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Questions CIOs need to answer before committing to Generative AI

Unlocking the potential of artificial intelligence (AI) is a top priority for many forward-thinking organizations. And one area that has been gaining significant attention in recent years is generative AI. This revolutionary technology holds the promise of creating new and unique content, from art and music to writing and design. But before diving headfirst into the world of generative AI, CIOs (Chief Information Officers) should consider several important questions. How can they ensure success with this powerful tool? Is it right for their business? Below are the key questions and insights into how CIOs can make informed decisions about adopting generative AI within their organizations.

  • Understand your business needs: Before implementing generative AI, CIOs must have a clear understanding of their organization’s specific goals and challenges. What specific business problem or opportunity will generative AI address? CIOs should clearly define the use case or application for generative AI within their organization. This will help determine if generative AI is the right solution and ensure it provides tangible value. By identifying the areas where generative AI can make a tangible impact, CIOs can ensure that its implementation aligns with strategic objectives.
  • What data is required for generative AI? Generative AI models typically require large amounts of high-quality data to learn and generate meaningful outputs. Ensuring access to high-quality datasets is crucial for achieving successful outcomes with generative AI applications. CIOs should identify the data sources available within their organization and assess if they meet the requirements for training and deploying generative AI models. Also, they should work closely with data scientists and domain experts to curate relevant and diverse datasets that reflect the desired output goals.
  • Choose the right tool and platform. Not all generative AI solutions are created equal. CIOs must carefully evaluate different tools and platforms to find one that best suits their business requirements. Factors such as ease of use, scalability, customization options, and integration capabilities should be considered before making a decision.
  • Required expertise and resources: Implementing generative AI may require specialized skills and expertise in areas such as machine learning, data science, and computational infrastructure. CIOs should evaluate if their organization has the necessary talent and resources to develop, deploy, and maintain generative AI systems effectively. Also, generative AI should not replace human creativity but rather augment it. Encouraging cross-functional collaboration between employees and machine learning models can lead to innovative solutions that blend the best of both worlds.
  • Continuously monitor performance: Monitoring the performance of generative AI systems is essential for maintaining quality output over time. Implementing robust monitoring mechanisms will help identify any anomalies or biases in generated content promptly.
  • How will generative AI be integrated with existing systems and technologies? CIOs should consider how generative AI will interface with their organization’s current IT infrastructure and whether any modifications or integrations are necessary. Compatibility with existing systems and technologies is crucial for seamless adoption.

How to determine if generative AI is right for your business

Determining whether generative AI is the right fit for your business requires careful consideration and evaluation. Here are a few key factors to consider before making a decision:

  • Business Objectives: Start by assessing your company’s goals and objectives. What specific challenges or opportunities could be addressed through the use of generative AI? Consider how this technology can align with your long-term vision and help drive innovation.
  • Data Availability: Generative AI relies on large datasets to learn patterns, generate content, or make predictions. Evaluate whether you have access to sufficient high-quality data that can fuel the algorithms behind generative AI models.
  • Industry Relevance: Analyze how relevant generative AI is within your industry sector. Research existing use cases and success stories in similar industries to gain insights into potential benefits and risks associated with implementation.
  • Resource Investment: Implementing generative AI may require significant investment in terms of time, budget, infrastructure, and skilled personnel. Assess if your organization has the necessary resources available or if acquiring them would be feasible.
  • Ethical Considerations: Generative AI raises ethical concerns regarding privacy, bias, fairness, accountability, and transparency aspects since it involves creating synthetic content autonomously using trained models based on real-world data. Evaluate these considerations thoroughly before committing to generative AI solutions. Compliance with data protection, intellectual property, and other applicable laws is essential.
  • Risk Assessment: Conduct a risk assessment to evaluate potential drawbacks such as model limitations, security vulnerabilities, compliance issues or reputational risks that might arise from adopting generative AI technologies.

By evaluating these factors thoughtfully and engaging stakeholders across different areas of expertise within your organization along with external consultants when needed; you will be better positioned to determine if generative AI is suitable for driving innovation in support of achieving your business objectives.

In today’s rapidly evolving technological landscape, the potential of generative AI cannot be ignored. It holds immense promise for transforming industries by unlocking new levels of creativity and innovation. The key lies in understanding your specific business needs before committing fully to this technology. So ask yourself: How can your organization benefit from generating artificial intelligence? And what are the potential risks and challenges that need to be addressed?

 

How IOT can improve the Project Management Process

The world of project management is rapidly evolving, and with the emergence of Internet of Things (IoT) technology, managing projects has become even more efficient. IoT has opened up a whole new world of possibilities for project managers who are looking to improve their processes and enhance productivity. IoT has the potential to significantly enhance the project management process by providing real-time data, improving communication and collaboration, optimizing resource allocation, and enabling proactive decision-making. Here are several ways in which IoT can improve project management:

IoT can be a game-changer in project management by allowing real-time data collection and monitoring of various aspects of a project. For example, IoT devices, such as sensors and connected equipment, can gather real-time data on various project parameters, including progress, performance, environmental conditions, and resource utilization. This data can be automatically transmitted to project management systems, providing up-to-date insights that enable better monitoring, tracking, and decision-making.

 

IoT devices also allow project managers to remotely monitor project sites, equipment, and assets in real-time. Through connected cameras, sensors, and wearables, project managers can assess on-site conditions, detect potential issues or delays, and ensure compliance with safety protocols. This capability improves efficiency and reduces the need for physical presence at project locations. Thus, managers can easily access data on from any remote location to monitor performance metrics in real time.

 

In addition, IoT sensors embedded in equipment, machinery, and vehicles can collect data on their usage, performance, and maintenance needs. By analyzing this data, project managers can optimize resource allocation, schedule preventive maintenance, and reduce downtime. This ensures that resources are utilized efficiently, delays are minimized, and costs are optimized.

 

Another benefit of using IoT in project management is its ability to automate routine tasks through machine learning algorithms. These algorithms analyze large amounts of data generated from sensors and make predictions based on patterns identified over time.

 

Furthermore, IoT enables better communication among team members by providing a centralized platform for sharing information and updates. This leads to increased collaboration, as everyone has access to the same data and insights. IoT helps reduce costs associated with traditional project management methods by eliminating unnecessary paperwork and travel expenses. With everything managed digitally through connected devices, there are fewer physical resources required overall. Incorporating IoT into your project management process offers many valuable benefits that ultimately lead to smoother operations and successful outcomes.

 

Another benefit of IoT in project management is improved efficiency. By automating certain tasks with smart devices like sensors or drones, teams can save time and focus on more important aspects of the project. Additionally, data collected from these devices can be used to identify areas where improvements could be made further down the line

 

Conclusion

 

The Internet of Things (IoT) is a game-changer in project management. It is widely expected that the adoption of IoT will continue to grow across industries as more companies recognize its potential and benefits. The IoT market has been expanding rapidly in recent years, with a wide range of organizations implementing IoT solutions to improve their operations, enhance customer experiences, and drive innovation.

By leveraging the power of IoT, project managers can gain real-time insights, improve decision-making, optimize resource allocation, enhance collaboration, and mitigate risks. However, successful implementation requires careful planning, integration with project management systems, data security considerations, and a clear understanding of the specific project requirements and objectives.

The future of project management lies with IoT integration as it enables seamless collaboration among team members regardless of location or time zone. With proper utilization of this technology, businesses will achieve optimal performance levels leading to successful completion of projects within set timelines and budgets.

Why APIs are critical for modernizing your business

In today’s fast-paced digital landscape, businesses need to constantly adapt and evolve in order to stay ahead of the competition. One way to achieve this is by using application programming interfaces (APIs). APIs have become essential tools for modernizing businesses by allowing them to seamlessly integrate different systems and applications, streamline processes, and provide better customer experiences.

An API is an application programming interface, a set of protocols, routines, and tools for building software applications. APIs define how different software components should interact with each other, allowing applications to communicate and share data with each other. In simpler terms, an API is like a waiter in a restaurant who takes orders from customers and communicates with the kitchen staff to fulfill those orders.

 

APIs can be either public or private. Public APIs are made available to developers by companies or organizations to allow third-party developers to build software applications that can interact with their services. Private APIs, on the other hand, are used internally by companies to facilitate communication between different software components within their organization.

APIs are critical for modernizing your business because they allow different software applications to exchange information and perform various functions. This communication is essential for businesses that rely on multiple software applications to run their operations. For example, a business may use an accounting application to track financial data, a customer relationship management (CRM) application to track customer data, and a human resources (HR) application to track employee data. Without APIs, these different applications would not be able to communicate with each other and share data. This would make it exceedingly difficult for the business to run its operations effectively.

 

APIs also allow businesses to integrate new applications and services into their existing systems. This is essential for businesses that want to stay up to date with the latest technological trends. For example, a business may want to add a chatbot to its website in order to provide better customer service. If the chatbot provider does not have an API, then the chatbot integration would be impossible. This would limit the functionality of the chatbot and make it less effective at providing customer service.

 

BENEFITS OP API XORLOGICS

 

How to Implement an API

APIs are critical for modernizing your business as they provide a way to decouple your front-end and back-end systems. This means that your front end can be built on one platform and your back end on another, and they can communicate with each other through an API.

There are many ways to implement an API, but the most common is to use a RESTful API. To do this, you will need to define a set of endpoints (URLs) that your API will support, and then write code to handle requests to those endpoints. Some real-life examples are Twilio, Stripe, and Google Maps. If you’re not sure how to get started, contact us for step-by-step guidance.

 

The Future of APIs

As the world becomes increasingly digital, the importance of APIs will only continue to grow. APIs are the key to unlocking the power of the digital world for businesses. By making data and functionality available to developers, they can build applications that will drive innovation and growth.

The future of APIs is bright. They offer a way for businesses to open their data and functionality to the world, paving the way for new applications and new opportunities. In summary, APIs can help modernize business by improving operational efficiency, enhancing customer experience, enabling innovation, fostering collaboration, and enhancing security. As more businesses recognize the power of APIs, we can expect to see even more innovation in the years to come.

The biggest challenges of BigData in 2023

The use of big data is on the rise, with organizations investing heavily in big data analytics and technology to gain insights and improve business performance. With the rapid growth of the internet, social media, and the IoT, the amount of data being generated is increasing exponentially. As a result, there is a need for better tools and techniques to collect, store, analyze, and extract insights from this data.

 

Additionally, the growth of the global datasphere and the projected increase in the size of the big data market suggest that big data will continue to be a critical driver of innovation and growth across various industries. In a study by Accenture, 79% of executives reported that companies that do not embrace big data will lose their competitive position and could face extinction.

 

Advancements in big data technologies such as machine learning, artificial intelligence, and natural language processing are also foreseen. These technologies have the goal to enable businesses and organizations to make better decisions, gain a competitive advantage, and improve customer experiences.

Xorlogics participating Cebit 2016

Here are a few examples of how big data is being effectively used in various industries:

 

  • Healthcare: Big data is being used to improve patient care, disease diagnosis, and treatment outcomes. For instance, healthcare providers can analyze electronic health records to identify patterns and trends that may help diagnose diseases earlier and predict patient outcomes. Additionally, big data analytics can help hospitals and healthcare organizations optimize their operations, such as reducing wait times and improving patient flow.
  • Finance: Big data is being used to identify and prevent fraud, assess risk, and personalize financial products and services. For instance, financial institutions can use big data to analyze customer behavior and preferences, in order to develop personalized marketing campaigns and offers. Additionally, big data analytics can help banks and other financial organizations to detect fraudulent activity and reduce the risk of financial crime.
  • Retail: Big data is being used to personalize the shopping experience, optimize inventory management, and improve customer loyalty. For instance, retailers can use big data to analyze customer behavior and preferences, in order to develop targeted marketing campaigns and personalized recommendations. Additionally, big data analytics can help retailers to optimize their inventory levels, reduce waste, and improve supply chain efficiency.
  • Manufacturing: Big data is being used to optimize production processes, reduce downtime, and improve quality control. For instance, manufacturers can use big data to monitor equipment performance and predict maintenance needs, in order to reduce downtime and optimize production schedules. Additionally, big data analytics can help manufacturers to identify quality issues early, reducing waste and improving product quality.
  • Transportation: Big data is being used to optimize transportation networks, reduce congestion, and improve safety. For instance, transportation companies can use big data to analyze traffic patterns and optimize routes, reducing travel time and congestion. Additionally, big data analytics can help transportation companies to monitor vehicle performance and identify potential safety issues, reducing accidents and improving overall safety.

 

Generally, big data is being effectively used across a range of industries to drive innovation and create value, improve operational efficiency, reduce costs, and improve customer satisfaction. Along with the benefits of Bigdata, it’s challenges cannot be ignored. Here below are few potential challenges that bigdata may face in the future:

 

  • Data Privacy and Security: As the amount of data collected and stored increases, so does the risk of data breaches and cyber-attacks. Protecting sensitive information will be critical, particularly as more businesses move towards storing their data in the cloud.
  • Data Quality: As the volume of data grows, so does the risk of inaccuracies and inconsistencies in the data. Ensuring data quality and accuracy will become increasingly challenging, particularly as the data comes from a wide range of sources.
  • Data Management: Managing large amounts of data can be complex and costly. Businesses will need to invest in tools and technologies to help manage and process the data effectively.
  • Talent Shortage: The demand for skilled data professionals is growing rapidly, and there may be a shortage of qualified individuals with the necessary skills to analyze and interpret big data.
  • Data Integration: With data coming from various sources, integrating, and combining the data can be a challenging process. This could lead to delays in data processing and analysis.
  • Ethical Use of Data: As the amount of data collected grows, it becomes increasingly important to ensure that it is used ethically and responsibly. This includes addressing issues related to bias, fairness, and transparency.
  • Scalability: As the volume of data continues to grow, businesses will need to ensure that their infrastructure and systems can scale to accommodate the increased data load.

 

Overall, these challenges could impact the effective use of big data in various industries, including healthcare, finance, retail, and others. Addressing these challenges will require ongoing investment in technologies and skills, as well as a commitment to ethical and responsible use of data.

 

If you are looking for a partner who can give you both strategic and technical advice on everything to do with the cloud, than contact us so we can talk about your cloud project and evaluate the most suitable solution for your business.

How to measure Resilience and success in Machine Learning and Artificial Intelligence models?

ML and AI are powerful tool that can be used to solve complex problems with minimal effort. With the rapid advances in technology, there still exists many challenges when it comes to making sure these models are resilient and reliable.Resilience is the ability of a system to resist and recover from unexpected and adverse events. In the context of AI and ML systems, resilience can be defined as the ability of a system to continue functioning even when it encounters unexpected inputs, errors, or other forms of disruptions.

 

Measuring resilience in AI/ML systems is a complex task that can be approached from various perspectives. Fortunately, there are some steps you can take to ensure your ML models are built with robustness. There is absolutely no one-size-fits-all answer to measuring resilience in AI and ML systems. However, there are a number of factors that can be considered when designing a resilience metric for these systems.

 

  • It is important to consider the types of failures that can occur in AI and ML systems. These failures can be classified into three categories: data corruption, algorithm failure, and system failure. Data corruption refers to errors in the training data that can lead to incorrect results. Algorithm failure occurs when the learning algorithm fails to connect a correct solution. System failure happens when the hardware or software components of the system fail. In other terms it’s also called robustness testing. This type of testing involves subjecting the AI/ML system to various types of unexpected inputs, errors, and perturbations to evaluate how well it can handle these challenges. Thus the system’s resilience can be measured by how well it continues to perform its tasks despite encountering these challenges. A resilient system is one that is able to recover from failures and continue operating correctly.

 

  • It is necessary to identify what creates a resilient AI or ML system. It is also important for a resilient system to be able to detect errors and correct them before they cause significant damage. Usually, the fault injection method makes easier to evaluate how the system response to intentionally introduced faults and if it’s able to detect & recover. With this method, the resilience of the system can be measured by how quickly and effectively it can recover from these faults. It is also mandatory to develop a metric that can be used to measure resilience in AI and ML systems. This metric takes into account the type of failures that can occur, as well as the ability of the system to recover from those failures.

 

  • The performance monitoring of the AI/ML systems cannot be considered insignificant as this monitors the performance of the AI/ML system over time, including its accuracy, response time, and other metrics. The resilience of the system can be measured by how well it maintains its performance despite changes in its operating environment.

Overall, measuring resilience in AI/ML systems requires a combination of methods and metrics that are tailored to the specific application and context of the system. Along with that, we also need to ensure that the data which is use to train ML models is representative of the real-world data. This means using a diverse set of training data that includes all the different types of inputs our model is likely to see. For example, if our model is going to be used by people from all over the world, we need to make sure it is trained on data from a variety of geographical locations.

 

Last but not the least, ML systems need regular training “refreshers” to keep them accurate and up-to-date. Otherwise, the system will eventually become outdated and less effective. There are a few ways to provide these training refreshers. AI/ML systems are typically trained using large amounts of data to learn patterns and relationships, which they then use to make predictions or decisions. However, the data that the system is trained on may not be representative of all possible scenarios or may become outdated over time. One way is to simply retrain the system on new data periodically. In addition, the system may encounter new types of data or situations that it was not trained on, which can lead to decreased performance or errors.

 

To address these issues, AI/ML systems often require periodic retraining or updates to their algorithms and models. This can involve collecting new data to train the system on, adjusting the model parameters or architecture, or incorporating new features or data sources.This can be done on a schedule (e.g., monthly or quarterly) or in response to changes in the data (e.g., when a new batch of data is received).

 

Another way to provide training refreshers is to use transfer learning. With transfer learning, a model that has been trained on one task can be reused and adapted to another related task. This can be helpful when there is limited training data for the new task. For example, if you want to build a machine learning model for image recognition but only have a small dataset, you could use a model that has been trained on a large dataset of images (such as ImageNet).

 

Measuring the resilience of AI/Ml systems requires extended range of tools and expertise. We at Xorlogics make sure to produce the best model with the highest standard of resilience & accuracy. Tell us about your business needs and our experts will help you find the best solution.

Physical & Cloud #DataProtection: Best Practices for your #Backup and #RecoveryProcess

Data, one of the most valuable assets of organisations. Massive data is the new currency. Thanks to advancements in technology and connectivity, data creation is skyrocketing. According to IDC, Global DataSphere Forecast, 2021-2025, the global data creation and replication will experience a compound annual growth rate (CAGR) of 23% over the forecast period, leaping to 181 zettabytes in 2025. That’s up from 64.2 zettabytes of data in 2020 which, in turn, is a tenfold increase from the 6.5 zettabytes in 2012. These data are stored in ever-increasing environments and connected devices, therefore backup and restoring the capability of an information system is a real challenge to ensure business continuity and the availability of associated data.

Volume of data created and replicated worldwide

What must IT departments do to fulfill the data security mission? Well, the data security policy is at the heart of each business concern and should be a fundamental part of their security strategy. Planned security measures can then create tactical and operational rules through the joint efforts of security and storage teams. To this end, storage must be an integral part of the company’s security strategy.

 

To achieve these objectives, a company must establish a cluster around the following five essential aspects:
• Allocation of responsibilities;
• Risk Assessment;
• Development of a data protection procedure;
• Communication of data protection procedure;
• Execution and testing of the data protection procedure.

 

  1. Allocation of responsibilities

The goal is to make storage security a fully-fledged feature of the IT security architecture. Even if the company decides that the responsibility for backup or storage security rests within the storage team, it must nevertheless integrate any safety measures in this area with task to secure the rest of the infrastructure. This integration will contribute to the establishment of in-depth protection. It is also advisable to share responsibility for extremely sensitive data. It’s, therefore, better to ensure that the person authorizing access is not the same as the person responsible for enforcement.

 

  1. Assessment of storage risks in the area of ​​IT security

#Managers must review each step of their backup methodology to identify security vulnerabilities. Can an administrator secretly make copies of backup tapes? Are they stored in boxes accessible to everyone? Is there a rigorous end-to-end monitoring chain for backup tapes? If critical data is backed up and transported, vulnerabilities of this nature could make it easy prey. If the risk analysis reveals many vulnerabilities, the company must seriously question the encryption of its data.

 

  1. Development of an information protection program that guarantees the security of company data, at all times, wherever they are

Multi-level protection should be adopted by taking existing best practices for the data network in order to apply to the storage network, while adding specific layers adapted to the characteristics of the archived data, for example:

  • Authentication: application of multi-level authentication techniques and anti-spoofing (anti-identity or address spoofing).
    • Authorizations: access rights according to roles and responsibilities (as opposed to total administrative access).

It is imperative to duplicate backup tapes because it is never good to depend on a single copy of the data. Despite the longevity of the bands, they are still exposed to environmental and physical damage. A common practice is to perform nightly backups and then store these off-site tapes without any verification. Recommended best practices include duplicating backup tapes and then storing offsite copies.

Magnetic tapes remain the preferred storage mode for backups because they are economical and offer sufficient capacity to back up an entire operating system on a single cartridge. When stored properly, archival tapes have a lifetime of more than 30 years, making them an exceptionally reliable storage medium.

 

  1. Communication of the procedure to be applied with regard to the protection and security of information

Once the procedure for protecting and manipulating sensitive data has been defined, it is important to ensure that those responsible for their safety are informed and trained. Safety rules are the most important aspect of assigning responsibilities. Functional managers need to be aware of risks, countermeasures, and costs.

Data loss and intellectual property theft affect the entire enterprise, not just the IT department. As such, the Director of Security must undertake a data security approach by training the different functional frameworks in the risks, threats and potential harms arising from security breaches, as well as the cost of the various possible countermeasures in this area. In this way, company executives can raise awareness about the cost/benefit of investments in data security.

 

  1. Implementation and testing of Data Protection and Security Plan

Securing data is not about technology but about the procedure. This is why it is essential to test the procedure. In addition, as the growth of the company is accompanied by an evolution in security and data protection needs, IT security practices must also evolve. Once the complete security plan has been developed, defined, and communicated to the concerned team, only then it’s the right time to implement it. IT team must ensure the implementation of the tools, technologies, and methodologies necessary for the classification of information. New technologies may be required to classify information or label it with metadata so that it is backed up according to appropriate rules and procedures.

Once in place, the procedure must be tested, both concerning backup and restore. The test is to introduce, into the process, any possible and imaginable danger, whether it is the loss of a tape or a server, network problems, equipment or filing of data or any other scenario which could affect the company’s performance.

It is advisable to carry out tests with personnel who are less familiar with the procedure, to ensure that it can nevertheless be applied without difficulty in the absence of the usual supervisor (due to illness, holidays or departure).

Data Management: Cost of poor data quality

Organizations are collecting and generating more information/data than ever before. This information/data is used in almost all activities of companies and constitutes the basis for decisions on multiple levels. But, simply having a lot of data does not make a business data-driven, because issues related to data quality maintenance are infecting numerous businesses. Companies are witnessing that not only the data is growing rapidly in scale & importance but also in complexity. The topic of data quality and what companies should do to ensure a good level of data is one of the biggest priorities within companies that are always being worked on. Since poor data quality affects, among other things, business processes, it can lead to wrong decisions and make it more difficult to comply with laws and guidelines (compliance).

 

Organizations around the world gather so much data that sometimes it’s impossible for them to differentiate the valuable and outdated or inaccurate data. Studies have also shown that the data stays stuck in different systems in inconsistent formats, which makes it unreliable or impossible to share with other team members. According to Gartner’s research, “the average financial cost of poor data quality on organizations is $9.7 million per year.” In other words, the cost of poor data quality is 15% to 25% of revenue.

MASTER DATA MANAGEMENT

Having quality data means getting the right answer to every question. This requires that data is constantly checked for errors, redundancy, and usability. In addition to avoiding errors and gaps, it is also about making data available to every concerning person in a uniform way and making it as easy to use as possible. Master data management (MDM) helps companies to ensure that their data is accurate, trustworthy, consistent, and shareable across the enterprise and value chain by enabling greater data transparency & empowering you to drive better decisions, experiences, and outcomes that benefit your business and your customers.

 

Basically, master data management creates added value on two levels: on the one hand in the administrative areas, for example through more efficient master data maintenance processes or also in IT projects; on the other hand, through increased transparency in the operational areas and thus improved controllability. The benefit in mastering data processes is reflected, among other things, in the reduced effort involved in searching for data, less internal coordination effort, and the fact that there is no duplication of work when changing data or making initial entries. Furthermore, clean master data forms the basis for scalable automation options and reduces the effort for migrations.

 

Mastering your data challenges also delivers a significant competitive advantage. And as the pace of innovation accelerates, the importance of mastering your data will only be beneficial for your business. The benefits of MDM in the administrative and operational areas as well as for compliance ultimately increase the competitiveness of companies. Last but not least, good data quality ensures the satisfaction of customers, suppliers, and employees.

The Data Modelling Techniques for BI

The Data Modelling Techniques for BI

Business applications, data integration, data management, data warehousing and machine learning – they all have one common and essential component: a data model. Almost every critical business solution is based on a data model. May it be in the areas of online trading and point-of-sale, finance, product and customer management, business intelligence or IoT, without a suitable data model, business data simply has ZERO value!

 

Data models and methods for data modelling have been around since the beginning of the computer age. A data model will remain the basis for business applications for the foreseeable future. In the area of ​​data modelling, the basics of mapping complex business models are developed. In order to model data successfully, it is particularly important to understand the fundamentals and relationships between the individual topics and to reproduce them using examples. Data needs a structure, without it, it makes no sense and computers cannot process it as bits and bytes.

 

What is the business intelligence and why is it important?

 

The concept of business intelligence first appeared in the 1960s. Business intelligence, also known as BI, is a collective or generic term for the various sub-areas of business analytics, data mining, data infrastructure, data visualization and also data tools. In summary, BI analyses all the data generated by a business and makes reports, performance measures, and trends that helps management in decision making.

 

BI is essential when it comes to optimizing business processes and positioning yourself successfully for the future. As the goal of BI is to provide you with company data from all of your company areas, so can use it for the company’s efficiency & increase productivity and react to changes in the market. With business intelligence, you are able to identify and evaluate data and ultimately react to achieve goals.

 

Data modelling techniques – an overview

 

The following is an overview of the various data modelling techniques:

    • Flat data model: in this very simplest database model, all data is in a single two-dimensional table, consisting of columns and rows. Columns are assumed to have a similar types of values and in the row, elements are supposed to have relational value to one another.

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    • Hierarchical model: data is stored in a tree-like structure. Data is store in a root or top-level, directory that contains various other directories and files.

 

    • Network model: This model is very similar to the hierarchical model but the hierarchical tree is replaced by a graph. In this model, the records are connected to each other and their allocation takes place via a link table. In this manner, the hierarchy is maintained among the records.

 

    • Relational model: This model represents the database as a collection of relations. A relation is nothing but a table of values. A predicate collection over a fixed set of predicate variables, the possible values ​​or combinations of which are subject to restrictions.

 

    • Star schema model: A star schema is a database architecture model where one fact table references multiple dimension tables, optimized for use in a data warehouse or business intelligence.

 

    • Data Vault Model: Entries with long-term stored historical data from various data sources, which are arranged in and are related to the hub, satellite and link tables. At the core, it is a modern, agile way of designing and building efficient, effective Data Warehouses.

 

DMS: Facts that Encourages to go Paperless in 2021

DOCUMENT MANAGEMENT SYSTEM

The digital transformation due to the pandemic has led companies to adapt work from the home concept. Remote work went from an offer, that only a few companies made, to an inevitable and massive shift in the way that people work around the world. The percentage of home offices is expected to double in 2021, according to a survey from Enterprise Technology Research (ETR).

These statistics show that remote work is not likely to decline this year or anytime soon. It’s important as a company to set up digitally and to ensure that employees can work flexibly from their home office. But in many companies, some of the important business processes still require employees to use paper forms and they can’t access the corporate data they need immediately from home. Thus, 2021 is the best time to go paperless so companies don’t have to deal with employees’ unproductivity along with lost, damaged, or misplaced documents which can easily result in lost data, privacy violations, or lost customers.

 

What is the paperless office?

The term is actually self-explanatory. A paperless office means saving on paper-based documents and using only digitally-supported formats for all business processes. The idea behind building a paperless office has been around for a few decades since it was first conceptualized as “The office of the future” in an article in Business Week in 1975. A paperless environment closely resembles an office utilizing integrated information systems with multiple software tools to reduce paper consumption and improve efficiency in retrieving electronic documents. Paperless environments increase office productivity and collaboration and also helps to manage the company’s data in a more efficient way with the right processes.

 

Advantages of the paperless office

Nowadays, companies can no longer avoid digital work; the advantages are too obvious to affect pretty much all professional fields. Businesses that are always looking for new and efficient ways to optimize their operations must take a look at the benefits of a paperless office here below:

  • Sustainability (reduce printing costs, etc. – better for the environment)
  • More efficient time management through productive work
  • Collaborative work with several people or teams
  • Space and material savings
  • Flexible work environment and mobility, which is essential especially in corona times, but also in the future
  • Central storage in digital form – everyone can access it from anywhere
  • Automatic Data Retention & Digital Backups
  • The search function enables documents to be found quickly (effective time management)
  • Future viability and competitiveness
  • Departments become more productive; more time spent on important tasks
  • Simplified communication using digital tools and media

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While technology supports the move to paperless business practices, not all organizations can afford the IT infrastructure necessary to make the transition. A small and medium-sized business (SMB) often lacks the resources and IT infrastructure to quickly increase server performance and/or expand storage capacity both of which are required for paperless transition.

Cloud systems are known as the foundation for the paperless office and to instantly increase companies’ IT infrastructure. For businesses that are interested in moving to electronic storage and retrieval of documents, cloud computing offers a cost-effective means of making the change.

 

One important technology that enables a paperless office to efficiently capture, store, secure, and retrieve information is an electronic document management system (EDMS).  Document management is becoming increasingly important as the concept of a paperless office is becoming an everyday reality, especially with the growth in cloud storage services.  With a DMS businesses can create, track and store digitized documents. It manages structured data and is focused on documents in formats as Word, PDF, PowerPoint, Excel, etc. The key purposes of a DMS are regulatory compliance and workflow management. DMS applications also have advanced imaging and scanning capabilities, such as optical character recognition (OCR), handprint character recognition (HCR), optical mark recognition (OMR), and more.

 

Conclusion:

The paperless system offers organizations many benefits including increased employee efficiency, productivity, streamline workflows, and information security. The paperless office is a process and not a one-time thing, therefore this transformation requires a certain discipline, the courage to change, and requires good communication within the company. Organizations can use numerous solutions to help them attain paperless environments. But the first step and the most commonly used method of reducing paper is the digitization of current documents.

With the growing availability of electronic document management systems, implementing a good document management solution can go a long way in enabling your organization to reduce cost, improve your business process, increase customer satisfaction, as well as improve employee efficiency and comfort and increase productivity. Contact-us any time to help you find the right document management software for your business needs.

 

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The Most Common Myths About Cybersecurity

cybersecurity

Cybersecurity issues are becoming a day-to-day struggle for businesses. cybersecurity statistics reveal a huge increase in hacked and breached data from sources in 2019. according to the Ninth Annual Cost of Cybercrime global study by Accenture, security breaches have increased over the past year with 11% and over the past five years, security breaches have increased by 67%, according to Accenture’s global survey. But even in the age of digitalization, there are still numerous myths surrounding the topic of cybersecurity. This article underlines few common myths around cybersecurity.

 

Myth 1: Nobody wants to hack me! I’m not anybody important!

Reporting on cyber attacks often gives the impression that it only hits the big ones. Small business must pay attention to cyber security and stop thinking they’re not even worth being attacked, and their size doesn’t make them the perfect target. Even those who often have fewer resources for security solutions or who are targeted by criminals in the event of mass attacks are a worthwhile goal. Hackers will be focusing to gain access to the data of SME in order to steal consumer information along with their credit card details. According to Accenture research, forty-three percent of cyberattacks are aimed at small businesses, but only 14% of them are prepared to defend themselves. Small businesses make up to 13% of the entire cyber security market, surprisingly small businesses invest less than $500 in cyber security.

 

Myth 2: Cybersecurity is only IT department’s problem

Most organizations are approaching cybersecurity totally wrong. Of course, the IT department is primarily responsible for the implementation of security standards and the implementation of protective measures. However, business leaders, either they run a small-medium company or an international corporation, they can’t just leave everything at their IT department and technology systems, instead to make a safer workplace and to protect their valuable assets, they need to make IT department work in collaboration with the rest of trained employees and give their management support to identify key risks, imagine potential threats and develop a plan for safe work environment.

 

Myth 3: My Antivirus software is enough to protect me

Your antivirus software is helpful and to fight against malicious software and viruses but they don’t stop the attack from happening. But the “threat landscape,” is changing and today’s hackers are way more sophisticated than you may think. They are finding new ways around antivirus software and to go mostly undetected to circumvent privacy. Even with antivirus software installed, you still need to be careful and wary of your online activities. Therefore, it is very important to take security measures that match your business requirements and your risk situation.

 

Myth 4: I have a Mac computer, they don’t get viruses

It’s true that Macs don’t get as much malware as PCs. However, it’s a BIG lie that they are 100% safe, they do get viruses, and beyond that, they’re getting more than ever. During the first quarter of 2019 alone Mac malware jumps more than 20% in three months, massive uptick in adware. A report from Malwarebytes claims, with detected threats up by more than 60% from the fourth quarter of 2018 to the first quarter of 2019, and adware becoming more prevalent with an increase of over 200% for the same period. The threat of malware has increased for Mac users in a short space of time.

 

Myth 5: Hacker attacks can be detected instantly

Did you know that the average time to identify a breach in 2019 was 206 days (IBM)? The average time to contain a breach was 73 days (IBM). It represents a great danger for business as longer hackers can access your systems undetected, the further they can go in their attack and cause some major damages is therefore all the more important in order to be able to react quickly in an emergency.

By conducting routine assessments and Continuous monitoring of potential vulnerabilities in your organization, you can save money, mitigate the damage of breaches, and perhaps even identify vulnerabilities before a breach takes place.

 

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