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The role of AI-driven Forecast Models in Business Operations

 

In today’s fast-paced business landscape, organizations are constantly seeking innovative ways to optimize their operations and unlock hidden sources of value. Artificial Intelligence (AI) has emerged as a game-changer, revolutionizing various industries with its data-driven insights & predictive capabilities. AI-driven forecast models, in particular, have the potential to transform how businesses make decisions and operate efficiently. In this article, we will explore the power of AI-driven forecast models and their impact on enhancing operational efficiency and value creation.

What are AI-driven forecast models?

AI-driven forecast models are advanced analytical tools that leverage machine learning algorithms and data analysis techniques to predict future outcomes based on historical data patterns. These models can process vast amounts of structured and unstructured data, learning from historical trends and making accurate predictions about future events.

 

The Role of AI in Operations

AI plays a crucial role in transforming traditional operational processes. By analyzing complex datasets at unparalleled speeds, AI-driven forecast models empower businesses to make well-informed decisions promptly. They enable organizations to proactively address challenges and opportunities, thereby optimizing various aspects of their operations.

 

  • Extracting insights from vast data: One of the primary advantages of AI-driven forecast models is their ability to process and analyze vast amounts of data from multiple sources. Businesses can gain valuable insights from this data, allowing them to identify patterns, trends, and correlations that were previously hidden or too complex to discover using conventional methods.

 

  • Improving accuracy and reducing errors: AI-driven forecast models boast exceptional accuracy levels when predicting future outcomes. By minimizing human intervention, these models eliminate the risk of human errors and biases, providing reliable and consistent forecasts. Organizations can rely on these predictions to make better decisions and allocate resources more effectively.

 

  • Allocating resources effectively: Resource allocation is a critical aspect of operational management. AI-driven forecast models can help organizations optimize resource allocation by analyzing historical data and predicting demand patterns. This enables businesses to allocate their resources efficiently, ensuring that they meet customer demands while minimizing waste and unnecessary costs.

 

  • Inventory management and supply chain optimization: AI-driven forecast models revolutionize inventory management by predicting demand fluctuations and inventory needs accurately. With this information, businesses can streamline their supply chains, reducing inventory holding costs and avoiding stockouts or overstock situations.

 

  • Predicting customer preferences: Understanding customer behavior is vital for businesses to tailor their products and services to meet customers’ preferences effectively. AI-driven forecast models analyze customer data and behavior to predict trends and preferences, helping organizations stay ahead of the competition and retain their customer base.

 

  • Anticipating market trends: In a dynamic marketplace, predicting market trends is crucial for business survival and growth. AI-driven forecast models leverage historical data and market indicators to anticipate upcoming trends, enabling organizations to respond proactively to changing market conditions and gain a competitive advantage.

 

  • Real-time monitoring and detection: AI-driven forecast models facilitate real-time monitoring of operations, enabling organizations to identify inefficiencies promptly. With instant alerts and insights, businesses can take immediate corrective actions, preventing potential disruptions and enhancing operational efficiency.

 

  • Implementing corrective actions: By pinpointing operational inefficiencies, AI-driven forecast models guide organizations in implementing targeted corrective actions. Whether it’s optimizing production processes or improving customer service, these models provide valuable recommendations to enhance overall operational performance.

 

  • Streamlining processes with AI: AI-driven forecast models can streamline complex processes within an organization, reducing manual intervention and associated time delays. By automating repetitive tasks, businesses can free up resources and focus on strategic decision-making, driving efficiency and productivity.

 

  • Automating repetitive tasks: AI automation streamlines routine tasks, enabling employees to concentrate on high-value activities that require human creativity and problem-solving skills. Automation also minimizes the risk of errors, leading to increased productivity and cost savings for businesses.

 

The Future of AI-Driven Forecast Models

 

  • Advancements and potential applications: As AI technology continues to evolve, so will AI-driven forecast models. Advancements in machine learning algorithms, computing power, and data availability will unlock new possibilities for forecasting accuracy and expand the range of applications across industries.

 

  • Ethical considerations in AI adoption: As AI-driven forecast models become more ubiquitous, ethical considerations become critical. Organizations must adhere to ethical guidelines and principles to ensure responsible AI deployment, safeguarding against potential negative impacts on society and the workforce.

 

Conclusion

AI-driven forecast models are a transformative force in today’s business landscape. By leveraging vast amounts of data and powerful algorithms, these models enable businesses to optimize operations, enhance decision-making, and unlock multiple sources of value. As organizations embrace AI’s potential, they must also address challenges related to data privacy, bias, and ethical considerations to harness the true power of AI-driven forecast models.

Best Practices for Managing and Analyzing Big Data

 

From social media posts and customer transactions to sensor readings and online searches, the sheer volume of data generated on a daily basis is staggering. It’s understood that with this flood of information comes great opportunity – if one knows how to manage and analyze it effectively. Data analytics plays a crucial role in today’s business landscape. It enables organizations to uncover valuable insights from the vast amount of data they collect and make informed decisions based on these findings.

Managing and analyzing big data effectively requires adopting certain best practices. Here are some key considerations:

 

Define clear objectives: Managing and storing big data can be a daunting task, but with the right approach, it becomes much more manageable. The first step is to prioritize your business needs. Start by identifying the key objectives and goals you want to achieve through data analysis. This will help you determine what type of data you need to collect and store and ensure your analysis aligns with your business needs.

 

Data quality and preprocessing: Ensure data quality by addressing issues such as missing values, outliers, and inconsistencies. Preprocess the data by cleaning, transforming, and integrating it to make it suitable for analysis. Embrace all data collection and storage practices that align with your business needs.

 

Data storage and infrastructure: There are numerous analytics tools available today that can help you make sense of your big data. Choose appropriate storage and infrastructure solutions that can handle the volume, variety, and velocity of big data. Consider investing in scalable storage solutions that can grow as your data grows. A robust infrastructure that can handle large volumes of data efficiently is mandatory! Consider options like distributed file systems, cloud storage, and scalable databases. Cloud platforms offer flexible storage options, allowing you to scale up or down based on demand. They also provide automated backup and disaster recovery capabilities, ensuring the safety and availability of your data.

 

Scalable and parallel processing: Utilize distributed processing frameworks like Apache Hadoop or Apache Spark to handle the processing of large-scale data sets across clusters of machines. This enables parallel processing and improves efficiency.

Data security and privacy: Implement robust security measures to protect sensitive data. Access controls, encryption, monitoring, and regular audits are essential for safeguarding against unauthorized access or breaches. Protecting privacy should always be a top priority when working with large datasets.

 

Data governance and compliance: Establish data governance policies and procedures to ensure compliance with relevant regulations, such as data retention, privacy laws, and industry standards. Document data lineage, establish data ownership, and maintain proper documentation.

 

Data visualization: Use effective data visualization techniques to present complex data in a clear and meaningful way. Presenting findings in a visual format helps stakeholders easily understand complex insights derived from big data analyses. Use charts, graphs, infographics or interactive dashboards to convey key messages effectively.

 

Machine learning and statistical techniques: Employ appropriate machine learning algorithms and statistical techniques to analyze big data. These techniques can uncover patterns, identify correlations, make predictions, and derive actionable insights.

 

Iterative and exploratory analysis: Big data analysis is often an iterative process. Explore different algorithms, models, and parameters to refine your analysis iteratively. Document and communicate your findings throughout the process.

 

Collaboration and interdisciplinary approach: Encourage collaboration among data scientists, domain experts, and business stakeholders. This interdisciplinary approach fosters a better understanding of the data, improves analysis, and promotes data-driven decision-making.

 

Continuous learning and improvement: Stay up to date with the latest tools, techniques, and advancements in big data management and analysis. Continuously learn from previous projects, experiment with new methods, and strive for improvement.

 

By following these best practices for managing and analyzing big data, your organization will gain valuable insights that can fuel innovation, drive informed decision-making, and ultimately lead to success in today’s highly competitive business landscape. But remember, the specific best practices may vary depending on the nature of your data, industry, and objectives. Regularly assess your processes and adjust them as needed to ensure you’re effectively managing and analyzing big data.

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.

Cloud Computing: A Life-Saver for Businesses in Crisis Situations

In times of crisis, businesses need reliable solutions to keep their operations running smoothly. From natural disasters to pandemics, the right digital infrastructure can make a world of difference in terms of both cost and efficiency. That’s why more and more companies are turning to cloud computing – a digital solution that promises maximum data security while being highly scalable and cost-effective.

 

It’s proven that Cloud computing is a life-saver for businesses in crisis situations. It allows businesses to continue operating even when faced with power outages, natural disasters, and other unexpected events. By storing data and applications in the cloud, businesses can keep their critical operations running and avoid costly downtime.

 

The benefits of cloud computing for businesses in crisis situations include:

 

  • Increased Flexibility: Cloud computing gives businesses the ability to scale up or down as needed, which can be a lifesaver during times of crisis when demand is unpredictable.
  • Reduced Costs: The pay-as-you-go model of cloud computing can help businesses save money during times of crisis when every penny counts.
  • Enhanced Collaboration: The collaborative features of many cloud-based applications can help businesses stay connected and work together effectively even when employees are working remotely.
  • Improved Disaster Recovery: With cloud backup and disaster recovery solutions, businesses can protect their critical data and systems from being lost or damaged in the event of a disaster.
  • Greater agility: In today’s fast-paced world, the ability to quickly adapt to change is critical for survival. Cloud computing gives businesses the agility they need to make changes on the fly and keep up with the competition.

 

 

How Does Cloud Computing Help with Business Continuity?

Business continuity is an important consideration for any business, and cloud computing can be a valuable tool in maintaining business continuity. Cloud computing can provide businesses with the ability to maintain access to their data and applications in any given circumstance. Additionally, cloud computing can provide businesses with the ability to scale their resources up or down as needed, which can help to ensure that they have the resources they need during times of increased demand.

There are many different cloud computing solutions available, and the best one for your business will depend on your specific needs. However, there are some general best practices that you should follow when implementing a cloud solution:

 

Best Practices for Implementing Cloud Solutions

There are many different cloud computing solutions available, and the best one for your business will depend on your specific needs.
However, there are some general best practices that you should follow when implementing a cloud solution:

 

    • Define Your Goals: Before you even start looking at different cloud solutions, you need to take a step back and define what exactly you want to achieve with the move to the cloud. What are your specific goals and objectives? Once you have a clear understanding of what you want to achieve, you can start evaluating different options to see which one is the best fit for your business.
    • Do Your Research: Don’t just go with the first cloud solution that you come across. Do your research and compare different options before deciding. Consider factors such as pricing, features, scalability, security, and support when making your decision.
    • Work with a Reputable Provider: When it comes to choosing a cloud provider, it’s important to work with a reputable and experienced company. Choose a provider that has a good track record and is able to meet your specific needs. Ask for references from other businesses that have used the provider’s services before making your final decision.
    • Implement a Pilot Program First: Before moving all of your data and applications to the cloud, it’s often helpful to implement a pilot program first. This will allow you to test out the cloud solution and make sure that it works well for your business. It’s also a good way to get a feel for the provider’s customer service and support.
    • Stay Up to Date: Cloud technology is constantly evolving, so it’s important to stay on top of the latest trends and changes. Make sure that your cloud solution is up to date with the latest features and security measures in order to ensure that you’re getting the most out of your investment.

 

Following these best practices will help ensure that your cloud solution is implemented successfully and meets your business needs.

 

Nevertheless, there are a few critical considerations to take into account when choosing a cloud provider, particularly for businesses in crisis situations. The 1st is scalability: can the provider scale up or down to meet the changing needs of the business? 2nd is reliability: is the provider’s infrastructure reliable and robust enough to handle mission-critical workloads? 3rd is security: how well does the provider protect data and ensure compliance with industry-specific regulations? 4th is cost: what is the total cost of ownership for using the provider’s services? And lastly, flexibility: how easy is it to use the provider’s services and how much control does the customer have over their own data and applications?

 

To choose the best possible cloud provider for your business, it’s important to understand your own requirements and objectives so you can evaluate different providers to see which one best meets your needs.

At Xorlogics, we advise you on all questions regarding the introduction, update or optimization, maintenance, and further development of your IT systems according to your needs and are at your side as a competent partner. We are happy to assist you in all technical areas. Thanks to our many years of experience, we know what is important, and which hardware and software make sense for your work processes. Just contact us and we will be happy to advise you.

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).

Master Data Strategy: How to achieve greater operational efficiency and improve the customer experience?

Master Data Strategy How to achieve a greater operational efficiency and improve the customer experience

Without a doubt, the corona pandemic has led to a holistic rethinking in many areas of the company. Companies have implemented solutions that make their employees work easier, help them to reduce overall costs, and improve existing business processes and their customer’s experience in parallel. All this can’t be done without good master data. Master data is at the heart of all operational processes. Sourcing, product development, manufacturing, shipping, marketing, and sales all depend on the ability to efficiently collect, manage, and share trusted data on time.

 

Master data management also helps to automate and control error-prone manual processes, enable transparency and insights to make better operational decisions and so organizations can improve the quality of products and services, and accelerate time-to-market.

In order to achieve increased productivity, profitability, and business performance while reducing costs, one must not ignore the quality of the master data, regardless of whether it is customer master data, supplier master data, or article master data. Only superior quality data has a decisive influence on the efficiency of business processes and the quality of corporate decisions. Outdated, incorrect, or missing master data can lead to a loss of sales or weaken the reputation of the customer or supplier.

 

What mistakes can one make in master data management?

 

Management is not involved

Without the support and coordination with the management, the master data management project is doomed to failure. The support of the management right from the start is the only way to dissolve cross-departmental thinking. The senior management officer must ensure that the project team can not only streamline the management of data across departments but also that business processes and procedures can be adjusted across departments if necessary. Such huge changes are rarely received positively, so effective communication in change management is necessary.

 

Master data management is not an IT issue

Master data management is not a technical challenge or problem that only the IT department can solve. This topic must be addressed by the specialist departments. Only the various specialist departments know the content-related requirements for correct and up-to-date data. And they know their own business processes in which the various data are generated or changed. IT can help with the selection and the implementation of MDM solutions, but the specialist departments must take on the technical part here.

 

The long-term vision of the MDM project

As with any project, the MDM project also needs good management within the organization based on a correct goal matrix and a long-term vision for data management. However, this must not tempt you to create the scope of the project in such a way that it is no longer possible to carry it out quickly and efficiently. Agile project management makes it possible for achieving the goals step by step. With an unrealistic project scope, the entire project can quickly fail, and you end up with no result. Most of the time an experienced project manager, possibly external, can help get the project off the ground.

 

Organizational and cultural changes are ignored

No matter how good the project, the goals, and the vision, it will fail if all the different parties in the organization are not brought on board. Those affected and opinion leaders play a key role in the success of the project. The project team often gambles away its own success by doing everything in a quiet little room and in the end, everyone is surprised by the new solution, the result = is rejection. Good change management communication to the affected groups is an essential component of building awareness and support for organizational change and achieving long-term success.

 

The goal of mastering data management is the optimization, improvement, and long-term protection of data quality and data consistency. The main problem is when the master data is stored redundantly in different databases. This leads to time-consuming and costly data comparisons or the introduction of a central MDM system that, as a central data hub, provides the data for all other systems.

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.

2021: Intelligent Data Management Will Enable the Future of Your Business

2021 Intelligent Data Management Will Enable the Future of Your Business

The EU’s GDPR has a major impact on the data privacy ecosystem. The regulation is an essential step to strengthen individuals’ / Business fundamental rights in the digital era we are living in. After two years of the introduction of the GDPR, the following question still arises: What will 2021 bring in terms of data management and data protection? According to Gartner, by 2023, 65% of the world’s population will have its personal data covered under some kind of modern privacy regulations.

 

It’s predicted that the technology for the preparation, control and administration of data will become much more efficient so that data is available more quickly and reliably. With the focus on foundational components of data integration, data governance, and data preparation the effectiveness of big data projects can be improved. With the right technology, data management can also drive enormous business value and support digital transformation. It’ll certainly help organizations to better manage the availability, usability, integrity, and security of their enterprise data.

 

Data has evolved over the years and will continue to evolve. Today’s organizations are data-centric; they accumulate enormous amounts of information in many different formats. Those who are unprepared to deal with the amount of data will be left behind compared to those ready to welcome all business opportunities that big data has to offer. Here below are 5 main areas that play a huge role in the good preparation of data management.

 

  • Data orchestration

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A frequently used term in the sales and marketing domain for whom data has a high priority as their data is the foundation of just about everything, they do. Simply put, data orchestration is the automation of data-driven processes that includes data preparation, making decisions based on that data, and taking actions based on those decisions. Data and API integration and data movement need to grow together to support all kinds of DataOps (data operations) methods. It’s a process that often spans across many different systems, departments, and types of data. It also requires a combination of different technologies that ensure a central data flow. This is the only way to orchestrate data-related activities – across different locations, on-premise or in the cloud.

 

  • Data discovery

In this process, relevant data insights are uncovering and transferred to the business users who need them. A comprehensive directory for searching, making available, saving, and interpreting data and other objects is becoming more and more important. Advanced Analytics enables the automation of mundane data management tasks and frees up resources to actually generate added value from the data. With the right use of data discovery tools, even the non-IT staff can easily access complex data sets and draw out the information they need. This process of knowledge discovery can be performed by anyone, without the technical know-how that was required in the past.

 

  • Data preparation

“Data preparation is one of the most difficult and time-consuming challenges facing business users of BI and data discovery tools, as well as advanced analytics platforms, Rita Sallam – Research Vice-President at Gartner.” However Artificial intelligence (AI) has solved this problem by creating the basis for advanced data transformation and by enabling automatic cleansing and consolidation of data. This enables users without any prior technical knowledge to use data.

 

  • Model management

Model Management technologies help organizations consistently and safely in developing, validating, delivering, and monitoring models that create a competitive advantage. The focus is to put the central control of all models in a single application instead of the separate management of individual models. In view of the fact that many analytical models never go into production or quickly become obsolete (model decay), it is important that companies can quickly and easily register new models, adapt, track, evaluate, publish, regulate and document them.  Previously, model management referred just to monitoring production models, but it’s beyond that. Models drive new breakthroughs and operational improvements for businesses. According to a McKinsey study, organizations that leveraged models extensively showed a 7.5% profit margin advantage over their peers, whereas those that did not use models had a 2.5% profit margin deficit compared to their peers.

 

  • Data governance

“A data governance plan, supported by effective technology, is a driving force to help document the basis for lawful processing.” Data protection laws require companies to have data governance programs that provide “data privacy by default” and define policies, roles, and responsibilities for the access, management, security, and use of personal data. If they do not proactively advance standards and programs, they not only run the risk of contradicting legal requirements but they could also lose the trust of their partners/customers. With the use of advanced analytics and artificial intelligence in decision-making, they are therefore even more challenged to bring transparency to the algorithms.

 

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What is Edge Computing and why does it matter for the Internet of Things

 

What is Edge Computing and why does it matter for the Internet of Things

Possibilities are that you have already heard the term “edge computing” as many organizations use it in their daily business when it comes to handle, process and deliver data from millions of devices around the world. Multiple definitions of edge computing can be found on the internet, but I like the one from Gartner.

 

Gartner defines edge computing as “a part of a distributed computing topology in which information processing is located close to the edge – where things and people produce or consume that information.” “The emerging category of edge computing has been steadily rising in importance and maturity, and 2020 will be the most interesting year yet for vendors and users in this exciting space,” Forrester  said on Forbes.

 

The IoT is having a significant role in the digital transformation of organizations and industries. The explosive growth of IOT, mobile computing along with new applications that require real-time computing power, either to receive information from the cloud or deliver the data back to the cloud are the main reason of edge-computing systems’s growth.

The amount of data generated by digital devices is escalating day by day as 127 new IoT devices are connected to the web every second. The installed base of active Internet of Things connected devices is forecast to reach 21.5 billion units by 2025. IoT applications important growth place challenging demands on the IT infrastructure. It requires the lowest latency with the highest scalability, reliability and availability. This explains the fact that edge computing has become a special and increasingly important discipline within a network infrastructure.

 

One of the biggest benefits of edge computing is the ability to process and store data faster, enabling for more efficient real-time applications that are critical to companies. As the distributed, open IT architecture of Edge Computing processes a lot of data in real time directly on site and thus drastically reduces the bandwidth requirements and the latency times that inevitably arise during data transmission and data processing in a data center. And regardless of whether an in-house or a cloud data center is used.

 

At the same time, the high and constantly growing data volume and the increase in critical applications at the endpoints (edge) mean that the quality features typical of a data center such as scalability, reliability and high availability are also indispensable for edge infrastructures. Therefore, edge computing and the IOT are the perfect match for several reasons.

Apart from simplifying the data processing at the edge of a network, close to the sensors, and thus avoids the issues of sending all the data directly to the cloud, edge computing in the IoT environment is characterized by these four advantages compared to cloud and data center-centered approaches:

 

Speed

The longer it takes for data to be processed, the less relevant it becomes. Not only, edge computing reduces the total volume of data traffic and thus increases the performance of applications and services, but also makes latency-sensitive applications possible, for example autonomous driving, which would not be possible with data center-based data processing or in areas with insufficient network coverage.

 

Security

The number of IoT devices is constantly growing. This makes them increasingly targets for potential network attacks. Due to the centralized structure, cloud computing is vulnerable to DDoS attacks and failures. Edge computing, on the other hand, distributes applications and processes across different devices, making it much more difficult and complex for attackers to infiltrate the network. Edge computing can also filter sensitive information and, if necessary, only transmit uncritical data in order to meet security and compliance requirements. This means that less data can be intercepted, which makes compliance with security standards easier.

 

Cost

By its nature, the edge is closer to the IoT device than the core or cloud. For IoT use cases, connecting thousands or even millions of devices directly to the cloud is often not feasible due to costs, privacy, and network issues. But, with edge computing, the data can be filtered at the point of origin and does not have to be sent to a data center. Companies therefore have the choice of using the perfect mix of local services and cloud-based applications for a cost-effective IoT solution. Data processing and storage in edge devices reduces the expensive bandwidth requirements and thus optimizes the overall costs.

 

Scalability

Edge computing enables companies to expand their capacity requirements at any time and efficiently by combining IoT devices and edge data centers. The use of edge devices optimizes the scaling costs because processing is decentralised and each additional device is associated with a far lower bandwidth with less load placed on the network.

 

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