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Data-driven business competitive advantages

Data is now one of the most important corporate resources. It’s helping more and more companies to anticipate future actions through their past activity. Regardless of whether they want to optimize their business processes with data-driven approaches, or expand or improve the interaction with customers based on data, or even introduce a new digital business model. They can diversify their portfolio with a range of different products – from classic to computerized to data-driven – in order to meet increasingly complex customer needs.

Data-driven business competitive advantages

In this context, Data-driven business models are increasingly becoming a decisive factor in competition. More and more companies are asking themselves how they can use data profitably in their business. But one should know that success does not depend on the pure availability of infinite (often unused) amounts of data. The challenge here is to combine structured, partially structured and unstructured data from different contact points with customers such as social media, sensors, mobile devices, sentiment data and call logs. With the right analysis of the data, more targeted marketing campaigns and better customer integrations desired results are achieved.

 

The analysis of data offers many advantages. Here below is a selection of possible application scenarios:

 

  • Analysis of customer behaviour: Insights and actionable information are gained through data analysis such as how the customers interact with your company so you can uncover ways that can better suit your customers’ behaviour patterns and increase your conversion rate.
  • Profiling of customer groups: Trends and patterns can be identified such as which customers looked at which products for how long? This also gives the opportunity to tailor the future collection to the tastes of certain customer groups. The importance of this process cannot be overestimated, especially for small businesses with limited resources.
  • Dynamic pricing: Thanks to analytics, companies can adjust their prices in real-time according to the market demand, consumer behaviour, available inventories and competitive situation as well as to financial targets and delivery conditions.
  • Personalized customer journey: Today’s customers are interacting with companies across multiple channels. Therefore, touchpoints across all those channels must be analysed so that a personalized and precise approach with the individual offers can be offered. AKA, The right product at the right time at the right price.
  • Next Best Action: Automated suggestions for further campaigns, special prices, additional offers, the type of customer approach (e.g. e-mail or telephone) or the intelligent control of online campaigns enable companies to track a dynamic view of their customers and use this information to better anticipate customer needs.

 

The data thinking concept is definitely a key to corporate success, which is based on data. Data-driven businesses open up countless possibilities such as improved customer and market knowledge, better coverage of customer needs, better sales forecasts, more targeted marketing, higher efficiency and much more. The first step is to look inward and work with the data that is already available to develop and advance use cases. The focus must be put on customer needs and the sensible implementation of the use cases so economic success and a positive ROI can be guaranteed.

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

2021 Intelligent Data Management Will Enable the Future of Your Business

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

 

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

 

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

 

  • Data orchestration

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

 

  • Data discovery

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

 

  • Data preparation

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

 

  • Model management

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

 

  • Data governance

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

 

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2021: Ensure Your Business Growth by Becoming Data-Driven Company

ensure Your Business Growth by Becoming Data-Driven Company

 

In 2021, government agencies and businesses will need to be able to make decisions based on current/real-time data faster and more accurately than before. Because: due to COVID-19, markets, supply chains and customer behavior have changed in recent months, only data-driven businesses are able to respond quickly and effectively in a rapidly changing world. In order to transform into a data-driven business, it’s not only important to understand the importance of data quality and governance. But it’s also key to drive a data strategy that is aligned with your business strategy. By integrating analytics into business strategies, businesses can transform data into decisions that improve lives and results.

 

A study of more than 3,500 business executives and senior IT decision-makers across the UK, France, Germany, and the Netherlands found a gap between companies using data to inform decisions during the pandemic and those who are not. The YouGov survey, commissioned by Tableau, asked executives of small, medium, and large businesses about their use of data during the pandemic, lessons learned, and confidence in implementing long-term business change. For executives in data-driven companies, a majority (80%) believe they had a key advantage during the pandemic.

 

These leaders are also deeply committed to the important role data plays in the future of their business. A large majority of 76% plan to increase investments in data literacy; especially after the long bumpy ride we have all been on since the start of 2020. Additionally, 79% are confident that they will ensure business decisions are supported by data. The results show that non-data-driven companies are slower to grasp the meaning of data in these uncertain times. Only 29% see this as a key benefit and 56% say they will reduce or stop investing in data skills. Additionally, only 36% are confident that the data will support business decisions.

 

“This year has accelerated change for businesses and ushered in a fully digital world faster than anyone could ever have imagined. Data is at the heart of this digital world,” said Tony Hammond, Vice President Strategy and Growth EMEA. at Tableau. “In this age of data, our research shows that data-driven companies see clear benefits and are more confident about the future of their business. As a result, they really rely on the power of their data. Companies that haven’t woken up to it run the risk of falling behind. But businesses big and small can rest assured that it’s not too late to harness the power of data – the time is now.”

 

When asked how it helps to be data-driven during the pandemic, company leaders recognized several benefits. At the top of the list are: more effective communication with employees and customers (42%), the ability to make strategic business decisions faster (40%) and improved collaboration between teams for decision making and problem-solving (36%).

 
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“We started building data skills in our company in 2013, and due to the pandemic, we have definitely benefited from these functions,” explains Dr. Dirk Holbach, Senior Vice President and CSCO Laundry & Home Care at Henkel, one of the world’s leading consumer goods and industrial companies. “For example, within a few days, we were able to record all of our personal protective equipment controls so that each facility can see how we are equipped in this regard so that our business can continue to operate. I am confident that we will take some good lessons with us in the future, especially when it comes to working together. “

 

For all respondents, the key takeaways from the pandemic are: the need to be more agile (30%), prioritize and implement projects faster (26%), and access to more accurate, up-to-date, and cleaner data (25%). Jay Kotecha, the data scientist at full grocery brand Huel, said of his data strategy: “Our data-driven strategy helps the company respond to consumer behavior and enables us to pivot and react faster and more clearly. It’s about empowering the entire organization through data. Employees examine data from across the company and turn it into insights we can act on, whether it’s sales projections, sales effectiveness, or marketing spend. “

 

Across Europe, the results show that just over half (56%) of business leaders consider their companies to be data-driven, while one in three (38%) think they do not. These results indicate a clear way for organizations to leverage data to support business resilience and decision making during this time. German companies are taking the lead with 62% as their business is data-driven, while the UK lags behind with just 46%.

 

The promise of digital change is based on the ability to harness the power of technology to grow your business, open up new markets, and acquire new customers. It also means that you need to understand all of the data (the digital exhaust – the trail of data left behind by browsing the Internet) that new customer experiences create. A data governance strategy as well as information and data quality management as an integral component of management systems significantly supports the achievement of the organization’s goals, ensures compliance conformity, increases throughput, and supports organizations in the transformation to a data-oriented culture. Organizations thus secure their competitiveness and can expand this further through increased data intelligence.

 

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Data-driven companies are more resilient and confident.

Data-driven businesses vastly more optimistic – research

Corporate Data Movement: How to Practice Good Data Security

Corporate Data Movement How to Practice Good Data Security

Data Movement refers to the transfer of files or information that can be critically important to all stakeholders in your organization: employees, suppliers and everyone you do business with. Companies are using all kinds of latest and supplicated technologies and techniques to protect their critical and valuable business assets. But, often forgotten, the most important factor, in any cybersecurity program, is trust.

 

In delivering their work rapidly and efficiently, whether it’s the marketing, sales or the finance team, employees forgets the essential data security measures if they are not trained well. In this article, we cover how business and technology leaders can ensure that their critical and valuable corporate assets remain safe and how they can teach employees to move data in safe way because if your employees don’t know how to recognize a security threat, how can they be expected to avoid it, report it or remove it? This is where IT needs to draw a line between balancing security issues and giving people the tools and accesses, they need to get their job done.

 

  • Shearing files containing corporate data via Emails

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A report on data security based on Ipswich File Transfer confidential surveys of IT professionals, revealed that a vast majority (84%) of the respondents send classified or confidential information as email attachments. Of that majority, 72% do this at least once per week, and 52% do this at least once per day. Try to imagine the quantity of data which is transferred via your email server every day with the information in between departments or with the personal information of employees. Mails with containing data of potential clients transferred from marketing to sales. As this sensitive data is transmitted via email servers with inadequate security controls. By doing it, employees are certainly putting company’s data at risk without realizing how dangerous they can be to the organization’s cybersecurity. Hackers are always looking for new ways to steal sensitive data by hacking email accounts to get the job done.

 

  • Sharing files containing corporate data via Cloud Servers

 

 As more and more companies switch to cloud or hybrid cloud environments, much more sensitive data is transferred via these cloud services such as Google Drive, Dropbox and Microsoft Azure. These clouds services are becoming more and more a regular part of any business process. The main problem while using a third-party file sharing service, corporate sensitive and confidential data is taken outside of company’s IT environment and causing a risk to the data’s privacy. Therefore, the IT department must have a sufficient overview of who has access to these services and which data is in the cloud or being transferred via cloud. The most important measure for the security of data in the cloud is who is responsible for protecting this data. It leaders must be certain that your company is well insured against breakdowns or security margins before signing any contract with cloud services. According to your own risk assessment, it may be concluded that certain types of data are better stored locally because IT there has more control over the security and compliance of this data.

 

  • FTP Servers

 

FTP is an application layer protocol used for the transfer of files between a client and server and is as old as the email and is used far more often than IT experts would like to admit. FTP is an ideal file sharing platform for small to medium-size enterprises which have basic file transfer requirements.  The problem with this method is that it is not considered as a secured protocol per today’s standards. Users don’t get the ability to encrypt and protect data as FTP has no encryption. Which leads they data in the transferred files front of a huge security risk. In addition to that, FTP has become over 45 years old and it is not suitable for high-volume transactions and multiple requirements. Therefore, companies must find better alternatives to FTP such as SFTP, HTTP, MFT.

 

Company must educate their workers about the best and safest data transfer approach and train them regularly on the pros and cons of each solution. The best way for businesses to handle this is to set a company-approved application that will demonstrate safe data-transfer. Employees whose companies provide them with a secure, easy-to-use file sync service will see no need to bring other file sharing services into the workplace. At the end of the day, it doesn’t matter which solution you or your end users use. Ultimately, your company is only responsible for the sensitive data, regardless of which tools and services you use.

 

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RPA & AI in Health Care

Smart systems affect our professional and private lives. Artificial intelligence (AI) is getting sophisticated and forming the basis for all the technologies we use on a daily basis. Obsolete technologies are making ways for new systems, entrepreneurs must reorient and restructure their business processes towards digitisation. As AI is getting increasingly sophisticated at doing what humans do, more efficiently, more quickly and at a lower cost, it’s use in healthcare can play a major role. Many industries already rely on automation of business processes using robotic process automation (RPA) or even artificial intelligence. Is this a good solution for the healthcare industry too?

 

With the advancements in technology applications and apps that encourage healthier behaviour, the population is increasingly having longer life expectancies. Hospitals struggle with mountains of data and inefficient and complex ways of processing all these data. Moreover, caregiver spent a significant amount of their time doing these repetitive tasks.Thus, the need for medical care is growing and the shortage of skilled workforce in the care sector is growing as well.

 

The complexity and rise of data in healthcare means by making AI and robotics a part of our healthcare Eco-system, and with the integration of automation of processes, we can counteract these problems. With the use of software robots, we can shifts repetitive, time-consuming routine activities, such as administrative or billing operations, away from employees to virtual “colleagues”. Faster access to data also makes medical care easier for patients: with the data generated via patient wearables and other medical devices combined with AI, it’s helpful to oversee early stage heart disease, enabling doctors and other caregivers to better monitor and detect potentially life-threatening episodes at earlier, more treatable stages. Doctors and caregivers can have more personal contact with patients and often fewer mistakes.

 

AI can be used to analyse and identify patterns in large and complex datasets faster and more precisely than it has previously been possible. It has the potential to aid the diagnosis of disease. Using AI to analyse clinical data, research publications, and professional guidelines could also help to inform decisions about patient’s treatment. But as AI depends on digital data, so any inconsistencies in the availability and quality of data restrict the potential of AI. 

 

Robotic Process Automation is digital techniques form an interface between the management concept and the automation of business processes using software solutions. Virtual employees take over the handling of individual user interfaces or even entire business processes. With the help of automation, workers get more time for tasks that require individual interaction and emotional judgment. Software robots mimic and perfectly scalable business processes in all areas around the clock with almost zero error rates.

 

Opting for RPA can represent a lot of advantages for medical institutions, such as:

 

  • Automation of routine tasks = relieves employees of frequently repetitive tasks so they can apply their skills to cases that require a human touch
  • Process, input and extraction of user interface-driven data from clinical applications, lab information systems, third party portals, insurance portals, radiology information systems= increase productivity and improves quality of work
  • Software robots work faster without any human error = Patient’s data up to date
  • An RPA robot can enter pertinent and specific health information into a patient’s Electronic Medical Record (EMR).
  • With the use of RPA, doctors have patient’s digital traces all the time = more insights into work process flows
  • RPA productivity also translates into return on investment as Software robots work much faster than their human counterpart
  • Helps caretakers not only reduce costs and gain efficiency but also increase the quality of patient care and amount of time spent with them
  • RPA can optimise patient registration, appointment scheduling, billings and payment posting = decrease in operational costs and improvements in patient experience 

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RPA is offering great benefit to health care professionals and administrators in helping them perform their tasks at a higher capacity with less effort. The combination of RPA and artificial intelligence forms the next level of automation because systems make their own decisions, learn and optimise processes independently. AI can assign tasks, which are then executed by RPA. The software robots provide the hands for the cognitive systems. In the future, for example, automation solutions could be even more involved in the monitoring of people in need of care. However, it often turns out to be sufficient and above all more meaningful to use RPA alone as it enables them to provide immediate productivity gains, achieve near zero error rates, reduce operational costs and greatly increase the quality of patient care.

 

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Privacy Policies: Keeping up to date with all the potential Cyber Security Threats 

 

The amount of time we spend every day on the internet on different devices for work of private purpose isn’t ignorable. That’s why security and privacy on the internet affect nearly everyone nowadays. However, there are many myths than reality. In this article you’ll find tips that you can use to handle security issues.

 

  • I have nothing to hide and why should criminals be interested in me at all?

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Believing that cybercriminals are not interested in me, so I don’t have to protect myself is absolutely wrong. Even if we all know that cybercriminals are mainly interested in all the available data left over from online shopping or online banking. These are mainly access data and account information that can be abused.

Alternatively, cybercriminals, for example, use ransomware to encrypt data on their computer.I’m positive that you’ve heard about ransomware. Anyone can come under threat from aggressive ransomware attacks, it’s a type of software designed to block access to a machine and files until a sum of money is paid out, it has the ability to cause massive disruption to productivity within the workplace.

Only after the transfer of a certain amount of money, you get back to your data. In addition, your device could be misused as part of a network of bots for criminal purposes.

 

  • There are no cyberattacks on trusted sites!

Many users think that they are safe because they only visit websites with a secure connection. But malware is becoming increasingly more common on popular and trusted domains. Cybercriminals are hacking trusted sites using stolen access credentials in order to launch attacks that are out in the open. Also, the malware can be hidden behind advertisements and installs itself without your intervention when clicking on it. No matter where you surf, protect yourself with antivirus software, firewalls, and important updates.

 

  • New smartphones are automatically secure

For years, people would believe that new generation smartphones can’t get viruses because they are just so secure. This led users to believe that they are safe from cybercriminals and that they could do what they liked online without running the risk of catching a virus or being the victim of a cyber-attack. However, this isn’t the case.Even when you buy a new smartphone you have to check yourself whether you use the latest version of the operating system.

If this is not the case, you should install available updates to close any security vulnerabilities. Likewise, you should also first check the security settings of your new device. Be sure to activate the backup of your SIM card and the device, as well as the encryption of the data – if available.

 

  • Online shopping scams

Online shopping scams involve scammers pretending to be legitimate online sellers, either with a fake website or a fake ad on a genuine retailer site. Almost every day new fake shops goes online, which never send prepaid goods to the buyer. If you get to experience this, it’s not only the loss of money that you’ll face but you might also face misuse of your personal data and credit card information if you have entered them on a fraudulent website. You should therefore urgently observe further movements on your account and contact your bank if in need. Therefore, always pay attention to the features of a secure online shop, such as the Trusted Shops seal of approval with buyer protection and information such as a complete imprint.

 

    • The risks of public Wi-Fi

 Wi-Fi is available at most public places or accommodations. Especially when staying in other countries, it is therefore often used. But do you know you might be unaware of some threats lurking in the background on public Wi-Fi while you balance your bank account?What many users ignore that the data transfer between their device and the router is usually unencrypted. This is used by cybercriminals to tap private data and, for example, take over their online accounts. In addition, you could play malicious software on your device.

While there are several ways to protect yourself when using a public network, one way beats the rest is using a VPN. A VPN encrypts your Internet connection to secure it and protect your privacy. When connecting with a VPN, no one – not the Internet service provider, the business/network owner nor any third-party hacker or snoop – can see the information you send over the network. When using Wi-Fi with a VPN your privacy and security is protected at all times.

 

Any devices that are connected to the network, such as laptops, smartphones, smart devices, even an installed USB key, can be read by the attacker. A malicious actor could use this vulnerability to steal sensitive information, and also insert malware or ransomware that would make a website unsafe to visit. So the tips above can help you to protect yourself against latest ways of cyber-crime.

Machine Learning and a powerful Customer-Service

Machine learning: powerful customer service

Every business-customer couple is looking for a certain harmony. But like any other private couple, it cannot exist without first having a strong knowledge of one another.

Monday, is your birthday. You open your emails and, wow surprise, your favorite shoe brand address their vows with a discount code. But before you go further to use this delicate attention, you note that the recipient is not good, means the mail and promo code might not be for you. And what’s more annoying than receiving a mailing from one’s favorite brand with such error? Unfortunately, this kind of mistake is not so rare in the context of written exchanges between a customer and a business. And although regrettable, this is sometimes the tip of the iceberg in terms of cutting edge of customer relationship.

 

Actually such errors in the commercial couple not only irritate, but they can also be the cause of branch of the company-client marriage contract. All loyal customers may fly away if their trusted brand is not even able to store essential information about them. Because Customers expect to be heard and acknowledged, to be treated with the utmost care and personalization, and to receive responses promptly.

As any other couple, the business couples thus have its ups and downs. However, with a little effort we can make this relation stable. And for a business, knowing a customer on the fingertips is a must.

 

Machine learning is based on algorithms that can learn from data without relying on rules-based programming. To be able to work on its commercial couple and ensure its present and future, every company therefore needs to know how to collect and operate effectively its customer data.

Development of data gathering tools, databases, behavioral segmentation techniques, connected data feedback from the field, are just as many opportunities on which it is necessary to invest in order to create a certain connection with the customer. Only by knowing the “who”, “what”, “when”, “how” and “why” about the act of buying, companies will be able to provide personalized service.

 

But so far, the collection of data alone is not the happy ending of the story. Like any old couple who knows each other by heart, if one doesn’t anticipate the desires, expectations and limits of others and does not act according, misunderstandings and conflicts takes birth. For a long term harmonious relationship, companies must therefore capitalize on these new gathered data and to do this machine learning is the best option. By describing the ability of a computer to not only calculate but to learn without being explicitly programmed, machine learning analyzes the raw data, synthesizes and then leaves it to companies to operate according to the relevance of their “data driven strategy”.

 
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Duo of anticipation and empathy, a win-win for commercial couple:

Today, the customer data volumes is exploding, more data has been created in the past two years than in the entire previous history of the human race thanks to the advent of digital channels and connected communication tool for customer interactions and business have thus become very complex. Machine learning makes it possible not only to sort and keep only the essentials, but also helps to learn about what are needs, expectations and requirements of customers, so companies can anticipate their actions and harmonize the client relationship.

 

A well-synthesized insight, driven by approaches such as machine learning, can provide opportunity for any company to predict. In particular, I would say that customer-profiling based on data from touch-points can allow companies to not only determine the stage of a customer within the sales funnel, but to predict their actions and reactions in the future. While only the biggest players have access to the technological know-how to do this well right now, it’s only a matter of time before SMEs can replicate it and take advantage of the computing power already at their disposal.

 

Customer service teams are expected not only to react to requests and questions coming their way, but to also proactively anticipate customer needs. Machine learning is also anticipation. It’s a powerful tool to analyze the actions of customers and sales assistant, but also to identify some keywords used throughout their conversations to recognize problems and find within the company’s knowledge base information a solution to the problem. Companies can identify urgent request of customers and respond quickly. A bit like when it comes to detect in the long conversations THE topic which should not be overlooked and which fully deserves our attention.

 

Beyond anticipation, machine learning also increases the empathy of any business capacity. By learning from their exchanges, companies eventually learn a lot about their customers and can offer personalized services. The challenge is then to provide new goods and services by knowing from customer purchase history, to give free shipping or reductions when it comes to a loyal customer or his birthday. A professional error can happen, in that case a company must admit their mistake and do everything to compensate the client at the right time, but also must learn from their errors and take extra precautions to reduce the risk of problem in the future.

 

So Machine learning is a predictive (and increasingly prescriptive) analytics approach that teaches computers to think and solve problems like a human, continuously adapting to new information. With machine learning, you can monitor the entire customer experience to not only gain new perspective but actual guidance on the best next steps to take, because it’s virtually impossible to grow your business over time without putting the customer first. While there are plenty of tools and services that allow you to streamline aspects of marketing and customer service, be wary of letting these resources overtake your entire business model. The only way to build a profitable business is by humanizing your brand and developing lasting connections with your customer base.

 

Machine Learning: An Artificial Intelligence approach

I’ve heard a lot of people saying that Machine Learning is nothing else than a synonymous of Artificial Intelligence but that’s not true at all. The reality is that Machine Learning is just one approach to AI (in fact it’s called the statistic approach).

 

Let me first give a definition of Machine Learning. It’s a type of artificial intelligence that gives computers the ability to learn to do stuff via different algorithms. On the other hand Artificial Intelligence is used to develop computer programs that perform tasks that are normally performed by human by giving machines (robots) the ability to seem like they have human intelligence.

 

If you are wondering what it means for a machine to be intelligent, it’s clear that “learning” is the KEY issue. Stuffing a lot of knowledge into a machine is simply not enough to make it intelligent. So before going far in the article, you must know that in the field of Artificial Intelligence, there are 2 main approaches about how to program a machine so it can perform human tasks. We’ve a Statistical Approach (also known as probabilistic) and Deterministic Approach. None of these two approach are superior to the other, they are just used in different cases.

 

The Machine Learning (=Statistic AI) is based on, yes you’ve guessed right, statistics. It’s a process where the AI system gather, organize, analyze and interpret numerical information from data. More and more industries are applying AL to process improvement in the design and manufacture of their products.

 

There’ll be around 5 to 20 billion connected devices within 3 years and so many capture points will be used to make live decisions, to recommend, provide real-time information and detect weak signals or plan of predictive maintenance. Whether it’s at the level of business uses, the sectors of industry and services (health, distribution, automotive, public sector …) or even the use of Business Intelligence, everything is changing! With the Machine Learning and voice recognition technology based on AI, even the Big Data technology might be quickly overtaken by real-time information.

 

In a preview of an upcoming e-book, “AI & Machine Learning”, UMANIS talks about The Data, machinery and men. In the e-book they have elaborated problems and expectations that different companies are facing in the technological era.

 

Based on the responses of 58 participants who responded to the survey “AI & Machine Learning”, here below you’ll find identified trends and indicators.

 

  • 44% of companies believes that AI and Machine Learning have become essential and latest trend in various fields including education, healthcare, the environment and business sector,
  • One company on two is curious about the technological innovations in order to understand the collection of data (via machine)
  • 1/3 of companies are currently on standby on AL & Machine Learning topics,
  • 21% of IT decision makers were informed about Cortana suites (Microsoft) and Watson (IBM)
  • 36% want to go further on this type of technology,
  • 88% are planning to launch an AL project within more than 6 months,
  • 50% of respondents are unaware of the purpose of these technologies in the company.

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TOP 5 issues:

  • Detect abnormalities
  • Using machine learning to optimize the automation
  • Integrating a Learning Machine module into an existing SI
  • Remodeling of the real-time Data architecture to gather big volumes with high computing power
  • Finding a permanent solution of storage and backup of the collected data

 

There’s no doubt that machine learning area is booming. It can be applied to high volumes of data to obtain a more detailed understanding of the implementation process and to improve decision making in manufacturing, retail, healthcare, life sciences, tourism, hospitality, financial services and energy. The machine learning systems can make precise result predictions based on data from training or past experiences. By gathering relevant information for making more accurate decisions, machine learning systems can help manufacturers improve their operations and competitiveness.

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