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How Technology can Enhance and Elevate Business & Employee Performance?

Technology has been advancing at an ever-increasing rate over the past few decades, and it has had a profound impact on how we live our lives. It’s no wonder, then, that technology is also having a huge impact on performance and enhancingperformance in both individuals and teams. Nowadays, the high level of performance is achieved by automating repetitive tasks, providing real-time feedback and analysis, facilitating communication and collaboration, enabling remote work, increasing efficiency and accuracy, and providing access to a wealth of information and resources. Additionally, emerging technologies such as artificial intelligence and machine learning are continuously helping to optimize and streamline complex processes & operations and decision-making leading to better outcomes and increased productivity within companies.

 

Below are the most common technologies that are used to enhance business performance, including:

 

  • Cloud computing: Provides access to on-demand computing resources, allowing businesses to scale up or down quickly, reduce costs, and increase flexibility.
  • Big data analytics: Is helping businesses make more informed decisions by analyzing large data sets to identify trends and patterns.
  • Artificial intelligence and machine learning: Helping business to automate routine tasks, make predictions, and optimize processes to improve efficiency and productivity. If integrated correctly, AI and AL can play a significant role in performance enhancement by analyzing vast amounts of data to identify patterns and insights to make predictions that humans may not be able to detect. For example, AI and ML can be used to optimize manufacturing processes, predict equipment failures, and analyze customer behavior to improve marketing strategies.
  • Internet of Things: These technologies are used to collect and analyze data from connected devices, providing insights into performance and enabling proactive maintenance.
  • Customer relationship management software: CRM software can help businesses manage customer interactions, improve customer service, and identify new opportunities for growth.
  • Collaboration and communication tools: These tools can help teams work together more effectively, whether they are in the same office or working remotely.

By leveraging these technologies, businesses can streamline processes, increase efficiency, and gain a competitive edge, resulting in increased revenue, profitability, and customer satisfaction.

How Technology can Enhancing and Elevate Business & Employee Performance?

Let’s now have a look on the most common technologies that are used to boost employee performance, these include:

 

  • Performance management software: This type of software can help track employee progress, set goals, and provide feedback and coaching to improve performance.
  • Learning management systems: These systems can help employees acquire new skills and knowledge through online courses, webinars, and other forms of e-learning.
  • Employee engagement platforms: These platforms can provide a forum for employee feedback, recognition, and collaboration, helping to increase employee motivation and satisfaction.
  • Data analytics and reporting tools: These tools can help managers track key performance metrics, identify areas for improvement, and make data-driven decisions.
  • Collaboration and communication tools: These tools can enable employees to work together more effectively, whether they are in the same office or working remotely.
  • Personal productivity tools: These tools can help employees manage their time and tasks more efficiently, reducing stress and improving work-life balance.

By leveraging these technologies, organizations can create a more engaging, productive, and efficient work environment, resulting in higher employee satisfaction, retention, and overall business performance.

AI: The next step in Software Development

AI has been revolutionizing businesses worldwide, from healthcare to banking, from automobiles to logistics. It’s innovations are developing very quickly and growing significantly on a global scale. AI refers to technologies that make it possible to equip computer systems based on algorithms with human abilities such as thinking, learning, problem-solving, etc., to make them intelligent and thereby help people to carry out different tasks.

 

With advances in machine learning, natural language processing, and data analysis, also in the world of software development, technology is changing rapidly and AI is leading the way. The global AI market reached USD 93.5 billion in 2021 and will expand at a growth rate of 38.1% annually by 2030. Innovations such as Edge AI, computer vision, decision intelligence (DI) and machine learning (ML) are shaping the market in the years to come. Additionally, robots are increasingly penetrating our everyday lives. And the current research suggests that this trend will continue in the coming years when robots and drones can take on more and more tasks in a meaningful way. These advances are related to the popularity and widespread use of AI & its promises of impressive growth opportunities.

 

The aim of AI is to create machines that can work and react like humans. However, AI is not just about creating human-like machines; it is also about making machines that can work better than humans. For example, a machine is able to process data much faster than a human can & also remember more information than a human can.

 

Since AI offers great potential for different areas, it is no wonder that its use cases are becoming more diverse with each passing year. AI solutions are already helping with:

  • Business process automation
  • Automated document creation
  • Management of production processes
  • Predictive Maintenance
  • Customer Analytics
  • Risk management
  • Supply chain management
  • Personalized service delivery
  • Software development

 

Since its inception, AI has made significant progress in software development. Early successes included creating programs that could play checkers and chess, as well as solve simple mathematical problems. In recent years, AI has been used to develop more complex applications such as autonomous vehicles, facial recognition systems, and machine translation. Looking to the future, AI will continue to play an important role in software development. With the rapid advancements being made in machine learning and natural language processing, there is no limit to what AI can achieve. As we move forward into the next era of computing, it is exciting to think about all the new possibilities that AI will enable us to realize. Let’s explore how AI is impacting software development and how it will continue to revolutionize the industry in the years ahead.

AI Software Development

Software development aka application development consists of winding together instructions for one or more programs that carry out required tasks or actions. The development team carries the task of translating problem-solving processes & algorithms into program code. Basically, we’ve known the classic methods of software development such as the agile and waterfall methodology. However, AI development works differently than classic software development. In AI development, data plays a central role – it is the center. In AI software development the behavior of AI depends on the self-training with the data. In the classic approach, the programmer had to set the rules himself, something that isn’t possible with AI development.

 

With AI, developers can create smarter and faster algorithms that can more accurately comprehend our intentions and behaviors within their applications. AI platforms promise faster development, more accurate prediction of user needs and behaviors, and continuously improving algorithms for data processing. This helps developers automate various tasks, from code quality analysis to bug fixing & save time on repetitive tasks. For example, if a developer needs to fix a bug that occurs often, they can train an AI system to automatically detect and fix that bug. This frees up the developer’s time so they can focus on more important tasks.

 

AI can also improve the quality of code. By using AI-powered static code analysis tools, developers can identify potential errors and bugs before they even write any code. This not only saves time and money by preventing buggy code from being deployed, but it also helps improve the overall quality of the software.

 

AI can help developers create more user-friendly applications. By using machine learning algorithms, developers can automatically generate user interface (UI) designs that are optimized for conversion and usability. This means that users are more likely to have a positive experience with the application, which could lead to increased customer retention and loyalty.

 

The work & results in AI development are characterized by the acquisition, analysis, and preparation of data and by training the models. We can say that an AI solution is gradually approached through smaller experiments and experience is gained in the process. For this reason, exact, conscientious, and transparent documentation of every single step & every attempt are essential.

 

To achieve quicker results, however, several work streams can run in parallel, all of which are dedicated to solving the same topic. This requires a high degree of flexibility.  It is evident that the rise of AI will revolutionize software development and open up a world of new possibilities. With its ability to process information faster than ever before, AI technology can help streamline projects and shorten production times. As more companies continue to invest in this form of technology, we can expect even greater advancements from artificial intelligence in the future. There’s no doubt that there are both positive and negative implications associated with embracing this kind of technology but for now, we must use what advantages it offers us to move forward into the digital age.

How Hyper-automaton is changing the digital landscape?

In the past two years, the shift from the workplace to the home office has led to increasing demands for artificial intelligence (AI) and automation in our daily life. Hyperautomation is a term that keeps coming up while discussing digitalization processes in businesses. For some, this is simply a detailed kind of process optimization, whereas hyperautomation is the key for the long term success for others.

 

The term hyperautomation goes back to the market research company Gartner. It refers to a well-founded methodology and a disciplined approach that organizations use to automate as many business and IT processes as possible. This technique uses a variety of technologies to speed up the automation of complicated business processes; in essence, businesses are attempting to maximize the efficiency of available digital opportunities and advance their Process Excellence initiatives.

 

Hyperautomation-Enabling Software

Hyper-automation has gained popularity over the previous 18 months, which is not surprising. The industry has adopted a somewhat hopeful attitude toward the development in light of Gartner’s identification of hyper-automation as one of the main strategic technology trends and its prediction of significant progress in years to come.

 

It’s true that hyper-automation opens up many opportunities for companies, especially when it comes to process improvement initiatives, lower operational expenses, fewer mistakes, and better outcomes, such as higher customer satisfaction through tailored customer experiences. Although it may seem thrilling and promising, the implementation is always the most difficult part. Because hyperautomation only functions as a holistic approach, you need to develop a sustainable and long-term plan before you start implementing it in your business. Organizations must also deploy the effective automation tools & techniques that form the strong foundation of hyper-automation.

 

Organizations run the risk of failing on these initiatives if they don’t take essential and key steps to understand the potential of automation as well as its capacity to generate ROI through increased productivity and cost reductions. In order to automate at such a high degree, businesses must first digitize widely.

 

While hyper-automation remains a concept, technologies such as robotic processing automation (RPA) are being deployed to create more dynamic industrialization and promote seamless collaboration between humans and bots. Plus many pure RPA applications can be implemented as small islands in the company almost overnight. Because it enables businesses to enhance their workflows and use AI-based automation, RPA will continue to be a key instrument for the digitization.

For example, an RPA process discovery platform can be used to automatically identify work processes that are suitable for automation. “Automating automation” is an crucial step to achieve scalability, as only 8 percent of automation projects reach more than 50 bots. Hyper-automation at scale is impossible without RPA.

 

According to Forrester, return on investment (ROI) in the form of both cost and time savings is expected to boost the market for RPA software from $13.9 billion to $22 billion by 2025. “Hyperautomation has shifted from an option to a condition of survival”, says research vice president at Gartner. While advances in hyper-automation will no doubt continue to evolve, RPA will help leverage this technology—ultimately “to automate automation”—and support the longer-term goal of hyper-automation.

Effective methods to avoid Data loss and Data leakage

In the age of digitization and technological developments such as Industry 4.0, companies are confronted with ever-increasing amounts of data that need to be stored, analyzed, and evaluated according to business activity/priorities. Even though data is playing an increasingly significant role as a resource, it also comes along with huge security challenges. It is becoming increasingly lucrative for hackers to steal data to use it for a competitive advantage or even to monetize stolen data. When the data is stolen, companies lose a lot of money. To counteract this, data security, i.e., the protection of data from unauthorized access, is of crucial importance.

 

The protection of a company’s valuable data from unauthorized access is the task of data loss prevention (DLP) tools. Data Loss Prevention (DLP) solutions have been an integral part of the IT security strategy of many companies for more than ten years now. It is one of the most used technologies, by worldwide companies, to prevent the loss of sensitive data.  The aim is to protect any form of data against manipulation, disclosure, loss, and other forms of threats.

 

Various countermeasures can be taken to minimize the loss of a company due to data loss & to protect critical business assets. When implementing them, it is important to know what value the respective data generates for the company. Data that leads to high financial losses in the event of damage must be given the highest priority in the implementation of data loss prevention.

 

  • Backups: The most used method to counteract data loss are backups. These do not directly prevent the data loss process, but if data is lost, it can at least be recovered. Thus, it is important that the backups are carried out on a regular basis. They must also be regularly checked for recoverability and malware.

 

  • Permission Restrictions: Another technique to limit accidental data loss by employees is to restrict permissions/access to valuable files. The permission layer supports the company’s data privacy by protecting access to restricted data. Also, if an employee does not have permission to delete a file cannot delete it either.

 

  • Training and antivirus programs: There are several measures that must be taken to protect against viruses. First, the employees should be trained so that a virus has no chance of being invited into the system. However, since errors can still occur here, network anti-virus programs must be installed on every computer, every server, and every communication interface. It makes sense not to rely on just one provider here to be able to intercept several viruses.

 

  • Data leakage prevention: Analogous to data loss prevention, data must be inventoried and categorized. It ensures that users do not send sensitive or critical information outside the corporate network. Business confidential and critical information is classified and protected so that unauthorized users cannot accidentally or maliciously share data, which would put the organization at risk.

 

  • E-mail scanning: To prevent unauthorized internal sending of confidential documents, companies can prevent outgoing e-mails with attachments. However, since this cannot be practically implemented in everyday life, it makes sense to scan outgoing e-mails and only deliver them if previously set rules for sending have been observed.

 

  • Training and antivirus programs: Finally, incoming electronic communication can also be checked. This is to ensure that no Trojan or other form of malicious software can nest in the corporate network. Incoming documents in particular offer opportunities for this. Anti-virus programs must be used here to prevent a virus from being loaded. Employees also need to be trained so that fraudulent e-mails don’t stand a chance.

Data loss prevention & data leakage prevention are two main data security strategies that are adopted by worldwide companies. Companies that store sensitive and critical data, such as personal data, should place a greater focus on data leakage prevention. Operators of universally available assets, on the other hand, should consider data loss prevention as a priority.

Invoice Management: Introducing the Digital Invoice Processing

 Digital Invoice Management

The cost of classic invoice management, such as costs for paper, printing & shipping material, proper and audit-proof archiving of all tax-relevant documents has been increasing for years. The growing expenses burden gets not only internationally active corporations with hundreds of thousands of billing transactions per month. Also, SMEs with a few hundred or thousands of invoices per year are in chaos and are increasingly looking for cost-effective ways to counteract & reduce the cost of invoices and improve processes.

 

So, it is not surprising that the topic of obvious savings and optimization is on everyone’s lips and that the optimization process of invoices and system support is in high demand. Therefore, Digital Invoice Management is more and more seen as a possible way out of misery. But how much potential is there in the real processes of today?

 

In the digital invoice management solution, invoice receiving, invoice output, and invoice archiving are the main processes that most companies are looking for, regardless of transaction volume or industry focus. The biggest challenge when receiving invoices is usually the effort involved in receiving, opening, distributing, and forwarding incoming invoices of various types, formats, and transmission routes caused. Because those times are long gone when the supplier invoices arrived into the company only via post, the electronic invoice receipts continue to increase parallelly.  In most companies, the invoices still arrive in different departments, making the automation process difficult. With the digital invoicing management system, companies can automate how, in what form, to whom, and where the various invoices are to be received/sent. A central mailbox must also be set and dedicated for all incoming invoices. Employees from different departments can then forward all incoming invoices to this mailbox.

 

Digital invoice management ensures noticeable time savings and reduces the process costs within the company. Digital invoice management has proved to have a positive effect on the waiting and processing times of invoices. With digital invoice management, it’s easy to create the necessary transparency and at the same time ensure that customers, business partners, or suppliers can provide a high level of information. Reporting also provides a real-time overview. So, you always know the exact status of all invoices in your company. Also, as the options for the degree of automation of invoices can be set from simply, knowing the status of the bill, reading out a few metadata to fully automated processes, only relevant data is transferred to the accounting department.

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82% of finance departments are overwhelmed by the high numbers of invoices they are expected to process on a daily basis and the variety of formats they’re received in. Thanks to digital invoice management in combination with a DMS, all documents are automatically filed in the correct digital file. It’s easy to find invoices again quickly at any time and benefit from high availability and the provision of specific information. For example, you can search for a specific invoice number and find it in seconds. After recording/archiving the invoices digitally, employees can have a holistic overview of invoice management in the company. The use of digital invoice management with the right software also complies with all the requirements of GDPR.

 

Although digital invoice management alone has numerous advantages, it is only through intelligent interfaces, e.g., to a document management system (DMS), that the full potential of digital invoice management can be achieved. Once the companies’ entire invoice workflow is digitally mapped and invoice management can become a routine task.

 

Digital invoice processing improves your workflow through automated checking and approval processes. With personalized workflows, you can easily route information to the right person for further processing or approval. By eliminating paper-based and manual processes, you can bring immediate quality improvement and productivity gains. In addition, errors and delays in the recording and processing of invoices can be reduced. It can be stated that you not only save time but also workload through automated invoice processing. With the implementation of digital invoice processing, you make the process simpler, leaner, and more transparent.

 

Would you like to find out more about digital incoming invoice processing? Our competent and experienced consultants are at your disposal.

CRM Automation: How can you elevate your Customer Relationship Management?

CRM Automation How can you elevate your Customer Relationship Management

The automation of companies has become a trend of our time. The modern market has set its own rules. The perfect product alone is no longer enough. In addition to the ideal product, customers also demand impeccable service. If a good product and competitive pricing are no longer enough to gain customers’ loyalty, then a personalized experience will have to be your differentiator. The competition is very strong, so a modern entrepreneur should pay close attention to customer management. Because one thing that 2021 has taught us, is to add good service to even the best product. Thus, automation is one of the most effective ways to effortlessly streamline your business.

 

The corona pandemic has further accelerated existing trends in automation. More and more companies want to automate their processes with the help of their CRM. The trend of collecting, unifying, and transforming customer data is becoming difficult and time-consuming without a proper data integration tool, but new offerings are finally making it possible for small and medium-sized businesses to do the same. One of the biggest benefits of automation is that instead of manually filling out all the documents, your team can let the software do it automatically. This gives them the opportunity to spend their valuable time on more important things. The software is programmed to work flawlessly, saving you a lot of problems and headaches and making the choice between tradition and automation obvious. CRM is becoming more and more synonymous with automation. The global CRM market is poised to reach about $113.46 billion by the end of 2027 (Globe Newswire, 2021).

 

As CRM takes care of a significant portion of the work processes; you don’t need to hire a big team to do the job but some entrepreneurs are still afraid to introduce the innovation because they are not sure if this solution really works and will be accepted by their team. Along with the fear of change, often companies don’t have an integrated approach to contact information and use different customer relationship management solutions that don’t communicate with each other. Without data integration tools, the process becomes difficult and time-consuming. In such a situation, employees need to log into multiple systems, download multiple sets of data to create their own unified customer database.

 
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On the other hand, the organizations which have successfully adapted to the new situation, use new tools and technologies to review and adapt their sales process to close more deals. Businesses that implement CRM automation experience an average of a 451% increase in qualified leads. (Annuitas Group). The CRM system, allows them to manage all contacted data of their customers and business partners in a structured and clear manner. They can record interactions and collect all important information about them along the customer journey, the journey from lead to purchase. Also, by bringing valuable data together, they are able to better understand and respond to the needs of their customers. Without electronic support, this mass of data cannot be handled at all.

 

Software for consolidating customer data easily connects disparate silos so data can be shared across systems for trend analysis, better decision-making, and greater customer satisfaction. For instance, using chatbots for communicating with customers, automating helpdesk tickets, or using automated email workflows to assist prospects in the sales process, customer management automation is on the rise. This automation enables companies to provide quality customer service while optimizing operational costs. The tools for this are also becoming easier and cheaper, which is why this is becoming more and more interesting, even for smaller companies.

 

However, it’s very important to choose software that is suitable for your team. Many CRMs look too mysterious but work as simple appointment schedulers. Therefore, the demand for quality software is very high among both small and medium-sized businesses. Well-designed software is user-friendly, which makes it easier for managers to explain the importance of the new software to their employees. It should definitely be done, and in this case, it’s not complicated.

A fully-integrated CRM system offers significant benefits but you must look for implementation options, scalability, adaptability, business value, and, of course, best value for money. The focus must always be on the individual requirements and needs of your company and the associated tasks, but also on employees who will use the system in the future.

From RPA to Intelligent Automation

RPA Intelligent Automation

 

RPA – Robotic Process Automation is changing the way companies operate around the world. The global RPA market was worth $271 million in 2016, and in 2020 that number hit $2.5 billion, an enormous increase by any metric. By mimicking structured, repetitive, and rule-based processes and tasks that are carried out by employees, this innovative technology shows its strengths. This ability can be used in many business processes in various sectors. Along with the increasing spread of RPA, the integration of artificial intelligence (AI) into the corresponding RPA software offerings is also increasing. More and more processes can be automated and transformed. Intelligent automation promises more insights, financial benefits, customer experiences, and higher business value.

 

The two types of process automation: fully automated and partially automated

 

In robot-based process automation, a distinction is made between partially automated solutions on the one hand and fully automated solutions on the other. In general, the idea behind RPA is that the robots work through the processes independently so that there is as little human interaction as necessary.

 

  • Fully automated processes (unattended automation)

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With fully automated processes, the robot works completely independently without the need for human intervention or, depending on the scenario or context, only necessary in exceptional cases. The software robot carries out transaction-based activities and processes on a large scale fully automatically without human interaction, even if the employee is logged off from the system. This type of automation is often used for back-office systems when it comes to collecting, sorting, analyzing, and distributing large amounts of data to specific employees within an organization.

 

  • Partly automated processes (unattended automation)

With partial automation, the focus is on bot/human interaction in processes. In partially automated processes, the robot reacts like a digital assistant to the employee by taking on certain homogeneous tasks. His work is triggered by certain events, actions, or commands that an employee executes in a certain workflow. While full automation concentrates on independent processing with little human intervention, the idea behind partial automation is a cooperation with the employee, in which human actions are supported by smaller automated processes.

 

 

Intelligent automation for competitive business results

 

Leading RPA software providers are continuously working to make their solutions smarter. While conventional RPA technologies often require rule-based processes and therefore do not need to make decisions based on their own judgment, intelligent automation, a combination of AI and RPA, opens up completely new possibilities: Virtual robots or bots monitor transaction processing, take notes if necessary, draw conclusions and make predictions. You can even refine the process execution approach based on insights.

 

Many RPA vendors have invested heavily in developing native solutions in their workflow design modules for bots, and have partnered with other leading technology companies. In this way, they can offer numerous innovative functions for processes that can be automated using RPA – while increasing the potential for added value at the same time. For example, some existing manual processes require reading an email or a poorly scanned PDF document and performing certain actions based on the content – or inserting extracted data into a data visualization tool and predictive or prescriptive analysis. In such cases, the use of natural language processing, computer vision, intelligent optical character recognition, or even data analysis and visualization tools may be necessary. All of this is available through the leading intelligent RPA tools. Another application example: Intelligent automation detects anomalies by virtual robots reviewing large data sets of payments, invoices, medical records, or customer feedback and identifying outliers, patterns, or topics that ultimately influence decision-making.

 

Many executives are well aware of the benefits of intelligent automation and how it can be integrated into their business transformation. These intelligent systems can detect and produce vast amount of information and can automate entire processes or workflows while self-learning and adapting. Companies that are looking to implement an RPA program should think ahead and choose an RPA platform that offers cognitive capabilities, reusable elements, and comprehensive libraries that are compatible with multiple applications.

While some companies struggled with their investments in the past year, the COVID-19 pandemic has further increased the demand for strong RPA resources as part of the digitization of processes.
Companies moving from traditional RPA to intelligent automation implementations have normalized the optimization and standardization of processes and strengthened the collaboration between IT and business. Instead of concentrating on the automation of various routine tasks, an intelligent solution enables the use of bots for end-to-end business processes and the identification of automation candidates through task or process mining. In doing so, the appropriate solutions are able to understand the data read and improve their own performance over time.

 

RPA can accelerate digital transformation. However, the real future lies in intelligent automation. As RPA providers expand their native AI offerings and the integration of technology partnerships progresses, digital team members will be able to execute increasingly complex processes – which further increases the value of intelligent automation. Therefore, companies need to review all their options before implementing the right technology that can improve their overall operational efficiency and take their business performances to the next level.

 

Sources:

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.

 

The role of Data Modelling & Prediction for Business Transformation

The role of Data Modelling & Prediction for Business Transformation

IT teams in small and medium-sized companies struggle with budget constraints and a shortage of skilled workers. When the demand for IT services increases, they are heavily overloaded and look for ways to increase efficiency. Additionally, organizations are reaching a point where their data storage and computing are unable to keep up with the growth of data and technological advancements.

 

As data, a critical asset for organizations continues to rise exponentially, business executives around the world are heavily investing in IT automation. Also, the digital transformation is pushing the boundaries, enticing businesses entities to invest in technologies that can predict possible outcomes, and to gain a competitive advantage. One of the emerging and appealing technology that businesses can benefit from in many ways is Predictive analytics. By definition, predictive analytics is a mathematical principle that uses algorithms and artificial intelligence (AI) to derive probabilities from historical and current data. It is currently one of the most important big data trends. The predictive analysis leverages statistical techniques such as predictive data modeling, machine learning, and even artificial intelligence to uncover patterns in big data.  It helps organizations to make data-driven decisions and get useful, business insights that can help them increase company profit.

 

It is a process that uses data mining and probability calculations to predict results. It includes the collection, analysis, and interpretation of data from various operational sources. The method uses structured and unstructured data, for example from internal and external IT systems (big data/data mining). Predictive Analytics collects this information using text mining, among other things, and combines it with elements of simulation processes. Thanks to machine learning, the algorithms automatically draw findings from their own data processing and use this as a basis to automatically develop predictions. The aim is to predict complex economic relationships and future developments based on the analysis of the existing data in order to make better decisions and gain a competitive advantage. Each model consists of a number of predictors, which are variables that can influence future results.

 
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The underlying software has become more accessible and user-friendly over time thanks to user interfaces that are suitable for specific departments. The goal is to identify trends, announce disruptive industry changes, and enable more data-driven decision-making. Such predictions serve to optimize the use of resources, save time and reduce costs. Optimized timelines for the introduction of new products or services can also be created. The models developed in the process are intended to help achieve or support the goals set.

 

Any area in which data is being collected is suitable for predictive analysis as there are many uses for it. These include detecting data misuse, improving cybersecurity, optimizing marketing programs, and improving business processes. Predictive analysis can use adaptive algorithms to examine systems, applications, and network performance by allowing companies to take a more proactive approach to IT operations management. With this technology, IT security experts can identify potential vulnerabilities, determine the likelihood of cyber-attacks and work on improving the company’s security structure.

 

Adapting to advanced analytics will allow your organization to stay on top. Just as technology is constantly innovating, so should companies adapt. Predictive analytics focuses on improving profitability, productivity and reducing costs through process optimization.

Do you have areas of the company in which you want to improve prediction/reporting?  If you answered yes, please contact us directly, our experts will gladly support you.

Hyper-automation: The Future of Digital Transformation

When it comes to digitization processes in companies, there is more and more talk of hyper-automation. But what is it actually? For some it is just a comprehensive form of process optimization; for others, hyper-automation is the future strategic technology that is not to be ignored.

“Hyperautomation has shifted from an option to a condition of survival,” said Fabrizio Biscotti, research vice president at Gartner. “Organizations will require more IT and business process automation as they are forced to accelerate digital transformation plans in a post-COVID-19, digital-first world.”

 

Hyperautomation The Future of Digital Transformation

 

What actually is hyper-automation?

The term hyper-automation goes back to the market research company Gartner. This means a well-founded methodology for achieving tactical and strategic goals through the automation of business processes. Thus, Hyperautomation considers the automation of business processes on a large scale by combining a wide range of coordinated digital technologies.

According to Gartner, hyper-automation focuses on two aspects of business operations: The first is to automate whatever can be automated within an organization. The second is to combine different approaches, tools, and technologies to automate only individual tasks (RPA) but also complex processes with the help of artificial intelligence (AI), virtual assistants, and machine learning (ML).

 

How does hyper-automation work?

Hyperautomation is able to unlock maximum potential by combining a number of technologies that support each other and automate complex processes with unstructured data and a significant level of ambiguity. By using AI, ML, NLP, process mining, and intelligent technologies, the ability to discover processes independently is enhanced, and RPA bots are enabled to do much more than just perform the previous repetitive tasks.

Basically, hyper-automation takes on another level of human work. It’s not only a tool but a unified enterprise strategy or initiative with the ultimate goal of creating and optimizing end-to-end processes to achieve an even higher degree of automation that supports innovative new business propositions.

Hyperautomation only works if bots ultimately also perform the tasks that a machine learning (AI) has identified. Advanced artificial intelligence can better analyze unstructured data and implant it in an efficient workflow. With RPA being the fundamental part of hyper-automation, intelligent bots continue to process the data in hyper-automation and ultimately ensure that the work is done.

 

Benefits of hyper-automation

As already described, hyper-automation enables automation that goes beyond simple, repetitive process sections. With hyper-automation, comprehensive automation can cover even complex processes. Reducing costs and maximizing profits, but also conserving resources and designing a smart working environment are also considered as main benefits. When used correctly, hyper-automation leads to a higher degree of automation and higher productivity in the company. A significant increase in customer satisfaction can also be achieved by integrating the personalized customer service. The side-by-side collaboration of man and machine is the ultimate goal of hyper-automation.

 

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