Page 27 – DataToBiz

Pawan Chabra
November 21, 2022
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AWS Data Engineering Guide: Everything you need to know

Enterprises are increasingly moving towards cloud platforms to achieve business objectives and optimize their business operations including data management. Not only these services have transformed the game of managing data and applications, but many Cloud Services have also dished out brilliant user experience at an inexpensive cost with the addition of Data Analytics for research. It further leads to the simplification of processes, allowing organizations to focus more on business growth. Several Data Engineering processes have come into the picture for the seamless management of Cloud Services. Names like Google Cloud, AWS, and Microsoft Azure have designed proper Cloud Infrastructure for organizations and individuals. To provide a seamless experience to users, these Cloud Platforms use several solutions such as Data Migration, Data Engineering, and Data Analytics. AWS Data Engineering encompasses one of the core elements in the AWS data platforms which provide a complete solution to users. It also manages data pipelines, transfers, and storage. For instance, to transform data into a uniform schema, AWS Engineering utilizes AWS Glue to give out all the functionalities. Moreover, it handles the Data Catalog that poses as a central repository of metadata. AWS Glue is capable of handling tasks completed in weeks rather than months. On the other hand, Data Visualization, with an apt representation of data using interactive charts, graphs, and tables, plays an important role in AWS Data Engineering. All the information from the Data Warehouse and Data Lakes poses as inputs for the tools to generate reports, charts, and insights from AWS data tools. Simply explained, data warehouse stores and uses structured data that is ready for strategic analysis while a data lake uses and stores both structured and unstructured data for use in the future. Advanced BI tools powered by Machine Learning provide deeper insights from data and help users find relationships, compositions, and distribution in data. What is Data Engineering? For us to understand Data Engineering, we have to understand the “engineering” part better. What do Engineers do? They design and build things. Therefore, data engineers can be thought of as people who design and make pipelines that change and help transport data in a format. This format helps the data to reach the Data Scientist or other users in a highly usable state. These solutions collect data from several sources and accumulate them in a single warehouse that holds all the data as a single source of truth. Over the years, the definition of Data Engineering has not changed much even though the technology and the tools have changed drastically. In simple words, Data Engineering is the foundation that holds data science and analytics together with the use of technology and data processing. Moreover, while conventional technologies like relational and transactional databases still have a place in big data architecture, fresh tools and technology have created innovation in the space. What is AWS? AWS, short for Amazon Web Services, is an on-demand cloud service provider that has various offerings under its umbrella. The organization is a subdivision of Amazon that can provide infrastructure, distributed computing facilities, and hardware to its customers. The various offerings from the organization are known as Infrastructure as a service (IaaS), Software as a service (SaaS), and Platform as a service (PaaS). AWS competes with names like Microsoft Azure, Alibaba Cloud, and Google Cloud. All these organizations are focused on improving the performance of an organization and reducing costs at the same time. Most of these platforms charge their users on a per-use basis. In comparison, an organization need not invest in setting up and maintaining complex IT infrastructure for its requirements at its premises. AWS data centers are located in various parts of the world and the customer has the choice to select the data center that is closest to their target customer. The various services offered by AWS include Security, Data Warehouse, Data Analytics, Cloud Computing, Database Storage, etc. AWS data management allows auto-scale with which a user can scale up or down the requirements for storage and computing capabilities based on the requirements of the business. What is AWS Data Engineering? There has been a phenomenal increase in the volume of data that is being generated by businesses and consumers. Organizations are looking at solutions to help manage, process, and optimally utilize this data. As a result, AWS Data Engineering came into the picture which can package and handle all the requirements of the customers as per their needs. An AWS Engineer is expected to analyze the customer requirements and propose an integrated package that can provide an optimal performance ecosystem to the organization. AWS Data Engineering is also used to ensure that data presented to the end users are in an analysis-ready form and can deliver the right insights. AWS Data Engineering Tools In recent times, we have seen several changes because of different tools designed by AWS for specific needs. The various tools used in the AWS ecosystem can be explained as follows: Data Ingestion Tools These tools are used to extract various types of raw data like text from multiple sources, real-time data, logs, etc which are then used to store in a storage pool. The data ingestion tools provide solutions with which users can collect data from multiple sources. It is one of the most time-consuming processes in the AWS Data Engineering cycle. The data ingestion tools provided by AWS are as follows: Amazon Kinesis Firehose The Kinesis Firehose tool from Amazon can deliver real-time streaming data to the S3 tool. It also can configure the data transformation before it is stored on the S3. Kinesis Firehose supports encryption, compression, and data batching features The scalability and volume depend on the data streaming yield. Kinesis Firehose is used in the AWS ecosystem to provide a seamless transfer of encrypted data. AWS Snowball Snowball from AWS is an amazing tool that can handle enterprise data from on-premise databases to the S3 tool. To avoid data and effort duplication, AWS used a snowball technique that can be

Yashica Chopra
November 17, 2022
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HireLakeAI – Most Advanced AI-Powered Resume Parser

HireLakeAI is a free resume parsing online tool that processes CVs to gather information and organize it in a structured format. We’ll discuss why HireLakeAI is the leading resume parser in the market. HireLakeAI is a smart resume parser to screen, evaluate, and filter resumes based on thirteen parameters. It assists recruiters in processing thousands of resumes quickly and in identifying potential candidates for the job. The resume parsing software is flexible and scalable. It can be used by SMBs, MSMEs, large enterprises, and recruitment agencies. The software is a complete solution to speed up the initial screening stage when shortlisting candidates for a job. HireLakeAI uses advanced artificial intelligence and machine learning algorithms to deliver accurate parsing results from resumes with all the necessary entities. It is developed by experienced AI engineers and innovators with years of experience. This smart recruitment intelligence tool can assess each job applicant in three categories- communication skills, technical skills, and personality traits. In addition, it can match the resumes with the job description and create rank-wise summaries after the interviews. HireLakeAI is the most advanced AI-powered resume parser that offers end-to-end services to recruiters and HR teams. It assists at each stage of recruitment to streamline the process and make it more effective. Organizations can hugely benefit from using the HireLakeAI resume parsing tool. Our API can seamlessly integrate with the existing HR systems to collect, process, and share data. The tool can also predict the possibility of a candidate accepting the job offer based on behavior analysis. Benefits of HireLakeAI Resume Parser Free to Use HireLakeAI is a free resume parser that works online. It also comes up with a paid API plan with complete flexibility of usage. The free version can be accessed from the website by creating a free account. It takes a couple of minutes to get started. Bulk Resume Parsing Since resume parsing is the process of using software to sort data from resumes based on keywords and user-provided criteria, HireLakeAI can screen numerous resumes in a quick time. It is the best tool for bulk resume parsing using AI and NLP (Natural Language Processing) algorithms. Shortlist with JD Matching Use the JD matching feature to shortlist resumes with qualifications, experience, and skills to identify the right candidates. The tool works based on the input job description details you provide. Communication Skills Assessment HireLakeAI uses seven parameters to assess the communication skills of a candidate based on interview data. The AI algorithm reads their confidence levels to determine if they are suitable for leadership roles. Technical Skills Assessment Similarly, HireLakeAI can be used to evaluate the technical skills of the candidates provided in the resume. It filters resumes that don’t have the necessary technical skills for the job. Personality Traits Assessment The tool helps recruiters identify personality and behavior traits to determine if the candidate can work with the organization. It provides insights about whether the candidate is a good fit for the existing work culture. Clean Interface HireLakeAI has a neat interface that is easy to customize and use. HR teams can automate the process using the tool and stay up to date about the number of candidates on the shortlist, those interviewed, hired, and so on. API Integration Use HireLakeAI resume parser API to integrate it with third-party HR software like ZOHO, BambooHR, etc. It can also be integrated with online job portals like Indeed, Naukri, Monster, Hirist, etc. Save Time and Resources Use HireLakeAI to save time, money, and resources spent on recruiting. Work smart instead of working hard to streamline hiring. You can use the offer acceptance predictor to identify candidates more likely to accept the job offer and spend your resources on them. To Sum Up HireLakeAI is the best and most advanced resume parsing tool that fine-tunes recruitment by offering the latest features and services to simplify the process. It is versatile, flexible, cost-effective, and highly efficient. Try HireLakeAI for free today! Originally published on HireLakeAI

Yashica Chopra
November 15, 2022
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2025’s Top 13 Data Analytics Companies: Who’s Dominating?

Data analytics companies provide offshore services for data management, analytics, & visualization. We’ll discuss the top data analytics companies in 2025 that are leading the industry with AI, BI & one-stop analytics. Who’s dominating the market and why! Data analytics is the process of collecting, analyzing, and transforming raw data into actionable insights and useful information. The insights are used to get a better understanding of the business and make data-driven decisions. Data analytics is a combination of statistics, business, and technology. It is a detailed and streamlined process that connects data sources to centralized data storage units and business intelligence tools. According to Fortune Business Insights, the global big data analytics market was worth $240.56 billion, and it is projected that the market will grow to $655.53 billion by 2029. Data analytics are a part of every industry, ranging from IT to healthcare to manufacturing, travel, healthcare, entertainment, and more. Globe Newswire predicts that Big Data in the healthcare industry alone will be worth $67.82 billion in 2025. In such instances, it is vital to invest in data analytics and adopt data-driven models to make business decisions. However, there’s no need to build the infrastructure from scratch or make heavy investments in developing the model in-house. Several SMBs, SMEs, and large enterprises hire offshore top data analytics companies to deliver the necessary insights and visualization reports. But which data analytical company should a business hire and why? Let’s go through the list of the top data analytics companies in the world to understand what they offer. Top Data Analytics Companies Dominating 2025 1. DataToBiz DataToBiz is a data analytics company that also offers data science, business intelligence, and artificial intelligence technologies to small, medium, and large enterprises. The company works with organizations from different industries like EdTech, FinTech, manufacturing, retail, eCommerce, IT, healthcare, and so on. It provides the following services: DataToBiz is one of the top data analytics companies with clients from the US, UK, Middle East, and Australia. The company provides descriptive and predictive analytics to help establishments reduce costs, minimize errors, and increase ROI. DataToBiz uses the latest technology and has a team of passionate and experienced data strategy consultants. It is an official partner of Google, AWS, and Microsoft and is widely renowned on Clutch. 2. Accenture Accenture is one of the top data analytics consulting firms with clients from 40+ industries. It is a multinational company with offices in different countries. The company provides transparent and trustworthy data analytics and insights to help businesses grow and become successful. It offers: Accenture provides scalable services and empowers businesses to reinvent their performance methods to increase productivity. It helps organizations make faster decisions based on actionable information. The company aims to help business organizations unlock the power of data to transform their processes and make the most of cloud computing solutions. 3. SAP SAP is a German multinational company that provides enterprise-level solutions to manage business operations using data-driven models. The company has acquired several smaller organizations that provide cloud-based technologies and solutions in the global market. SAP is well-known for cloud data analytical services. It provides: SAP helps businesses access the data necessary to make informed decisions. It promotes collaborations, cloud migrations, and system integrations between multiple software/ tools in the enterprise. The company offers flexible solutions to set up self-servicing data governance and analytics within the business. This allows employees to access the insights directly instead of requesting the information from other departments. 4. Capgemini Capgemini is one of the top data analytics firms with a presence in more than 50 countries. The company offers a range of outsourcing, data analytical, and cloud solutions to create a sustainable and inclusive future. It emphasizes the importance of knowing how to use data and insights for business growth and development. The company offers: Capgemini offers a full-service portfolio to businesses from varied industries. It empowers establishments to take advantage of data and artificial intelligence to increase their business potential and reach new heights in competitive markets. The company activates data to amplify business outcomes. 5. Wipro Wipro is among the top companies for data analytics, with business partners in 66 countries around the world. The company’s data analytics and AI services help organizations unleash new improvements and develop agile processes within the business. It provides businesses with the necessary infrastructure to create new business models and identify revenue streams while ensuring quality customer services. The company offers: Wipro has a robust AI framework named Imbibe the Sense-Think-Respond-Learn (STRL) system that converts raw data to actionable insights to help businesses make better and faster decisions. Next-gen technologies and industry-leading solutions are a part of Wipro’s offerings to its clients. 6. Sisense Sisense offers infused analytics everywhere to help businesses look beyond traditional approaches to data and analytics. The analytics can be embedded into employee and customer applications to streamline business processes and increase customer satisfaction through quality services. The company offers: Sisense provides a highly customizable dashboard, Sisense Fusion, which provides artificial intelligence-based cloud analytics in real-time. Employees can use it anywhere and anytime to access the latest insights required for day-to-day work. The embedded analytics offered by Sisense are used by more than 2000 leading businesses in the global market. 7. Deloitte Deloitte is a popular data analytics consulting company with diverse talents and services. The multinational company has clients from around the globe. It provides strategic, consulting, and technological services to manufacturing, finance, supply chain, operations, and government, energy & resource industries. The company offers: Deloitte offers auditing and taxation services by moving the systems to the cloud and streaming the process. Artificial intelligence and data modeling are part of the company’s offers to aid with digital transformation. The company has a presence in 150+ countries and territories and works with a vast range of clients. 8. Teradata Teradata offers a multi-cloud data platform with enterprise-level data analytics. Flexibility and scalability are the strong points of the company. It aims to transform businesses with the power of data and human expertise. Perspective analytics, predictive analytics, and data modeling

Shashank Dharasurkar
November 09, 2022
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A Guide to Outsourcing Power BI Development

Finding an experienced business intelligence developer or Power BI expert can be challenging for many business owners. As a result, most companies outsource business intelligence projects rather than developing them in-house. When you choose to outsource BI projects, you can lower the project development cost while reducing administrative expenses and taxes. Business owners of every size wonder whether they should hire more Power BI developers or hire an outside firm for business intelligence. Outsourcing BI projects versus in-house development can affect your organization’s efficiency, costs, and workflow. Now the question is Why should you outsource BI projects over hire Power BI developers in-house? Keep reading to find out! BI Outsourcing: How To Make It Work? The use of outsourcing today is seen as a vital tool that can help organizations become more efficient and successful if used appropriately. Outsourcing, however, has remained a constant in the IT world. In the past, The main goal of IT outsourcing was to reduce costs and standardize processes before outsourcing. As a result, the business became profitable. It is, however, different when it comes to Business Intelligence outsourcing. The aim here is to avoid failures and justify BI investments. To deliver results, BI outsourcing companies use a variety of techniques. The process of acquiring and modeling data gets automated by using offshore resources or software. It can either get replaced with a highly integrated BI stack or cloud-hosted BI solutions or a combination of these solutions. In-House Team Vs. Outsourcing Power BI Developer Outsourcing Power BI project development is typically a more cost-effective option than in-house development because you will get the skills and experience necessary to address your specific, immediate needs. An outsourcing firm with a business intelligence developer has experience in solving different problems while building solutions. So that experience makes them more capable of achieving your goals and objectives. For example, if you want to conduct a huge holiday sale, you can work with an independent marketing organization to plan an excellent strategy and market the product appropriately to the proper people. It will enable your company to take advantage of its limited sales days. It is best to outsource Power BI projects to companies with years of experience. As stated before, outside Power BI developers have encountered various challenges and can bring insights and practices that your team might not be familiar with. Your project will not only get resolved more quickly but also more effectively with their help. An outsourcing company, for example, could advise you on making the most of big data available to you if you hire them to assist with data-driven decision-making. Also, they can provide accurate and comprehensive answers to your questions and concerns about the BI project. How To Choose BI Outsourcing Specialists: Here is the list of things you should consider while choosing a BI Outsourcing firm or hiring power BI developers for your business. The DataToBiz team, for instance, provides business intelligence outsourcing and other data-driven services. The platform offers services including Big Data, Data Science (predictive analytics), and Business Intelligence (gathering and presenting information from diverse sources). Benefits Of Outsourcing Power BI Development Businesses often find it difficult to hire talented business intelligence and Power BI developers. As a result, most companies outsource business intelligence projects instead of choosing in-house development. Following are some of the benefits that most companies leverage when they choose to outsource. Increased Expertise The main reason companies outsource their business intelligence is to acquire increased expertise. Your company benefits from the expertise and knowledge of an employee when a business intelligence system gets implemented. BI companies, on the other hand, provide you with decades of expertise and experience. Thus, implementations, optimizations, and troubleshooting can get completed more quickly. The BI services team is made up of experts who have specialized knowledge about BI. In this way, action items and challenges can get accomplished faster and more effectively than with an in-house team. Data security The benefits of outsourcing business intelligence operations include access to a team that stores and manages important data across multiple platforms, which is a vital aspect of information security. There is a thorough understanding of data regulations and compliance requirements among their staff. Business intelligence is essential for today’s businesses, and outsourcing such a valuable aspect should not be overlooked. Hiring a Business Intelligence Company, like DataToBiz, allows you to concentrate on business development while utilizing BI technology. Flexibility When you outsource software development, you will no longer be responsible for interviewing, recruiting, training, and finding the best match for your projects. In addition, you shouldn’t be concerned about scaling if labor requirements change based on the project. Savings In Both Money And Time To successfully operate internal BI, your staff must be able to figure out what tools and systems your company needs, as well as regularly learn new skills and tools. The result can be costly for your company and a distraction from more productive tasks. Power BI outsourcing saves you money by eliminating the need for data specialists, software engineers, and project managers. You gain access to the best talent by outsourcing Power BI project development over hiring in-house developers. In addition to their proficiency with Power BI, outside developers are more familiar with emerging technologies and trends. As a result of working with an experienced project management team, you can get started sooner. You may also get valuable assistance from them during the process. No matter what you need to change or what concerns you have, they can effectively handle everything for you. Power BI Developers: Roles & ResponsibIlity With Required Skills An organization’s data-driven decision-making depends on the ability of Power BI developers to convert data into stories that enable data-driven decision-making. Required Experience In Data-Specific Roles The minimum experience required to become a Power BI developer is 2 or 3 years of working with BI tools. Besides, they should have a solid understanding of database management, SQL queries, data modeling, data warehousing, business intelligence, and OLAP (Online

Yashica Chopra
November 04, 2022
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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

Sunaina Agarwal
November 02, 2022
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Business Intelligence Consultant: 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

Kavika Roy
October 31, 2022
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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

Shashank Dharasurkar
October 27, 2022
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A Definitive Guide To Machine Learning Consulting

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

Kavika Roy
October 21, 2022
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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

Yashica Chopra
October 20, 2022
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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