CCNA vs. CCNP vs. CCIE Security Certification

As more companies turn to cloud-based software and other advanced solutions, demand for expert IT professionals in the field increases. One popular vendor, Cisco Systems, Inc., makes underlying software and hardware businesses will use for their networks.

If you’re interested in pursuing a career in the data security industry, you may want to consider earning a Cisco security certification. However, there are many types of certificates available, and each one will deliver unique benefits to you and your job marketability.

Learn more about Cisco certifications and learn the difference between CCNA, CCNP and CCIE certifications to help you choose which path is right for you.

Why Earn Cisco Certifications?

The main reason why Cisco provides these security certifications is so IT professionals can fine-tune their skills and build upon their knowledge. When IT professionals earn a Cisco certification, they can use Cisco products and services more easily, help guide customers and troubleshoot customer problems.

A future employer may perceive candidates with certifications as more qualified, productive and someone with a “go-getter” attitude. According to Cisco’s website, 81% of employers associate certifications holders with higher quality and value of work contribution.

However, it’s important to research the various Cisco certifications to learn which ones are most suitable for you and what job you’re interested in. For example, Cisco offers different levels of certifications, ranging from entry-level to expert.

Below are three certifications from Cisco that may be a good fit for you.

CCNA — Cisco Certified Network Associate

A CCNA certification is highly sought after. This certification demonstrates a professional’s ability to install, configure, operate and troubleshoot networks, both routed and switched. No prerequisites are necessary for the CCNA certification. It’s considered an associate-level certification and is available in a few prominent areas, including:

  • Cloud
  • Collaboration
  • Industrial/IoT
  • Security
  • Routing and Switching
  • Service Provider
  • Wireless

One challenge in the data industry is the increased reliance on cloud environments. Using only one cloud provider is a business risk some companies are concerned about. Uptime Institute cites the concentration risk of cloud computing as a major challenge for data centers in 2022.

Earning a CCNA cloud certification may help you get hired for an entry-level position at a company and allow you to support a senior cloud engineer.

Common jobs that you can earn with a CCNA are an IT network engineer, associate networking engineer, network system administrator and cloud architecture and security professional.

CCNP — Cisco Certified Network Professional

The Cisco CCNP certification is a more advanced professional-level certification than the CCNA certification. With the CCNP, you should be able to implement higher-level networking solutions for a company. It will cover the fundamentals of LAN and WAN infrastructures. Here are some of the different areas you can earn a CCNP in:

  • Enterprise
  • Security
  • Service Provider
  • Collaboration
  • Data Center

You must pass some core exams before earning the CCNP certification. Someone looking for the CCNP certification must also qualify for Cisco’s IP switched network and IP routing technologies. This will help determine the candidate’s readiness for the CCNP certification.

Some jobs you may get with a CCNP certification are senior security/network engineer, network architecture, network manager and troubleshooting assistant.

CCIE — Cisco Certified Internetwork Expert

IT professionals who’ve secured the knowledge and technical skills to design, implement and configure security for Cisco solutions and IT resources would be ready to earn the CCIE certification. According to Cisco, an expert-level certification is accepted worldwide as the most prestigious certification in the tech industry. Here are some of the CCIE certifications:

  • Enterprise Infrastructure
  • Collaboration
  • Enterprise Wireless
  • Data Center
  • Security
  • Service Provider

CCIE certifications can open up a range of job opportunities, but it’s a challenging certification to earn. Earning a CCIE means that your end-to-end IT lifecycle skills are valid. You know exactly what you’re talking about regarding networking, LAN/WAN, IPv4 and IPv6 protocols, switches and routers, general information and installation and configuration of various network types.

Jobs you can earn with a CCIE certificate include network security architect, network security specialist, infrastructure consulting practitioner and cloud engineer/architect.

Where to Earn Cisco Certifications

Because Cisco certifications are in such high demand and can open up job opportunities, you may want to know how you can earn them. You earn certificates directly from Cisco’s website. Under Cisco’s Learn tab, there’s plenty of information about certifications, training, events, webinars, support and other services.

There are many online training programs that you can complete to help you prepare for the Cisco certification exams. Here are some websites that offer programs you may want to explore based on the certification you’d like to earn:

For CCNA

  • Udemy
  • ICOHS College
  • Pluralsight
  • Cybrary

For CCNP

  • Udemy
  • INE
  • Global Knowledge
  • Varsity Tutors

For CCIE

  • Udemy
  • Skillshare
  • PluralSight
  • Network Lessons
  • Koenig solutions

These examples are only a few, as other online training programs and resources can set you up for success.

Additionally, Cisco offers several resources on its website to help individuals prepare for certification exams. These include guided study groups and a free Cisco Networking Academy program.

Earning Cisco Certifications

Because many companies, especially large ones, will use Cisco products for their technology infrastructure. Potential IT candidates who list certifications on their resume or job application will have a competitive advantage in the hiring process.

Depending on your current skill level and knowledge, you should be able to determine which Cisco certification is right for you. Cisco’s website has extensive information on each certificate and what topics you’ll learn about. Consider earning a Cisco certification, whether it’s CCNA vs. CCNP vs. CCIE, to bolster your skills and improve your marketability.

Key Points on AI’s Role In The Future Of Data Protection

Artificial Intelligence is transforming every industry as we speak, and data protection might be the biggest of them all. With a projected market size of USD 113390 Million, there’s a lot to protect—and humans won’t be able to do it all.

Luckily for us, Artificial Intelligence solutions are here to help us out. Because AI can do a lot more than just collect and analyze data — it can also protect it. In this article, we’ll explain what the role of Artificial Intelligence is in the future of data protection.

Here’s AI for data protection in summary:

3 Ways AI serves in data protection

  • AI Can Improve Compliance: from the GDPR to the CPRA, AI can help you track down gaps in your compliance with the most important data protection legislation.
  • AI as an ally against cyberattacks: cyberattacks are becoming increasingly sophisticated, but so is AI. It can help you recognize the patterns that indicate an attack is underway and put in automated reactions to minimize damage.
  • AI can protect against phishing attempts: together with ML and NLP, AI is a valuable tool in detecting phishing attempts—especially since they are becoming increasingly hard to spot.

Why AI is so valuable in the fight against cybercrime

  • AI can handle more and more complex data than humans: with the amount of data that is being processed and collected every second, it’s incredibly inefficient to not let AI do the work—and AI can cut costs drastically as well.
  • AI can quickly classify data and keep it organized: before you can protect your data, make sure it’s organized properly. No matter the amount or complexity of the structure, AI can help you stay on top of it.
  • No humans needed to keep sensitive data secure: scared of human errors and have trust issues? With AI, you don’t need to rely on people for protection and discreteness.

The threats your data faces on a daily basis

It’s not just the good guys who are using technologies like artificial intelligence to up their game—hackers and people after sensitive data can also reap the benefits of AI. There are more than 2,200 cyberattacks per day—which means one every 39 seconds, so the threat is substantial.

While the clock is ticking, research found that fewer than 25% of businesses think they’re ready to fight off a ransomware attack. That leaves 75% of organizations all the more vulnerable to data privacy threats.

Leaks of personal information, data hacks and other privacy scandals are costly: it’s estimated that cybercrime will cost companies worldwide an estimated $10.5 trillion annually by 2025, with an ​​average cost of $3.86 million per breach—not including the harm done to users and the reputation of a business.

That makes investing in a solid data protection system all the more useful, which is shown in the spending habits of businesses all over the world: global spending on privacy efforts are expected to reach $8 billion by 2022. Luckily, with the rapid developments in AI and other smart security tools, it has become more attainable—even for smaller businesses.

3 Ways AI serves in data protection

What does Artificial intelligence in data protection look like in practice? Let’s look at some of the ways AI can assist your organization in warding off cyber criminals.

1.    AI Can Improve Compliance

How compliant is your organization with all the data protection and privacy regulations? It can be incredibly hard to keep up, understand and check whether your systems are up-to-date on the latest compliance regulations.

But—no need to worry! AI has taken over the nitty-gritty of it all. It’s expected that by 2023, over 40% of privacy compliance technology will rely on AI.

What kind of legislation can you hold up with the use of AI? Two big names are the GDPR and CPRA. AI can help you identify blind spots in your data protection efforts and warn you when you’re not living up to the standards governments put in place.

One tool that does this is SECURITI.ai. With AI-driven PI data discovery, DSR automation, documented accountability you get a clearer view of your data processing activities and can make sure you’re compliant.

An alternative AI solution is Claudette, a web crawler that assesses the privacy policies using supervised machine learning technologies. After it’s done scanning and collecting information, it checks if the data is used in a way that’s GDPR proof. It shows you issues such as incomplete information, unclear language, or problematic data processing tactics.

Of course, you can’t solely rely on AI to do all the work when it comes to privacy and data protection. You and your employees also need to understand and handle data in ways that are compliant with the rules set in place.

Start with understanding what the GDPR and CPRA are all about. Osano’s guide to CPRA is a great place to start to learn what the CPRA, which will replace the CPPA on January 1, 2023, is all about. Educate yourself on the rules of data protection, and it will be even easier to select an AI tool that will help you protect your valuable data.

2.    AI as an ally against cyberattacks

With the combination of big data, artificial intelligence and machine learning, you have a great recipe for tracking down the patterns that indicate a cyberattack is happening. Why is that helpful?

It’s all about identifying patterns. When AI and ML work together, they can map out what happened during previous attacks. Together, they can identify the actions hackers have taken before and find weak spots in your security system, so you can fill those gaps and be extra alert.

AI can assist in quickly alerting the right people and systems that there’s a threat. This can even kick off a series of extra measures to be taken, so the cyberattack can be beaten back.

AI can also make sure malicious websites and unauthorized data transactions are automatically blocked before any harm can be done.

3.    AI can protect against phishing attempts

​​Sometimes its employees who unknowingly are letting the cyber criminals in. Many people roll their eyes when they hear about yet another phishing attempt—shouldn’t we all know better by now not to click on certain links? — but cyber criminals are creating increasingly sophisticated phishing attacks. Even the most tech-savvy and internet-native people are able to fall for it.

Because phishing is all about what’s happening in the details, or in the background of a message—something the untrained human eye won’t immediately see.

Ai does see it, however. With technologies like Natural Language Processing and Machine Learning, it can automatically spot if a phishing attack is at play, and warn users.

There are even AI and ML tools on the market that are able to analyze the context of a message and the relationship between the sender and receiver, for even greater accuracy.

Why AI is so valuable in the fight against cybercrime

But why AI? Can we really rely on yet another robotic system to keep a digital framework safe? Isn’t it safe to have it handled by humans? We’ll expand on the three main benefits AI offers in the data protection game.

1.    AI can handle more and more complex data than humans

With all the data that is being processed and stored nowadays, there are barely enough people on the planet to keep an eye on every sensitive piece of information.

Good data protection is extremely time-consuming, because it’s constant. Checking servers manually is virtually impossible.

AI can work automatically and 24/7, no matter how much data there is to handle. On top of that, AI can be put in place to handle the more complex data structures, which can be hard to analyze and protect for humans. All while keeping costs low.

2.    AI can quickly classify data and keep it organized

Before you can even start protecting data, you will need to put it in place—efficiently. With the large volumes of data that organizations deal with, AI comes in handy. AI can quickly classify and manage data to keep it organized.

3.    No humans needed to keep sensitive data secure

AI can work independently from humans, which means nobody necessarily needs to have direct access to the sensitive data you’re trying to predict. Not only does that decrease the changes of human error, but it also builds an extra layer of trust.

Ready to call in the help of AI for your data protection?

Start by looking at the legislations that are important for your organization, and build on the needs you have for your specific business. Want to know more about the power of AI for data driven businesses? Keep reading in our blog section dedicated to artificial intelligence!

Why Is Physical Security Vital for Data Security?

Modern businesses hold on to an increasing amount of sensitive and sometimes confidential data. As a result, they’ve had to invest in new technology and practices to keep that data safe.

Many of these businesses, when developing their data security or cybersecurity protocols, focus on the security of their hardware, software and business network. Prioritizing these assets is essential — however, if physical security gets left behind, even the best digital tech may not keep a company’s data safe.

There’s practically no stopping someone with physical access to your data storage from stealing info or compromising your business network.

This is why companies that prioritize digital security also need to carefully consider physical security — and what may happen when physical security is neglected.

Physical Access Can Allow Criminals to Bypass Even the Best Digital Security

It’s almost impossible to protect any device from a physical attack. If a hacker has sustained access to device hardware, they’ll be able to breach its defenses eventually — potentially giving them access to the information on that device, as well as any stored security credentials.

Devices that are digitally secured but not physically secured — like a laptop left behind in a coffee shop, or an IoT sensor in an unlocked case — can provide a valuable vector of attack for hackers. In some cases, that vector may be all they need to create serious trouble for a company.

In some cases, poor building security may enable hackers to sneak into server rooms or gain access to off-site devices, like IoT sensors. Often, hackers also gain access to hardware either by theft — for example, swiping a laptop left sitting in a coffee shop — or by using social engineering to gain remote access.

Even large devices that are rarely moved or accessed by staff — like servers in a data center — can be at risk.

This is why large, high-budget data centers often have what’s colloquially called a mantrap — a set of two interlocking doors, somewhat like an airlock, that one has  to pass through to reach the server hardware. These doors serve as a final access check for the data center and help to minimize the risk of unauthorized server access.

These threats aren’t an abstraction — hackers and other criminals have used physical access to steal data in the past.

In 2015, for example, hackers stole five servers from the offices of a British charity, PlanUK. Those servers contained a wealth of information on donators, including names, addresses, bank account numbers and sort codes.

In 2018, the theft of a laptop exposed the data of more than 43,000 patients of the West Virginia-based Coplin Health System — part of the reason that laptop theft is ranked the number one cause of health data breaches.

Valuable Hardware and Essential Systems May Be at High Risk

Hackers may also use physical attack vectors if they need to gain access to critical infrastructure, which may otherwise be air-gapped from internet-connected systems and impossible to attack with digital-only methods.

This is part of why major physical security manufacturers dedicate entire product lines to physical security for nuclear power plants, for example, or airports or international organizations — and why those kinds of institutions take physical security so seriously.

Enterprise-grade computer hardware can also be very valuable — making that hardware a major target. While you may expect criminals to be driven more by data or network access than by the resale value of your servers, theft for resale or reuse has happened before.

In 2018, for example, Icelandic criminals stole 600 bitcoin-mining servers in one of the biggest tech heists on record. Rising cryptocurrency prices may encourage some criminals to plan similar heists of powerful hardware. Owners of data centers, rendering farms and other facilities with high-value hardware should be aware of these risks, as well as how good physical security is necessary to keep their hardware safe.

Using Physical Security to Complement Your Digital Security Planning

Without strong physical security practices, your data can be vulnerable — even if you have a great digital security plan in place.

Hackers, when faced with strong cyber defenses, sometimes turn to physical attacks to gain access to critical hardware. In other cases, they may also be after the hardware for sale or personal use.

Even a basic physical security plan — one that involves ID verification and access control — can go a long way in complementing a digital security strategy and keeping data safe.

Data Security for Data Scientists & Co. – Infographic

Data becomes information and information becomes knowledge. For this reason, companies are nowadays also evaluated with regard to their data and their data quality. Furthermore, data is also the material that is needed for management decisions and artificial intelligence. For this reason, IT Security is very important and special consulting and auditing companies offer their own services specifically for the security of IT systems.

However, every Data Scientist, Data Analyst and Data Engineer rarely only works with open data, but rather intensively with customer data. Therefore, every expert for the storage and analysis of data should at least have a basic knowledge of Data Security and work according to certain principles in order to guarantee the security of the data and the legality of the data processing.

There are a number of rules and principles for data security that must be observed. Some of them – in our opinion the most important ones – we from DATANOMIQ have summarized in an infographic for Data Scientists, Data Analysts and Data Engineers. You can download the infographic here: DataSecurity_Infographic

Data Security for Data Scientists, Data Analysts and Data Engineers

Data Security for Data Scientists, Data Analysts and Data Engineers

Download Infographic as PDF

Infographic - Data Security for Data Scientists, Data Analysts and Data Engineers

Infographic – Data Security for Data Scientists, Data Analysts and Data Engineers

How Healthcare Is Cracking Down on Data Privacy

The COVID-19 pandemic emerged more than a year ago, and come March, the United States will also pass the one-year anniversary of the novel coronavirus’ arrival in our nation. Hospitals have become overrun with patients, having to adjust for space even when they’re at full capacity. The colder months are bringing on more infections as well.

With such high demands on health care providers, technology has been an area of assistance through it all. Telehealth in particular allows patients to stay at home and receive care without putting themselves at risk. However, security and privacy concerns accompany this reliance on technology.

The digital world can be dangerous. Hacks and breaches can occur at any time. The novel coronavirus pandemic has accelerated these attacks. Through August 2020 alone, 305 healthcare data breaches occurred — which is up from 2019’s 136 breaches in the same time frame. These vulnerabilities cannot continue to occur, since health care facilities hold vital patient information like Social Security numbers, medical records and financial information.

The industry is resilient, though. Adapting to new norms and protocols is part of the healthcare field. With the new focus on technology to connect patients and providers through the ongoing pandemic, practices have been cracking down on keeping data safe and secure.

Health Care Industry Adapts

Data presents itself in the health care industry in several ways. Standard patient data includes personal information about health history, relationships and private matters. Other forms of data may include connections from medical devices that use the internet — something like a digital blood pressure monitor may transmit data. Then, providers must store and send this data at various times.

The Health Insurance Portability and Accountability Act sets forth two main regulations facilities must follow. The security rule mandates that the use of all electronic personal health data must be stable in any form or use. The privacy rule indicates that all medical records, insurance information and private data must have the best protection.

In 2017, 477 breaches affected about 5.6 million patient records, breaching what should have been secure HIPAA data. To uphold HIPAA regulations and prevent breaches like these from happening, health care providers have taken several steps.

First, education is crucial. Bringing all staff in on up-to-date privacy protocols will go a long way. For instance, using encryption on mobile devices, backing up all data, creating strong passwords and consistently patching and updating the systems and firewalls are critical for staff to understand.

Access is another form of protection. Multi-factor authentication, like passwords, keys, PINs and biometrics, will keep systems secure and only give access to those who need it the most. Then, facilities can monitor data at all times — unauthorized access, emails and transfers. If something suspicious happens, IT departments can see it in real time and flag it or stop it.

Last, consistent evaluations are more necessary than ever. Health care facilities will want to make sure they comply with industry and privacy requirements, and that staff members know the protocols to follow. Then, data privacy remains a top priority.

The Lasting Impact

Vaccines are slowly rolling out and becoming more available to residents across the world. However, even with a vaccine, global spread will slow gradually, especially in areas where cases are high and rising. For instance, the United States cases are still rising and breaking records daily.

Data will continue to be a central focus throughout the pandemic and afterward. Right now, specifically, with big tech companies facing scrutiny and investigations for privacy faults, data is at the forefront of Americans’ minds. Health care companies must excel in ways that big tech has not.

One sign of progress is new mental health startups popping up that focus on virtual dynamics. With services like Real Therapy or Two Chairs, you can make a virtual appointment. Since privacy is already an inherent part of therapy, data privacy will be critical to integrate into these business models.

Getting Ahead of the Curve

While the pandemic may seem uncontrollable at times, health care facilities have more agency. They can smooth relationships with patients and operate more efficiently with stricter data privacy protocols in place. In an uncertain time, ensuring data security is one of the best things health care providers can do.

5 Data Privacy Predictions for 2021

2020 has been a significant year for data management. As businesses face new technological challenges amid the COVID-19 pandemic, issues of privacy have spent some time in the spotlight. In response, data privacy could see some substantial changes in 2021.

Few people will emerge from 2020 with an unchanged perception of data security. As these ideas and feelings shift, some trends will accelerate while others get replaced. Businesses will have to adapt to these changes to survive.

Here are five such changes you can expect in 2021.

International Data Privacy Standards Will Increase

Privacy concerns over Chinese-owned app TikTok caused quite a stir in 2020. With the TikTok situation bringing new attention to privacy in international services, you’ll likely see a rise in international regulations. China has already announced new security standards and asked other countries to follow.

2020 has cast doubt over a lot of international relations. More countries will likely issue new standards to ease tension and move past these doubts. This trend started before 2020, as you can see in Europe’s GDPR, but 2021 will further it.

Customers Will Demand Transparency

Governments aren’t the only ones that will expect more of tech companies’ privacy standards. Since things like TikTok have made people more aware of what apps could access, more people will demand privacy. In 2021, companies that are transparent about how they use data will likely be more successful.

According to a PwC poll, 84% of consumers said they would switch services if they don’t trust how a company uses their data. Data privacy isn’t just important to authorities or businesses anymore. The public is growing more concerned about their data, and their choices will reflect it.

Security Will Become More Automated

In response to these growing expectations, businesses will have to do more to secure people’s data. Cybersecurity companies are facing a considerable talent shortage thanks to pandemic-related complications, though. The data security world will turn to automation to fix both of these problems.

With so many businesses changing the way they operate, cybersecurity will have to become more flexible too. Automating some processes through AI will allow companies to achieve that flexibility. Security AI is still relatively new, but as it develops, it could take off in 2021.

Security Data Analytics Will Become the Norm

Big data analytics have already become standard practice in many business applications. In 2021, more companies will start using them to improve their data privacy measures, too. With major companies like Nintendo and Marriott experiencing significant data breaches this year, more will turn to analytics to find any potential shortcomings.

No one wants to be the next data breach news story, especially with more people paying attention to these issues now. Data analytics can highlight operational improvements, showing companies how to better their data security measures. With data privacy in the spotlight in 2021, taking these steps is crucial.

Third-Party Risk Assessments Will Be More Crucial

As people demand better privacy protection, businesses will have to consider their third-party partners. Consumers will be more critical of companies giving third parties access to their data. As a result, companies will have to perform more risk assessments on any third party.

Third-party data breaches affected companies like General Electric and T-Mobile in 2020, exposing thousands of records. Customers will expect businesses to hold their partners to higher standards to avoid these risks.

2021 Could Be a Landmark Year for Data Privacy

Data privacy is more prominent than ever before, mostly due to a few notable scandals. Now that the general public is more aware of these issues, businesses will have to meet higher standards for data privacy. Implementing data security processes may cause some disruption and confusion at first, but it will ultimately lead to a safer digital landscape.

All of these changes could make 2021 a turning point for data security. With higher expectations from consumers and authorities, data management will become more secure.

Glorious career paths of a Big Data Professional

Are you wondering about the career profiles you may get to fill if you get into Big Data industry? If yes, then Bingo! This is the post that will inform you just about that. Big data is just an umbrella term. There are a lot of profiles and career paths that are covered under this umbrella term. Let us have a look at some of these profiles.

Data Visualisation Specialist

The process of visualizing data is turning out to be critical in guaranteeing information-driven representatives get the upfront investment required to actualize goal-oriented and significant Big Data extends in their organization. Making your data to tell a story and the craft of envisioning information convincingly has turned into a significant piece of the Big Data world and progressively associations need to have these capacities in-house. Besides, as a rule, these experts are relied upon to realize how to picture in different instruments, for example, Spotfire, D3, Carto, and Tableau – among numerous others. Information Visualization Specialists should be versatile and inquisitive to guarantee they stay aware of most recent patterns and answers for a recount to their information stories in the most intriguing manner conceivable with regards to the board room. 

 

Big Data Architect

This is the place the Hadoop specialists come in. Ordinarily, a Big Data planner tends to explicit information issues and necessities, having the option to portray the structure and conduct of a Big Data arrangement utilizing the innovation wherein they practice – which is, as a rule, mostly Hadoop.

These representatives go about as a significant connection between the association (and its specific needs) and Data Scientists and Engineers. Any organization that needs to assemble a Big Data condition will require a Big Data modeler who can serenely deal with the total lifecycle of a Hadoop arrangement – including necessity investigation, stage determination, specialized engineering structure, application plan, and advancement, testing the much-dreaded task of deploying lastly.

Systems Architect 

This Big data professional is in charge of how your enormous information frameworks are architected and interconnected. Their essential incentive to your group lies in their capacity to use their product building foundation and involvement with huge scale circulated handling frameworks to deal with your innovation decisions and execution forms. You’ll need this individual to construct an information design that lines up with the business, alongside abnormal state anticipating the improvement. The person in question will consider different limitations, adherence to gauges, and varying needs over the business.

Here are some responsibilities that they play:

    • Determine auxiliary prerequisites of databases by investigating customer tasks, applications, and programming; audit targets with customers and assess current frameworks.
    • Develop database arrangements by planning proposed framework; characterize physical database structure and utilitarian abilities, security, back-up and recuperation particulars.
    • Install database frameworks by creating flowcharts; apply ideal access methods, arrange establishment activities, and record activities.
    • Maintain database execution by distinguishing and settling generation and application advancement issues, figuring ideal qualities for parameters; assessing, incorporating, and putting in new discharges, finishing support and responding to client questions.
    • Provide database support by coding utilities, reacting to client questions, and settling issues.


Artificial Intelligence Developer

The certain promotion around Artificial Intelligence is additionally set to quicken the number of jobs publicized for masters who truly see how to apply AI, Machine Learning, and Deep Learning strategies in the business world. Selection representatives will request designers with broad learning of a wide exhibit of programming dialects which loan well to AI improvement, for example, Lisp, Prolog, C/C++, Java, and Python.

All said and done; many people estimate that this popular demand for AI specialists could cause a something like what we call a “Brain Drain” organizations poaching talented individuals away from the universe of the scholarly world. A month ago in the Financial Times, profound learning pioneer and specialist Yoshua Bengio, of the University of Montreal expressed: “The industry has been selecting a ton of ability — so now there’s a lack in the scholarly world, which is fine for those organizations. However, it’s not extraordinary for the scholarly world.” It ; howeverusiasm to perceive how this contention among the scholarly world and business is rotated in the following couple of years.

Data Scientist

The move of Big Data from tech publicity to business reality may have quickened, yet the move away from enrolling top Data Scientists isn’t set to change in 2020. An ongoing Deloitte report featured that the universe of business will require three million Data Scientists by 2021, so if their expectations are right, there’s a major ability hole in the market. This multidisciplinary profile requires specialized logical aptitudes, specialized software engineering abilities just as solid gentler abilities, for example, correspondence, business keenness, and scholarly interest.

Data Engineer

Clean and quality data is crucial in the accomplishment of Big Data ventures. Consequently, we hope to see a lot of opening in 2020 for Data Engineers who have a predictable and awesome way to deal with information transformation and treatment. Organizations will search for these special data masters to have broad involvement in controlling data with SQL, T-SQL, R, Hadoop, Hive, Python and Spark. Much like Data Scientists. They are likewise expected to be innovative with regards to contrasting information with clashing information types with have the option to determine issues. They additionally frequently need to make arrangements which enable organizations to catch existing information in increasingly usable information groups – just as performing information demonstrations and their modeling.

IT/Operations Manager Job Description

In Big data industry, the IT/Operations Manager is a profitable expansion to your group and will essentially be in charge of sending, overseeing, and checking your enormous information frameworks. You’ll depend on this colleague to plan and execute new hardware and administrations. The person in question will work with business partners to comprehend the best innovation ventures to address their procedures and concerns—interpreting business necessities to innovation plans. They’ll likewise work with venture chiefs to actualize innovation and be in charge of effective progress and general activities.

Here are some responsibilities that they play:

  • Manage and be proactive in announcing, settling and raising issues where required 
  • Lead and co-ordinate issue the executive’s exercises, notwithstanding ceaseless procedure improvement activities  
  • Proactively deal with our IT framework 
  • Supervise and oversee IT staffing, including enrollment, supervision, planning, advancement, and assessment
  • Verify existing business apparatuses and procedures remain ideally practical and worth included 
  • Benchmark, dissect, report on and make suggestions for the improvement and development of the IT framework and IT frameworks 
  • Advance and keep up a corporate SLA structure

Conclusion

These are some of the best career paths that big data professionals can play after entering the industry. Honesty and hard work can always take you to the zenith of any field that you choose to be in. Also, keep upgrading your skills by taking newer certifications and technologies. Good Luck 

Interview: Does Business Intelligence benefit from Cloud Data Warehousing?

Interview with Ross Perez, Senior Director, Marketing EMEA at Snowflake

Read this article in German:
“Profitiert Business Intelligence vom Data Warehouse in der Cloud?”

Does Business Intelligence benefit from Cloud Data Warehousing?

Ross Perez is the Senior Director, Marketing EMEA at Snowflake. He leads the Snowflake marketing team in EMEA and is charged with starting the discussion about analytics, data, and cloud data warehousing across EMEA. Before Snowflake, Ross was a product marketer at Tableau Software where he founded the Iron Viz Championship, the world’s largest and longest running data visualization competition.

Data Science Blog: Ross, Business Intelligence (BI) is not really a new trend. In 2019/2020, making data available for the whole company should not be a big thing anymore. Would you agree?

BI is definitely an old trend, reporting has been around for 50 years. People are accustomed to seeing statistics and data for the company at large, and even their business units. However, using BI to deliver analytics to everyone in the organization and encouraging them to make decisions based on data for their specific area is relatively new. In a lot of the companies Snowflake works with, there is a huge new group of people who have recently received access to self-service BI and visualization tools like Tableau, Looker and Sigma, and they are just starting to find answers to their questions.

Data Science Blog: Up until today, BI was just about delivering dashboards for reporting to the business. The data warehouse (DWH) was something like the backend. Today we have increased demand for data transparency. How should companies deal with this demand?

Because more people in more departments are wanting access to data more frequently, the demand on backend systems like the data warehouse is skyrocketing. In many cases, companies have data warehouses that weren’t built to cope with this concurrent demand and that means that the experience is slow. End users have to wait a long time for their reports. That is where Snowflake comes in: since we can use the power of the cloud to spin up resources on demand, we can serve any number of concurrent users. Snowflake can also house unlimited amounts of data, of both structured and semi-structured formats.

Data Science Blog: Would you say the DWH is the key driver for becoming a data-driven organization? What else should be considered here?

Absolutely. Without having all of your data in a single, highly elastic, and flexible data warehouse, it can be a huge challenge to actually deliver insight to people in the organization.

Data Science Blog: So much for the theory, now let’s talk about specific use cases. In general, it matters a lot whether you are storing and analyzing e.g. financial data or machine data. What do we have to consider for both purposes?

Financial data and machine data do look very different, and often come in different formats. For instance, financial data is often in a standard relational format. Data like this needs to be able to be easily queried with standard SQL, something that many Hadoop and noSQL tools were unable to provide. Luckily, Snowflake is an ansi-standard SQL data warehouse so it can be used with this type of data quite seamlessly.

On the other hand, machine data is often semi-structured or even completely unstructured. This type of data is becoming significantly more common with the rise of IoT, but traditional data warehouses were very bad at dealing with it since they were optimized for relational data. Semi-structured data like JSON, Avro, XML, Orc and Parquet can be loaded into Snowflake for analysis quite seamlessly in its native format. This is important, because you don’t want to have to flatten the data to get any use from it.

Both types of data are important, and Snowflake is really the first data warehouse that can work with them both seamlessly.

Data Science Blog: Back to the common business use case: Creating sales or purchase reports for the business managers, based on data from ERP-systems such as Microsoft or SAP. Which architecture for the DWH could be the right one? How many and which database layers do you see as necessary?

The type of report largely does not matter, because in all cases you want a data warehouse that can support all of your data and serve all of your users. Ideally, you also want to be able to turn it off and on depending on demand. That means that you need a cloud-based architecture… and specifically Snowflake’s innovative architecture that separates storage and compute, making it possible to pay for exactly what you use.

Data Science Blog: Where would you implement the main part of the business logic for the report? In the DWH or in the reporting tool? Does it matter which reporting tool we choose?

The great thing is that you can choose either. Snowflake, as an ansi-Standard SQL data warehouse, can support a high degree of data modeling and business logic. But you can also utilize partners like Looker and Sigma who specialize in data modeling for BI. We think it’s best that the customer chooses what is right for them.

Data Science Blog: Snowflake enables organizations to store and manage their data in the cloud. Does it mean companies lose control over their storage and data management?

Customers have complete control over their data, and in fact Snowflake cannot see, alter or change any aspect of their data. The benefit of a cloud solution is that customers don’t have to manage the infrastructure or the tuning – they decide how they want to store and analyze their data and Snowflake takes care of the rest.

Data Science Blog: How big is the effort for smaller and medium sized companies to set up a DWH in the cloud? Does this have to be an expensive long-term project in every case?

The nice thing about Snowflake is that you can get started with a free trial in a few minutes. Now, moving from a traditional data warehouse to Snowflake can take some time, depending on the legacy technology that you are using. But Snowflake itself is quite easy to set up and very much compatible with historical tools making it relatively easy to move over.

Establish a Collaborative Culture – Process Mining Rule 4 of 4

This is article no. 4 of the four-part article series Privacy, Security and Ethics in Process Mining.

Read this article in German:
Datenschutz, Sicherheit und Ethik beim Process Mining – Regel 4 von 4

Perhaps the most important ingredient in creating a responsible process mining environment is to establish a collaborative culture within your organization. Process mining can make the flaws in your processes very transparent, much more transparent than some people may be comfortable with. Therefore, you should include change management professionals, for example, Lean practitioners who know how to encourage people to tell each other “the truth”, in your team.

Furthermore, be careful how you communicate the goals of your process mining project and involve relevant stakeholders in a way that ensures their perspective is heard. The goal is to create an atmosphere, where people are not blamed for their mistakes (which only leads to them hiding what they do and working against you) but where everyone is on board with the goals of the project and where the analysis and process improvement is a joint effort.

Do:

  • Make sure that you verify the data quality before going into the data analysis, ideally by involving a domain expert already in the data validation step. This way, you can build trust among the process managers that the data reflects what is actually happening and ensure that you have the right understanding of what the data represents.
  • Work in an iterative way and present your findings as a starting point for discussion in each iteration. Give people the chance to explain why certain things are happening and let them ask additional questions (to be picked up in the next iteration). This will help to improve the quality and relevance of your analysis as well as increase the buy-in of the process stakeholders in the final results of the project.

Don’t:

  • Jump to conclusions. You can never assume that you know everything about the process. For example, slower teams may be handling the difficult cases, people may deviate from the process for good reasons, and you may not see everything in the data (for example, there might be steps that are performed outside of the system). By consistently using your observations as a starting point for discussion, and by allowing people to join in the interpretation, you can start building trust and the collaborative culture that process mining needs to thrive.
  • Force any conclusions that you expect, or would like to have, by misrepresenting the data (or by stating things that are not actually supported by the data). Instead, keep track of the steps that you have taken in the data preparation and in your process mining analysis. If there are any doubts about the validity or questions about the basis of your analysis, you can always go back and show, for example, which filters have been applied to the data to come to the particular process view that you are presenting.

Consider Anonymization – Process Mining Rule 3 of 4

This is article no. 3 of the four-part article series Privacy, Security and Ethics in Process Mining.

Read this article in German:
Datenschutz, Sicherheit und Ethik beim Process Mining – Regel 3 von 4

If you have sensitive information in your data set, instead of removing it you can also consider the use of anonymization. When you anonymize a set of values, then the actual values (for example, the employee names “Mary Jones”, “Fred Smith”, etc.) will be replaced by another value (for example, “Resource 1”, “Resource 2”, etc.).

If the same original value appears multiple times in the data set, then it will be replaced with the same replacement value (“Mary Jones” will always be replaced by “Resource 1”). This way, anonymization allows you to obfuscate the original data but it preserves the patterns in the data set for your analysis. For example, you will still be able to analyze the workload distribution across all employees without seeing the actual names.

Some process mining tools (Disco and ProM) include anonymization functionality. This means that you can import your data into the process mining tool and select which data fields should be anonymized. For example, you can choose to anonymize just the Case IDs, the resource name, attribute values, or the timestamps. Then you export the anonymized data set and you can distribute it among your team for further analysis.

Do:

  • Determine which data fields are sensitive and need to be anonymized (see also the list of common process mining attributes and how they are impacted if anonymized).
  • Keep in mind that despite the anonymization certain information may still be identifiable. For example, there may be just one patient having a very rare disease, or the birthday information of your customer combined with their place of birth may narrow down the set of possible people so much that the data is not anonymous anymore.

Don’t:

  • Anonymize the data before you have cleaned your data, because after the anonymization the data cleaning may not be possible anymore. For example, imagine that slightly different customer category names are used in different regions but they actually mean the same. You would like to merge these different names in a data cleaning step. However, after you have anonymized the names as “Category 1”, “Category 2”, etc. the data cleaning cannot be done anymore.
  • Anonymize fields that do not need to be anonymized. While anonymization can help to preserve patterns in your data, you can easily lose relevant information. For example, if you anonymize the Case ID in your incident management process, then you cannot look up the ticket number of the incident in the service desk system anymore. By establishing a collaborative culture around your process mining initiative (see guideline No. 4) and by working in a responsible, goal-oriented way, you can often work openly with the original data that you have within your team.