taxonomies Library Can the software identify multiple versions of the same company name or the short forms of a degree/ course? The library has to be vast and regularly updated to ensure that the latest companies are added to the list and resumes in different word forms are automatically considered and shortlisted. Unbiased and Accurate Diversity and inclusivity are being given additional prominence in the current scenario. Is the software’s algorithm capable of being unbiased based on religion, ethnicity, race, nationality, physical limitations, etc.? Can certain fields be turned off to minimize bias and shortlist resumes based on merit? Processing Speed Since speed is the priority, test the software to determine the duration it takes to automatically collect data from resumes, auto-fill the sheets, and create tables for your recruiter to proceed to the next step. Try the free/ trial versions of different tools to compare speed and accuracy. Pricing and Budget You can find quite a few free resume parser software tools in the market. However, an established business or recruiting agency cannot rely on free software forever. You should choose a resume parser that provides your desired features and fits within your budget. Opt for the ones that offer customized pricing for convenience. 🥊 HireLakeAI vs. Affinda vs. Sovren Let’s compare the features of the top three resume parser software tools and their features. 🔶 Overview Using a resume parser, you can automatically collect data from resumes and present it in an easily readable format. The software can offer additional features and services that further assist recruiters in streamlining the hiring process. HireLakeAI HireLakeAI is a comprehensive solution to screen, evaluate, and filter resumes in bulk. Developed by DataToBiz, it is the latest resume parser tool in the market with an advanced AI algorithm. HireLakeAI offers solutions like Resume Parsing and Job Description Matching. The vision is to add more modules to make it a one-stop solution to handle all stages of recruiting. HireLakeAI aims to create a seamless and hassle-free recruiting system in your business, that too within the budget. Affinda Affinda is developed by a Melbourne-based company with global clients. Affinda resume parser is an

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A Definitive Guide To Machine Learning Consulting for 2023

Planning to integrate artificial intelligence and machine learning into your daily business operations?  If yes, then what’s the best way to go about it?  If you are confused about how to leverage machine learning or artificial intelligence capabilities to scale your business, then you need an expert to guide you. This guide will walk you through the whole process of ML and AI consulting and whether it’s the right choice for you.  What is Machine Learning Consulting? The goal of AI/ML consulting is to help businesses improve their performance and productivity by using AI technologies. The main focus of artificial intelligence is to exploit machine learning to increase companies’ value in an environment of rapid change and intense competition.  AI consulting companies help their clients design and develop advanced AI-based products and solutions by developing AI-driven algorithms and machine learning tools.  In order to ensure maximum results, it is essential to design, implement, and integrate Artificial Intelligence into the business environment of various organizations.  The demand for artificial intelligence-driven products and systems is high at present. But, there are still limited options available. The services offered by AI consultants include: What Does A Machine Learning Consulting Firm Do? There are many reasons why companies need consultants, such as a lack of expertise, existing commitments, government regulations, or the need for an unbiased perspective.  While consulting firms perform varying roles and responsibilities depending on the industry and the company’s needs, their primary goal is to improve business efficiency and profitability. Consulting firms begin their work with the following tasks. The Process Starts With Knowing What Your Business Needs Every consulting project begins with some business need. Business requirements are still one of the biggest reasons for the failure of consulting and software projects. It can be choosing the right location to install your next branch or selecting the right audience.  There is a particular issue with ML consulting, as it is at the intersection of consulting and software development. Setting Up the Team and the Process Machine learning is not necessary for all business problems. Problems that cannot get reduced to a set of rules make sense for machine learning and other approaches.  The performance and maintenance of rule-based systems are better than machine learning when the rules are well known. ML can be useful for solving particular problems, but the project team, stakeholders, and high-level targets needed get decided. The Process of Collecting and Exploring Data It is relatively simple to do this if the company already has the data. Else consultant has to collect all the data to understand the business problem.  Afterward, consultants must verify that data labels are accurate and do not conflict with each other. If data isn’t readily available, you can also have options like leveraging online data, paying for labeling, or using novel machine learning techniques such as one-shot learning. The Model Development Process. Developing a machine learning model that performs more accurately requires thousands of experiments.   It is a trial-and-error process involving the latest research, a thorough understanding of business dynamics, and data analysis skills. For accuracy testing, all models get tested against the same data set. Full-Stack Application Development The model development and integration for production require additional time and resources.  In most cases, ML models get embedded into APIs which makes it easy to integrate them into applications.  Application development, which implements the ML model and integrates it into decision-making processes, can be more challenging than modeling itself.  When developing applications, integrating them into existing enterprise systems is necessary, which requires an external developer’s involvement. To put the model into action, it is necessary to address the scalability and data security issues. Why Does Your Business Need Machine Learning Consulting Services? If you are just getting started with machine learning, it can be difficult to differentiate a top consulting firm from an upstart. You should consider these points before working with a consulting firm. They Are A Time Saver McKinsey estimates that artificial intelligence and machine learning will replace 30% of current jobs by 2030. Machine learning service providers can save you a lot of time. You can find running a machine learning campaign difficult if you don’t have any AI training.  The advantage of hiring a highly regarded machine learning company is that they have expertise in all aspects of machine learning and can launch your campaign quickly. Experienced consultants are familiar with best practices. For instance, A lean consultant can quickly pinpoint inefficiencies in a company’s manufacturing process.  Business owners do not need to create something from scratch or waste valuable time on tasks an expert contractor can complete. They Are Result Driven  A good machine learning firm will always take the time to understand your business, your goals, and your needs before creating a machine learning campaign for you.  In this way, they will design and implement a machine-learning strategy that corresponds to your business goals and provides you with the desired outcomes. It Is Financially Profitable It is important to have a lot of data to make good decisions when creating a machine learning campaign. In order to hire a machine learning consultant, you will need access to various programs and software, which can cost quite a bit over time. When you hire a machine learning consultant, you will have access to many different programs and software, which can be expensive over time. Additionally,  you pay only for the services that you need, when you need them.  An ML consultant with similar expertise can save hundreds of dollars compared to hiring salaried employees. Besides, consultants with expertise in many areas can identify pinpoints where you are currently overspending and suggest ways to cut it.  Providing Support For Data Preparation The data preparation and collection are essential for modeling and training ML, as well as the foundation for deep learning and AI.  Also, for combining data sets, renormalizing data, and preparing your database for successful implementation, a good ML consultancy can provide you with helpful advice.  Consultants

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How Can Data Analytics Enhance Investment Decisions to Maximize Private Equity Returns?

As the data coming from company portfolios becomes vast and complex, a standard approach to private equity (PE) business analysis crumbles. It is no longer sufficient to get precise and data-rich insights to measure, track, and grow the performance of your company portfolios. As technology keeps evolving and businesses and their underlying operational models become more complex, making profitable and smart private equity investment decisions becomes even more daunting. Some key questions arise: The other key pain points that need a data-driven remedy include operational efficiency, increasing market valuation, performing due diligence, etc. Furthermore, the ongoing pandemic has also led to a paradigm shift in the PE investment sector and investors are more positive about potential portfolio companies that can adapt to the market changes as well as stay on the growth path. All this compiles into a highly relevant and massive use case umbrella for data analytics or private equity analytics. Combine that with automation and digital transformation and we have a very thought-provoking discussion topic: the rise, challenges, and applications of analytics and automation in the PE sector. Let’s explore the various ways analytics can help both companies as well as investors to make well-informed investment decisions. Data Analytics For Private Equity Investment Decisions: An Introduction A recent study published by Deloitte identified the huge potential and different applications data analytics offers in private equity investment decision-making. The study outlined seven key points that make data analytics extremely relevant and crucial for investment decision-making. Data and analytics are the key drivers of success when it comes to tech startups and organizations having their roots or branches in IT. These companies are spurring worldwide disruptions and giving birth to innovative and digital business models. Some common examples include Rocket Loans and Quicken Loans, where approvals for mortgages and loans are given based on simple and automated processes. These companies are more relevant for data analytics-based investment decisions, as data is readily available in the ready-to-process form. However, even brick-and-mortar establishments can respond to technology-enabled disruptors. Such is the power of data analytics and automation! Key Benefits Offered In Decision-Making For Private Equity Investments Automation and data analytics can offer three major benefits for the digital transformation of private equity investments (PEIs), which are discussed in detail in this section of the article. 1. Tracking Project Profitability Investors can establish a preferred vendor approach by adopting a hybrid model for investment decision-making. This hybrid model can help investors develop better governance policies and performance expectations and calculate better scale efficiencies. Hence, the investors can see the projects running behind schedule, or the companies running over budget, or identify the other key problems that are leading to unprofitable businesses. Thus, the investors can change, manage, or increase investments in their portfolio companies in a better and data-driven manner. 2. Ensuring Transparency Of Cash Flows Volatile cash flows can not only amount to outstanding loans or debts, but they can also drag massive investments down as they crash or become a steady drain of resources. Using predictive analytics in association with quantitative and qualitative studies done specifically on portfolio companies can lead to highly consequential results or data insights. Investors can see how well their investments are going to be used or are going to spur results in the long run. They can conduct these studies again after some time and compare the results with the previous analysis to identify any hidden patterns or discrepancies in the reports. Such comparative studies will also help them in understanding the various points of concern that arise over time and whether they can escalate into business risks or not. 3. Comparative Analysis Almost all PE investors are prone to the common occurrence, where the same brand is yielding different returns in different locations. While some branches tend to bring higher revenues, others fail to even stay afloat. In such cases, data analytics can be of immense importance as it can help in collecting, processing, and evaluating multiple types of data, such as: Thus, data analytics is of extreme importance and can have many amazing and crucial applications in PEI decision-making for better returns. Up next, we discuss the three major trends in PEIs that are all set to transform the sector and reinforce the hold and relevance of data analytics in the sector. Private Equity Investment Decision-Making: Why Data Analytics? Below, we discuss the various reasons PE stakeholders, right from PE firms to investors, are looking for data analytics-based solutions for key decision-making and strategizing. 1. The Role Of PE CFOs Is Evolving Given the massive change spurred by the COVID-induced meltdown, the roles and responsibilities of PE CFOs have seen a major revision and evolution. CFOs now need to be more tactical, technical, strategic, and empowered with insights stemming from data and analytics rather than hunches or business acumen. Recent studies done by E&Y suggest that 78% of CFOs are looking for raising larger investments that have led to a serious race for assets among investors. So, PE firms are looking at ways to make more strategic decisions, in terms of capturing investor allocations for alternative investments, such as: All this requires a heavily data-driven mindset and resourcefulness that screams for data analytics-based offerings such as software tools, analytics apps, or custom SaaS offerings. 2. The Talent Profile Of PE Firms Is Also Evolving Amid the increasing skill gap and the Great Resignation, and COVID-wave scares, companies are also focusing on increasing workplace engagement and employee motivation to retain and enhance their talent profiles. Studies reveal that 73% of PE managers focus on employee productivity and engagement for talent management. Talent management activities and attitude require an immersive understanding of the various factors that influence the entry and exit of an employee, such as: Such specific decision-making further creates the grounds for embracing data analytics to gain visibility into the granular level of organizational operations and processes. 3. Process Improvement And Innovation Within a short span of just four or five years, the digital transformation and innovation vision of

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Why Should You Hire A Data Engineering Consultant – Full Guide

Data engineering is the process of establishing the data architecture to perform data analytics and create visualization reports. Data engineers offer a range of services for enterprises to use real-time insights for decision-making. We’ll discuss the need to hire a data engineering consulting company for a business.   Businesses need a competitive advantage in today’s market. Technology plays a major part in empowering an organization to be ready for market opportunities. Data is another vital and integral part of a business. Combining both aspects makes an enterprise capable of using data to make better decisions.   However, raw data cannot be of much use to end-users and decision-makers. It has to be cleaned, formatted, structured, and formatted to prepare for analytics and business intelligence. That’s the responsibility of data engineers and data engineering consultants. These are the ones who ‘engineer’ vast amounts of data and prepare it for data scientists to perform advanced analytics using AI and ML algorithms.  According to Zippa, there’s a high demand for Data Engineers in several parts of the US. From Silicon Valley in California to New York, Chicago, Virginia, Miami, Dallas, Washington, and many more, almost every US state has businesses looking for Data Engineers to help with digital transformation.  The Data Engineering market in India is also growing rapidly. Indian Data Engineering is expected to grow at a CAGR (compound annual growth rate) of 36.7% by 2027 and touch 86.9 billion USD. The banking and insurance sector has employed the highest share of data engineers in India at 37.7%, compared to other non-IT sectors.  Let’s find out why you should hire a data engineering consultant and what you need to consider when choosing a consulting company for your business needs.  What is Data Engineering?  Data engineering is a critical part of the data science hierarchy. It is the process of preparing and managing big data in an enterprise for data scientists to perform analytics and drive actionable insights. Data engineering ensures that the data used for business intelligence is of high quality and delivers accurate results.  Data engineering involves the building of data models and connecting them to data pipelines. It includes ETL (Extract, Transform, and Load) of big data to avoid redundancy and filter poor-quality data.  As the demand for business intelligence and actionable insights grows in the market, the need for qualified data engineers has also rocketed. According to a survey conducted by Interview Query, the demand for data engineers grew by around 40% in 2020. Data engineering is the foundation on which date scientists build their AI and ML models.  What Does a Data Consultant Do?  A data consultant creates the base work to collect data and process it for analytics to drive insights. They make use of the latest tools and technologies to clean, format, and structure large volumes of data in a quick time. The main job of a data engineering consultant is to identify the right technologies, processes, and strategies to manage data in an enterprise.  In 2017, Gartner reported that 85% of big data projects don’t succeed. Two years later, in 2019, Gartner reported that 80% of the projects continued to fail. The lack of proper big data processing infrastructure is considered the reason for this failure. A data engineer or a data engineering consulting company works to set up and streamline the infrastructure. They use a plethora of technical skills to achieve this.  SQL, programming languages (python, java, etc.), cloud computing, etc., are just a few things a data engineer should work with. Data engineering is further classified into three types- generalist, database-centric, and pipeline centric.  While a generalist data engineer works with startups and small enterprises, the other two deal with mid-sized and large enterprises with multiple data warehouses and complex data flow. You can hire a data engineering consultant from a consulting company or a solution provider to handle data pipelines and infrastructure within your enterprise. They work with various tools like:  What Do Engineering Consulting Firms Do?  Data engineering firms offer end-to-end solutions to collect, store, process, analyze, visualize, and manage big data in an organization. The consulting firms provide one-stop solutions to adopt and implement the data-driven model in a business. They help the employees and top management understand how to use data and insights in real-time to make faster and better decisions.  The role of data engineers has changed over the years. The companies no longer limit themselves to providing ETL services. Managing and scaling the data infrastructure is also a job for data engineers. They support data teams in your enterprise to improve performance and efficiency.  Data engineer consulting firms constantly work on improving, scaling, and customizing the infrastructure to suit your business needs. They help reduce the cost of projects in the long run by creating a reliable and strong foundation to build new models without starting from scratch every time. The firms also offer off-the-shelf solutions to help enterprises develop their own algorithms and data flows. Airbnb and Netflix are two examples of companies that took this approach.  In short, data engineering consulting companies have a versatile role to play in the current scenario. They provide primary services for businesses that rely on available technology and scalability (plus management) services for organizations that have self-servicing internal models. Either way, the data engineer is a must-have on the team.  Why Hire Data Engineering Companies?  You need a data engineer to establish the groundwork for big data analytics. Business intelligence is used at all levels in an enterprise and in different departments. From production to quality testing to accounting, recruiting, logistics, sales, marketing, and customer service, insights derived from data analytics are used to make the right decisions for your business.  But why do you need a data engineering company? Why can’t you simply hire a data engineer on the payroll? The answer is simple. A consulting company has a team of qualified and experienced data engineers working together to offer tailor-made solutions for your business.  The increasing demand for data engineers makes it difficult to find

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Azure Data Engineering Services : Adapt to Changing Data Needs

AWS and Azure data engineering services are offered by top data engineering services companies to build, develop, deploy, and maintain a customized IT infrastructure on the cloud. Know more about them! Businesses today can collect enormous amounts of data. Analytics, traffic monitoring, and everything else depend on data. For handling such big data, businesses need an infrastructure that trains their personnel to sort and analyze this amount of data. That’s where data engineering services come into action. AWS and Azure data engineering services are offered by top data engineering services companies to build, develop, deploy, and maintain a customized IT infrastructure on the cloud. Businesses can partner with the service providers to streamline their data, systems, and processes to adopt the data-driven decision-making model.  But what does data engineering mean? What is the role of a data engineer? Let’s find out. What are Azure Data Engineering Services? The term data engineering is the process of creating systems for almost all industries that collect and manage information.  In other words, data engineering is the process of sourcing, transforming, and managing data from different sources.  Data engineers mine data for insights. Their skill set allows them to construct architectures for extracting value from data, which are then applied to benefit a company. As a result, data is accessible and useful.  An essential aspect of data engineering is the practical use of collected and analyzed data.   Thus, data engineering uses different methods to gather and authenticate data, ranging from data integration tools to artificial intelligence.  The same applies to data engineering services; sophisticated processing systems get designed and monitored to put found data in realistic situations.  Essential Data Engineer Skill Set for Azure Data Engineering Services SQL A data engineer must be proficient in SQL as a foundational skill. The SQL language is essential for managing RDBMS (relational database management system).  To achieve this, you will have to go through practicing many queries. To learn SQL, you don’t need to memorize a query. Learning how to optimize queries is crucial. Data Warehousing Understanding how to build and use a data warehouse is an essential skill. Using data warehouses, data engineers can collect unstructured data from several sources. After that, the information gets compared and evaluated to improve a company’s efficiency. Data Architecture For businesses to build complicated database systems, data engineers must have the necessary knowledge. Data engineering services & solutions include data architecture as a core offering. The term refers to data operations, which handle data in motion, data in rest, and datasets, with the relationship between applications and data. Programming Skill It is essential to improve your programming skills if you want to link your databases and work with different types of applications such as web, mobile, desktop, and IoT.  To achieve this, you will need to learn a language that is suitable for enterprise use, such as Java or C#. Both are useful as part of open-source tech stacks, and the latter is helpful in Microsoft-based stacks for data engineering.  Python and R, however, are the most important ones. Python can be used for various data-related operations with an advanced amount of knowledge.  Data Analysis  Data science is mostly associated with machine learning. A data engineer will be in a better position to excel if they understand how data can be used to analyze and model data. Having an understanding of the basic concepts will help you to better understand data scientists’ needs.  Who are Azure Data Engineering Services Experts? With the help of data engineers, companies can replace their in-house data infrastructure with a robust information pipeline and transform their data into insights for business analytics.  Across industries and businesses, data engineering services are now gaining popularity as a tool to extract valuable data.  Not just Microsoft Azure, but data engineering services in AWS are also in high demand. In fact, Azure, AWS, and Google Cloud form the top three cloud platforms in the global market.  With these services, you can ensure that valid data will be available at the right time, in the appropriate format, and in the right place. Azure Data Engineering Services: Roles and Responsibilities The following are some of the roles and responsibilities Data Engineers need to perform: Work on Data Architecture Data architects use a systematic approach in planning, creating, and maintaining data architectures while aligning them with business needs.  Collect Data Getting the appropriate data from valid sources is the first step in building a database. The storing process of optimized data begins after data engineers plan a set of dataset processes.  Conduct Research Data engineers conduct research in the industry to find a solution to a business problem.  Improve Skills Theoretical database concepts aren’t enough for data engineers. They must have the knowledge and expertise necessary for successful development. Furthermore, they need to keep up with various machine-learning algorithms. They should have expertise in analytics tools like Tableau, Knime, and Apache Spark. These tools allow businesses to generate valuable business insights. Furthermore, a data engineer should also offer big data engineering services to handle vast amounts of data in real-time.  Create Models and Identify Patterns In order to extract historical insights from data, data engineers use a descriptive data model.  They use forecasting techniques to gain actionable insights about the future while developing a predictive model. Additionally, they provide recommendations for different outcomes using their prescriptive model.  Why Do Modern Businesses Need Azure Data Engineering Services? Data Science tends to be the only way organizations can gain meaningful insights from their data.  Companies can, however, build large, maintainable data reservoirs through Data Engineering.  Data Science and Data Analytics can obtain useful results from these design data processes that are scalable.  In order to enhance the efficiency and effectiveness of data analytics, accurate and reliable insights must be provided.  Using AI and ML, companies are able to achieve higher efficiency, become agile, tap into new market opportunities, launch new products faster, and provide better service to their customers.  Yet, according to an MIT Tech Review survey, 48% of

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6 Reasons To Hire a Machine Learning Developer

Artificial intelligence and Machine Learning are undeniably transforming and integrating themselves into every aspect of our everyday lives, from entertainment and social interactions to the workplace. Artificial intelligence, according to experts, will advance a company into the next generation, revolutionize data management, and improve the whole consumer experience. There are many business applications for AI and machine learning, and as these applications become more widespread and sophisticated, we can only anticipate an expansion of this list.  6 Reasons to Hire a Machine Learning Developer Here are a few of the reasons you should hire a machine learning developer: 1. Supports Real-Time Decision Making Big data’s potential is expanding quickly, thus you must connect users and providers (buyers and sellers) instantly. Businesses must instead use data and analytics to quickly determine the best course of action based on the current situation. Your consumers will appreciate personalized services, which will increase profits. A machine learning developer helps businesses achieve real-time decision-making by drawing insightful conclusions from the data. 2. Enables Automation That Replaces Manual Efforts ML technology advances industrial automation by replacing manual jobs that incorporate unpredictable elements like changeable parameters, external causes, and internal system changes. Industrial automation in the 20th century focused on employing machines to reduce predictable and repetitive human tasks. An ML developer gives businesses the ability to make quick judgments and automate operations by creating predictive data models. One such is Adobe Target‘s Auto Target, a potent AI technology that employs machine learning to create automated tailored experiences. Personalization in Adobe Target is based on a set of experiences chosen by the marketer. 3. Effectively Address Security and Network Challenges Cyberattacks, network breaches, and other security flaws sometimes occur in real-time without warning, giving users little opportunity to take action. One of the biggest cyberattacks in history occurred in 2018 when a series of DDoS strikes targeted GitHub and Arbor Networks in the US. Any network intrusion must be promptly detected and stopped by enterprises in order to avoid service interruptions and data leakage. ML developers are capable of creating algorithms that are capable of tracking network activity to quickly identify anomalies so that preventative steps can be taken. Additionally, as ML algorithms take the place of manual investigation and analysis, they self-learn and adapt to change, which further enhances the condition of cyber-security. 4. Improves Marketing Results The branch of AI known as machine learning focuses on creating intelligent machines that can operate independently of human intervention. Additionally, a machine learning developer helps businesses save time and money in the marketing sector as well. In marketing initiatives, machine learning can be an amazing and potent multiplier. Constant messaging, trends and changes in consumer behavior, targeted marketing techniques, etc. are all made possible by machine learning engineers. Social media websites can be used to develop micro-marketing campaigns. 5. Manages Unstructured Data Large amounts of unstructured data can take a long time to clean up. Any organization that evolves over time gathers a lot of fresh information. For instance, if you are in the air freight forwarding business, you will need to process large amounts of data on services, rates, etc. within a short period of time in order to evaluate it and obtain insights about potential future patterns. Managing and removing massive information indexes is one of machine learning’s most important applications. The information is easier to process, obtain, and retrieve thanks to machine learning. 6. Increasing Customer Satisfaction Currently, machine learning is used to improve customer experience, which results in increased customer loyalty. It is done by looking at the customer’s buying habits. Many organizations all over the world already use this technology. According to research, 76 percent of business owners agreed that their profitability increased as a result of incorporating machine learning into their processes to understand client behavioral patterns. Google and many other large websites are using this technology. You may have observed that when using websites like YouTube, LinkedIn, etc., you frequently receive recommendations for various goods to buy or individuals to connect with. With the aid of machine learning, the ads and friend suggestions are handled. How To Hire A Machine Learning Engineer When hiring a machine learning engineer, who can be a valuable asset for your business, it is advised to exercise extreme caution if the company is investing in the development of artificial intelligence and machine learning. As a result, there are some essential factors to consider when hiring a machine learning engineer or specialist. What to take into consideration when you want to hire a machine learning developer is covered in full below. 1. Technical Skills For these professionals, full-fledged technical skill in coding and programming is essential in order to tackle such assignments. Here, you must check the applicant’s technical specialization and administer the coding test to confirm that she/he is capable of overseeing the creation of AI and ML models. Make sure you have qualified professionals to hire the best applicant so you can evaluate their technical talents. A machine learning developer should have knowledge of languages like python, frameworks like Pytorch or TensorFlow, algorithms like Scikit, and many others. Make sure you test all the necessary technical skills before hiring. 2. Interactive Communication Skills The ability to communicate with others, which is necessary for collaboration with colleagues, should be taken into account when trying to hire a machine learning engineer. Before beginning the project, AI and machine learning professionals should agree on a framework for communication with the company. Therefore, if you are concerned about a candidate’s communication skills, you should assess their capabilities to ensure effective communication. Wrapping Up Finding a suitable AI or machine learning professional would be difficult and time-consuming. To prevent confusion between data analysts, data scientists, and machine learning engineers, it provides basic stages of vacancy description with a precise set of responsibilities. In order to recruit a machine learning engineer that is an expert in his field, one must have a complete understanding of technology and competence. So, to

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Why Hire an AI Software Development Company?

Whether or not a business project is a success largely depends on how solid the technical team is. Today, AI development is faster than ever, pushing businesses to gain a competitive advantage.  Every project is unique and so are its requirements. A company must choose the most convenient and efficient model for AI software development, depending on the project’s scale, features, goals, and budgetary constraints. However, there is also a slew of factors that determine the success of a project.  Factors Responsible for Outsourcing AI Development Needs of a Company  Requirements Before you decide whether to outsource a project or handle it in-house, clearly assess the project goals. What are the features you are looking for in your project? What are the services you will require for the successful execution of the project? When are you planning to launch the project?  Next, you need to assess if an in-house team has all the necessary skills to fulfill your expectations from the project or if an external agency can provide you with the level of customization you require for the project. Budget  Maintaining a budget is one of the most crucial aspects of a business. So you need to figure out how much capital you can invest into developing your project. Next, calculate the expenditure required for in-house handling and outsourcing and choose the option that best fits your budget. The Results  It is one of the most crucial factors you need to consider to decide which of the two options is more viable for you. Compare the services that your in-house team will offer with an external agency. Understand the complete scope of work and the exact services you will be paying for. Now, the next question that might come into your mind is, “How do you ensure that your project idea is executed in the best possible manner? Do you hire an in-house software development team? Do you partner with an AI outsourcing agency?” Let us deep dive into the pros and cons of both these options so you can decide what suits your company the best. Advantages and Disadvantages of In-House AI Development In-house AI development means hiring team members within your organization and training them with the crucial IT skills and abilities needed to handle the project. Although this method is more cost-effective in the long run, it can seriously load the company’s budget in a shorter span.  Disadvantages of In-House AI Project Development Lack of Expertise  In-house AI developers demonstrate lesser expertise and the skill to develop AI-based ideas as compared to ones in an agency. They do not have an interdisciplinary perspective which is a prerequisite for creating meaningful models for making business decisions and getting faster results. Need for Resources Hiring an entire team is even more difficult than it sounds. As an organization, you must equip yourself with all the resources such as infrastructure, overhead, additional employee benefits, training fees, etc. making an in-house team an expensive affair. Time Crunch  Hiring an in-house AI development team is an extensive process. From evaluating potential team members to recognizing the skill gap of every member, training every member is a long process. If you run out of time to execute a project, it is natural to not go with this option. Advantages of In-House AI Project Development High level of Personalization By developing your own AI solution, you can remove all the unnecessary features and complicated settings and bring down costs just by sticking to the product requirement.  Ownership of Intellectual Property (IP) If your company invests in developing advanced technology like AI by building an in-house team, you can gain patent rights for the value you add in this field. It can give you an edge over your competitors in the industry. Advantages and Disadvantages of Outsourcing AI Project Development Outsourcing means your company partners with an external AI development agency and delegates the project execution job. Though outsourcing technical projects can be expensive, it is worth the investment. You get access to domain experts with a lot of experience working on similar projects. It is especially beneficial if you do not want to wait too long to launch your product in the market. Advantages of Outsourcing AI Project Development Instant Access to High-Quality Data Tons of high-quality data are needed for developing machine learning and building deep learning models. Even if your business has access to big data, working with such massive data requires a lot of experience and is time-consuming. Only an external AI development agency can bring such expertise to the table. Before starting any AI project development, proper data cleansing and preparation are prerequisites. An outsourcing agency can feed a Machine learning platform with high-quality data sets to leverage it to your advantage. Outsourcing Is a Remedy for Invisible Costs Outsourcing might be expensive, but you pay for the skills and expertise. But while building an in-house team, you pay in terms of both time and money for additional things apart from the skills and expertise. Everything is an additional cost to the company, from recruitment and software licenses to training and employee benefits to providing refreshments. Also, hiring people in technical fields such as AI is tough. A lot of time is needed to search, interview, recruit, and onboard new employees. Your company can save all of this time and capital just by partnering with an outsourcing AI agency. Scalability and Flexibility  If you are a fast-growing startup in a highly competitive industry, you need to scale dynamically to meet market needs. In such a situation, the in-house team” approach might not be helpful, especially in challenging, specialized projects. Also, if you are a small company, you probably do not have the resources needed to implement all the in-house services and systems. So to ensure that the quality of the final product is not compromised and to compete with the big players in the market, outsourcing is a better option.  You can achieve high levels of scalability and

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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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