Search Results

Both data science and business intelligence (BI) focus on data processing. However, there are some key differences to be aware of. For instance, data science can predict trends, while business intelligence provides analysis of past events. Moreover, data science uses a more technical skill set compared to business intelligence’s practical approach. To make these concepts sound much easier for you, Sencury defines each and compares them in a nutshell. What is Data Science? To initiate the process of data science, there is a need for concrete data sets. So, data scientists collect and maintain data beforehand. Afterward, this data is processed via data mining, modeling, model training, and summarization. To receive future forecasts, data scientists use machine learning, descriptive analytics, and other analytics tools. According to Harvard Business School, Data Science requires you to: make hypotheses gather data while running experiments assess the quality of data clean and optimize datasets organize and structure data for analysis While analyzing specific datasets (raw data) it is possible to spot patterns. These patterns are the basis for forming future predictions. This kind of analysis requires text mining, regression, descriptive, and predictive analytics. Being helpful in gaining future insights based on their data, many industries turn to data science services. Businesses can prosper with such a convenient approach at their disposal. For example, by analyzing customer preferences, it is possible to predict future market trends and develop new products that will be close to 100% successful. Data Scientists need 6 steps to do the work: Craft the problem Organize raw data for the problem Process raw data for analysis Analyze data Conduct in-depth analysis Report analysis results What industries can benefit from Data Science? Here’s the list of industries that find Data Science a successful approach: Healthcare and Pharmaceuticals Finance Retail and eCommerce Manufacturing Automotive Energy and utilities Government Construction Communications, Media, and Entertainment Data Science Tools Among some of the most popular tools used by Data Science are: Python PyTorch Pandas TensorFlow Scikit-learn Project Jupyter DataRobot Databricks Keras Apache Spark Matplotlib Apache Hadoop NumPy Orange MATLAB SAS Julia BigML What are the advantages of using Data Science for businesses? Data Science advances your organization’s growth as it: improves business predictions cooperates with business intelligence helps in sales and marketing increases information security interprets complex data helps in decision-making automates recruitment processes These are the main facts to know about Data Science. Therefore, let’s proceed with Business Intelligence and discuss it next. What is Business Intelligence? The process of business intelligence requires proactiveness. To drive changes, business leaders need to process and analyze data to obtain actionable insights. For instance, you may use your business's key performance indicators (KPIs) for strengths and weaknesses identification. With this knowledge, it becomes easier to improve operating efficiency and the company's business performance as a whole. Business Intelligence requires the following: organizing data collection process storing the data obtained analyzing data generating reports for further insights Data can truly support an organization's decision-making. Great improvements in BI technology are equal to improvements in speed, efficiency, and effectiveness. For example, transformational effects in BI and significant changes can be achieved with the help of automation and data visualization. Business Intelligence analysts require 7 steps to perform the process. These are: Identifying the use case Forming a hypothesis Examining data Collecting data Drawing conclusions Presenting findings Implementing solutions What industries can benefit from Business Intelligence? There are various industries that would like to learn from past data. For example, Retail Telecommunication Fashion Human resources Healthcare Fintech and Banking Sales and marketing Business Intelligence Tools These are the most used BI tools on the market. Some of them are already in use by Data Science. What are the advantages of using Business Intelligence for businesses? Among the advantages businesses can obtain there are: implementation in different industries and departments usage of Artificial Intelligence and Predictive Analytics provision of 24/7 real-time monitoring and data access exploitation for both data analysts and business users Now, let’s compare the two approaches – Data Science vs Business Intelligence, to understand what makes them different. Key differences between Data Science vs. Business Intelligence The practice of Data Science as well as Business Intelligence turns data into information, which is new knowledge. This information is crucial for business decision-making. However, the concepts are still different. Let's visualize these differences. As you can see from the table above, the differences are in the main goal, skills needed to perform Data Science and Business Intelligence processes, gather data, maintain it, and manage, and the complexity of business operations. Sencury’s Experience Our company offers customers Sencury’s top Data Engineering Services. We carry out both Data Science and Business Intelligence methods choosing the best toolsets on the market. Sencury provides smart personalized recommendations, transforms company data, and enhances organizational performance. Our team focuses on customer requirements and, also, can suggest or advise on the best-fit solutions per your case. Choose Sencury to get quality company data analysis. Today, data insights are the best way to initiate business growth and become a leader in the competitor market.

Technologies never stop evolving. The global markets are constantly producing innovations to meet human demand for automation and convenience. With all the technologies present, humans produce data volumes exponentially. Information is stored in media and audio files, written in e-documents, and on different devices. Information introduces Big Data – large data sets that can be analyzed computationally. By 2025, Statista projects that there will be more than 181 zettabytes of human-produced data. With such a quantity of data sources, it would be unwise not to use the data obtained. Therefore, we have Data Mining – a practice to analyze the given data and receive/create new information. At first glance, these notions are completely different. To understand what exactly makes Big Data differ from Data Mining, let’s discuss these concepts in a nutshell. Big Data Explained The term “big data” has been coined since the 1990s, owing to John R. Mashey - an American computer scientist, director, and entrepreneur. It refers to data sets of enormous sizes. This voluminous information is beyond the ability to capture, curate, manage, and process unless using specific software tools. Big Data is formed from structured, unstructured, and semi-structured data. However, only unstructured data has the biggest focus and usage. The size of Big Data has no universal quantity because this number is constantly increasing in capacity. Moreover, to process these changing volumes of information, newer tools and techniques are required every now and then. Characteristics When we speak about Big Data, we understand it according to the following six V criteria: Volume. Quantity of data that has been generated or stored (a lot larger than terabytes and petabytes). Variety. Data type and nature. Velocity. The speed with which data is being generated and processed. Veracity. Data reliability impacts data quality and data value. Value. The significance of information gained via processing and analysis of large datasets. Variability. Big data has formats, structures, or sources with their own characteristics that are constantly changing. Tools Big Data has the core components and ecosystem it uses. McKinsey Global Institute defines the following applications: Techniques: A/B testing, Machine Learning, and Natural Language Processing Technologies: business intelligence, cloud computing, and databases Visualization: charts, graphs, and others Industry Applications Big Data has the potential to be unveiled almost in every industry there is. The other question is how it is going to be used to receive direct benefits. The industries that make use of Big Data are: Government International development Finance Healthcare Education Media Insurance Internet of Things (IoT) Information technology As it was mentioned, Big Data stands for large data sets that humanity can analyze with the help of appropriate tools and techniques. However, the process of analyzing data and creating new knowledge out of it is called Data Mining. Therefore, let’s find out more about it. Data Mining Explained Data Mining processes different large data sets and aims to discover data patterns using machine learning, statistics, and database systems. It belongs to the subfield of computer science and statistics. The main goal of Data Mining is to intelligently extract information and transform it into usable valuable information for others. The first to use this term was Michael Lovell in 1983. Within years, this term had different positive and negative connotations. Nowadays, it is used interchangeably with “knowledge discovery”. Characteristics Before you use the Data Mining algorithms, there is a need to assemble the target data set. As Data Mining finds patterns that are 100% present in the data, the target data set must be large enough to contain these patterns. Also, it has to be concise enough to meet the acceptable time frame. This process is called pre-processing. There are six classes of Data Mining tasks: Anomaly detection. Identification of unusual data records or errors that require further investigation. Dependency modeling. Relationships between variables or the search of such. Clustering. Discovery of groups and structures that are similar (patterns). Classification. Structure generalizations to apply to new data. Regression. Search for a function that models data without errors and estimates relationships among data sets. Summarization. Compact data set presentation via visualization and reporting. After the data has been discovered it is crucial not to misuse it. However, anything can happen if you have tested lots of hypotheses at the same time. For this reason, it is vital to perform proper statistical hypothesis testing. With hypothesis testing you can make probabilistic statements about certain parameters. Tools According to Javatpoint, the following are the latest and most popular Data Mining tools in use: Orange Data Mining SAS Data Mining DataMelt Data Mining Rattle Rapid Miner Modern technological stack allows the usage of multiple open-source components mostly as Python modules. It requires custom data engineering as a service which Sencury is happy to provide. Industry Applications Healthcare and Structured Health Monitoring Customer Relationship Management Fraud Detection and Intrusion Detection Manufacturing Engineering Financial Data Analysis Retail Telecommunication Media and entertainment Logistics and trucking Biological Data Analysis Other Scientific Applications This is the basic information you should know about Big Data and Data Mining. Let’s also make a comparison of both. Big Data vs Data Mining: Comparison To understand the basic differences between Big Data and Data Mining, let’s structure and visualize them in a comparison table. From the table above it is clear that Big Data is a whole concept that includes tools and techniques to process data. However, Data Mining is one of the tools that helps to deal with Big Data and find value within it. Sencury’s Experience with Data Mining and Big Data Our team provides customers with a broad scope of Data Engineering Services. Sencury has vast data engineering knowledge and experience. We create data-driven solutions that can enhance your performance optimization. Our data engineering can enhance your business decision-making by leveraging business intelligence and advanced reporting! Different organizations accumulate huge amounts of data daily. With data engineering, it becomes easier to make use of this data the right way. Sencury’s data engineers can assess your raw data and create predictive models that display short-and long-term trends. We also identify data insights, find correlations, and derive new business data that is valuable for your company's growth. Interested in trying? Contact us for the details.

You may have heard tons of information concerning DevOps practices. The latter has raised great interest among businesses. Google Trends show 100% user interest in DevOps for the past couple of years. Sencury also posted articles based on DevOps culture and Cloud-specific DevOps. However, do you know that DevOps has given insights to deliver better and faster within other engineering technologies? For example, MLOps, where ML stands for Machine Learning. It has a part of DevOps (Operations) in it. Where do we use MLOps?What are its components and benefits for your business? When do you need MLOpsand what is its difference from DevOps practices? Let’s find out with Sencury! What is MLOps? MLOps is the shortened term for Machine Learning Operations. It stands at the core of Machine Learning engineering. The main goal of MLOps is to streamline machine learning models to production. And, also, track and maintain ML models throughout the whole process. MLOps Usage Quality ML and AI solutions require using an MLOps approach. Basically, it adopts CI/CD practices. Here, ML models are being monitored, validated, and governed. Who adopts it? Data scientists and DevOps engineers, who work together to achieve success. MLOps Components There's no defined MLOps timeline in projects dedicated to machine learning technology. Moreover, it can cover the process from data pipeline to model production. Or any part of the process the project requires. E.g., model deployment only. MLOps principles can be beneficial and are applicable in the following cases: Exploratory Data Analysis (EDA) Exploration, sharing, and data preparation in iterations for an ML lifecycle. This can be achieved via the creation of datasets, tables, and visualizations that can be reproduced, edited, and shared. Data Preparation & Feature Engineering Refined features require iterative data transformation, aggregation, and removal of repetitive information. Model Training & Fine-tuning, RLHF (Reinforcement Learning from Human Feedback) There are two options: via open-source libraries that can perfectly train and improve model performance. Or via automated ML tools including those available in major clouds (e.g. AWS SageMaker JumpStart). The latter can perform trial runs and create code that can be reviewed and deployed afterward. Model Evaluation Perform model evaluation in experimental and production environments. Consider: Evaluation datasets to validate model performance Multiple continuous training runs to track prediction performance across them Performance comparison and visualization between different models Using AI techniques (interpretable) for model output interpretation Model Governance ML lifecycle requires end-to-end tracking of model origins and versions and managing model artifacts and transitions. Model Inference & Serving Manage both how often a model can be refreshed and the inference request times. To automate the pre-production pipeline, use CI/CD tools such as repositories and orchestrators. Model Optimization for Deployment There are several options for a model to be optimized before deployment. For example, by Data quantization. This is the process of compressing an AI model by reducing its high computational, storage, and energy requirements. In other words, every ML model has numerical representations that are being lowered to drastically decrease the model’s size. This allows computations to run more quickly and requires less memory. There are two ways to perform quantization: Quantification of weight and activation. Model pruning. Pruning an ML model requires setting a 0 value to certain weights. This makes a model unable to overfit. There are various ways to prune a model. E.g., at the start prune a random number of weights. Or at the end of the training process to make a model lighter. The main idea of pruning is to keep a complex model architecture and increase the model's capacity (with as many interactions between features as possible), due to limiting its size. Model Deployment & Monitoring Put the actual model to the test by defining an actual use case. These may be Single-sample inference deployment, Batch deployment, and Deploying models onto edge devices. To get registered models into production faster, automate permissions and cluster creation. Then, enable REST API model endpoints. Model Retraining (Automated) Create alerts and automation to be able to correct any data deviations between model training and model inference. Why is There a Need for MLOps? Getting machine learning models into production is not an easy task. Mostly, due to the complex components of the ML lifecycle (see the picture above). Often, it requires hundreds of GPUs and weeks of time which constitute a serious cost constraint. It is a great challenge to keep the processes synchronized and work together to reach a goal. It requires extra accuracy and precision to do the task. Collaboration of DevOps, Data engineers, Data scientists, and ML engineers becomes critical as well. As MLOps includes experimentations, iterations, and continuous improvements of the ML lifecycle. The biggest benefits of MLOps are its efficiency, scalability, and risk reduction. Efficiency: faster results; faster production Scalability: vast scalability and management of thousands of models at once Risk reduction: greater transparency and faster response to risk events; policy compliance Differences Between MLOps vs DevOps MLOps focuses on data management and model versioning. In its turn, DevOps prioritizes overall application performance and reliability. Let’s see the full comparison of MLOps vs DevOps in the table below. Sencury on MLOps vs DevOps MLOps is an important service provided by Sencury. It was shaped by leveraging the strengths and capabilities of our Data Science, Data Engineering, and DevOps specialists. It leverages an out-of-the-box consulting package for the cloud, on-premise, edge computing, or hybrid ML ecosystem. Contact us today to receive more details!

Benjamin Treynor Sloss, who is the founder of Site Reliability Engineering (SRE), once said: “Hope is not a strategy.” And he was right. A strategy is defined as having the plan to act upon to achieve short or long-term goals. That’s what we can achieve with DevOps and SRE. It’s about actions rather than hope. However, DevOps has a broader focus. It stands for Development and Operations. While reliable systems can be built with the help of Site Reliability Engineering - a set of principles and practices that are based on software engineering and can be applied to software development and operations. Both DevOps and SRE are strategies that have different goals, focuses, approaches, use cases, and tools. We have explained DevOps culture in one of our previous articles. Therefore, let’s dive deeper into what Site Reliability Engineering vs DevOps is and what it can do for your business. What is Site Reliability Engineering? Site Reliability Engineering (SRE) aligns closely with DevOps principles. It uses software engineering and automates operations tasks that system administrators carry out manually. For example, such tasks as: production system management change management incident response emergency response These tasks can be carried out by a single software engineer or a team of qualified experts. In any case, the responsibility includes ensuring system availability, latency, performance, efficiency, change management, monitoring, emergency response, and capacity planning. SRE focuses on automation, system design, and improvements to support system resilience. Aligning with DevOps principles makes SRE a certain part of DevOps, but it still has different objectives at its core. Let’s see what these differences are. These differences are so significant that it would be logical to figure out exactly which DevOps fundamentals SRE adheres to. Site Reliability Engineering Principles Monitoring of Applications In SRE, monitoring software performance is a more reliable process than eliminating all the errors. The latter might still occur, so the team tends to provide service-level agreements (SLAs), service-level indicators (SLIs), and service-level objectives (SLOs). By observing and monitoring performance metrics after the application has been deployed, it is possible to provide quality and conformity. Implementing gradual change System reliability is maintained by the constant release of frequent and small changes. With the help of consistent and repetitive processes, it is possible to: Reduce risks that might occur due to changes Provide feedback loops to measure system performance Increase speed and efficiency of change implementation Automating reliability improvement SRE attempts to use various policies and processes to merge with reliability principles during delivery. Some of the problem-solving strategies from these policies and processes are: Early issue detection by developing quality gates based on SLOs Build testing automation using SLIs Architectural decision-making to ensure system resiliency initially How Does Site Reliability Engineering Work? During the production stage, a site reliability engineer uses automation tools to monitor and observe software reliability. In addition, this expert has good coding skills and can find problems in a software product and fix them by altering the code. Formerly, a site reliability engineer was a system administrator or an operation engineer. The responsibilities of such a qualified engineer include operations, system support, and process improvement. The tools a Site Reliability Engineer uses are: Container orchestrator. With the help of this tool, software developers run containerized applications on different platforms. Hence, a container is a single package, where containerized applications store their code files and other resources. For instance, Amazon Elastic Kubernetes Service. On-call management tools. With the help of these tools, SRE teams receive timely alerts on software issues. Therefore, they can plan, arrange, and manage support experts to deal with the reported software problems. Incident response tools. These tools are helpful in categorizing issue severity and dealing with the most crucial ones first. In addition, you can have a post-incident analysis report, which may resolve the problem of similar issues occurring. Configuration management tools. The tools mentioned help automate software workflow by removing repetitive tasks. For instance, AWS OpsWorks sets up and manages servers automatically. SRE metrics to adhere to: Service-level objectives (SLOs): uptime, system throughput, system output, and download rate, which are quantifiable goals achieved at a reasonable cost. The delivery is done through software to the customer. Service-level indicators (SLIs): actual measurements of the metric. Real-life situations may give out values that are different from or match the SLO. Service-level agreements (SLAs): legal documentation including key information about procedures when SLO is not met. For example, if the team does not cope with the task within the set time. Error budgets: SLO-based noncompliance tolerance. Benefits of Site Reliability Engineering Improved collaboration. SRE improves collaboration between development and operations teams. Enhanced customer experience. SRE ensures the customer experience will still be positive even if there are software errors. Better operations planning. Teams are always prepared for events they can foresee. Therefore, they plan the appropriate incident response to minimize the negative impact both on businesses and end users. Choose Sencury for your Site Reliability Engineering vs DevOps Practices Sencury also has our SRE consulting services to offer. Sencury's experience includes DevOps and SRE practices. We make strategic decisions on a concrete approach and build our development strategy on flexibility and mutual success factors. By consulting you on your system reliability we enhance your internal processes. We deliver applications and services at a high speed and give outstanding recommendations to prevent application errors and crashes. Our SRE consulting services combine cultural philosophies, practices, and tools. Contact us to receive top-notch SRE consulting services on the market.

Smart contracts are becoming popular in the software development market and among various industries. These are the digital programs that automatically execute, control and document the terms and conditions of an agreement between the two parties. Using blockchain and AI technologies, smart contracts become more efficient, accurate, and adaptable. With smart contracts, it is possible to achieve high levels of security, transparency, and automation. Do we need lawyers then? Would smart contracts substitute human experts? Sencury has an answer for that. How do Smart Contracts Work? Smart contracts change the way of implementing and enforcing agreements and contracts. Now they are decentralized, secure, and automated. Nonetheless, how do they work? The procedure looks as follows: Smart contracts are blockchain-powered self-executing programs. They are built to provide automatic enforcement of different rules and regulations based on a particular agreement or contract. It means there is no need for intermediaries e.g., lawyers and other third parties. Everything starts from the identification of an agreement, where the parties involved identify cooperation terms and conditions and the desired outcome. Then, smart contract conditions are set. Containing the terms and conditions of a concrete deal and the rules of execution, smart contracts are written in code and are stored on a blockchain network, where they can be accessed by everyone involved. Automatic validation and enforcement of the contract terms are done via the blockchain network. Blockchain technology makes this process transparent, immutable, and secure. Therefore, it is difficult to alter or manipulate the contract without being detected. Also, there are network updates. Here, all the nodes update their ledger. AI in Smart Contracts AI can enhance smart contracts in a number of ways. It brings both intelligence and automation into the process of decision-making that is core in smart contracts. For example, Oracles Oracles, or specific data feeds, take data from off-the-blockchain data sources and put this particular data on the blockchain for smart contracts to use. Oracles are powered by AI, which allows them to collect and analyze real-time data from various sources. I.e., IoT devices, social media, or financial markets. The data obtained is used to prompt certain actions within the smart contract or initiate the decision-making process on the information that has been previously analyzed. Risk assessment The other brilliant way to be on the safe side with smart contracts is to use AI algorithms for risk assessment. Historical data and related patterns are the basis for AI models. The latter identifies potential risks, predicts outcomes, and recommends appropriate actions. Industries such as insurance, lending, or supply chain might benefit from using AI in the mentioned cases. Dynamic changes to terms and conditions Using AI capabilities, smart contracts can adjust their terms and conditions based on changing circumstances. E.g., an AI-powered smart contract in a supply chain or any other industry can dynamically adjust pricing, delivery schedules, and other conditions based on the current market state. Resolution of disputes AI is an assisting tool in smart contract disputes. It helps to analyze the terms of the contract, the history of transactions made, and all the data within the contracts that might resolve any problem occurring. Therefore, AI algorithms offer automated dispute-resolution mechanisms. Hence, all the stakeholders involved will save time and costs that would have been wasted during the traditional dispute resolution procedure. Contract optimization AI is the number one tool to learn from past experiences and avoid repeating problematic occurrences in future contacts. It easily identifies negative patterns and trends that might be improved. In addition, AI suggests what improvements might help in a particular case. I.e., AI recommends the best contract terms, resource allocation, and risk management strategies. Can Smart Contracts Substitute Lawyers? With all the features and encryption benefits that smart contracts can offer; it is still highly unlikely these self-executing programs will substitute legal experts. The main reason lies in the limitations smart contracts possess and their inability to replace lawyer expertise and comprehensive legal guidance. Lawyers are needed to support the following legal procedures: Complex legal matters There are contracts that include various complex nuances and regulations, which cannot be executed by smart contracts. The latter requires predefined conditions. Therefore, lawyers are the main sources of expertise that will provide legal analyses, advice on different matters, and interpret contract conditions even better than the smart contract is able to. Legal counsel Lawyers also provide legal advisory customized to any existing case. To protect the client’s interest, a lawyer would assess the possible risks based on the terms of the contract, negotiate according to certain agreements, and many more. Concerning smart contracts, they are unable to understand the contexts of the legal documents, negotiate terms per need, or provide custom pieces of advice. Contract formation Smart contracts are the executors. However, if you need to draft the possible terms and conditions of a legal document, unfortunately, smart contracts will not cope with the work. They only automate and execute things that are established. Lawyers, in their turn, can provide drafts, ensure legal compliance, and customize client needs to meet business or personal requirements. Dispute resolution Smart contracts use AI to better resolve disputes within their terms and conditions. However, if the problem is complex and requires proceedings and advocacy for the client’s rights, no matter how smart a program is, it has limits. Hence, a legal IT lawyer carries out duties this self-executing software cannot. For example, lawyers have the needed skills and experience to navigate the legal system, engage in negotiations, and pursue litigations. Legal framework A lawyer is a person who studies the legal market with its evolutions, new regulations, legislations, and judicial precedents shaping the interpretation of contracts and legal rights. Lawyers really update their knowledge to provide up-to-date services. Smart contracts, in their turn, cannot adjust to changes on their own. They need a particular input. However, even with the input, new conditions require human expertise to process them rather than a program that bases its knowledge on outdated artifacts. Sencury’s Answer on Smart Contracts, AI, and Lawyers Smart contracts are one of the many great ways to establish partnerships, set business goals, and define particular rules. Moreover, these self-executing programs will ensure all the terms and conditions are being met precisely. Adding AI to power smart contracts is a good investment, especially with NLU capabilities. AI can automate legal procedures and provide great knowledge-sharing on negative contract patterns, resolve minor disputes, and offer transparency and efficiency. Notwithstanding the benefits smart contracts’ AI has, there are situations and legal disputes, where the skills and experience of a human expert are a necessity. That’s why lawyers are greatly valued in the software development market. Smart contracts cannot substitute lawyers as the program is unable to think outside its input measures, at least before hypothetical artificial general intelligence is developed. We shape our agreements according to current laws and regulations of the software development market. Therefore, we possess the skills and knowledge needed to guide clients in the intersection of legal and technology matters. Sencury’s most common technological stack to build smart contracts is Solidity on top of the Ethereum blockchain. Contact us to receive an extensive consultation on Smart contracts, AI integration into smart contracts, and why you need to have a lawyer when signing digital contracts. Our consultants will provide answers to your questions and many more.

Quality Assurance Consulting is an extremely needed service in the software development life cycle. With its help, you can receive a wide range of benefits that improve software quality, reduce costs, and enhance overall business performance. A Quality Assurance Consultant is an expert in the field that knows how to set up an end-to-end software development process and promote quality on every level of production. However, do all businesses need to be consulted on Quality Assurance Strategy? Are there specific prerequisites? Let’s find out with Sencury! What is QA Strategy Consulting and Why Do You Need it? Quality Assurance Strategy Consulting is a type of service that aims to help different organizations both create and implement a custom-made QA strategy for the software development life cycle (SDLC). This service requires working with an expert, who will be able to identify current QA process weaknesses within the organization, create a strategy to overcome them and implement this strategy into the SDLC. The QA Strategy Consultant thoroughly analyses the following procedures: software development process testing methodologies testing tools, and techniques organizational structure communication channels roles and responsibilities of team members Based on this analysis, the QA Strategy Consultant gives concrete recommendations on how to improve the quality, efficiency, and effectiveness of the software development process. You might need QA Strategy Consulting in three prominent cases. For example, if your organization has unoptimized QA process non-effective QA process lack of in-house QA expertise Having these specific knowledge gaps and the lack of skills to achieve success requires help from the outside. Therefore, a great plus is that QA Strategy Consulting exists as a service. What are the Pillars of QA Strategy Consulting? The QA Strategy Consulting consists of several steps. It is a process of Discovery, where a QA consulting expert assesses the organization’s QA practices and identifies what can be improved. Analysis, where the data obtained during Discovery is scrutinized to create a valid QA strategy. Recommendations, where the QA Strategy Consultant recommends the best ways to implement the new QA strategy, and how to utilize tools, techniques, and methodologies correctly to improve the company’s software quality, working efficiency, and the team’s effectiveness. Implementation, where the QA Consulting Expert works with the internal team to ensure the new QA strategy is being implemented and integrated into the software development process. Therefore, QA Strategy Consulting exists as a service to help organizations meet their unique needs and goals. The main idea of this service is to improve the quality of software products, reduce costs, and improve efficiency and effectiveness through the advancement of business workflows. What are the Benefits of QA Strategy Consulting? Despite a business client's focus-industry and direction, QA Strategy Consulting can be beneficial in a variety of ways. For example, the most crucial ones are: Improved Quality Quality software is important for any business. With QA Strategy Consulting software quality improves drastically. QA experts identify and address quality issues and reduce the risk of defects early in development. Increased Efficiency A tailor-made QA strategy should align with an organization's goals and needs. Therefore, expert consulting on the QA strategy results in improved testing efficiency and reduced testing time and costs. Your software products will be delivered faster and with high quality. The resources used for product development will be used more efficiently. Risk Management Unfortunately, there are plenty of risks throughout the whole software development process. With QA Strategy Consulting it is possible to identify and manage these risks, especially security vulnerabilities, compliance issues, and performance inconsistencies. This way organizations mitigate the impact of any risks and avoid costly consequences. Competitive Advantage Companies with outstanding software quality and efficiency are more competitive in the software development market within their industry. Best quality impacts customer satisfaction in a positive way. With user preference comes an improved brand reputation, and a stronger market position. Compliance Such industries as healthcare, finance, and government require a regulated approach according to industry standards. Thus, QA Strategy Consulting ensures compliance and reduces the risk of non-compliance penalties and legal consequences. Scalability If your business needs to scale, QA Strategy Consulting can develop a scalable testing approach that adapts to changing business needs during the period of growth. Adjusting to changes, a QA strategy remains effective and efficient even over time. What QA Strategy Consulting Services Sencury Provides? Sencury is competent in different types of technology consulting services. Our team possesses unique knowledge and expertise in many software development fields and processes. Therefore, we can help businesses out by creating the best QA Strategy for their requested needs and goals. We tend to consult businesses following the pillars of the QA Strategy Consulting process and taking into account the specificity of the business workflow and their weak points. To ensure our strategy for the business client will be efficient and effective, we analyze all the given requirements and recommend a scheme to implement that can reduce the organization’s spending and time waste. Contact Sencury today to achieve the desired business growth and success in software development.

Today, code quality is of paramount importance. Every software product surrounding us consists of a code and ensuring this code is reliable is of the utmost importance. Test-driven development (TDD) can offer a systematic approach and achieve these goals. For example, TDD allows the creation of automated tests before writing code. If tests fail, developers can write code to make them pass. This process enhances development, reduces defects, and fosters a more robust software ecosystem. In this article, Sencury describes the fundamentals of TDD, its benefits, and practical applications that foster software development with confidence and precision. What is Test-Driven Development? TDD stands for a process of software development that uses pre-written test cases to test the software against requirements and ensure it meets all of them. TDD roots come from extreme programming, which aims to boost productivity by organizing people and their working routines. Therefore, all the programming code written is based on test cases – a set of steps to ensure the correct functionality, behavior, features, and quality of an application are being met. The TDD process focuses on a repetitive short cycle of "red-green-refactor". It is accurately visualized in the picture below. Here, "red" is equal to “fail”, so it marks the test that is intended to fail. "Green" is equal to “pass” and determines the exact quantity of code that should be written to pass the test. “Refactor" is the actual code improvement, but without changing its functionality. Developer Test-Driven Development Developer’s test-driven development requires writing a single developer test as well. This test is also called the unit test. Unit tests need little production code written to complete them. The focus of developer TDD lies on every small functionality of the system. It is exactly what TDD is about. Acceptance Test-Driven Development (ATDD) Acceptance test-driven development promotes writing only one acceptance test, which will meet software specification requirements. Also, this test can satisfy the behavior of the system. To ensure the test is completed, write the minimum needed production code. As the main idea of ATDD is the focus on the overall behavior of the system, it is also referred to as Behavioral-Driven Development (BDD). Click here to read more about BDD in our corresponding article. Test-Driven Development Process Basically, the TDD process is the following: Writing Tests It is essential to write a test case that describes the required behavior beforehand the code is produced. This test is supposed to fail due to the code, which does not exist yet. Running Tests It is obligatory to execute every test. The one written beforehand has to be executed as well. This test is to fail as there is no functionality that the test describes. Writing Code To pass the test, you need to write an essential part of the code. So, the goal is to make the test that previously failed to pass. There is no need to write more code than is needed as this is not the final implementation. Running Tests All the tests have to be executed once again. Including the new ones and the previously passed tests. If they all pass, the new code and the code that already exists are working correctly. Refactoring When all the tests pass, it is time to refactor the code. It is done without changing the code's behavior but improving structure, readability, and performance. At the same time, functionality stays intact. Repeating The TDD process is repetitive. First, you write the test, then it fails, the next step is to write the code to make it pass, and so on. These iterations are needed to implement all the desired features. Practical Applications of Test-Driven Development Being a valuable methodology, TDD is applicable to various software development scenarios. Here are some practical applications of TDD in real-world development: Web Development TDD is effective in web development projects. It suits best to ensure web applications function correctly in different browsers and across various devices. The focus of developers is to write tests that simulate user interactions and verify expected behaviors. This way, software experts can catch frontend and backend issues early in development and promote a significantly greater user experience. API Development API building can also be enhanced with the help of pre-defined functionality and behavior of the end product. Written tests allow you to cover various use cases, edge cases, scenarios that handle errors, etc. Here, developers can ensure the correct work of APIs in both delivering accurate responses and handling data in the appropriate way. Legacy Code Refactoring Legacy codebases can also make use of the TDD approach. Critical functionalities can be tested to see whether there is a need for changes. It is a perfect way to ensure the refactored code meets the expected behavior and has no other flaws. TDD mitigates possible risks of error due to code modifications and acts as a safety net while the code is being refactored. Agile Development TDD can work side-by-side and produce positive results with Scrum and Kanban, which belong to Agile methodologies. In Agile, we break tasks into smaller user stories or backlog items to work in iterations. The same idea applies to TDD. Software engineers can test each separate functionality before it is implemented. Therefore, TDD is perfect for iterative and incremental development. With TDD, every sprint ends with the delivery of flawless software. Open-Source Contributions Open-source projects can benefit from TDD as well. Making code contributions and writing tests allows software engineers to align changes with project requirements without regression. These tests can substitute documentation as they serve as one. In addition, every code contributor can understand the end-point behavior and collaborate, communicate, or discuss changes based on the documentation provided. Continuous Integration/Continuous Deployment (CI/CD) TDD can perfectly integrate with CI/CD pipelines. Automated tests produced during the TDD incorporated into the CI/CD process positively impact frequent code integration, automatic test execution, and quick feedback on the quality of the code. A stable and reliable codebase is not a problem with TDD and CI/CD workflows. Collaborative Development Team collaboration is one of the pillars of successful development. Here, TDD can help by defining the expected behavior via tests. Therefore, software engineers will communicate more efficiently until their understanding of software requirements matches. Tests can act as a shared point of reference that impacts collaboration, code reviews, and knowledge sharing within a team. Why Do You Need Test-Driven Development? Many organizations ask themselves whether there is a potential need for TDD. It depends on the organization itself, its basic needs, and its capabilities. With TDD any business will: be able to deliver continuously and innovate faster with the robust code achieve extensible and flexible code with the ability to refactor or move it receive tests that were also tested by a development team that verifies test failure obtain an easily testable code have an effortless implementation of requirements with the written function Test-Driven Development at Sencury Sencury offers our QA strategy consulting service as a starting point to decide on the need for the TDD and its extent. Also, QA strategy consulting exists for designing a proper balance between manual and automatic testing. It is of great benefit as there are many other complex methodological decisions to make. For instance, cooperation between QA and the development team (particularly, sanity testing and regression testing), cooperation between QA and BA, planning of QA team mobilization for different phases such as the test documentation phase, dev process support by QA, UAT, production support phase, and change request implementation phase. Naturally, Sencury offers manual and QA automation testing teams. The latter amends a unit test that developers write. Both constitute crucial elements of TDD.

Cloud and DevOps are related concepts. They are used together in the process of efficient and scalable software development and operations. Cloud Computing delivers servers, storage, databases, networking, and software over the Internet. You pay per use and have no need to invest in your own physical infrastructure. These resources are flexible, scalable, cost-effective, and globally available. DevOps, in its turn, is a set of practices and cultural philosophies. Its goal is to improve collaboration and communication practices between software development teams such as development (Dev) and operations (Ops). DevOps automates the process of software delivery and also maintains the high quality, reliability, and scalability of the software released. Cloud Computing and DevOps intersect in many ways. What might this intersection bring to your business? Sencury rationalizes. Cloud-Specific DevOps and its Benefits Working together, DevOps and Cloud Computing can offer potentially useful advantages to different businesses. Within the Cloud, DevOps makes an impact by promoting team collaboration and communication. What’s more, the team can be present in different parts of the world. Hence, the process will still be continuous and efficient. Organizations can experience the following benefits: increased efficiency improved communication greater scalability better quality control faster time to market reduced costs Every benefit is a result of developers’ ability to respond to business needs faster, almost in real time. DevOps makes it possible to remove software development’s latency, or at least a good part of it. Aspects of Cloud-specific DevOps Infrastructure as Code (IaC): Management of infrastructure happens with the help of coding. Therefore, servers, networks, and databases are defined and managed via code. This enables version control, automated provisioning, and consistent deployments. That's why Cloud-specific DevOps heavily relies on infrastructure as a code. Elasticity and Scalability Whenever there is a business demand to scale resources, Cloud resources are the ones to do it dynamically. What can Cloud-specific DevOps offer here? Mainly, the design and implementation of scalable architectures to handle non-flexible workloads. To leverage elasticity, Cloud-specific DevOps uses autoscaling, load balancing, and container orchestration tools (Kubernetes). Continuous Integration and Continuous Deployment (CI/CD) Cloud-specific DevOps should offer CI/CD practices. Making code changes needs frequent integration into a shared repository. Then, such processes as automated build, test, and deployment are on to ensure the quality of the changed code and deliver efficiency on production. Cloud platforms have tools and services for CI/CD processes like AWS Code Pipeline and Azure DevOps. Cloud Resource Management Managing Cloud resources is a priority for DevOps teams. What do they do specifically? Provision and configure the components of the infrastructure, monitor the usage of resources, and optimize costs. Automation of resource management tasks is the key procedure in Cloud-specific DevOps practices. It is done via scripts, templates, and configuration management tools, adding both to the consistency of the process and reducing manual work. Monitoring and Logging Cloud-based systems also need performance monitoring and maintenance. For this reason, Cloud-specific DevOps uses solutions for comprehensive monitoring and logging to capture metrics, logs, and events from multiple cloud resources. To enable troubleshooting, proactive monitoring, and performance optimization, DevOps allows the integration of tools like AWS CloudWatch, Azure Monitor, or Prometheus. Security and Compliance Cloud-powered systems and data should be protected by any means. Security is critical when it comes to DevOps and cloud-specific DevOps. Appropriate security protection measures are usually taken by teams, who would like to prevent cloud-based threats. These measures include strategies like identity and access management, encryption, secure network configurations, vulnerability management, and compliance with industry regulations. Therefore, Cloud-specific DevOps focuses on using the benefits of cloud platforms to promote agile and scalable software development and operations. It revolves around automation, collaboration, and efficient use of cloud resources, where teams deliver software faster and with improved quality. Cloud-Specific DevOps Tools There are numerous DevOps tools created to operate in cloud environments on the market. What’s more, these tools aim to enhance the efficiency and effectiveness of your cloud-based DevOps processes. Therefore, here’re the most popular cloud-specific DevOps tools: AWS CodePipeline If you need a completely managed continuous integration and delivery service, choose AWS CodePipeline offered by Amazon. It allows automation of building, testing, and deployment of applications on AWS. Azure DevOps If there is a need for a comprehensive set of development tools, then Microsoft for Azure Cloud is the perfect match. It includes features like source control, build automation, continuous integration/continuous deployment (CI/CD), and more. Google Cloud Build Continuous Integration and Development can be provided by Google Cloud Build by Google Cloud Platform. Using Docker containers you can build, test and deploy applications on Google Cloud. Jenkins Continuous integration and delivery can be achieved using Jenkins – an open-source automation server. It is quite popular today among software engineers. Jenkins is compatible with different cloud environments via plugins and integrations, e.g., AWS, Azure, and Google Cloud. Kubernetes To manage containerized applications in the cloud, choose Kubernetes. It is an open-source container orchestration platform and the number one choice for deployment and management, enabling automated scaling, self-healing, and efficient usage of the other cloud resources. Terraform The best infrastructure as a code tool that defines, and provisions cloud infrastructure resources in declarative configuration language is Terraform. It supports AWS, Google Cloud, and Azure cloud providers. The latter makes it faster to automate infrastructure changes. Sencury's Cloud-specific DevOps Services Our company offers you quality DevOps Services with development and operation teams working together throughout the SDLC. Within Sencury’s DevOps Services, we provide Cloud-specific DevOps Services to ensure scalability and efficiency of the teams’ communication and the project workflow. We ensure you will receive top-notch services of non-cloud apps adoption and migration to the cloud consulting on your specific needs and cloud solutions assessment performance and scalability enhancement via the cloud automation and optimization of processes integration of IT infrastructure security measures Our pool of experts includes DevOps architects, Cloud and Automation engineers, and Security professionals. We focus on business automation, integration of software development with cloud infrastructure, and more. Call on us today if you are thinking about the Cloud-Specific DevOps solution. Let’s discuss the details involved.

L1, L2, and L3 are the three levels of customer technical support within an organization. Each of these is the level of services that a company offers to provide to its customers and potential end-users. What’s more, these levels offer completely different sets of technical support. Therefore, let’s find out what makes L1 vs L2 vs L3 different and what are their main characteristics. Level 1 Support Within a company, L1 technical support is the first level of assistance to customers or end-users. It includes resolving minor issues and troubleshooting. The L1 Support Team is responsible for user request acknowledgment and handling. This kind of assistance team refers primarily to FAQs and pre-defined scripts for uncomplicated technical issues. To review user requests or issues, the L1 Support Desk Operators use different channels. For example, a web form, email, call, or support chat. Every action is done through an internal ticketing system. If an organization has a physical space allocated for the L1 Support Team, employees, who are also users, can request technical help in person. In the majority of cases, the L1 Support Team is an internal company team available round-the-clock. Of course, the team has shifts and employees rotate in order to provide real-time response continuity. Key Characteristics Support on the first level of technical service involves the following actions: processing initial customer inquiries and support requests providing basic technical assistance and guidance to resolve common issues troubleshooting to resolve simple software/hardware problems forwarding complex issues to higher support levels documenting and tracking support incidents The L1 Support Team transfers the support request/incident to the second level of support if it cannot be solved via FAQs or the pre-written scripts. Level 2 Support L2 technical support is the next level service of technical assistance. It presupposes more in-depth knowledge and expertise. The L2 Support Team consists of one or several engineers responsible for fixing configuration issues and guiding users on advanced troubleshooting. Most of the issues that come to L2 Support can be resolved by standard procedures located in the administration guide. Moreover, problems the L2 Support Team fix require competency in a cloud configuration, database administration, network engineering, operating system administration, etc. If the organization has a DevOps culture, cloud technologies support is highly automated within the company. A critical part of L2 Support is to maintain software and system infrastructure by promoting routine checks, and automatic, or proactive monitoring. Key Characteristics Support on the second level of technical service involves the following actions: handling complex support tickets transformed by L1 Support Team providing advanced troubleshooting and diagnostics of issues resolving complex technical problems requiring deeper knowledge and expertise helping configure, install, and update software and applications applying security updates and critical patches of the operating system and its applications ensuring antivirus and other core cybersecurity routines proactively monitoring and ensuring core IT security requirements (e.g., 2-factor authentication enforcements) maintaining operating system and hardware state: disk space, (v)CPU and RAM utilization collaborating with other technical teams to resolve complex problems documenting and maintaining knowledge base scripts and support documentation When the ticket is too complex to resolve or requires a code change, the L2 Support Team transfers it to a higher level of support that deals with complicated issues and possesses both specific skills and knowledge. Level 3 Support The third level of technical support is the highest level an organization can have. It involves highly competent experts, who can handle extremely complex issues forwarded to them from the second support level. Mainly, it is the level that carries out changes in the software code. The L3 Support Specialists should possess knowledge about a product or service and a great scope of skills. Therefore, the L3 Support Expert is responsible for database administration and development, server repairs, and network and infrastructure maintenance. Sometimes, the L3 Support Specialist is an internal developer that works in current product development and is available for on-call duty within the SLA requirements. Key Characteristics Support on the third level of technical service involves the following actions: providing advanced troubleshooting and issue resolution for critical or highly complex tickets investigating and resolving technical issues related to networks, servers, databases, and infrastructure collaborating with other specialized teams, e.g., network engineers, system administrators, and developers developing and implementing long-term solutions, system improvements, and patches conducting root cause analysis and proactive problem prevention providing expertise and guidance to L1 and L2 support teams These are the primary levels of support an organization can provide. Therefore, it depends on the company which line of support to offer to the end-users - from the in-house team (usually, L1) and which to outsource. Our Experience with L1, L2, L3 Support Sencury’s services offer two types of support expertise. These are the L2 Support and L3 Support. Our team of skilled Support Specialists has the perfect knowledge and skills needed for 24/7 support services. Sencury offers two types of support services: Dedicated support team SLA-based support service A dedicated team is usually requested by enterprises with a significant support incident flow, requiring a focused team. It can be also required by the IT security and Data Protection requirements when the support team works for a single client. SLA-based support service corresponds to a certain service level agreement – an agreed timing for response and solution of incidents with different severity levels. This contract can correspond to a shared support team, optionally. We value our clients and desire to resolve their technical issues as soon as possible. Therefore, the team puts all our efforts into ensuring a systematic and quality approach to the technical incident management procedure. Sencury’s skilled tech support professionals offer the following services: Tell us about your main issues that require quick solutions and let’s support your software’s continuity and excellence together!

One of the most needed requirements during the software development lifecycle is the ability to communicate and collaborate. Businesses, development teams, and stakeholders have a huge gap when it comes to shared understanding. However, they are responsible for putting all efforts into effective partnerships. One of the ways to align business requirements with development outcomes is with the help of Behavior-Driven Development (BDD). Therefore, let's dive into the key components and benefits of BDD, explore how it fosters effective communication, facilitates specification, and ensures the delivery of top-notch software. Get to know how proper behavior can transform software development with Sencury. What is Behavior-Driven Development? Behavior-Driven Development (BDD) is a type of software development with a unique focus on collaboration between developers, testers, and business stakeholders. All these parties are essential in SDLC as they ensure the delivery of high-quality software that meets business requirements. BDD is a methodology combining, augmenting, and refining different practices used in test-driven development (TDD), and acceptance testing. BDD prioritizes the behavior of the software system. Also, it uses a common language to describe and define the desired behavior. Key Components of Behavior-Driven Development There are three core components of BDD to draw attention to. For instance, Collaboration – an essence in cross-functional teams. Clear communication, support, and understanding help achieve the desired outcome faster and with better quality. Technical implementation details are being backgrounded. Of course, they still matter, but the “what should be done” environment is more efficient than the one where the team brainstorms and focuses on “how it should be done”. Scenarios - clear and concise explanations of “what should be done”. There is no need to focus on describing the system's steps and how the system is going to deal with the workflow. On the contrary, describe in an understandable language the desired outcome. Also, there is a need to mention the expected behavior without specific details of how it should be achieved. The Given When Then Structure – a scenario framework. This framework is divided into several parts: the “given”, which is the context of the prerequisites needed for the scenario to trigger actions; the “when” which discusses the actions taken by the end-user; and the “then” part is the exact expected outcome after the end-user has performed the actions. Sounding as easy as these concepts are, it is vital to understand how to apply them within a project. The following picture answers this particular question. How Behavior-Driven Development Works In BDD, there are behavior-driven tests that are also called functional specifications. These specifications include an outline of the scenarios of application development. For example, The principle of “5 Whys” or “If-Then” generates user stories and relates application features to a business purpose in a clear way; Single outcome identification for every behavior; Each scenario is translated into a DSL (domain-specific language) for better communication purposes; All documentation is placed into one set available for every party involved (testers, developers, stakeholders). Business Application of Behavior-Driven Development Behavior-Driven Development is a collaborative and customer-focused approach. Therefore, businesses tend to use it for clear communication, shared understanding, and iterative development. This way, it is possible to produce top-notch products and services. Let’s have a look at the best business applications of BDD: Requirement Gathering and Analysis BDD needs clear requirements in a natural language format. To ensure your team understands what has to be done, businesses should adhere to BDD during requirements gathering. Your team will define and document acceptance criteria, user stories, and end-to-end business processes in a faster and more reliable way. User Acceptance Testing (UAT) To describe the expected behavior of a system, BDD helps create executable specifications in the form of scenarios. Businesses can use executable specifications for user acceptance testing. Any business stakeholder can write test scenarios with a desired outcome, but also it will include a system that meets all the needs. Process Improvement and Automation BDD ensures continuous collaboration and feedback between all the parties involved in product software development. E.g., business stakeholders, developers, and testers. With BDD you can easily improve the SDLC procedures. Here, BDD can help identify inefficiencies, bottlenecks, and areas for improvement. Then you can automate repetitive tasks and make some of the processes faster. Agile Project Management Agile methodology is compatible with the BDD approach via Scrum and Kanban. Therefore, likewise in test-driven development, BDD business requirements are broken into smaller fragments with clear acceptance criteria. This allows quick responses to feedback, and more adaptability if the business needs are changeable. Iterations allow better productivity during project management processes. Customer-Focused Development End-users are the main audience when it comes to software development. Here, BDD helps understand and focus on the needs of customers and their feedback. BDD can both define and validate user stories with regard to end-user expectations. Such an approach allows mutual understanding, and, this way, provides better services. Training and Documentation It is best to have clear and executable specifications when it comes to documentation and training purposes. Here, BDD scenarios can help document system behavior and every process involved. This way, it would be essential to provide training materials or documentation that is easily understandable and extra user-friendly. It reduces the time of knowledge transfer in onboarding practices and increases operational efficiency. Benefits You Get with Behavior-Driven Development Application of the BDD approach to your organizational workflow will enhance every single process in the SDLC. Therefore, you will receive: stronger collaboration shorter learning curve higher visibility quicker iterations BDD test suite elimination of waste focus on user needs 100% met business objectives Behavior-Driven Development Practices at Sencury As a part of the DevOps and Agile culture implementation organization, BDD is the ultimate and advanced implementation of these philosophies. It is not only the cross-functional team preference or methodology the team follows but also the very nature of artifacts Sencury implements. Based on multi-years of industrial experience, Sencury can implement the right balance for each case and proper convergence of test documentation, requirements specifications, and test automation scripts per se. In this case, a traceability matrix matching the use case ID and test case ID is no longer needed and can be considered redundant. Sencury offers QA strategy consulting, PM services within BDD methodology, and DevOps implementation to implement the BDD philosophy, methodology, culture, and a toolset for your particular case. Contact us to receive a consultation.

Quantum computing is one of the biggest leveraging technologies to come. We have defined and explained what quantum computing stands for, where it is applicable, and its future development in our previous article, Quantum Computing Software Development Kick-off: When? To get a better understanding of quantum technologies, do read this article. We also mentioned that quantum computers can break lots of security rules. They are extremely fast in their actions, and it takes a blink of an eye to fulfill a request. Therefore, unprotected transactions, data, and other sources might be compromised via such a computer. Quantum computers can break through some of the security measures, especially in vulnerable environments. Humanity has created quantum-resistant algorithms that ensure data security cannot be breached. Of course, these algorithms are being advanced to protect even better against quantum computer threats. However, there are cryptocurrencies such as Bitcoin that are only partially protected. As the security question involves the most popular cryptocurrency, it is vital to understand whether Bitcoin will be cracked by quantum computing. Sencury decided to look for an answer and see what we found out! What is Bitcoin and How Does it Work? Bitcoin is a digital form of money, an electronic currency. It does not have a physical form. So, we cannot hold it in our hands like traditional coins or banknotes, because it exists only in a digital format. To operate, Bitcoin uses a decentralized network called the blockchain. The latter is a public ledger and records all Bitcoin transactions. These transactions are conducted with the help of cryptographic technology. This measure is needed to prevent fraud and enhance security. Each Bitcoin transaction is transparent and accessible to the public as it is verified and added to the blockchain. To send and receive Bitcoin, you must have a Bitcoin wallet, digital cryptocurrency storage with the user’s Bitcoin addresses, and private keys. Bitcoin is unique because it is limited and the world’s very first cryptocurrency. There are 21 million Bitcoins worldwide and this factor makes them extremely valuable. Nowadays, there is a global deflation of Bitcoin. The main factor of this drawback is due to the human possibility to lose or forget private keys/seed phrases. Without proper keys, Bitcoin volume is locked forever and will not circulate in the economy. This makes Bitcoin even more pricy. However, the price of one Bitcoin highly depends also on demand and supply and is determined by the market. For example, Bitcoin’s price reached 27,639.73 on May 11, 2023. As Bitcoin represents digital money, its secure transactions are a priority. Hence, a quantum computer poses a threat to these transactions by cracking a specific part of Bitcoin’s cryptographic algorithms. Therefore, let’s discuss this vulnerability in detail. The Impact of Quantum Computing on Bitcoin Bitcoin appears to be vulnerable when it concerns quantum computers. Its weak spot lies in the cryptographic algorithms called ECDSA - the elliptic curve digital signature algorithm. This particular cryptographic algorithm is used for generating and verifying Bitcoin addresses and transaction signatures. The digital signatures are protected by certain mathematical problems and computational difficulty to solve them. ECDSA relies on ECDLP - the elliptic curve discrete logarithm problem. These algorithms presuppose that ECDLP will be hard to crack. Traditional computers are unable to solve this problem due to the big size of the keys and unapproachable timeframes. However, quantum computers can solve these and even more complex problems via Shor’s algorithm. Using this method and taking advantage of quantum bit properties, calculations are done faster and appear to be more precise. Classical computers are unable to perform the same. We all expect quantum computers to enter the market soon. With quantum computer availability, there is a chance they will break the ECDSA algorithm of Bitcoin, inflicting the security of the whole network. In the wrong hands and acknowledging the possibilities of private key calculations, a fraudster can crack the Bitcoin address and spend the digital money related to that address. As a result, researchers still work on quantum-proof algorithms to avoid digital thefts of a kind. As there is limited availability of quantum computers due to their not having enough scale for industrial use, it is hard to tell what exactly they will be capable of after their “experimental” stage is over. However, this kind of threat exists, and it should be prevented rather than dealt with afterward. The World Economic Forum highlights the following statement: “Quantum world is not yet here and the time to shape its contours is now.” Sencury’s Reflections on Bitcoin Security Issues Sencury has been founded by fundamental scientists and engineers, which allows us to easily spot potential threats and their mitigation in the topic of quantum computing threats for Bitcoin and the cryptocurrency industry. Concerning quantum-proof cryptographic algorithms, a new cybersecurity paradigm should be developed in advance before it is too late. Scientists will eventually add a protection layer to both Bitcoin and cryptocurrency transactions. For now, it is just a matter of time. In addition, addressing such leveraging technology as Web 3.0, a new quantum reality might require the creation of a completely new web and IT cybersecurity ecosystem. Sencury would be more than happy to be a part of these innovations and make our contribution as well. If you are interested in quantum technologies in general and their implications/prospects for Bitcoin and cryptocurrency safety, contact us today!

Being a top software development provider in the market requires leveraging new tools and technologies to build up-to-date products and be competitive among organizations alike. It is always good to look ahead and be prepared for digital changes. Your tech outlook can be formed via an understanding of what technologies gain the most traction. But, also, you need to know how to implement these technologies into your business workflows. That’s where Sencury might help you out! Let’s discuss some of the most popular technologies that are emerging these days. The Spread of Hyperautomation Hyperautomation is the rapidly increasing automation of businesses and processes, which leads to operational efficiency. It is used as a means of identification, assessment, and further automation of outdated systems and workflows. Gartner reports that about 70% of organizations are going to implement automation to their infrastructure by 2025. And there are, perhaps, no limits to what kind of industries hyperautomation can be leveraged to. But, surely, one type of automation cannot fit all. Many institutions acquire automation and find a need for supportive cohesive technologies to prevent conflict between automated systems. That’s why hyperautomation requires the use of multiple technologies, tools, and platforms. Some of the well-known use cases suggest using: OCR (Optical Character Recognition) to understand documentation NLP (Natural Language Processing) to understand e-mails Big Data, algorithms, AI, etc. to forecast stocks and automate restocking AI/ML (Artificial Intelligence/Machine Learning) to enhance automation flows On considering the hyperautomation of your business, also implement a combination of the following technologies: artificial intelligence (AI) and machine learning (ML) event-driven software architecture robotic process automation (RPA) business process management (BPM) intelligent business process management suites (iBPMS), integration platform as a service (iPaaS), low-code or no-code tools packaged software Of course, there are many other types of decision, process, and task automation tools you can find useful for your industry/organization. It depends on the goal you would like to pursue. However, don’t delay the decision to hyperautomate to grow your business organically. Another leveraging technology that should be paid attention to is Web 3.0. The Arrival of Web 3.0 Web 3.0 belongs to the third generation of the World Wide Web. It has not been introduced yet, but we expect it to be coming soon. Web3 is going to be decentralized, open to use, and built on top of blockchain technologies. It should form the Semantic Web - a network of meaningfully linked data. Even though it's still being developed, many are already looking forward to exploiting its possibilities. Let’s look closer at Web 3.0 features: Decentralization In version 3.0 the search for information will be based on its content, and this information will be stored in many locations simultaneously. No single server and the extra control users can have over their information are the main pillars of decentralization. In comparison, Web 2.0 requires a unique web address to find information, the location of which is fixed on a single server. Openness/Blockchain The other great feature of Web3 is its being based on open-source software. It gives network users the possibility to interact directly without any trusted intermediary. And no permissions are needed as anyone can participate without authorization from a governing body. As a result, Web 3.0 applications will run on the blockchain or decentralized peer-to-peer network, or a combination of both called dApps (decentralized applications). Semantic Web/AI/ML/NLP Web 3.0 allows computers to understand information similar to a human. This becomes possible with the help of the Semantic Web and NLP. In addition, to perfectly imitate the way a human learns, the third version of the Web will use machine learning and artificial intelligence. These technologies will also add to information accuracy and a faster response time while searching. How exactly can businesses leverage Web 3.0? Mainly, to make decentralized blockchain-driven data or cryptocurrency transfers that are also encrypted and tracked relieve people from relying on banks (centralized organizations) via open smart contracts increase revenue in the entertainment sector with the help of Metaverse (new physical, virtual, and augmented reality) produce NFTs and other digital goods instantly with the help of blockchain technology that also protects intellectual property and PII earn from users’ data as Web 3.0 will function as a global brain that can interpret any content contextually and conceptually This list can be extensive as Web 3.0 is quite promising. Let’s wait and see together. Besides hyperautomation and Web3 there is one more leveraging technology to discuss. It’s Quantum Computing and the related technology boost. Emerging Quantum Computing Technologies Quantum computing is an emerging technology with a fast-growing presence in the software development market. It uses phenomena in quantum physics to create new ways of computing. 40% of enterprises are planning to be involved in quantum computing by 2025, as Gartner reports. A quantum computer is also called a supercomputer because it solves complex problems that classical computers simply can’t. With the right input, quantum computing may greatly contribute to a lot of sectors. E.g., financial, security, artificial intelligence (AI), machine learning (ML), Big Data search, digital manufacturing, nuclear fusion, polymer design, aerospace designing, drug discovery and design, and military affairs and intelligence. What might Quantum Computer’s contribution be? For instance, quantum computing would improve security measures while sharing information radar's ability to detect missiles and aircraft water purity via smart chemical sensors trading simulators design of investment portfolios genetically targeted medical care DNA research data encryption and fraud detection How Does Quantum Computing Work? Every quantum computer works on quantum principles. Quantum algorithms approach complex problems and create multidimensional spaces, where the linking individual data points emerge like patterns. On the contrary, classical computers are deprived of the ability to create these computational spaces and will not find any patterns. To perform operations, a classical processor uses bits. However, to run multidimensional quantum algorithms a quantum computer uses qubits (CUE-bits). The quantum information a qubit holds is placed into superposition, or in other words, a combination of all possible configurations of this qubit. Groups of qubits in superposition are the ones to create complex, multidimensional computational spaces. These spaces allow us to have a bigger outlook on complex problems. Also, qubits become entangled, which means they behave differently from each other and impact one another in this way. Quantum algorithms leverage those relationships to find solutions to complex problems. The idea of resolving complex problems leverages demand. Scientists and software engineers around the world are working to make things easier for people and Quantum Computing is a technology that can make a difference. Learn more about Quantum Computing by following the link. Become Sencury’s Business Partner Today Sencury is a leading software development provider on the market. We use our scientific knowledge and technical skills to deliver quality products to stimulate your business growth. Our dedicated team of specialists looks forward to enhancing your business processes. Let us know what becomes a block on your road to success and we will take up the challenge! Let’s leverage the newest technologies on the market together. Become Sencury’s trusted partner to scale! Choose our developers' techy solutions and get quality results. Contact us and let’s talk!