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Covid19: Evolution of the Digital Transformation within companies

Covid19 Evolution of the Digital Transformation within companies

The corona pandemic is continuously changing the framework for digitization. According to IBM 96% of leaders report that Covid-19 will accelerate their digital transformation by an average of 5.3 years. Another study by Celerity shows that 63% of leaders state that the Covid-19 pandemic prompted them to embrace digital transformation sooner than originally planned. The goal of promoting innovations faster and keeping up with the times is the most important reason for digital transformation for companies.

 

With the adoption of digital solutions, companies have greater resiliency. They can not only streamline their operations but also automate all manual processes in order to generate more revenues. With data driven insights, companies can make decisions faster and adapt or change course at any point. They are also better prepared to fight against cyber threats, also when their employees work remotely. All these advantages give companies a boost for their own digital transformation, but they also have to overcome various hurdles as the transition to a digital company is anything but easy. Limited resources, rigid legacy systems as well as unclear goals and rules – all these things delay IT departments when it comes to innovations.

 

Even tough 30% of organizations will increase innovation and reinvent their business models in order to future-proof their companies, digitization continues to be a major challenge for many companies. Companies’ IT departments are experiencing the greatest change: they are developing into a service provider who, on the one hand, strives to ensure that the IT systems and applications run properly and, on the other hand, acts as a full-service provider of IT-supported business processes. The requirements continue to increase. A total of four hurdles in particular slow down innovative projects.

 

Skill shortage:

According to the KMPG CIO survey, 54% of organizations reported that skill shortages were holding them back from pursuing their transformation goals. In particular, they were lacking expertise in the following areas: Cybersecurity, Technical architecture, Enterprise architecture, Advanced data analytics. Current IT teams are made of few people who have the essential technical skills but don’t have training time to develop the skills. But if your business is suffering from skills shortages, ignoring training is not the right solution – it’s much easier to train existing staff than to hire new employees.

 

 

Legacy systems

Legacy systems, which form the backbone of many enterprises, are holding them back from leveraging new digital technologies and creating new experiences for their customers/partners. Outdated business software consumes a lot of resources in companies. According to Forrester, companies invest 70 to 80 percent of their IT budget in maintaining rigid back-office systems. These systems are difficult to integrate or customize when it comes to supporting new digital initiatives. Slow development methods combined with legacy systems make the problem worse. Outdated networks and servers no longer meet the needs of companies. Taking the right step and modernizing the legacy is the way forward. The IT modernization promises cost savings, efficient management of IT infrastructure, efficient utilization of human capital, better security and risk management, enhanced user experience, and last but not the least, a direct impact on competition.

 

Limited resources

Digital transformation is necessary in order to become more agile, more innovative and more resilient, but often only limited resources are available for converting old processes to more modern tools. Building a business case for such investments can be quite challenging in terms of budget approval. Also, all legacy systems require modernization. Otherwise, they can be exposed to crashes anytime. Therefore, the introduction of new technologies can be introduced in steps, often with a free software test phase.

 

In order to be really successful, that means first and foremost to be economically positive, but also to recognize and use the opportunities of the future as a driver of innovation, a digital roadmap should be planned. Not the hard change, but a targeted and coordinated development in digitization should be the way in which all members involved within a company pull together. Strategic concepts that include optimal resource planning are required here. This means that a networked transfer of knowledge and experience must take place. Using the strengths of individual individuals and combining them with scientific innovations should be the path to productive and efficient profitability. The digital evolution is a permanent further development and with all step forward new opportunities become visible that can be realized through a stable foundation.

 

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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.

 

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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.

4 Cloud Computing myths, debunked

Flexibility, scalability, and long-term business resilience are the huge boost to cloud adoption. The future of the cloud is bright. Over $ 287 billion growth is expected during 2021-2025 for the global cloud computing market. Yet there are many myths surrounding the use of cloud solutions that prevent companies from taking benefits of cloud services. Even after twenty years, the use of cloud applications such as Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS) keeps coming up against it vague fears and rejection. It’s because, in addition to data security, access and control options also play a hugely important role. Cloud solutions certainly pose certain risks for companies and their data if not managed correctly. However, many myths are way much exaggerated. In addition, many risks not only affect the cloud, but also locally operated networks.

The following misconceptions about cloud computing prevent companies from taking advantage of the cloud and have made the acceptance of cloud services particularly difficult.

 

The company will lose control over its own data

Many CIOs and IT managers or administrators often feel that they will completely lose control of companies’ sensitive data and its management once it’s migrated to the cloud. Additionally, they also carry the fear of dependence on providers as the control over the server is also given away to the cloud provider. Overall companies are particularly concerned with the location of the data and a possible loss of control over their own data. But even though they have passed the operation and maintenance of servers into third-party hands, they are and will remain the sole owner at all times and can retain all rights and control over their data and can decide independently, depending on the services used, where the company-critical backup and archiving data is stored. Because administrators and those responsible for data no longer have to worry about small details such as updates or background processes, they can spend more time optimizing the infrastructure and suitable strategies for business growth.

 

The data is not safe in the cloud

For a long time, it was believed that cloud solutions are more susceptible to attacks than the company’s own IT. Cloud services, in themselves, are exceptionally secure. However, many companies are reluctant to cloud adoption and have huge concerns about cyber-attacks, data theft, and industrial espionage. Because there is no such thing as absolute security, more and more cloud providers are creating a secure cloud for their customers. Their business model hinges on preventing breaches and maintaining public and customer trust. Additionally, all cloud providers have to comply with stringent regulations and this requires them to put robust security measures in place, including the use of strict protocols and advanced security tools. Also, the latest data centers are equipped with various security measures and offer users a guaranteed high level of security for their data.

 

Migrating to the cloud is complicated

The companies’ IT departments are often considered to be busy maintaining ongoing day-to-day operations. They don’t have enough time or know-how to modernize IT operations through the cloud. They are persuaded that migration to the cloud will come along with additional requirements, will also increase the complexity of the IT infrastructure and administrative effort. BUT every cloud provider offers their support whether it’s before or during the migration and ensures that everything runs smoothly. The greatest advantages only become visible after the conversion and cloud automation of many tasks and processes, on the one hand, it relieves computer scientists in their everyday work as they no longer have to worry about updates, backups, archiving, or the complicated maintenance of IT systems.  On the other hand, it can meet the requirements of the specialist departments faster than conventional infrastructures.

A company that plans to move its applications from a data center to a large cloud platform, must check whether their applications are cloud-ready or need to be revised before the migration. Otherwise, they’ll end up paying a high price for a platform that they cannot take full advantage of.

 

Cloud is more expensive than the in-house computing

Cloud migrations are complex projects that quickly lead to unexpected costs. As with all operating costs, it is not just the monthly cost that needs to be considered, but also the total cost of ownership (TCO). The cost of going to the cloud depends on several factors such as license obligations, data center, and the company’s ability to control and optimize cloud consumption. The big advantage of the cloud is the flexible scaling and that you only pay for the capacities that you actually use. The up-front costs of cloud migration are often significant, but the longer-term savings usually dwarf that initial cost. Choosing the right provider and achieving more performance and lower costs requires know-how and experience with the multitude of services.

 

Almost every company knows how important it is to keep up with the times in the digital age in order to remain competitive. Cloud computing is playing a vital role in responding to the challenges of these unpredictable times. The cloud is seen as a tried and tested method to achieve the necessary flexibility and agility. It has proven to be an important driver of digital transformation.

 

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Business Automation & Multi-Cloud Management: Micro and Maxi trends for 2021 and Beyond

iot IoB covid gartner xorlogics

The year 2021 is all about transformation processes, primarily resulted by the exceptional situation we’ve witnessed in 2020. As 2020 caused a major shift in how business and IT teams operate, the development around COVID-19 was and still is a great challenge for all organizations. In addition to classic customer service, IT service, in particular, is confronted with more tasks and service requests. So that the workforce can work productively and quickly, the IT service needs intelligent tools for automation. Many changes have been on the agendas of IT departments for several years and vary from micro changes that affect the big picture to maxi changes that will affect future generations of employees.

 

In this continuously changing environment, organizations are exploring new ways to operate and drive growth. Each year, Gartner, Inc. releases a series of studies mentioning trends/predictions that will impact the business environment, IT, and technology in the coming years. Here below, we’ve gathered the most relevant trends to the IT automation market to help IT, professionals.

 

“Hyper automation is irreversible and inevitable. Everything that can and should be automated will be automated.” Brian Burke, Research Vice President, Gartner

 

  • By year-end 2025, over half of the world’s population will be subject to at least one internet of behaviors (IoB) program (private, commercial or governmental).
  • By 2025, 50% of enterprises will have devised artificial intelligence (AI) orchestration platforms to operationalize AI, up from fewer than 10% in 2020.
  • By 2025, 40% of physical experience-based businesses will improve financial results and outperform competitors by extending into paid virtual experiences.
  • By 2025, half of the large organizations will implement privacy-enhancing computation for processing data in untrusted environments and multiparty data analytics use cases. 
  • By 2024, organizations with IT teams that understand the needs of customers will outperform other organizations’ customer experience metrics by 20%.
  • 2023, 40% of all enterprise workloads will be deployed in cloud infrastructure and platform services, up from 20% in 2020.
  • By 2025, traditional computing technologies will hit a digital wall, forcing the shift to new computing paradigms such as neuromorphic computing.
  • By 2025, most cloud service platforms will provide at least some distributed cloud services that execute at the point of need.
  • By 2025, customers will be the first humans to touch more than 20% of all products and produce.
  • By 2024, organizations will lower operational costs by 30% by combining hyper-automation technologies with redesigned operational processes.
  • By 2024, 80% of hyper-automation offerings will have limited industry-specific depth mandating additional investment for IP, curated data, architecture, integration, and development.
  • By 2024, more than 70% of the large global enterprises will have over 70 concurrent hyper-automation initiatives mandating governance or facing significant instability.

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Business Continuity: What is important in a VPN service?

Business Continuity What is important in a VPN service

The coronavirus crisis has hit us hard. It has dramatically transformed the companies by obliging them to make the abrupt shift to working from home. Remote work, home office, smart working, due to the health protection (Coronavirus restrictions), more and more companies are accessing them. In the current case, nobody expected a crisis in which offices would be closed for months and social distancing would change the way people work together. Companies in which home office was a foreign concept until recently suddenly had to send 90% or more of the workforce home for “remote work”. A step that had required precautionary measures on the part of the company so that compliance with data protection guidelines is not jeopardized by the fact that employees work from home. This was a huge challenge for IT teams.

 

The pandemic has made it clear that businesses need to rethink their continuity concepts. Because even the best emergency plan can never foresee all the consequences of a crisis. In order to remain active, under any circumstances, companies need a flexible IT environment that can quickly adapt to unforeseen events at any time – as far as possible.

Due to all these changes, IT departments must increasingly take remote employees and service providers into account when it comes to IT security. The increased use of home offices also increases the dangers of business-critical systems and confidential data. As a result, it is important to understand the different types of users who log into their systems from outside, as it is highly recommended to manage, secure, and monitor this access.

 

A sudden shift to home-office resulted that over the course of just a few business days, the use of Virtual Private Network (VPN) technology expanded from select remote users to entire employee populations. Most organizations already had a VPN for remote work connections, but adding an extra layer of security to your network is also a good idea. As VPNs became vital to today’s business operations, critical to keeping alive commercial, government, and healthcare organizations, it’s important to acknowledge what is really important in a VPN service.

A VPN is the best way to secure data in transit. It encrypts and secures all the internet traffic flowing between a laptop or smartphone and the VPN server. That makes it very hard for hackers to pry into confidential data. Ideally, the VPN provider does not collect any information about the online activities of its customers. But let me highlight that the internet is far from the ideal place. VPN providers store data about their users’ online activities in so-called “logs”. However, surveillance is not the goal here. Providers collect data only to optimize their services. Basically, they collect and store theses 4 types of information on their customers:

 

  • Connection data: The VPN providers save the times and data on their customers’ registrations and cancellations, the usage time, and the amount of data in the download/upload.
  • Online activities: The VPN providers essentially save the browser history, i.e. pages visited, search queries, and services used.
  • Original IP address: The providers save the IP address of the device with which the customer surfs the Internet.
  • Connection statuses, control, and error messages

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It is harmless if connection data and online activities are collected anonymously. This enables the provider to generate important information and preserve the anonymity of its users. It’s also stated in the provider’s logging guidelines, that the collected data is deleted after specified time intervals.

 

What you should know before registering for a VPN service?

Before registration of a VPN service, you must inform on the home country of the VPN provider as every company is subject to certain regulations by the state authorities in the state in which it is registered. In many countries, including the EU countries, the USA, and Australia, strict regulations apply to data collection and retention. The aim is to oblige Internet service providers (ISPs) to collect and store data. This concern, for example, visited websites or sent emails. With a VPN, this type of mass surveillance can be avoided because the VPN encrypts the data.

 

Encryption is the transformation of readable data into a meaningless sequence of data. Cyber distancing the device and ensuring that all data between the work and home network is sent over an encrypted channel provides additional protection for data and company resources. An encryption key is required to encrypt the data. Only those who have access to the key can decrypt and read the transmitted data. The gold standard for the protocols is AES (Advanced Encryption Standard). The VPN providers use either the AES-128 or AES-256. The former is very secure and is considered to be absolutely secure and currently the highest encryption standard on the market.

In addition to the encryption, a kill switch option is also offered. With the kill switch function, an Internet connection ends automatically if the secure VPN fails. This ensures that the online activities of the user remain hidden if the VPN connection fails.

 

As most providers use encryption, it slows down Internet speeds. This has an impact on the download and browsing speed. This can also adversely affect the stream quality of streaming services. There are a few things that users can do here, such as, choose a fast VPN, choose a VPN location nearby, the closer the location, the shorter the waiting time. Perform a server test, providers usually select a server automatically. But that’s not always the best choice. With the help of speed tests, it is possible to find the perfect server.

 

In addition to the use of VPN, IT risks associated with remote work are limited by companies using specific protective measures and technical solutions. Including:

  • Profiling of the home office / smart worker. It is critical for a company to define in advance profiles of remote workers based on their role, what information can they access from distance. The security mechanisms for occasional remote workers and for full-time home office workers must also not be the same.
  • Authentication of remote access. The introduction of a system for identifying the home office worker, as soon as he or she connects to the company system, is a huge step into preventing unauthorized access to the company’s valuable data. Ideally, multiple authentication systems must be used such as user name, password, one-time code, etc.
  • The most important measure to avoid any cyber risk, it’s important to carefully draft a BYOD policy in place with employees and educate them on the protection of confidential and proprietary information and trade secrets information. Not having a comprehensive policy becomes problematic when employees are fired or resign.

 

The past few months have shown that companies that are already further advanced with their digitization and use protection strategies are suffering less from the crisis. It’s always important to draw users’ attention to the IT security problems associated with smart working. Regular virus protection updates, the separation of private and professional inboxes, restrictions on the use of external devices (USB sticks, hard drives, etc.) for transferring data from one computer to another, etc. Smart workers must always be reminded of the best basic practices to achieve great protection against any inconvenience.

How can Digital Marketing benefit from Artificial Intelligence?

AI in Digital Marketing xorlogics

 

AI is now more accessible than ever with it’s potential to change business forever. With its positive impact, and being a lot more affordable than before, both big and small companies are able to benefit from the insights and automation options it provides. With the integration of AI brands can not only leverage customer’s data but also anticipate their customer’s next move, understand sales cycles better, correlate their strategies for converting prospects into paying customers and improve the overall customer’s journey with machine learning efforts.

 

As a subset of AI, machine learning involves the analysis of historical data from various business interactions with customers / prospects (for example: when and what was the last time the customer ordered?). As ML has the ability to analyse extremely large sets of data, its being used in the digital marketing departments around the globe to identify sales patterns and increase success factors. Algorithms for ML generate insights via predictive analytics, based on these insights, marketers can either take actions individually or automate AI to do the job. For example, they can automate emails that are aimed at re-targeting your audience, giving you a better chance of a higher ROI.

 

ML is also playing a major role in SEO. Even tough SEO algorithms change across major search platforms, with AL and ML tools, the insights from searchable content may become more relevant than specific keywords in the search process. In order to maintain a high-ranking place on search engine result pages, consider the quality of your content rather than simply the keywords included. By doing so, you’ll be ahead of the game when it comes to future-forward content creation and SEO.

Marketers can also benefit from ML tools to analyse what type of content, keywords, and phrases are most relevant to your desired audience. Once they have the key insight, they can optimize their dialogue and develop engagement across multiple online platforms, to drive brand awareness and create meaningful relationships with leads, prospects, and customers alike

 

The key difference between modern AI-based and traditional outbound marketing strategies is the integration of contextual data, i.e. information that results from the interaction with the customer is not stored in labour-intensive and time-consuming data warehouses but is available in real time and can be used for decision-making. With these customer profiles and interests recorded in parallel to the interaction, the previously defined decision strategies are fed in order to achieve high planning security for high, profitable conversion rates.

 

Artificial intelligence opens up possibilities in marketing domain far beyond currently available functions. PwC estimates that business could save $2 trillion globally by applying intelligent automation to many activities that were previously processed by humans and making employees more productive. In addition to that, AI, robotics and other forms of smart automation will bring great economic benefits and contribute up to $15 trillion to global GDP by 2030.

 

With AI integrated marketing, business can forecast customer behaviour and run data-based campaigns to have remarkable results. It helps them to save a lot of money on marketing and sales efforts by bringing them valuable leads. Not only business can achieve valuable leads and turn them into customers but they can also maintain a good relation and provide a better service to these customers by introducing a 24/7 customer service with the help of AI equipped chatbots. These chatbots are able to handle customer enquiries and provide customer support on time and appropriately, based on the needs of customers. Business are creating value through transforming customer journeys by providing immediate response to consumer’s queries or issues. According to McKinney’s study, 75% of customer demand NOW service within 5 minutes of online contact. If business can beat this time, they can convert a ‘visitor’ into a ‘paying’ customer.

Here below are some interesting statistics of AI in marketing:

 

  • According to research from Callcredit, 5 hours and 36 minutes is the amount of time that the average marketing professional spends collating data and getting it ready for presentation. An AI integrated marketing dashboard can do the collating in minutes and give you more time for reporting process. Statwolf
  • 97% of leaders believe that the future of marketing lies in the ways that digital marketers work alongside machine-learning based tools. QuanticMind
  • By 2020, 30% of companies worldwide will be using AI in at least one of their sales processes. Gartner
  • By 2020, 85% of customer interactions will be handled without a human. Gartner
  • For 61% of marketers AI is the most important aspect of their data strategy. MeMSQL
  • 80% of business and tech leaders say AI already boosts productivity. Narrative Science
  • When AI is present, 49% of consumers are willing to shop more frequently while 34% will spend more money. PointSource
  • Large businesses with more than 100,000 employees are most likely to have an AI strategy – but only 50% of them currently have one. MIT Sloan Management Review
  • Netflix is saving $1 billion per year by using machine learning to make personalised recommendations- Artelliq
  • 44% of consumers don’t even realise they’re already using AI-powered technology platforms. Pega
  • 45% of end users prefer chatbots as the primary mode of communication for customer service inquiries. Grand View Research

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The ways that ML is being used in digital marketing practices helps organizations to expand their understanding of their target consumers and how they can optimize their interactions with them.  So, when developing appropriate AI applications for marketing purpose, the collaboration between software developers and domain expert’s must not be neglected. The ultimate decision-making competence when it comes to the all-important question of which next best actions are to be implemented in order to achieve the greatest possible marketing success usually belongs to the domain experts. You should have the experience and expertise to incorporate the right NBAs into the self-learning models for decision-making strategies.

 

Source:

Demand Forecast Powered by Machine Learning

Demand Forecast Powered by Machine Learning

 

The business landscape is rapidly becoming more global. Largely due to improvements in communicationsand increasing globalization which are dramatically impacting the way business is managed. No area of a business is more affected by the trend of a global business environment than the supply chain. Supply chain logistic, known as the backbone of global trade, is a network of many partners involved such as customer, dealers, manufactures,transportation, external warehouse,suppliersand inventory. Sometimes a delivery comes along with delay, sometimes there is something wrong in a package, delivered article is different to ordered article and sometimes a shipment is lost. This is annoying for all sides. It costs time, energy, money and sometimes even the customer. Challenges for decision-makers in supply chain management are growing due to the widely networked supply chains and the constant change in the environment of companies.

 

In fact, many companies are facing hurdles in their existing business processes and technologies that aren’t flexible enough to deal with “large and global” business environments. Therefore, areas such as manufacturing, distribution, sourcing of materials, invoicing and returns are impacted by the increased integration of a global customer and supplier base.

Supply Chain specialist must deals with long-term planning in terms of location, make-or-buy decisions, supply relationships, capacity dimensioning, logistics strategy and general tasks along with cost optimization in structuring of the logistics and production processes. Hence, in order to initiate the demand forecasting, it’s highly recommended to understand the workflow of machine learning modeling. This offers a data-driven roadmap on how to optimize the development process.

 

Operational inefficencies in SCM often lead to potential revenue losses, increasing costs, and poor customer service, ultimately diminishing profits. With the help of AI, machine learning techniques are able to forecast the right number of products or services to be purchased during a defined time period. In this case, a software system can learn from data for improved analysis. Only good data produces good results!

Data interpretation is a vital part of supply chain management and demand forecast as it’s used to improve your ability to estimate future sales, reduce shortages and overstock. Once the data is interpreted correctly, both in national and international trade results in having the right products at the right time in the right number at the right place.
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So, for demand forecasts that are generated by self-learning algorithms require data that is closely related to sales. However, in order for machine learning to achieve a high quality of forecasting, a certain amount of quality data is required. The result of ML process depends solely on the quality and quantity of data provided.

To ensure that the data is up to date, the input data should not be older than 5 years. Data selection can be a special hurdle before using machine learning methods, because it can be very time-consuming. In connection with the data quality, it must be ensured that there are only a few missing values of the data records in the input data, otherwise the machine learning model may generate incorrect results. Data preparation is necessary for successful implementation and definitely pays off later. If the data record does not have sufficient data quality, it must be prepared through an intensive process and carry sufficient information for qualitative algorithms and for a good forecasting performance.

 

The goal of ML is to develop algorithms which can learn and improve over time and can be used for predictions. Therefore, ML fed with qualitative data can generate precise forecasts and thus ensure a secure basis for planning. The resulting benefits, such as reducing inventory levels and simultaneously optimizing the ability to deliver, also improve the operating result. ML uses learning algorithms to recognize patterns and regularities in data and is able to adapt automatically and independently through feedback and thus react to changes.

 

Compared to traditional demand forecasting methods, machine learning not only accelerates data processing speed but provides a more accurate forecast, automates forecast updates based on the recent data in order to create a robust system.

AI and Automation impact on medium-sized Companies and Organizations

 
The Business Benefits from AI and ML
  • Massive Data Consumption from Unlimited Sources
  • Rapid Analysis Prediction and Processing
  • Simplifies Time-Intensive Documentation in Data Entry
  • Handling Repetitive Jobs
  • Reduction of Error
  • Improves Precision of Financial Rules and Models
  • Easy Spam Detection
  • Interpret Past Customer Behaviours
  • Better Customer Segmentation and Accurate Lifetime Value Prediction
  • Recommending the Right Product
  • Digital Assistance and 24/7 Availability

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Hyper Automation and Artificial Intelligence (AI) are seeping into every aspect of business, from operations to production and from product development to business processes. Processes, tasks and role models – everything is changing with this revolutionizing in the commercial sector. This evolving area of Artificial intelligence (AI) and machine learning (ML) is the future for all businesses, and it’s already affecting the way we live and work today. In fact, research firm Markets and Markets estimates that the machine learning market will grow from $1.41 billion in 2017 to $8.81 billion by 2022!

 

The new digital technologies are having different impacts on jobs in the short and longer term. While automation may displace workers within a sector in the short term, studies are demonstrating that overall employment is growing when looking at a longer period comparing results from different sectors and the World Economic Forum has found that, by 2022, algorithms and other transformative technology could create more jobs – as many as 58 million. Because despite automation and artificial intelligence, people will continue to be important in the future.

 

Artificial intelligence opens up possibilities far beyond currently available functions. PwC estimates that business could save $2 trillion globally by applying intelligent automation to many activities that were previously processed by humans and making employees more productive.

 

Like business of all sizes, SMB are also flooded with data gathered from different sources such as, customer data, supplier data, market data, operations data and many more. AI can quickly pull out insights from these databases and extract valuable data. Managers can get valuable insights on business process from workflow management and/or make sales predictions easily by using data from ERP systems. As AI is able to pull out insights from data easier than human workforce and run the routine tasks to add value to existing business, company can use its human resources to solve potential problems, spend time on creative tasks and boost business ability to innovate.

 

SMBs can also benefit from AI to identify, understand and better target their customers. Business can forecast customer behaviour and run data-based campaigns to have remarkable results with the use of big data and artificial intelligence. It helps them to save a lot of money on marketing and sales efforts by bringing them valuable leads. Not only business can achieve valuable leads and turn them into customers but they can also maintain a good relation and provide a better service to these customers by introducing a 24/7 customer service with the help of AI equipped chatbots. These chatbots are able to handle customer enquiries and provide customer support on time and appropriately, based on the needs of customers.

 

Not only in customer service, artificial intelligence is demonstration it’s capacities in accounting, finance, auditing and tax controlling, fraud detection as well. Instead of recurring routines, data analysis, process control and optimization by automating procedural tasks would be on the agenda of SME by allowing employees to make even more valuable contribution to corporate management. As AI can decipher data and draw actionable insights easily from complicated situations and eliminate/reduce errors, it’s totally transforming and changing the way accounting and tax firms used human intelligence to perform same tasks. With an accurate integration of AI empowered workforce, accountants and bookkeepers can build a value-based business, attract more customers and increase revenue by spending less time that they were used to spend.

 

Customer service and accounting are not the only 2 departments getting aids from AI, sales and marketing are also getting their potential customer’s pipeline faster than ever before by finding interesting leads in their databases as contact information is available for the market you want to target.
By engaging each potential lead in a very personalized way, sales person can turn the lead into a paying customer and recommend the right product with the help of gathered data on the customer.

 

Sources:

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