Monitoring of Jobskills with Data Engineering & AI

On own account, we from DATANOMIQ have created a web application that monitors data about job postings related to Data & AI from multiple sources (, Google Jobs, and more).

The data is obtained from the Internet via APIs and web scraping, and the job titles and the skills listed in them are identified and extracted from them using Natural Language Processing (NLP) or more specific from Named-Entity Recognition (NER).

The skill clusters are formed via the discipline of Topic Modelling, a method from unsupervised machine learning, which show the differences in the distribution of requirements between them.

The whole web app is hosted and deployed on the Microsoft Azure Cloud via CI/CD and Infrastructure as Code (IaC).

The presentation is currently limited to the current situation on the labor market. However, we collect these over time and will make trends secure, for example how the demand for Python, SQL or specific tools such as dbt or Power BI changes.

Why we did it? It is a nice show-case many people are interested in. Over the time, it will provides you the answer on your questions related to which tool to learn! For DATANOMIQ this is a show-case of the coming Data as a Service (DaaS) Business.

The Role Data Plays in HR Analytics

Data analytics in HR can help businesses make informed decisions for hiring, promotion and digital transformation. While human resources is typically considered a “soft” discipline, information can reveal invaluable insights that help professionals deliver tangible improvements. What role does data play in HR analytics and success?

The Value of Data in HR

Data is crucial for the success of HR analytics tools. It increases visibility into business processes and the employee experience. The information analytics reveals allows decisions to be based on proven facts rather than subjective assumptions.

For example, professional absenteeism costs an estimated $24.2 billion annually worldwide. Reducing these rates relies on identifying the most common causes of missing work among employees. Some might have a chronic illness or unpredictable family obligations. Others might struggle to maintain motivation if the workplace culture does not fit them well.

Data highlights information like this, allowing HR professionals to act on sound evidence and insights. This applies to HR-specific choices, like hiring, as well as businesswide decisions, like the best way to implement a new app or technology.

Applications for HR Analytics Tools

What are the benefits of data in HR? There are many applications for the insights gained from HR analytics tools. Most business goals and challenges are connected to HR in one way or another, so data-powered solutions can have a significant ripple effect.

Data-Driven Hiring

Refining the recruitment process is a top priority for many HR professionals. Data analytics can streamline hiring, from finding potential candidates to choosing new hires.

Important KPIs for this category include time to hire, time to fill, offer acceptance rates and application sources. This data highlights how applicants hear about the business’s job openings, how long it takes to fill open positions and how frequently first-choice applicants accept job offers. Analyzing these key data types lets HR professionals pinpoint ways to improve their hiring process.

For example, HR analytics tools could reveal that a certain job board is more likely to attract applicants who accept offers. It might refer fewer candidates than another, but data would show it attracts higher-quality candidates. HR managers could then focus on prioritizing their postings on that specific site.

More Informed Employee Management

Data analytics in HR enable employee management decisions, like promotions, to be based on hard numerical data. This can be particularly helpful since getting or missing a promotion can affect workers emotionally. If they can see why they may not have received a promotion, they may be more likely to turn disappointment into motivation to improve.

HR professionals can track KPIs like average projects completed each month, client reviews, performance over time or job review results. Analyzing all this data can highlight employees who may fly under the radar while actually outperforming colleagues. Insights like this allow HR professionals to make more informed promotion decisions.

Digital Transformation Initiatives

Employees play a core role in the success of digital transformation. Surveys show that 27% of business executives are concerned about where to focus their efforts. Applying data in HR can reveal employees’ needs and the areas where technology upgrades can best serve them.

HR professionals can also use data analytics to measure the success of digital transformation initiatives once they are implemented. For example, employee performance and satisfaction surveys might show most workers like a new software program but find it confusing to learn. The HR department could use this information to suggest more thorough training for new tools moving forward.

Artificial Intelligence for HR

Data analytics in HR doesn’t need to be a manual process. Cutting-edge AI tools are now widely available to help with the data analysis process. For example, ChatGPT, one of today’s most popular AI models, can give users instructions on how to set up data analytics programs.

While ChatGPT won’t replace professional data analysts any time soon, it can help HR professionals navigate analytics. An HR department might want a data analysis program to predict employee success. It can use ChatGPT to generate instructions on creating that specific program. This technology can even write functional code.

Artificial intelligence for HR data analysis is still somewhat limited. ChatGPT may be smart but can only work with text input and output. However, AI does make a helpful assistant. Additionally, collecting large amounts of information is central to creating and training well-optimized AI models. Compiling HR data can help prepare businesses for the future.

Data-Driven Human Relations

Data analytics and artificial intelligence for HR can revolutionize decision-making. HR analytics tools ground promotions and hiring in clear numerical KPIs. Businesses can even use data to analyze the performance of big changes, like integrating a new digital transformation initiative. It opens the door to many possibilities that can lead to an even more productive human resources department.

Jobprofil des Data Engineers

Warum Data Engineering der Data Science in Bedeutung und Berufschancen längst die Show stiehlt, dabei selbst ebenso einem stetigen Wandel unterliegt.

Was ein Data Engineer wirklich können muss

Der Data Scientist als sexiest Job des 21. Jahrhunderts? Mag sein, denn der Job hat seinen ganz speziellen Reiz, auch auf Grund seiner Schnittstellenfunktion zwischen Technik und Fachexpertise. Doch das Spotlight der kommenden Jahre gehört längst einem anderen Berufsbild aus der Datenwertschöpfungskette – das zeigt sich auch bei den Gehältern.

Viele Unternehmen sind gerade auf dem Weg zum Data-Driven Business, einer Unternehmensführung, die für ihre Entscheidungen auf transparente Datengrundlagen setzt und unter Einsatz von Business Intelligence, Data Science sowie der Automatisierung mit Deep Learning und RPA operative Prozesse so weit wie möglich automatisiert. Die Lösung für diese Aufgabenstellungen werden oft vor allem bei den Experten für Prozessautomatisierung und Data Science gesucht, dabei hängt der Erfolg jedoch gerade viel eher von der Beschaffung valider Datengrundlagen ab, und damit von einer ganz anderen entscheidenden Position im Workflow datengetriebener Entscheidungsprozesse, dem Data Engineer.

Data Engineer, der gefragteste Job des 21sten Jahrhunderts?

Der Job des Data Scientists hingegen ist nach wie vor unter Studenten und Absolventen der MINT-Fächer gerade so gefragt wie nie, das beweist der tägliche Ansturm der vielen Absolventen aus Studiengängen rund um die Data Science auf derartige Stellenausschreibungen. Auch mangelt es gerade gar nicht mehr so sehr an internationalen Bewerben mit Schwerpunkt auf Statistik und Machine Learning. Der solide ausgebildete und bestenfalls noch deutschsprachige Data Scientist findet sich zwar nach wie vor kaum im Angebot, doch insgesamt gute Kandidaten sind nicht mehr allzu schwer zu finden. Seit Jahren sind viele Qualifizierungsangebote für Studenten sowie Arbeitskräfte am Markt auch günstig und ganz flexibel online verfügbar, ohne dabei Abstriche bei beim Ansehen dieser Aus- und Fortbildungsmaßnahmen in Kauf nehmen zu müssen.

Was ein Data Scientist fachlich in Sachen Expertise alles abdecken muss, hatten wir ganz ausführlich über Betrachtung des Data Science Knowledge Stack besprochen.

Doch was bringt ein Data Scientist, wenn dieser gar nicht über die Daten verfügt, die für seine Aufgaben benötigt werden? Sicherlich ist die Aufgabe eines jeden Data Scientists auch die Vorbereitung und Präsentation seiner Vorhaben. Die Heranschaffung und Verwaltung großer Datenmengen in einer Enterprise-fähigen Architektur ist jedoch grundsätzlich nicht sein Schwerpunkt und oft fehlen ihm dafür auch die Berechtigungen in einer Enterprise-IT. Noch konkreter wird der Bedarf an Datenbeschaffung und -aufbereitung in der Business Intelligence, denn diese benötigt für nachhaltiges Reporting feste Strukturen wie etwa ein Data Warehouse.

Das Profil des Data Engineers: Big Data High-Tech

Auch wenn Data Engineering von Hochschulen und Fortbildungsanbietern gerade noch etwas stiefmütterlich behandelt werden, werden der Einsatz und das daraus resultierende Anforderungsprofil eines Data Engineers am Markt recht eindeutig skizziert. Einsatzszenarien für diese Dateningenieure – auch auf Deutsch eine annehmbare Benennung – sind im Kern die Erstellung von Data Warehouse und Data Lake Systeme, mittlerweile vor allem auf Cloud-Plattformen. Sie entwickeln diese für das Anzapfen von unternehmensinternen sowie -externen Datenquellen und bereiten die gewonnenen Datenmengen strukturell und inhaltlich so auf, dass diese von anderen Mitarbeitern des Unternehmens zweckmäßig genutzt werden können.

Enabler für Business Intelligence, Process Mining und Data Science

Kein Data Engineer darf den eigentlichen Verbraucher der Daten aus den Augen verlieren, für den die Daten nach allen Regeln der Kunst zusammengeführt, bereinigt und in das Zielformat gebracht werden sollen. Klassischerweise arbeiten die Engineers am Data Warehousing für Business Intelligence oder Process Mining, wofür immer mehr Event Logs benötigt werden. Ein Data Warehouse ist der unter Wasser liegende, viel größere Teil des Eisbergs der Business Intelligence (BI), der die Reports mit qualifizierten Daten versorgt. Diese Eisberg-Analogie lässt sich auch insgesamt auf das Data Engineering übertragen, der für die Endanwender am oberen Ende der Daten-Nahrungskette meistens kaum sichtbar ist, denn diese sehen nur die fertigen Analysen und nicht die dafür vorbereiteten Datentöpfe.

Abbildung 1 - Data Engineering ist der Mittelpunkt einer jeden Datenplattform. Egal ob für Data Science, BI, Process Mining oder sogar RPA, die Datenanlieferung bedingt gute Dateningenieure, die bis hin zur Cloud Infrastructure abtauchen können.

Abbildung 1 – Data Engineering ist der Mittelpunkt einer jeden Datenplattform. Egal ob für Data Science, BI, Process Mining oder sogar RPA, die Datenanlieferung bedingt gute Dateningenieure, die bis hin zur Cloud Infrastructure abtauchen können.

Datenbanken sind Quelle und Ziel der Data Engineers

Daten liegen selten direkt in einer einzigen CSV-Datei strukturiert vor, sondern entstammen einer oder mehreren Datenbanken, die ihren eigenen Regeln unterliegen. Geschäftsdaten, beispielsweise aus ERP- oder CRM-Systemen, liegen in relationalen Datenbanken vor, oftmals von Microsoft, Oracle, SAP oder als eine Open-Source-Alternative. Besonders im Trend liegen derzeitig die Cloud-nativen Datenbanken BigQuery von Google, Redshift von Amazon und Synapse von Microsoft sowie die cloud-unabhängige Datenbank snowflake. Dazu gesellen sich Datenbanken wie der PostgreSQL, Maria DB oder Microsoft SQL Server sowie CosmosDB oder einfachere Cloud-Speicher wie der Microsoft Blobstorage, Amazon S3 oder Google Cloud Storage. Welche Datenbank auch immer die passende Wahl für das Unternehmen sein mag, ohne SQL und Verständnis für normalisierte Daten läuft im Data Engineering nichts.

Andere Arten von Datenbanken, sogenannte NoSQL-Datenbanken beruhen auf Dateiformaten, einer Spalten- oder einer Graphenorientiertheit. Beispiele für verbreitete NoSQL-Datenbanken sind MongoDB, CouchDB, Cassandra oder Neo4J. Diese Datenbanken exisiteren nicht nur als Unterhaltungswert gelangweilter Nerds, sondern haben ganz konkrete Einsatzgebiete, in denen sie jeweils die beste Performance im Lesen oder Schreiben der Daten bieten.

Ein Data Engineer muss demnach mit unterschiedlichen Datenbanksystemen zurechtkommen, die teilweise auf unterschiedlichen Cloud Plattformen heimisch sind.

Data Engineers brauchen Hacker-Qualitäten

Liegen Daten in einer Datenbank vor, können Analysten mit Zugriff einfache Analysen bereits direkt auf der Datenbank ausführen. Doch wie bekommen wir die Daten in unsere speziellen Analyse-Tools? Hier muss der Engineer seinen Dienst leisten und die Daten aus der Datenbank exportieren können. Bei direkten Datenanbindungen kommen APIs, also Schnittstellen wie REST, ODBC oder JDBC ins Spiel und ein guter Data Engineer benötigt Programmierkenntnisse, bevorzugt in Python, diese APIs ansprechen zu können. Etwas Kenntnis über Socket-Verbindungen und Client-Server-Architekturen zahlt sich dabei manchmal aus. Ferner sollte jeder Data Engineer mit synchronen und asynchronen Verschlüsselungsverfahren vertraut sein, denn in der Regel wird mit vertraulichen Daten gearbeitet. Ein Mindeststandard an Sicherheit gehört zum Data Engineering und darf keinesfalls nur Datensicherheitsexperten überlassen werden, eine Affinität zu Netzwerksicherheit oder gar Penetration-Testing ist positiv zu bewerten, mindestens aber ein sauberes Berechtigungsmanagement gehört zu den Grundfähigkeiten. Viele Daten liegen nicht strukturiert in einer Datenbank vor, sondern sind sogenannte unstrukturierte oder semi-strukturierte Daten aus Dokumenten oder aus Internetquellen. Mit Methoden wie Data Web Scrapping und Data Crawling sowie der Automatisierung von Datenabrufen beweisen herausragende Data Engineers sogar echte Hacker-Qualitäten.

Dirigent der Daten: Orchestrierung von Datenflüssen

Eine der Kernaufgaben des Data Engineers ist die Entwicklung von ETL-Strecken, um Daten aus Quellen zu Extrahieren, zu in das gewünschte Zielformat zu Transformieren und schließlich in die Zieldatenbank zu Laden. Dies mag erstmal einfach klingen, wird jedoch zur echten Herausforderung, wenn viele ETL-Prozesse sich zu ganzen ETL-Ketten und -Netzwerken zusammenfügen, diese dabei trotz hochfrequentierter Datenabfrage performant laufen müssen. Die Orchestrierung der Datenflüsse kann in der Regel in mehrere Etappen unterschieden werden, von der Quelle ins Data Warehouse, zwischen den Ebenen im Data Warehouse sowie vom Data Warehouse in weiterführende Systeme, bis hin zum Zurückfließen verarbeiteter Daten in das Data Warehouse (Reverse ETL).

Hart an der Grenze zu DevOp: Automatisierung in Cloud-Architekturen

In den letzten Jahren sind Anforderungen an Data Engineers deutlich gestiegen, denn neben dem eigentlichen Verwalten von Datenbeständen und -strömen für Analysezwecke wird zunehmend erwartet, dass ein Data Engineer auch Ressourcen in der Cloud managen, mindestens jedoch die Datenbanken und ETL-Ressourcen. Darüber hinaus wird zunehmend jedoch verlangt, IT-Netzwerke zu verstehen und das ganze Zusammenspiel der Ressourcen auch als Infrastructure as Code zu automatisieren. Auch das automatisierte Deployment von Datenarchitekturen über CI/CD-Pipelines macht einen Data Engineer immer mehr zum DevOp.

Zukunfts- und Gehaltsaussichten

Im Vergleich zum Data Scientist, der besonders viel Methodenverständnis für Datenanalyse, Statistik und auch für das zu untersuchende Fachgebiet benötigt, sind Data Engineers mehr an Tools und Plattformen orientiert. Ein Data Scientist, der Deep Learning verstanden hat, kann sein Wissen zügig sowohl mit TensorFlow als auch mit PyTorch anwenden. Ein Data Engineer hingegen arbeitet intensiver mit den Tools, die sich über die Jahre viel zügiger weiterentwickeln. Ein Data Engineer für die Google Cloud wird mehr Einarbeitung benötigen, sollte er plötzlich auf AWS oder Azure arbeiten müssen.

Ein Data Engineer kann in Deutschland als Einsteiger mit guten Vorkenntnissen und erster Erfahrung mit einem Bruttojahresgehalt zwischen 45.000 und 55.000 EUR rechnen. Mehr als zwei Jahre konkrete Erfahrung im Data Engineering wird von Unternehmen gerne mit Gehältern zwischen 50.000 und 80.000 EUR revanchiert. Darüber liegen in der Regel nur die Data Architects / Datenarchitekten, die eher in großen Unternehmen zu finden sind und besonders viel Erfahrung voraussetzen. Weitere Aufstiegschancen für Data Engineers sind Berater-Karrieren oder Führungspositionen.

Wer einen Data Engineer in Festanstellung gebracht hat, darf sich jedoch nicht all zu sicher fühlen, denn Personalvermittler lauern diesen qualifizierten Fachkräften an jeder Ecke des Social Media auf. Gerade in den Metropolen wie Berlin schaffen es längst nicht alle Unternehmen, jeden Data Engineer über Jahre hinweg zu beschäftigen. Bei der großen Auswahl an Jobs und Herausforderungen fällt diesen Datenexperten nicht schwer, seine Gehaltssteigerungen durch Jobwechsel proaktiv voranzutreiben.

Hybrid Cloud

The Cloud or Hybrid Cloud – Pros & Cons

Big data and artificial intelligence (AI) are some of today’s most disruptive technologies, and both rely on data storage. How organizations store and manage their digital information has a considerable impact on these tools’ efficacy. One increasingly popular solution is the hybrid cloud.

Cloud computing has become the norm across many organizations as the on-premise solutions struggle to meet modern demands for uptime and scalability. Within that movement, hybrid cloud setups have gained momentum, with 80% of cloud users taking this approach in 2022. Businesses noticing that trend and considering joining should carefully weigh the hybrid cloud’s pros and cons. Here’s a closer look.

The Cloud

To understand the advantages and disadvantages of hybrid cloud setups, organizations must contrast them against conventional cloud systems. These fall into two categories: public, where multiple clients share servers and resources, and private, where a single party uses dedicated cloud infrastructure. In either case, using a single cloud presents unique opportunities and challenges.

Advantages of the Cloud

The most prominent advantage of traditional cloud setups is their affordability. Because both public and private clouds entirely remove the need for on-premise infrastructure, users pay only for what they need. Considering how 31% of users unsatisfied with their network infrastructure cite insufficient budgets as the leading reason, that can be an important advantage.

The conventional cloud also offers high scalability thanks to its reduced hardware needs. It can also help prevent user errors like misconfiguration because third-party vendors manage much of the management side. Avoiding those mistakes makes it easier to use tools like big data and AI to their full potential.

Disadvantages of the Cloud

While outsourcing management and security workloads can be an advantage in some cases, it comes with risks, too. Most notably, single-cloud or single-type multi-cloud users must give up control and visibility. That poses functionality and regulatory concerns when using these services to train AI models or analyze big data.

Storing an entire organization’s data in just one system also makes it harder to implement a reliable backup system to prevent data loss in a breach. That may be too risky in a world where 96% of IT decision-makers have experienced at least one outage in the last three years.

Hybrid Cloud

The hybrid cloud combines public and private clouds so users can experience some of the benefits of both. In many instances, it also combines on-premise and cloud environments, letting businesses use both in a cohesive data ecosystem. Here’s a closer look at the hybrid cloud’s pros and cons.

Advantages of Hybrid Cloud

One of the biggest advantages of hybrid cloud setups is flexibility. Businesses can distribute workloads across public, private and on-premise infrastructure to maximize performance with different processes. That control and adaptability also let organizations use different systems for different data sets to meet the unique security needs of each.

While hybrid environments may be less affordable than traditional clouds because of their on-premise parts, they offer more cost-efficiency than purely on-prem solutions. Having multiple data storage technologies provides more disaster recovery options. With 75% of small businesses being unable to recover from a ransomware attack, that’s hard to ignore.

Hybrid cloud systems are also ideal for companies transitioning to the cloud from purely on-premise solutions. The mixture of both sides enables an easier, smoother and less costly shift than moving everything simultaneously.

Disadvantages of Hybrid Cloud

By contrast, the most prominent disadvantage of hybrid cloud setups is their complexity. Creating a system that works efficiently between public, private and on-prem setups is challenging, making these systems error-prone and difficult to manage. Misconfigurations are the biggest threat to cloud security, so that complexity can limit big data and AI’s safety.

Finding compatible public and private clouds to work with each other and on-prem infrastructure can also pose a challenge. Vendor lock-in could limit businesses’ options in this regard. Even when they get things working, they may lack transparency, making it difficult to engage in effective big data analytics.

Which Is the Best Option?

Given the advantages and disadvantages of hybrid cloud setups and their conventional counterparts, it’s clear that no single one emerges as the optimal solution for every situation. Instead, which is best depends on an organization’s specific needs.

The hybrid cloud is ideal for companies facing multiple security, regulatory or performance needs. If the business has varying data sets that must meet different regulations, some information that’s far more sensitive than others or has highly diverse workflows, they need the hybrid cloud’s flexibility and control. Companies that want to move slowly into the cloud may prefer these setups, too.

On the other hand, the conventional cloud is best for companies with tighter budgets, limited IT resources or a higher need for scalability. Smaller businesses with an aggressive digitization timeline, for example, may prefer a public multi-cloud setup over a hybrid solution.

Find the Optimal Data Storage Technology

To make the most of AI and big data, organizations must consider where they store the related data. For some companies, the hybrid cloud is the ideal solution, while for others, a more conventional setup is best. Making the right decision begins with understanding what each has to offer.

5G Is Here – Now What?

Before 5G began to roll out, people had questions and theories about it, especially since its popularity coincided in part with COVID-19.

What is 5G? How does 5G work? Will I get sick from coming close to it? These are some of the questions people asked. While some believed it endangers human health, others couldn’t wait to get the best of what it offers.

It is now 2022, and though 5G is being rolled out in different parts of the world, scientists continuously monitor the technology. They have assured users it’s the same as previous networks but faster and more efficient. Its ultra-reliable low-latency communication (URLLC) makes it 250 times faster in processing requests than humans.

For health care professionals, 5G’s technical attributes are an advantage to telehealth, remote surgery, transferring large medical files, and many other medical procedures to treat and support patients. By 2025, 5G is expected to have impacted the economy by driving up to $2.0 trillion in total gross output in Europe alone and creating up to 20 million jobs.

5G Advantages: What Difference Does It Make?

With a speed 100 times faster than 4G, 5G will make a huge difference in internet connectivity, leading to shorter loading times and quick completion of tasks. The world is highly dependent on technology and connectivity – with 5G, there will be a significant improvement in mobile broadband service quality.

Below are some of 5G’s advantages to expect in 2022 and beyond:

  • Energy savings. Unlike 4G LTE, 5G introduced a new standard called 5G New Radio (NR). 5G NR offers adaptable numerology and has the best features of LTE. However, one of the features that sets it apart from 4G LTE is the increased energy savings for devices using it.
  • Reduced latency. 5G URLLC is the most significant feature that makes 5G superior to other networks before it. URLLC offers increased reliability and reduced latency using a novel radio access technology (RAT). These features are the reason why 5G is fast, efficient, and reliable.
  • Higher frequency bands. Aside from the URLLC feature that makes it extremely fast, 5G uses a system of cellular installations that divide their network territories into sectors and use radio frequencies to exchange encoded data. This speeds up the process of encoding data.
  • Supports complex applications. 5G can support complex applications that enable the maintenance of self-operating machines and artificial intelligence (AI). As a result, companies are looking to build 5G-based smart factories that will allow the collection of massive amounts of data and substantially reduce manual labor requirements.
  • Disaster management. Integrated access and backhauling (IAB) is another new feature of 5G. This feature can be used in disaster management by enabling temporary, ad hoc IAB nodes.

5G and Artificial Intelligence

5G is expected to be available in every part of the world by 2025, and so is AI. The high speeds and low latency with which 5G operates would prove extremely efficient if combined with robots and cobots (collaborative robots) that use artificial intelligence.

Since AI is the foundation of an intelligent 5G network, these two systems will work together to process even faster and smarter information requests. Network operators can take advantage of AI in a 5G world to explore new services and improve existing ones.

5G Disadvantages You Need to Know

Just as there are pros to 5G, there are some potential downsides. Below are some disadvantages of 5G:

  • The improvement of technology also means there are new cybercrimes. 5G enables omnipresent computing in home and industrial settings, which raises cybersecurity requirements.
  • Limited rollout. 5G is being rolled out, but it will take time before it gets to every part of the world. As of 2022, the S. and China are far ahead of other countries in their 5G rollouts in 2022.
  • Obstruction issues. Even when 5G is active, obstructions like trees, tall buildings, and construction may limit the reach of 5G signals. Organizations may have to build many more cellular 5G towers than 4G towers in order to achieve the desired coverage.
  • Outdated devices. Existing devices may not support 5G and could become obsolete. Consequently, individuals who own devices that do not support 5G may have to discard them and get the latest ones in order to enjoy the benefits of the network.

What’s Next for 5G?

Though many countries have yet to enjoy the benefits of 5G, the network is already impacting advanced technologies like artificial intelligence and machine learning. A total of 204.6 million 5G connections are expected to be established by 2023.

The advantages of 5G outweigh its disadvantages. 5G will benefit many industries like retail, manufacturing, health care, and other sectors like customer service when it fully rolls out worldwide.

How to speed up claims processing with automated car damage detection

AI drives automation, not only in industrial production or for autonomous driving, but above all in dealing with bureaucracy. It is an realy enabler for lean management!

One example is the use of Deep Learning (as part of Artificial Intelligence) for image object detection. A car insurance company checks the amount of the damage by a damage report after car accidents. This process is actually performed by human professionals. With AI, we can partially automate this process using image data (photos of car damages). After an AI training with millions of photos in relation to real costs for repair or replacement, the cost estimation gets suprising accurate and supports the process in speed and quality.

AI drives automation and DATANOMIQ drives this automation with you! You can download the Infographic as PDF.

How to speed up claims processing with automated car damage detection

How to speed up claims processing
with automated car damage detection

Download this Infographic as PDF now by clicking here!

We wrote this article in cooperation with pixolution, a company for computer vision and AI-bases visual search. Interested in introducing AI / Deep Learning to your organization? Do not hesitate to get in touch with us!

DATANOMIQ is the independent consulting and service partner for business intelligence, process mining and data science. We are opening up the diverse possibilities offered by big data and artificial intelligence in all areas of the value chain. We rely on the best minds and the most comprehensive method and technology portfolio for the use of data for business optimization.

Better Customer Service Using Big Data

Big data is frequently discussed across many industries by more than just business owners, CEOs or IT managers. Big data and big data analytics are two critical elements of modern business that company leaders and their employees should understand if they want to make more informed decisions.

In addition to the highly data-driven business landscape, people’s needs and expectations are changing. Companies with superb customer service gain a competitive advantage over competitors with poor operations.

The power of big data analytics helps organizations take steps to improve their customer service offerings, ultimately meeting or exceeding the needs and expectations of existing and potential clients.

An Overview of Big Data

What exactly is big data and how is it different from traditional data?

Big data describes large, diverse datasets growing at increasing rates and proving highly useful in business. Datasets are so voluminous that traditional data processing software solutions cannot manage them properly.

Here are the “five Vs,” or essential qualities, that accurately describe big data:

  • Volume
  • Velocity
  • Variety
  • Veracity
  • Value

Businesses that leverage big data can address or even prevent a range of problems that would otherwise be more challenging to solve.

Organizations collect, combine and mine three types of data — structured, semi-structured and unstructured — for advanced analytics applications.

Benefits of Big Data Analytics

After analyzing big data, gathering new insights on company operations and other critical business issues helps companies overcome existing problems. Some of these might be costly and cause potential obstacles.

Here are two main benefits of big data analytics:

Customer Attraction and Retention

Big data analytics gives companies detailed insights into customers’ wants and needs.

For example, organizations can review customer data and adjust their current sales or marketing strategies to increase loyalty and satisfaction. Big data can also highlight changes in client sentiment and predict future trends.

Increased Employee Productivity

Monitoring employee performance is essential for most companies. Thankfully, big data analysis can show leaders how individual workers perform and measure their productivity.

Big data can analyze important factors such as absenteeism rates, number of sick days taken, workload and output. Once this information is collected, supervisors can relay findings to employees and make improvements to bolster productivity.

Other benefits exist, but these two examples provide a glimpse into the world of big data and how transformative it is in the modern business world.

How to Use Big Data to Improve Customer Service

There are a few ways businesses can harness big data analytics to gain insights and take actionable steps to improve their customer service offerings. Here’s how.

Solves Customer Inquiries More Effectively

Contacting a customer service center is often time-consuming and headache-inducing for a consumer, especially when the representative cannot answer a question or solve a problem.

Lack of effectiveness and speed are two of the most common causes of customer service frustration. Qualitative and quantitative big data analytics let customer service employees identify their weaknesses, such as their familiarity with a product or service, and take action accordingly.

For example, a representative can spend more time learning about customers’ most common issues while using a specific product, allowing them to solve problems faster and more effectively.

Increases Personalized Offers

A business can achieve significant revenue growth by aligning customer behaviors and marketing messages. Personalized offerings are becoming increasingly popular among consumers. In other words, people want companies to see them as individuals rather than a source of profit.

Big data analytics helps organizations increase the number and quality of personalized offerings. For example, analytics can reveal critical customer information, like how much money they spend, what products they buy and which services they use.

These details help employees create and automate personalized marketing offers. Customer service representatives can also use this data to make recommendations based on buyer preferences, improving the experience and building loyalty.

Empowers Customer Service Representatives

Big data analytics are a major boon to customer service representatives. These employees are considered the face of the company, meaning they must have access to all the resources they need. Insights from big data are no exception.

Representatives working with results from big data analysis are in a better position to respond to inquiries more quickly and provide effective customer solutions. They will likely perform well if they have insights at their disposal.

Provide Superior Customer Support With Big Data Analytics

No matter the industry, virtually every organization relies on data, whether it’s sales, web traffic, customer, supply chain management or inventory data.

Data is becoming increasingly important for companies in today’s competitive business environment. The role of big data will continue to grow as more organizations recognize its positive impact on customer service and satisfaction.

6 Best Podcasts On Big Data To Check Out

Podcasts are one of the best ways to learn about big data, as you can listen and absorb knowledge whether you’re on the move, doing the dishes, or just relaxing at home. If you want to know more about big data, then here are some of the best podcasts you’ll want to be listening to right now (Headlines of all entries are linked to each mentioned podcast!)

1. Freakonomics

 You may well know about the book Freakonomics by Stephen Dubner. In it, he uncovered the world of data science for the average reader, and showed them just how it affected their everyday lives. In this podcast, he carries on the work he started in the book to help you understand the world of big data.

There are several episodes that you’ll want to make sure you listen to, such as The Health of Nations, which looks at how health is measured across the world. Everybody Gossips is another good episode, as it covers how our Google search histories expose our true selves to those who are evaluating that data.

2. Data Framed

This podcast is a must listen if you’re looking to learn more about big data. Trends are changing all the time in this field, so you want to make sure you’re on top of the game. “Each episode brings on an expert in their field, so you can learn from the best” says tech writer Adrian Bowman, from Boom Essays and OXEssays. “You’ll get a real insight into how they use data, and what that means for you.”

Recent episodes have covered things like Salesforce was created to be a mature data organization, and how to build a data science team from scratch. They’re all fascinating to listen to, so you’ll want to make sure that you tune in.

3. Data Skeptic

 With so many episodes in the archive, you can go back and listen to this show for days on end. Every episode covers a different concept in data science, so it’s really helpful to anyone that’s learning about it for the first time. Even if you’re an expert though, you’ll find some new perspectives in here.

You don’t have to start at the beginning to listen, though. Instead, you can catch up with the latest episodes that cover everything new in data. For example, they’ve recently released episodes on the user perceptions of ‘bad ads’ online, and political digital advertising analysis.

4. Data Crunch

This podcast is very much aimed at people who are already working with big data in some way. As such, it won’t be as accessible to newcomers to the field. However, if you are someone in the field then you’ll want to subscribe to this show.

You’ll find lots of episodes on how machine learning is changing industries across the board, as well as some showing where it hasn’t been the success that companies were looking for. You’ll see a lot about what works and what doesn’t here, so you can see what will make your business thrive in the future.

5. Not So Standard Deviations

 On the other hand, this is the podcast you’ll want to be listening to, if you’re new to data science and want to learn more. “The chemistry between the hosts makes it a very easy listen” says Dean Simmons, a big data blogger at State Of Writing and Paper Fellows. “That makes it a lot more accessible for those who are beginning to learn about the subject.”

You’ll get all the basics on things like social media algorithms, deprecated packages, app testing, and much more here. You’ll learn a lot and enjoy listening, too.

6. Making Data Simple

 Finally we have this podcast, which looks at bringing you the very latest news in big data, in a way that’s easy to understand. It’s another show that’s worth listening to if you’re already working in data, as it looks at the news from the viewpoint of those in industries where data is vital.

Host Al Martin talks to experts every episode, so you’ll be able to get the news from the people who know about it, and see how it will affect you.

All these shows can give you a lot of info about big data, so give them a listen and see which one is right for you.

How Online Businesses Can Mitigate Fraud Risk

Fraud has the potential to shatter businesses of all sizes and in all industries. Now that many businesses operate online at least partially, if not completely, the fraud risks are more prominent than ever. Right alongside the perks of reaching an enormous audience and using endless marketing tricks for promotion, businesses have to find a way to mitigate such risks.

One global economic crime survey, from PwC, found that 47% of all businesses worldwide experienced some type of fraud in the last 2 years. While online sales are higher than ever and are expected to grow significantly, this is all matched by a growth in fraud.

If we stop to take a look at how the eCommerce world has progressed in just a few years, the risks are becoming more imminent. Nowadays, it is more important than ever to take action to mitigate risks.

These days, online retailers deal with approximately 206,000 attacks on their businesses each month, research shows. Cybercriminals keep looking – and finding – new ways to capture and use data obtained from businesses, something that can ruin the brand entirely.

If you operate your business online, it is your obligation to your customers and your company to protect if from fraudsters that will steal data and wreck your online reputation. A single instance of fraud can alienate many of your customers and damage your brand, often without any chance to fix it.

Your job is to continuously track the trends, know the risks, and practice data science security hacks to mitigate fraud risks. In this article, you’ll learn all about it. But first, let’s take a look at why fraud happens in the first place.

Why does online fraud take place?

There are two big reasons why fraudsters can get access to data on your website and ruin your business:

  • It is easy. Before the Internet existed and businesses were solely physical, fraudsters needed to do things like rob the place or steal physical cards to make purchases with. These days, fraudsters use their hacking skills to buy cards and make purchases, get access to customer data on your website, etc.
  • It’s often conducted anonymously. Scamming online stores gives fraudsters a high sense of anonymity. They cannot be caught on camera and they can operate from any location in the world. Most police departments don’t make this a priority, so most of them remain uncaught, while businesses suffer the consequences.

Unless you take precautions to prevent this from happening, you are opening your company to many fraud risks. The good thing is, you can actually take precautions and measures to prevent and minimize the effects of fraud when it happens.

How to mitigate fraud risks for your online business

Now that you know how frequently this happens – and why that is the case, it’s time to go through some actionable tips on how to minimize the risks.

1.    Use quality tools for modern fraud monitoring

Did you know that you can use tools to monitor and prevent fraud? Modern tools that are rich with features can protect your business’ data, as well as protect it from risky transactions. If you take a look at this guide on modern fraud monitoring, from SEON, a top-rated tool used for this purpose, you’ll find that there’s a lot to be done to mitigate such risks.

Some of the key features to benefit from when it comes to such tools are:

  • Real-time monitoring – at all times
  • Behavior tracking
  • Fraud scoring
  • Graph visualization
  • Risk-based authentication
  • Manual queries
  • Alerts and reporting
  • Sandboxing capacity

Thankfully, SEON has all that and more. Thanks to SEON, businesses can now authenticate their customers, automatically cancel or detect risky orders, block visitors based on geolocation, and create a variety of custom filters based on their preferences.


2.    Know your fraud risks

It’s impossible to prevent something that you don’t know anything about. Many companies aren’t even aware of the risks before they actually happen. When they realize it, the damage is already done.

Let’s go through the main types of fraud risks that you should work to mitigate today:

  • Credit card fraud

This type of fraud is a banking data crime. It’s a big term that includes all sorts of stealing and illegally using credit card information. In some cases, criminals will use stolen credit card information to buy services or products on your website.

In more severe cases, they’ll be able to get this from your website, which means that you aren’t keeping your customer’s payment details safe enough.

Either way, you are looking at grand losses and problems. Eventually, when people use stolen cards, this defrauds the business owners that have to refund the purchase.

  • Chargeback fraud

Chargeback fraud happens when a credit card provider asks the retailer to refund a disputed or fraudulent transaction. This happens when people buy a product or a service, receive it, but then request a full refund from the company that provided them with the card.  It is also known as friendly fraud. In most cases, criminals wait a few weeks or even a few months after receiving the goods, and then contact the bank to dispute a transaction ‘they don’t know happened’. Some merchants are too busy to notice this, so they are losing tons of money because of it.

  • Affiliate fraud

Affiliate fraud is done when criminals use fake data to generate affiliate commissions. In the affiliate marketing world, online businesses pay affiliates commission for clicks or sales they refer to the website. Criminals often game these systems and make it seem like there’s real activity to generate commissions or increase their amount.

  • Phishing schemes

This is one of the gravest and yet, most common frauds for online businesses. Most online businesses today provide their customers with accounts to facilitate their purchasing process and track their behaviors. This is where financial data, personal information, and purchase history are all stored. Through phishing schemes, fraudsters obtain this personal data, log into the accounts, and make unauthorized purchases.

These are just a few types of eCommerce fraud that occurs online. If you want to prevent them, you need to learn what your business is at risk for, and use the necessary tools to mitigate those risks.

3.    Audit your website regularly

Your website is your storefront and it is one of the most important things to work on. You shouldn’t just work on its design or the content you publish on it. If you want to discover flaws in it before fraudsters do and use it to their benefit, you need to audit it carefully – and regularly.

Using fraud detection tools is a great step toward this, but you should also make sure to check some other things, too.

For example, are your shopping cart plugins and software up-to-date?

Do you have a working SSL certificate or is it expired?

Does your site comply with the current data protection laws and regulations?

Is your store Payment Card Industry Data Security Standard (PCI-DSS) compliant?

Do you back it up as often as you should?

Have you updated your passwords recently, your hosting dashboard, and your CMS database?

4.    Pay close attention to high-value orders

Small frauds can cost you a bit of money and a bit of your reputation. Big frauds can kill your business and your reputation in the industry. This is why you should pay close attention to high-value orders before shipping them out.

Check these personally, even the gift cards. Such items are very often used by fraudsters who hope to resell them, but have obtained them illegally.

5.    Don’t be afraid to contact your customers

Customers that buy from you regularly will have similar behaviors every time they make a purchase. Your system will start flagging any unexpected behavior on their behalf. When that happens and you notice that an existing customer changed their patterns dramatically, don’t be afraid to reach out to them. This might save them and you a lot of money and keep them safer. Not to mention, it will make your brand even more trustworthy and secure in their eyes.

6.    Request the CVV number for purchases

The back of cards such as Visa, MasterCard, and Discover contains a three-digit security code called the Card Verification Value or CVV. American Express cards have a four-digit code on the back.

Why is it smart to request this number?

Most fraudsters have the card numbers and expiry date but don’t have the CVV. This will minimize the risks and make it impossible for them to make fraudulent purchases if they don’t have the physical card on them.

7.    Limit the amount of customer data you are collecting

It can be tempting to collect tons of customer data, especially for research. You can use this data to improve your marketing strategies and your brand and offer customers a more personalized experience. But, collecting a lot of data means that you are creating more risks for that data to be stolen.

That being said, make it your mission to collect and store as little data as possible. Collect only what is necessary.

Are you already doing these things?

Fraudsters are getting smarter about how they attack online businesses. It is your obligation to keep up with the scams in the digital world and find ways to mitigate the risks. This article gives you seven excellent starting points for this.

How to ensure occupational safety using Deep Learning – Infographic

In cooperation between DATANOMIQ, my consulting company for data science, business intelligence and process mining, and Pixolution, a specialist for computer vision with deep learning, we have created an infographic (PDF) about a very special use case for companies with deep learning: How to ensure occupational safety through automatic risk detection using using Deep Learning AI.

How to ensure occupational safety through automatic risk detection using Deep Learning - Infographic

How to ensure occupational safety through automatic risk detection using Deep Learning – Infographic