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.

Source: seon.io

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

How To Perform High-Quality Data Science Job Assessments in 4 Steps

In 2009, Google Chief Economist Hal Varian said to the McKinsey Quarterly that “the sexy job in the next 10 years will be statisticians.” At the time, it was hard to believe. But more than a decade later, we can’t get around the importance of data. Where once oil ruled the world, data is now catching up—quickly. That calls for more and better data scientists. In this article, we’ll explain to you how to find them.

Why is it so hard to find good data scientists?

The demand for data scientist roles has increased by 650 percent since 2012, and that number will continue to grow as the amount of data—and power it holds—grows steadily, too.

But unsurprisingly, there hasn’t been an increase of 650 percent in available data scientists on the job market. Even though the job is a lot sexier—and better paid—than ten years ago, many employers are still struggling to fill their empty seats with talented data scientists.  McKinsey predicted that there would be a shortage of between 140,000 and 190,000 people with analytical skills in the U.S. alone in 2018, and even in 2022 good data scientists, data analysts, forecasting analysts, modelling analysts, machine learning scientists, are hard to find.  Add to that another 1.5 million managers who will also need to at least understand how data analysis drives decision-making, and you can see how employers can be in a bit of a pickle.

Why thoroughly screening data scientists is still crucial

Even though demand is growing much faster than the number of data scientists, companies can’t simply settle for the first data lover who’s available from Monday to Friday. It’s no longer the company with the most data that wins the game. The ones who are taking the lead are the ones that are able to get the most out of data. They can pull valuable information that helps with decision-making and innovation out of even the smallest pieces of data—and they’re right, over and over again. This is why it’s vital to check if applicants have the skills you need to derive valuable input out of data. You’ll be basing a lot of business decisions on what these data scientists tell you, so best make sure they’re right.

But what makes someone a great data scientist? Some people turn their life around and go from being a maths teacher to following a 12-week data science boot camp or online data science course and quickly get the hang of it—others are top of their class, but aren’t confident enough data scientists to inform your business on its next big move. The truth is that the skills a valuable data scientist has, will have to develop over the years. It’s not just the data literacy, hard skills and the brain for maths—they’ll also need to be able to present and communicate their findings the right way.

Finding the right data scientists using a data science job assessment

So, you’ll want to choose your data scientists carefully, but how do you do that? Resumes and portfolios might seem impressive, but how do you actually find out if someone has the skills you’re looking for—especially if you don’t have anyone on board yet that knows what to ask. The easiest and most effective thing to do, is to screen candidates early in the process, using a data science test that’s been created by a real-life expert. This will ensure that relevant questions are being asked, and you get a clear idea of who’s worth going through the hiring process with — and who isn’t. In this article, we’ll walk you through four steps that will help you set up a data science job assessment that is of real value to your hiring managers. Let’s get started.

Step 1: Choose the right platform

You could, of course, draw up an online survey and create a test in there to send out to all applicants, but these might be hard to ‘grade’—although you’ll develop a tremendous respect for teachers along the way. In many cases, it’s better to choose a dedicated platform that has tests available, and will help you swift through the results effortlessly.

Before you start looking for platforms, make a list of absolute needs that you won’t compromise on. Ask yourself at least the following questions:

  • What types of tests are you looking for? Only hard skills, or also soft skills? If you need both, look for a platform that offers both—mixing and matching can be time-consuming.
  • Will there be tests readily available, or are you looking for a platform that allows you to create your own tests?
  • Does the platform have experience with companies like yours?
  • How are the tests presented to candidates, and how do you want the test results presented to your hiring managers?
  • And last but not least: what are you willing to spend on a job assessment platform? Do they charge per candidate, a flat fee, or would you prefer an annual subscription?

Once you’ve chosen a platform that is right for you, the fun can begin.

Step 2: Start with a hard skills assessment

For roles like data scientists, you’ll be initially focusing on whether they possess the right hard skills. Depending on the specific role, you can test core data science topics such as:


You’re expecting your future data scientist to be fluent in statistics. Depending on the level you’re hiring at, you might want to throw in a few questions that quickly test how fast someone can see through the woods in a mess of statistics, and if they can interpret them the right way.

Machine learning

For some more senior roles, machine learning is becoming increasingly important in the world of data science. If this is the case for the role you’re hiring for, test to see if someone knows how to use data to feed it to machine learning and build awesome products.

Neural networks

A big part of data science is knowing how to work with neural networks. Neural networks are a way to solve problems through trial and error, based on human and animal brains. It’s incredibly helpful if your data scientist’s brain can use them.

Deep learning

Deep learning is a subfield of machine learning that can be necessary in specific data science roles. It works more closely to the way the human brain makes decisions, so this will require a specific set of test questions.

Collecting data

All that data has to come from somewhere, right? Your data scientists should not only be able to read and process data, but also know where and how to get the most valuable input. For this, include some questions about data extraction, data transformation, and data loading. This can also include tests on Excel and querying languages like SQL.

Storing data

Databases should look nothing like the average teenage bedroom. Meaning that they should be nice and tidy, making it easier to extract valuable information from them. Since data isn’t just numbers, but can be anything from video to reviews, it’s crucial that you hire a data scientist who knows how to store this correctly.

Analyzing and modeling data

Data wrangling, data exploration, analysis, and modeling need in-depth understanding of math and programming, but luckily, even data scientists get some help.

Data scientists use analytical tools like Apache Spark, D3.js Python, and many, many more to analyze all that data. If you’re using a specific one in your company and want your data scientists to be able to hit the ground running, quickly test if they’re actually able to use the tools they list on their resume.

Visualizing and presenting data

At the end of the day, data scientists will have to be able to communicate their findings to other departments with people who are less data-savvy. For this, they often use tools that help them visualize data to explain it in a more easy-to-grasp way.

Test if your next data scientist is able to do that with a quick check on their skills in tools like Tableau, PowerBI, Plotly, Bokeh, or whichever one you use.

Step 3: Continue with a soft skill assessment

Your friendly neighborhood data scientist should not only be a math genius, they should possess the right soft skills too. If they’re impossible to work with, you won’t reap the benefits of their skill set. Productivity will suffer, and team morale might also take a hit. Here are some soft skills to test your candidates on:

  • Business-oriented: ultimately, your data scientist will be fueling your decision-making process. This means they’ll have to have a good head for business, on top of simply understanding the numbers.
  • Communication skills: sure, everyone in your company preferably has some of these, but since data scientists play such an important role in decision-making, you’ll want them to be able to express themselves well—and listen to what you’re asking from them.
  • Teamwork: your data scientists shouldn’t be on a little island somewhere in the company. The more they integrate with other departments, the easier it is for them to determine what your business needs from them.
  • Critical thinking skills: this one’s pretty self-explanatory, but the more critical your data scientist, the more reassurance you’ll have that data is correctly interpreted.
  • Creativity: data is less dry than it seems. From data storage to finding connections and problem-solving: it all requires some form of creative thinking.

Step 4: Follow up on the test results

If you want to make the most of your data science job assessment, it shouldn’t just be a test to see who goes through to the next round. For the candidates that ‘pass’, you can customize the questions in their follow-up interview based on the strengths and weaknesses they showed in their test. Because the test they took says a lot, but at the same time—it’s just a snapshot. Did they score remarkably high on certain skills? Ask them how they got to be so experienced in that, and what projects contributed most to that.

Did you notice that they struggled with questions about X? Ask how they are planning to improve on that and how they make sure this doesn’t impact the quality of their work for the time being—are they calling in help from a peer, or do they simply take more time to figure things out?

These types of follow-up questions steer a job interview in a much more real-life direction: it’s not a generic set of questions that any company could ask any employee, but a real conversation between you and the candidate, in which you can evaluate if they fit in the future of the company—and if your company fits in theirs.

Ready to start the hiring process?

With these tips, we’re sure you’ll get some extra reassurance that your next hire will be a great fit—not just based on their previous experience and a couple of interviews. If you want, you can keep reading about data science jobs—or simply start hiring. Good luck!

Mainframe Modernization: Making It Happen

In the fast-paced world of technology and business, it can be hard to keep up with what’s new. What’s new today can be obsolete in a few weeks, and adapting to this ever-changing landscape can become a challenge if an organization isn’t well prepared or equipped. Modernization of systems doesn’t necessarily mean transitioning to an entirely new system or platform; often, all it takes is actual modernization of existing tools to help them adapt to new business demands and requirements.

The mainframe is one system that has stood the test of time. A number of naysayers taut the system as “legacy” or obsolete, but the fact that mainframes handle 68% of the world’s production IT workloads indicate otherwise. Mainframes are proof that the latest isn’t always the greatest, standing firm as one of the foundations of business systems in today’s most successful businesses around the world. What some don’t realize is that the race toward digital transformation is not reliant on the system or platform an organization has in place; digital transformation initiatives rise and fall depending on how they approach data. Regardless of the platform used, data analysts who work with irrelevant or stale data are prone to achieve false or misleading results. Access to real-time data is key, and data gathered days or hours—even minutes—ago isn’t a current representation of the current situation. This can lead to an organization acting on miscalculations and opportunities that no longer exist. Actionable insights need to come from real-time data to ensure that your organization can make sound business decisions in a timely manner.

The Old vs. the New

Conventional methodologies have kept mainframe data and real-time data separate due to issues with accessibility. Most businesses traditionally use Extract, Transform, and Load (ETL) processes for data analysis, a logistically complex and time-consuming process that’s prone to errors and stale data because it’s performed only periodically. This can lead to hours or even weeks of delay that’s simply unacceptable in today’s always-connected, always-on digital business landscape. Today’s businesses depend largely on real-time business intelligence—and access to it—to get a competitive edge.

In light of this perceived separation between mainframes and real-time data analytics, data scientists have found that the creation of analytic models can be too slow at times due to the conventional process of offloading data from the mainframe to other platforms for analysis. Organizations should move away from ETL processes and find ways to make real-time data analytics from the mainframe quicker and more efficient for their business. Mainframe modernization is key in making mainframe systems work with modern solutions because it allows for data virtualization, integrating all disparate enterprise data into a logical data layer. This layer manages the unified data and provides centralized governance while delivering the required data in real-time to business users.

Depending on the industry, mainframe modernization can optimize key business processes like order processing, payment gateways, and internal business operations queries. Mainframes are known for performing high-volume transaction processing, and these transactions can make or break a business. Managed in real-time, it will help organizations battle fraud and manage business risks as they arise, or even before they do. The data gathered can also help paint a more accurate representation of who a company’s customers are, allowing them to better plan resources and come up with more personalized initiatives.

Making IT Happen

Mainframe modernization is a major undertaking that presents a host of options for every organization. These options will vary depending on a number of factors, including business size, tenure, and industry. The following, however, are a few of the key considerations in modernization.

  • Look for quick wins
    As all businesses know by now, time is of the essence in every undertaking, even mainframe modernization. Its success is dependent on how quickly it can deliver the desired results.
  • Automate migration to avoid disruption
    Accelerating modernization efforts means leveraging modern tools API’s. The platforms available today are designed to minimize the effects of the modernization process if not avoid disruption completely.
  • Focus on total cost of ownership (TCO)
    It’s a mistake to view the initial cost of modernization at face value. Amore accurate view of costs involves a focus on the total cost of ownership. Calculating the TCO, or the purchase costs plus operation costs, will help minimize it even before modernization initiatives commence.
  • Don’t just leave everything to IT
    The modern IT team is one that includes everyone in the organization. Mainframe modernization is more a business initiative than an IT concern, and as such, should involve decision makers and business leaders. System integrations and updates remain the responsibility of IT specialists, but choosing the appropriate modernization approach and ensuring that the initiative succeeds should be a responsibility shared by the entire organization.
  • Create business value
    Mainframe modernization isn’t simply the implementation of technology upgrades or migration to a new system; it should also be an opportunity to combine the old with the new. Improve existing business processes or create new ones accordingly while capturing institutional knowledge from mainframe systems to gain a competitive edge.

Options abound when it comes to mainframe modernization, but that doesn’t mean that you should apply them all or choose the latest and greatest. Choosing the right approach to modernization entails re-examining your business and its goals and deciding which solution will take you there—and take you there fast. There exists an “imaginary” gap between digital innovators and mainframes because of the challenges and costs in data accessibility and system availability. The goal of mainframe modernization is to bridge this gap in the best, and fastest, way possible.

Business Intelligence – 5 Tips for better Reporting & Visualization

Data and BI Analysts often concentrate on learning a BI Tool, but the main thing to do is learn how to create good data visualization!

BI reporting has become an indispensable part of any company. In Business Intelligence, companies sometimes have to choose between tools such as PowerBI, QlikSense, Tableau, MikroStrategy, Looker or DataStudio (and others). Even if each of these tools has its own strengths and weaknesses, good reporting depends less on the respective tool but much more on the analyst and his skills in structured and appropriate visualization and text design.

Based on our experience at DATANOMIQ and the book “Storytelling with data” (see footnote in the pdf), we have created an infographic that conveys five tips for better design of BI reports – with self-reflective clarification.

Direct link to the PDF: https://data-science-blog.com/en/wp-content/uploads/sites/4/2021/11/Infographic_Data_Visualization_Infographic_DATANOMIQ.pdf


DATANOMIQ is a platform-independent consulting- and service-partner for Business Intelligence and Data Science. We are opening up multiple possibilities for the first time in all areas of the value chain through Big Data and Artificial Intelligence. We rely on the best minds and the most comprehensive method and technology portfolio for the use of data for business optimization.


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How Microsoft Azure Is Impacting Financial Companies

Microsoft Azure has taken a large chunk of the cloud marketplace, transforming companies with the speed and security of the cloud. Microsoft has over the years used Azure to cushion companies against risk, deal with fraud and differentiate their customer experience. 

With Microsoft Cloud App Security, customers experience 75% automatic threat elimination because of increased visibility and automated threat protection. With all these and more amazing benefits of using Azure, its market share is bound to increase even more over the coming years.


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Financial companies have not been left behind by the Azure bandwagon. The financial industry is using Microsoft Azure to enhance its core functionsinvest money by making informed decisions, and minimize risk while maximizing returns. 

Azure facilitates these core functions by helping with the storage of huge amounts of data—  some dating back to decades ago—, data retrieval and data security. 

It also helps financial companies to keep up with regulatory compliance.

Microsoft Azure is not the only cloud services provider. But here’s why it is the most outstanding when it comes to helping financial companies achieve their business goals.

Azure Offers Hybrid and Multi-Cloud Computing for Financial Companies

The financial services industry is extremely dynamic. Organizations offering financial services have to constantly test the market and come up with new and innovative products and services. 

They are also often under pressure to extend their services across borders. Remember they have to do all of this while at the same time managing their existing customers, containing their risk, and dealing with fraud.

Financial regulations also keep changing. As financial companies increasingly embrace new technology for their services— including intelligent cloud computing— and they have to comply with industry regulations. They cannot afford to leave loopholes as they take on their journey with the cloud.

The financial services industry is highly competitive and keeps up with modernity. These companies have had to resort to the dynamic hybrid, multi-cloud computing, and public cloud strategies to keep up with the trend.

This is how a hybrid cloud model worksit enables existing on-premises applications to be extended through a connection to the public cloud. 

This allows financial companies to enjoy the speed, elasticity, and scale of the public cloud without necessarily having to remodel their entire applications. These organizations are afforded the flexibility of deciding what parts of their application remains in an existing data center and which one resides in the cloud.

Cloud computing with Azure allows financial organizations to operate more efficiently by providing end-to-end protection to information, allowing the digitization of financial services, and providing data security. 

Data security is particularly important to financial firms because they are often targeted by fraudsters and cyber threats. They, therefore, need to protect crucial information which they achieve by authenticating their data centers using Azure.

Here’s why financial companies cannot think of doing without Azure’s hybrid cloud computing even for just a day.


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  • The ability to expand their geographic reach

Azure enables financial companies to establish data centers in new locations to meet globally growing demand. This allows them to open and explore new markets. They can then use Azure DevOps pipelines to maintain their data factories and keep everything consistent.

  • Consistent Infrastructure management

The hybrid cloud model promotes a consistent approach to infrastructure management across all locations, whether it is on-premises, public cloud, or the edge.

  • Increased Elasticity

Financial firms and banks utilizing Azure services can respond with great agility to transactional changes or changes in demand by provisioning or de-provisioning as the situation at hand demands. 

In cases where the organization requires high computation such as complex risk modeling, a hybrid strategy allows it to expand its capacity beyond its data center without overwhelming its servers.

  • Flexibility

A hybrid strategy allows financial organizations to choose cloud services that fall within their budget, match their needs, and suit their features.

  • Data security and enhanced regulatory compliance

Hybrid and multi-cloud strategies are a superb alternative for strictly on-premises strategies when one considers resiliency, data portability, and data security.

  • Reduces CapEx Expenses

Managing on-premises infrastructure is expensive. Financial companies utilizing Azure do not need to spend large amounts of money setting them up and managing them. 

With the increased elasticity of the hybrid system, financial organizations only pay for the resources they actually use, at a relatively lower cost.

Financial Organizations Have Access to an Analytics Platform

As we mentioned earlier, financial companies have the core function of making financial decisions in order to invest money and gain maximum returns at the least possible risk. 

Having been entrusted with their customers’ assets, the best way to ensure success in making profits is by using an analytics system.

Getting the form of analytics that helps with solving this investment problem is the kind of headache that does not go away by taking a tablet of ibuprofen and a glass of waterintegrating data is not an easy task. Besides, building a custom analytics solution from scratch is quite expensive.

Luckily for financial companies, Azure has a dedicated analytics platform for the financial services industry. It is custom-made just for these types of organizations. 

Their system is quite intuitive and easy to use. Companies not only get to save the resources they would have otherwise used to build a custom solution, but they get to learn about their investment risks and get instant results at cloud speed. 

They can mitigate against negatively impactful market occurrences and gain profits even when operating in adverse market conditions.


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Financial Companies Get Advanced Data Management

Good analytics goes hand-in-hand with a great data management system. Financial companies need to have good data, create an organized data warehouse, and have a secure data storage system.

In addition to storing your data, Microsoft Azure ensures your storage can be optimized to support advanced applications, for example, machine learning and forecasting. 

Azure even allows you to compress and store documents for long periods of time when you write the data to Microsoft Azure Blob Storage. These documents can be retrieved anytime when the need arises for auditors’, regulators’, and lawyers’ perusal. 


Microsoft has over time managed to gain the trust of many industries, the financial services industry inclusive. Using its cloud computing giant, Azure, it has empowered these companies to carry out their functions efficiently and at the lowest cost and risk possible.

Azure’s hybrid cloud computing strategy has made financial operations flexible, opened doors for financial companies to establish their services in multiple locations, and provided them with consistent infrastructure management, among many other benefits.

With their futuristic model and commitment to growth, it’s only prudent to assume that Microsoft Azure will continue carrying the mantle as the best cloud services provider in the financial services industry.