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The biggest challenges of BigData in 2023

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

 

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

 

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

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

 

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

 

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

 

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

 

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

 

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

How 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.

Benefits & Challenges of Software Development with Machine Learning

Software development with machine learning involves using ML algorithms and techniques to build software applications. These applications can range from simple data analysis and prediction tools to more complex systems such as image recognition, natural language processing, and autonomous systems. The process of developing such software typically involves the collection and cleaning of data, selecting and training models, evaluating performance, and deploying the final product.

 

Several benefits are associated with software development, such as:

 

Automation and improved efficiency: ML models can automate tasks that would be time-consuming or difficult for humans to perform, such as image recognition or natural language processing. This can lead to improved efficiency and cost savings.

Increased accuracy: ml models can achieve higher levels of accuracy than traditional software in tasks such as prediction and classification.

Handling big data: ML models can handle and process large amounts of data, making it possible to extract insights and identify patterns that would be difficult or impossible to detect manually.

Personalization: ML models can be trained on individual user data, making it possible to personalize recommendations and experiences.

Real-time decision-making: With the development of edge computing, ML models can make decisions in real time, enabling the development of applications such as autonomous vehicles, robots, and IoT devices.

Innovation: Using ML models and techniques opens doors for new possibilities, which can lead to the development of new products, services, and business models.

Overall, software development with ML offers the potential for significant advancements in automation, accuracy, and efficiency in a wide range of industries and applications.

 

But, while there are multiple benefits of software development with ML, there are also some challenges that may arise:

Data availability and quality: ML models require a large amount of high-quality data to train and test on. If data is not available or is of poor quality, this can make it difficult to develop accurate models.

Model selection and tuning: There are many different ML algorithms and models to choose from, and selecting the right one for a given task can be challenging. Additionally, fine-tuning the parameters of a model to achieve optimal performance is a time-consuming process.

Overfitting: Overfitting occurs when a model is trained too well on the training data and does not perform well on new, unseen data. This can be a common problem and can be addressed using techniques such as cross-validation and regularization.

Explainability: Some ML models, such as deep neural networks, can be difficult to interpret and understand. This can make it challenging to explain how a model is making its predictions and to identify any potential biases in the data.

Deployment and maintenance: Deploying ML models in production environments can be complex and requires specialized knowledge. Additionally, these models need to be continuously updated and maintained as the data and requirements change over time.

Ethical concerns: There are many ethical concerns that arise when using ML such as bias, transparency, and accountability. It’s important to consider these concerns when developing and deploying such models.

 

ML is becoming increasingly popular in many industries and is expected to have a significant impact on the economy in the near future. In general, ML and AI are considered to be one of the most promising fields in technology and key driver of digital transformation and innovation. As companies are investing in this technology to improve their products and services, automate tasks, and gain a competitive edge. And this is across different industries such as healthcare, finance, retail, logistics, and manufacturing.

 

According to a report by the Belgian government, the AI market in Belgium is expected to grow rapidly in the next few years, with the government investing heavily in research and development in this field. VLAIC, aka AI research center Vlaamse AI-coalition, is an initiative from the Flemish Government to support the development and use of Artificial Intelligence (AI) in Flanders, Belgium. The goal is to make Flanders a leading region in AI by 2025.

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.

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.

Data: A key resource for Digital Business Models

Businesses are generating and collecting large amounts of data, due to the rapid digitalization in all sectors and industries. Digitization has come to stay so not only the volume but also the variety of data – structured and unstructured – is growing exponentially in companies around the globe. New sources of income and growth opportunities are what companies strive for today. The evaluation and analysis of data help significantly and are becoming more and more important for companies – because the results of such analysis are considered as a strategically valuable asset.

Data A key resource for Digital Business Models

Companies don’t just want to collect the data, but their goal is to get real benefit from it. The analysis of data, which arises from individual digital interactions, generates significant added value for companies. This also includes the processing of omnichannel data, regardless of whether they are structured or unstructured, to get better insights into the entire customer journey. In order to unlock this potential in companies, a data strategy is required, i.e. a plan for data value creation in the company. Because only those who use data in a targeted and intelligent way can make better and faster decisions, optimize processes, improve planning or develop new approaches, define new customer-specific offers, or even innovate the entire business model & define short- and long-term objectives.

If implemented correctly, a data strategy offers companies from all industries and, in all areas of the company, sustainable benefits. In the transformation process, management must take into account the data infrastructure, data management, the general data strategy approach, and data analysis and its use.

 

Data infrastructure

The development of future-proof and efficient data infrastructure is based on a high degree of flexibility in data generation, virtualization, and processing. For high-quality data analysis, companies should therefore increasingly focus on independent software components (microservices) that have interfaces with common communication standards and thus enable quick and flexible integration into new applications without detours. Particular attention should be paid to fast network architecture, the prevention of data silos (e.g. by introducing a data lake), and the inclusion of secure cloud solutions.

 

Data strategy

The development and implementation of a data strategy often present companies with challenges. A suitable data strategy must be adapted to an organization and its objectives. It defines how data is handled and which goals are pursued. Since data value creation is not just a technology issue, but rather affects almost all areas of the company as the core of digital transformation, uncoordinated and decentralized approaches often do not lead to success.

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Therefore, a correspondingly fast and agile strategy is required to achieve results quickly. If the decision is made on a static approach, the motto is usually “defense instead of attack”. The focus is clearly on strict regulations in the handling of data and data quality. With a strategic approach, companies choose the middle ground between strict guidelines and targeted data usage. Companies that take a dynamic approach to data handling use a large amount of data in order to be able to react quickly to market changes and to improve their own analytical skills. Fast data access and quick results are important here. In the dynamic approach, cost savings and profitability through the use of data come to the fore. Flexibility in analysis and quick availability of results, for example through user-friendly data visualization, is essential.

 

The data warehouse

No matter what you need your data for and how you ultimately create your data strategy, every modern organization with a lot of data needs modern data warehousing. The data warehouse is the universal place where data from many different sources is collected. If you are planning a suitable data strategy, the data warehouse is one of the most important core elements to be reviewed. In many companies, IT teams are looking for a suitable data strategy that simplifies administration and helps to draw value from all the existing data. A modern data warehouse must therefore be part of a successful data strategy. Companies working on a data strategy cannot avoid modernizing their data warehouse. And one of the most important tasks in this modernization is the automation of the data warehouse.

 

The goal of a strong data strategy should always be to build an organization where agility is an integral part of its DNA. Such an organization works with an operating model that focuses on the employee and the customer. The model equips the company with the ability to quickly recognize changing business requirements and to continuously develop. We support you in designing, implementing, and integrating data correctly into your existing system landscapes and thus into your processes. Feel free to reach out to our consultants that offer a wide range of business and technical know-how as well as the necessary instinct to solve the individual challenges of your organization. We can’t wait to help you find success.

 

 

Sources:

SAP White Paper: Digital Transformation – Digital Business Modeling A Structural Approach Toward Digital Transformation

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 Next Generation Intelligent IT Infrastructure

 

The rapid adoption of cloud computing, big data, and IoT devices proliferation contribute to the increasing complexity of business IT environments today. These and other key trends are challenging organizations to effectively and efficiently manage and secure their IT environment and to assure IT service levels and achieve business success. It doesn’t matter whether it’s hosting or outsourcing your own hardware to a colocation. The first question from customers is always: How safe is it to give your own data to someone else? Hardware, software, infrastructures, either of a start-up of a big enterprise, everyone needs the latest, high-quality, and powerful IT. But of course, these facilities come with an important amount of financial resources.

 

In addition to that, today’s IT infrastructures are being overwrought to the breaking point by new technologies and applications, such as requirements of controlling mobile devices, maintaining visibility into virtualized resources and services, including on-premise, cloud, hybrid-cloud, virtualized, distributed, and mobile components, achieving increasingly demanding SLAs for critical business applications and so on. Managing IT services and automating all aspects of IT management in remote and distributed environments with ease is often labor-intensive and costly for organizations because their available IT management tools are often poorly integrated. Effective IT service management becomes more challenging when some resources are on-premise and some are in the cloud.

 

This post outlines what IT professionals should look for when determining the enterprise legacy infrastructure transformation and make it ready for the digital future.

 

    • Even in 2020, many enterprises still use outdated systems, without considering that they can be exposed to crashes any time and they can be left with no backup plan!. If the X software worked perfectly fine for years, it doesn’t mean that you can run it for life. Outdated software applications can, unfortunately, hurt your business and waste time by slowing down production, wasting money in the long run, and even increasing vulnerability to security threats. Thus, one should not ignore or underestimate the hidden costs of legacy software.
      Research indicates that costs related to maintaining legacy software can exceed the original development budget in just 5 years and take up as much as 75% of the IT budget. In addition to that, the estimated average cost of a data breach is $4 million according to IBM.
      So instead of being stuck with your old software, letting new opportunities in your industry go unnoticed, you need to follow industry trends by making sure that you are focusing 100% on your business demands. This creates more business openings for you and you can take over your market share.

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    • Call it a digital transformation strategy or call it smart business, by removing unnecessary software your company can re-organize your IT infrastructure to align with existing or new business goals.  As enterprise licenses aren’t cheap and every single seat is worth from hundreds to thousands, a regular software audit allows you to cut the cost of unused software and potentially save millions a year.

 

    • Despite all of the technology at our disposal, many enterprises still rely on manual, repetitive tasks in industries such as transportation and logistics, financial services, manufacturing, insurance and finance, and accounting. Mostly the reason for performing manual tasks instead of opting for automation is because old legacy systems are difficult to integrate with modern applications. By automating every manual, repetitive task to the company can multiply efficiency and profitability, increase workflow efficiencies, and freeing up their staff for higher-value work.

 

    • In Cloud computing ear, the business landscape is revolutionizing. Cloud computing offers increased flexibility, efficiency, accessibility, scalability, and collaboration. Plus, cloud computing can help you get your entire staff on the same up-to-date software. The biggest concern for many IT teams has been tackling the growth of data. If your organization is still not taking advantage of the cloud, it’s time for an IT infrastructure upgrade. This shift will not only give you the increased processing power and data storage your company needs, but it will also give your staff the consistency, flexibility, and improved collaboration tools to help you get the most from your technology.

 

Sources:

Accenture: Intelligence unlocks vision

Accelerated Automation: RPA as a Service (RPAaaS)

RPA – Robotic Process Automation – is the automated processing of business processes by digital software robots. This technology beings its support in clearly structured, repetitive, and rule-based processes and tasks that are carried out by people. With RPA, companies can implement a level of precision in the processing of tasks that is often unattainable by human alone. In this robot-controlled process automation, the bots take over the roles and tasks of users and interact with other software systems. Almost half of all processes in companies can be automated with the help of RPA.

 

RPA as a Service is the easiest and fastest way to use the advantages of Robotic Process Automation (RPA). RPA as a service means companies can take advantage of the many benefits of RPA without the upfront cost, inconvenience, and maintenance of buying their own RPA technology or hiring in-house RPA developers. RPAaaS offers a selection of out-of-the-box RPA services and tools accessible through a monthly subscription in order to meet the needs of every business for quick and cost-effective intelligent automation.

Accelerated Automation RPA as a Service RPAaaS
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Every organization has different needs, timelines, and budgets, therefore, RPAaaS solves the problem of a lack of in-house development resources not only by providing the development platform but also by offering bookable, qualified RPA experts, such as, RPA business analysts, RPA developers, or RPA architects. Thanks to the model of resources that can be booked as required, load peaks can be flexibly balanced and the schedules for scaling bots can adhere. The main objective of RPAaaS is to reduce development and deployment costs and quickly and effectively meet the need for effective and reusable automation components.

 

The relocation of automation assets to the cloud opens up a way for financially constrained companies to tackle the planned business process automation or to scale existing automation approaches without having to hire qualified automation personnel and also by eliminating the need for infrastructure and maintenance.

 

Business continuity, supporting a distributed workforce or digital transformation, whatever the business goal is, with RPA as a service, a company does not need to purchase its own servers, licenses, or professional services. RPAaaS is therefore also suitable for companies that only plan to use it for a limited period of time. Because in this case, there is no point in purchasing expensive RPA technology that will no longer be used after a certain project has been completed.

How sales and marketing Automation can boost your business?

One of the greatest challenges in marketing today is the introduction of marketing automation or the digitization of sales. Automation of sales & marketing is the implementation of various technology, e.g. in the form of software to take on administrative and monotonous activities. The automation of these repetitive tasks helps boost operational efficiency and ease decision-making. Due to Covid-19, the sales processes within companies showed a positive attitude towards the automation of marketing and sales processes. Research and Markets anticipated the global marketing automation market size to reach USD 8.42 billion by 2027 (March 2020 survey). According to Gartner’s latest annual CMO Spend Survey, 68% of CMOs expect their martech budget to increase by 2021.

 

By means of automation, companies put useful, relevant content online in order to generate qualified leads for marketing and sales activities. This is how the digital sales process gets going. The task of marketing is to gain customer contacts with convincing content on websites, social media or other dedicated portals.  On average, 51% of companies are currently using Marketing automation. With more than half of B2B companies (58%) plan to adopt the technology.

 

Sales automation is not just about email marketing, it doesn’t send useless spam, nor is it a solution for marketers who want to work less. Instead, as part of sales automation, workflows are developed that make work easier and turn prospects into customers. Marketing automation tools are used in sales automation. These tools include lead generation, lead segmentation, email marketing, lead nurturing, and lead scoring. With the right automation and the data of your customers and your communication goals, you create workflows that send the right information at the right moment. Through the intelligent interaction of contact lists, website tracking, smart content, and email workflows, you can achieve a highly personalized approach. Automated sales are all about creating fast and efficient sales processes that lead to more leads or more sales.

 

How sales and marketing Automation can boost your business

Reports have shown that employees spend more than half of their time doing administrative tasks in the CRM system rather than spending time to really sell products. The primary goal of automation is to give your employees more time and hand over administrative tasks to suitable software! To make the road in automation easier, here below typical tasks in Sales and Marketing can be easily automated.

 

  • Morning reports and emails

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Most organizations start the day with a morning summary that includes data such as current team goal achievement, deal status, and employee productivity. Instead of always sending individual emails to all employees, you can automate these reports and have them sent to your employees every morning.

 

  • Lead distribution

Leads have to be processed individually by the sales manager without automation. However, it’s possible that the interest of the potential customer lapses and the lead is no longer usable. You can prevent this with an automated lead assignment. You determine in advance which lead category must be assigned to which member of the sales team. As a result, leads are processed more quickly and, in a time-saving manner, without a single manager having to take care of everything.

 

  • Lead prioritization aka lead scoring

The leads generation is also a topic that costs a lot of time, which is often not valued by the corresponding success. Once a list of target customers has been created, these should be registered according to their priority. An automated lead evaluation system helps with the selection of the most important (potential) customers. This calculates how promising a lead is based on specified criteria. Not only it saves you a lot of time, but also helps to invest in the most promising leads. No more leads are lost as better reporting and analysis options are availed through central tracking of all processes and activities in the sales process.

 

  • Creation of data sets

After the sale, the CRM system needs to be brought up to date. This can take several hours, but it is actually wasted time. Because you can also easily automate this task! It is true that entering data cannot be relieved of you, but functions for a better overview can pop up automatically when you need them, such as, if a lead visits a page with prices, a new task automatically appears in the CRM, when someone requests a demo version of your product, an opportunity is created, another task is created when a demo version expires. With such functions, you make sure that you never miss a sale opportunity!

 

  • Automated library with all sales materials

In most cases, the sales & marketing content is stored in multiple different locations. A nightmare for every employee who now needs different information about a customer or project. With a CRM system in sales, you always automatically have all the data in one place. With the help of search terms, your employees can then easily find what they are looking for.

 

In addition to those listed above, there is a lot more is possible with automation in sales, such as automated billing, automated planning of meetings and appointments, automated reporting for the summary of the activities by the employees, independent lead tracking, and monitoring to understand customer movements, etc.

 

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