How to Ensure Data Quality in an Organization?

Introduction to Data Quality

Today, the world is filled with data. It is everywhere. And, the value of any organization can be measured by the quality of its data. So, what actually is the quality of data or data quality, and why is it important? Well, data quality refers to the capability of a set of data to serve an intended purpose. 

Data quality is important to any organization because it provides timely and accurate information to manage accountability and services. It also helps to ensure and prioritize the best use of resources. Thus, high-quality data will lead to appropriate insights and valuable information for any organization. We can evaluate the quality of data in certain aspects. They include accuracy, relevancy, completeness, and uniqueness. 

Data Quality Problems

As the organizations are collecting vast amounts of data, managing its quality becomes more important every single day. In the year 2016, the costs of problems caused due to poor data quality were estimated by IBM, and it turned out to be $3.1 trillion across the U.S economy. Also, a Forrester report has stated that almost 30 percent of analysts spend 40 percent of their time validating and vetting their data prior to its utilization for strategic decision-making. These statistics indicate that the scale of the problems with data quality is vast.

So, why do these data quality problems occur? The main reasons include manual entry of data, software updates, integration of data sources, skills shortages, and insufficient testing time. Wrong decisions can be taken due to poor data management processes and poor quality of data. Because of this, many organizations lose their clients and customers. So, ensuring data quality must be given utmost importance in an organization. 

How to Ensure Data Quality?

Data quality management helps by combining data, technology, and organizational culture to deliver useful and accurate results. Good management of data quality builds a foundation for all the initiatives of a business. Now, let’s see how we can improve the data quality in an organization.

The first aspect of improving the quality of data is monitoring and cleansing data. This verifies data against standard statistical measures, validates data against matching descriptions, and uncovers relationships. This also checks the uniqueness of data and analyzes the data for its reusability. 

The second one is managing metadata centrally. Multiple people gather and clean data very often and they may work in different countries or offices. Therefore, you require clear policies on how data is gathered and managed as people in different parts of a company may misinterpret certain data terms and concepts. Centralized management of metadata is the solution to this problem as it reduces inconsistent interpretations and helps in establishing corporate standards.  

The next one is to ensure all the requirements are available and offer documentation for data processors and data providers. You have to format the specifications and offer a data dictionary and also provide training for the providers of data and all other new staff. Make sure you offer immediate help for all the data providers.

Very often, data is gathered from different sources and may include distinct spelling options. Hence, segmentation, scoring, smart lists, and many others are impacted by this. So, for entering a data point, a singular approach is essential, and data normalization provides this approach. The goal of this approach is to eliminate redundancy in data. Its advantages include easier object-to-data mapping and increased consistency.

The last aspect is to verify whether the data is consistent with the data rules and business goals, and this has to be done at regular intervals. You have to communicate the current status and data quality metrics to every stakeholder regularly to ensure the maintenance of data quality discipline across the organization.

Conclusion

Data quality is a continuous process but not a one-time project which needs the entire company to be data-focused and data-driven. It is much more than reliability and accuracy. High level of data quality can be achieved when the decision-makers have confidence in data and rely upon it. Follow the above-mentioned steps to ensure a high level of data quality in your organization. 

The Future of AI in Dental Technology

As we develop more advanced technology, we begin to learn that artificial intelligence can have more and more of an impact on our lives and industries that we have gotten used to being the same over the past decades. One of those industries is dentistry. In your lifetime, you’ve probably not seen many changes in technology, but a boom around artificial intelligence and technology has opened the door for AI in dental technologies.

How Can AI Help?

Though dentists take a lot of pride in their craft and career, most acknowledge that AI can do some things that they can’t do or would make their job easier if they didn’t have to do. AI can perform a number of both simple and advanced tasks. Let’s take a look at some areas that many in the dental industry feel that AI can be of assistance.

Repetitive, Menial Tasks

The most obvious area that AI can help out when it comes to dentistry is with repetitive and menial simple tasks. There are many administrative tasks in the dentistry industry that can be sped up and made more cost-effective with the use of AI. If we can train a computer to do some of these tasks, we may be able to free up more time for our dentists to focus on more important matters and improve their job performance as well. One primary use of AI is virtual consultations that offices like Philly Braces are offering. This saves patients time when they come in as the Doctor already knows what the next steps in their treatment will be.

Using AI to do some basic computer tasks is already being done on a small scale by some, but we have yet to see a very large scale implementation of this technology. We would expect that to happen soon, with how promising and cost-effective the technology has proven to be.

Reducing Misdiagnosis

One area that many think that AI can help a lot in is misdiagnosis. Though dentists do their best, there is still a nearly 20% misdiagnosis rate when reading x-rays in dentistry. We like to think that a human can read an x-ray better, but this may not be the case. AI technology can certainly be trained to read an x-ray and there have been some trials to suggest that they can do it better and identify key conditions that we often misread.

A world with AI diagnosis that is accurate and quicker will save time, money, and lead to better dental health among patients. It hasn’t yet come to fruition, but this seems to be the next major step for AI in dentistry.

Artificial Intelligence Assistants

Once it has been demonstrated that AI can perform a range of tasks that are useful to dentists, the next logical step is to combine those skills to make a fully-functional AI dental assistant. A machine like this has not yet been developed, but we can imagine that it would be an interface that could be spoken to similar to Alexa. The dentist would request vital information and other health history data from a patient or set of patients to assist in the treatment process. This would undoubtedly be a huge step forward and bring a lot of computing power into the average dentist office.

Conclusion

It’s clear that AI has a bright future in the dental industry and has already shown some of the essential skills that it can help with in order to provide more comprehensive and accurate care to dental patients. Some offices like Westwood Orthodontics already use AI in the form of a virtual consult to diagnose issues and provide treatment options before patients actually step foot in the office. Though not nearly all applications that AI can provide have been explored, we are well on our way to discovering the vast benefits of artificial intelligence for both patients and practices in the dental healthcare industry.

Interview – Customer Data Platform, more than CRM 2.0?

Interview with David M. Raab from the CDP Institute

David M. Raab is as a consultant specialized in marketing software and service vendor selection, marketing analytics and marketing technology assessment. Furthermore he is the founder of the Customer Data Platform Institute which is a vendor-neutral educational project to help marketers build a unified customer view that is available to all of their company systems.

Furthermore he is a Keynote-Speaker for the Predictive Analytics World Event 2019 in Berlin.

Data Science Blog: Mr. Raab, what exactly is a Customer Data Platform (CDP)? And where is the need for it?

The CDP Institute defines a Customer Data Platform as „packaged software that builds a unified, persistent customer database that is accessible by other systems“.  In plainer language, a CDP assembles customer data from all sources, combines it into customer profiles, and makes the profiles available for any use.  It’s important because customer data is collected in so many different systems today and must be unified to give customers the experience they expect.

Data Science Blog: Is it something like a CRM System 2.0? What Use Cases can be realized by a Customer Data Platform?

CRM systems are used to interact directly with customers, usually by telephone or in the field.  They work almost exclusively with data that is entered during those interactions.  This gives a very limited view of the customer since interactions through other channels such as order processing or Web sites are not included.  In fact, one common use case for CDP is to give CRM users a view of all customer interactions, typically by opening a window into the CDP database without needing to import the data into the CRM.  There are many other use cases for unified data, including customer segmentation, journey analysis, and personalization.  Anything that requires sharing data across different systems is a CDP use case.

Data Science Blog: When does a CDP make sense for a company? It is more relevant for retail and financial companies than for industrial companies, isn´t it?

CDP has been adopted most widely in retail and online media, where each customer has many interactions and there are many products to choose from.  This is a combination that can make good use of predictive modeling, which benefits greatly from having more complete data.  Financial services was slower to adopt, probably because they have fewer products but also because they already had pretty good customer data systems.  B2B has also been slow to adopt because so much of their customer relationship is handled by sales people.  We’ve more recently been seeing growth in additional sectors such as travel, healthcare, and education.  Those involve fewer transactions than retail but also rely on building strong customer relationships based on good data.

Data Science Blog: There are several providers for CDPs. Adobe, Tealium, Emarsys or Dynamic Yield, just to name some of them. Do they differ a lot between each other?

Yes they do.  All CDPs build the customer profiles I mentioned.  But some do more things, such as predictive modeling, message selection, and, increasingly, message delivery.  Of course they also vary in the industries they specialize in, regions they support, size of clients they work with, and many technical details.  This makes it hard to buy a CDP but also means buyers are more likely to find a system that fits their needs.

Data Science Blog: How established is the concept of the CDP in Europe in general? And how in comparison with the United States?

CDP is becoming more familiar in Europe but is not as well understood as in the U.S.  The European market spent a lot of money on Data Management Platforms (DMPs) which promised to do much of what a CDP does but were not able to because they do not store the level of detail that a CDP does.  Many DMPs also don’t work with personally identifiable data because the DMPs primarily support Web advertising, where many customers are anonymous.  The failures of DMPs have harmed CDPs because they have made buyers skeptical that any system can meet their needs, having already failed once.  But we are overcoming this as the market becomes better educated and more success stories are available.  What’s the same in Europe and the U.S. is that marketers face the same needs.  This will push European marketers towards CDPs as the best solution in many cases.

Data Science Blog: What are coming trends? What will be the main topic 2020?

We see many CDPs with broader functions for marketing execution: campaign management, personalization, and message delivery in particular.  This is because marketers would like to buy as few systems as possible, so they want broader scope in each systems.  We’re seeing expansion into new industries such as financial services, travel, telecommunications, healthcare, and education.  Perhaps most interesting will be the entry of Adobe, Salesforce, and Oracle, who have all promised CDP products late this year or early next year.  That will encourage many more people to consider buying CDPs.  We expect that market will expand quite rapidly, so current CDP vendors will be able to grow even as Adobe, Salesforce, and Oracle make new CDP sales.


You want to get in touch with Daniel M. Raab and understand more about the concept of a CDP? Meet him at the Predictive Analytics World 18th and 19th November 2019 in Berlin, Germany. As a Keynote-Speaker, he will introduce the concept of a Customer Data Platform in the light of Predictive Analytics. Click here to see the agenda of the event.

 


 

Marketing Attribution Models

Why do we need attribution?

Attributionis the process of distributing the value of a purchase between the various channels, used in the funnel chain. It allows you to determine the role of each channel in profit. It is used to assess the effectiveness of campaigns, to identify more priority sources. The competent choice of the model makes it possible to optimally distribute the advertising budget. As a result, the business gets more profit and less expenses.

What models of attribution exist

The choice of the appropriate model is an important issue, because depending on the business objectives, it is better to fit something different. For example, for companies that have long been present in the industry, the priority is to know which sources contribute to the purchase. Recognition is the importance for brands entering the market. Thus, incorrect prioritization of sources may cause a decrease in efficiency. Below are the models that are widely used in the market. Each of them is guided by its own logic, it is better suited for different businesses.

First Interaction (First Click)

The value is given to the first touch. It is suitable only for several purposes and does not make it possible to evaluate the role of each component in making a purchase. It is chosen by brands who want to increase awareness and reach.

Advantages

It does not require knowledge of programming, so the introduction of a business is not difficult. A great option that effectively assesses campaigns, aimed at creating awareness and demand for new products.

Disadvantages

It limits the ability to analyze comprehensively all channels that is used to promote a brand. It gives value to the first interaction channel, ignoring the rest.

Who is suitable for?

Suitable for those who use the promotion to increase awareness, the formation of a positive image. Also allows you to find the most effective source.

Last Interaction (Last Click)

It gives value to the last channel with which the consumer interacted before making the purchase. It does not take into account the actions that the user has done up to this point, what marketing activities he encountered on the way to conversion.

Advantages

The tool is widely used in the market, it is not difficult. It solves the problem of small advertising campaigns, where is no more than 3 sources.

Disadvantages

There is no way to track how other channels have affected the acquisition.

Who is suitable for?

It is suitable for business models that have a short purchase cycle. This may be souvenirs, seasonal offers, etc.

Last Non-Direct Click

It is the default in Google Analytics. 100% of the  conversion value gives the last channel that interacted with the buyer before the conversion. However, if this source is Direct, then assumptions are counted.

Suppose a person came from an email list, bookmarked a product, because at that time it was not possible to place an order. After a while he comes back and makes a purchase. In this case, email as a channel for attracting users would be underestimated without this model.

Who is suitable for?

It is perfect for beginners who are afraid of making a mistake in the assessment. Because it allows you to form a general idea of ​​the effectiveness of all the involved channels.

Linear model attribution (Linear model)

The value of the conversion is divided in equal parts between all available channels.

Linear model attribution (Linear model)

Advantages

More advanced model than previous ones, however, characterized by simplicity. It takes into account all the visits before the acquisition.

Disadvantages

Not suitable for reallocating the budget between the channels. This is due to the fact that the effectiveness of sources may differ significantly and evenly divide – it is not the best idea. 

Who is suitable for?

It is performing well for businesses operating in the B2B sector, which plays a great importance to maintain contact with the customer during the entire cycle of the funnel.

Taking into account the interaction duration (Time Decay)

A special feature of the model is the distribution of the value of the purchase between the available channels by increment. Thus, the source, that is at the beginning of the chain, is given the least value, the channel at the end deserves the greatest value.  

Advantages

Value is shared between all channel. The highest value is given to the source that pushed the user to make a purchase.

Disadvantages

There is no fair assessment of the effectiveness of the channels, that have made efforts to obtain the desired result.

Who is suitable for?

It is ideal for evaluating the effectiveness of advertising campaigns with a limited duration.

Position-Based or U-Shaped

40% receive 2 channels, which led the user and pushed him to purchase. 20% share among themselves the intermediate sources that participated in the chain.

Advantages

Most of the value is divided equally between the key channels – the fact that attracted the user and closed the deal..

Disadvantages

Underestimated intermediate channels.It happens that they make it possible to more effectively promote the user chain.. Because they allow you to subscribe to the newsletter or start following the visitor for price reduction, etc.

Who is suitable for?

Interesting for businesses that focus on attracting new audiences, as well as pushing existing customers to buy.

Cons of standard attribution models

According to statistics, only 44% of foreign experts use attribution on the last interaction. Speaking about the domestic market, we can announce the numbers are much higher. However, only 18% of marketers use more complex models. There is also evidence which demonstrates that 72.4% of those who use attribution based on the last interaction, they use it not because of efficiency, but because it is simple.

What leads to a similar state of affairs?

Experts do not understand the effectiveness. Ignorance of how more complex models work leads to a lack of understanding of the real benefits for the business.

Attribution management is distributed among several employees. In view of this, different models can be used simultaneously. This approach greatly distorts the data obtained, not allowing an objective assessment of the effect of channels.

No comprehensive data storage. Information is stored in different places and does not take into account other channels. Using the analytics of the advertising office, it is impossible to work with customers in retail outlets.

You may find ways to eliminate these moments and attribution will work for the benefit of the business.

What algorithmic attribution models exist

Using one channel, there is no need to enable complex models. Attribution will be enough for the last interaction. It has everything to evaluate the effectiveness of the campaign, determine the profitability, understand the benefits for the business.

Moreover, if the number of channels increases significantly, and goals are already far beyond recognition, it will be better to give preference to more complex models. They allow you to collect all the information in one place, open up limitless monitoring capabilities, make it clear how one channel affects the other and which bundles work better together.

Below are the well-known and widely used today algorithmic attribution models.

Data-Driven Attribution

A model that allows you to track all the way that the consumer has done before making a purchase. It objectively evaluates each channel and does not take into account the position of the source in the funnel. It demonstrates how a certain interaction affected the outcome. Data-Driven attribution model is used in Google Analytics 360.

With it, you can work efficiently with channels that are underestimated in simpler models. It gives the opportunity to distribute the advertising budget correctly.

Attribution based on Markov’s Chains (Markov Chains)

Markov’s chain has been used for a long time to predict weather, matches, etc. The model allows you to find out, how the lack of a channel will affect sales. Its advantage is the ability to assess the impact of the source on the conversion, to find out which channel brings the best results.

A great option for companies that store data in one service. To implement requires knowledge of programming. It has one drawback in the form of underestimating the first channel in the chain. 

OWOX BI Attribution

OWOX BI Attribution helps you assess the mutual influence of channels on encouraging a customer through the funnel and achieving a conversion.

What information can be processed:

  • Upload user data from Google Analytics using flexible built-in tools.
  • Process information from various advertising services.
  • Integrate the model with CRM systems.

This approach makes it possible not to lose sight of any channel. Analyze the complex impact of marketing tools, correctly distributing the advertising budget.

The model uses CRM information, which makes it possible to do end-to-end analytics. Each user is assigned an identifier, so no matter what device he came from, you can track the chain of actions and understand that it is him. This allows you to see the overall effect of each channel on the conversion.

Advantages

Provides an integrated approach to assessing the effectiveness of channels, allows you to identify consumers, even with different devices, view all visits. It helps to determine where the user came from, what prompted him to do so. With it, you can control the execution of orders in CRM, to estimate the margin. To evaluate in combination with other models in order to determine the highest priority advertising campaigns that bring the most profit.

Disadvantages

It is impossible to objectively evaluate the first step of the chain.

Who is suitable for?

Suitable for all businesses that aim to account for each step of the chain and the qualitative assessment of all advertising channels.

Conclusion

The above-mentioned Ad Roll study shows that 70% of marketing managers find it difficult to use the results obtained from attribution. Moreover, there will be no result without it.

To obtain a realistic assessment of the effectiveness of marketing activities, do the following:

  • Determine priority KPIs.
  • Appoint a person responsible for evaluating advertising campaigns.
  • Define a user funnel chain.
  • Keep track of all data, online and offline. 
  • Make a diagnosis of incoming data.
  • Find the best attribution model for your business.
  • Use the data to make decisions.

The Power of Analyzing Processes

Are you thinking BIG enough? Over the past few years, the quality of discussion regarding a ‘process’ and its interfaces between different departments has developed radically. Organizations increasingly reject guesswork, individual assessments, or blame-shifting and instead focus on objective facts: the display of throughput times, process variants, and their optimization.

But while data can hold valuable insights into business, users, customer bases, and markets, companies are sometimes unsure how best to analyze and harness their data. In fact, the problem isn’t usually a lack of data; it’s a breakdown in leveraging useful data. Being unsure how to interpret, explore, and analyze processes can paralyze any go-live, leading to a failure in the efficient interaction of processes and business operations. Without robust data analysis, your business could be losing money, talent, and even clients.

After all, analyzing processes is about letting data tell its true story for improved understanding.

The “as-is” processes

Analyzing the as-is current state helps organizations document, track, and optimize processes for better performance, greater efficiency, and improved outcomes. By contextualizing data, we gain the ability to navigate and organize processes to negate bottlenecks, set business preferences, and plan an optimized route through process mining initiatives. This focus can help across an entire organization, or on one or more specific processes or trends within a department or team.

There are several vital goals/motivations for implementing current state analysis, including:

  • Saving money and improving ROI;
  • Improving existing processes or creating new processes;
  • Increasing customer satisfaction and journeys;
  • Improving business coordination and organizational responsiveness;
  • Complying with new regulatory standards;
  • Adapting methods following a merger or acquisition.

The “to-be” processes

Simply put, if as-is maps where your processes are, to-be maps where you want them to… be. To-be process mapping documents what you want the process to look like, and by using the as-is diagram, you can work with stakeholders to identify developments and improvements of the current process, then outline those changes on your to-be roadmap.

This analysis can help you make optimal decisions for your business and innovative OpEx imperatives. For instance, at leading data companies like Google and Amazon, data is used in such a way that the analysis results make the decisions! Just think of the power Recommendation Engines, PageRank, and Demand Forecasting Systems have over the content we see. To achieve this, advanced techniques of machine learning and statistical modeling are applied, resulting in mechanically improved results from the data. Interestingly, because these techniques reference large-scale data sets and reflect analysis and results in real-time, they are applied to areas that extend beyond human decision-making.

Also, by analyzing and continuously monitoring qualitative and quantitative data, we gain insights across potential risks and ongoing improvement opportunities, too. The powerful combination of process discovery, process analysis, and conformance checking supports a collaborative approach to process improvement, giving you game-changing insights into your business. For example:

  • Which incidents would I like to detect and act upon proactively?
  • Where would task prioritization help improve overall performance?
  • Where do I know that increased transparency would help the company?
  • How can I utilize processes in place of gut feeling/experience?

Further, as the economic environment continues to change rapidly, and modern organizations keep adopting process-based approaches to ensure they are achieving their business goals, process analysis naturally becomes the perfect template for any company.

With this, process mining technology can help modern businesses manage process challenges beyond the boundaries of implementation. We can evaluate the proof of concept (PoC) for any proposed improvements, and extract relevant information from a homogenous data set. Of course, process modeling and business process management (BPM) are available to solve the potentially tricky integration phase.

Process mining and analysis initiatives

Process mining and discovery initiatives can also provide critical insights throughout the automation and any Robotic Process Automation (RPA) journey, from defining the strategy to continuous improvement and innovation. Data-based process mining can even extend process analysis across teams and individuals, decreasing incident resolution times, and subsequently improving working habits via the discovery and validation of automation opportunities.

A further example of where process mining and strategic process analysis/alignment is already paying dividends is IT incident management. Here, “incident” is an unplanned interruption to an IT service, which may be complete unavailability or merely a reduction in quality. The goal of the incident management process is to restore regular service operation as quickly as possible and to minimize the impact on business operations. Incident management is a critical process in Information Technology Library (ITIL).

Process mining can also further drive improvement in as-is incident management processes as well as exceptional and unwanted process steps, by increasing visibility and transparency across IT processes. Process mining will swiftly analyze the different working habits across teams and individuals, decreasing incident resolution times, and subsequently improving customer impact cases.

Positive and practical experiences with process mining across industries have also led to the further dynamic development of tools, use cases, and the end-user community. Even with very experienced process owners, the visualization of processes can skyrocket improvement via new ideas and discussion.

However, the potential performance gains are more extensive, with the benefits of using process mining for incident management, also including:

  • Finding out how escalation rules are working and how the escalation is done;
  • Calculating incident management KPIs, including SLA (%);
  • Discovering root causes for process problems;
  • Understanding the effect of the opening interface (email, web form, phone, etc.);
  • Calculating the cost of the incident process;
  • Aligning the incident management system with your incident management process.

Robotic Process Automation (RPA)

Robotic process automation (RPA) provides a virtual workforce to automatize manual, repetitive, and error-prone tasks. However, successful process automation requires specific knowledge about the intended (and potential) benefits, effective training of the robots, and continuous monitoring of their performance and processes.

With this, process mining supports organizations throughout the lifecycle of RPA initiatives by monitoring and benchmarking robots to ensure sustainable benefits. These insights are especially valuable for process miners and managers with a particular interest in process automation. By unlocking the experiences with process mining, a company better understands what is needed today, for tomorrow’s process initiatives.

To further upgrade the impact of robot-led automation, there is also a need for a solid understanding of legacy systems, and an overview of automation opportunities. Process mining tools provide key insights throughout the entire RPA journey, from defining the strategy to continuous improvement and innovation.

Benefits of process mining and analysis within the RPA lifecycle include:

  1. Overviews of processes within the company, based on specific criteria;
  2. Identification of processes suitable for RPA implementation during the preparation phase;
  3. Mining the optimal process flow/process path;
  4. Understanding the extent to which RPA can be implemented in legacy processes and systems;
  5. Monitoring and analysis of RPA performance during the transition/handover of customization;
  6. Monitoring and continuous improvement of RPA in the post-implementation phase.

The process of better business understanding

Every organization is different and brings with it a variety of process-related questions. Yet some patterns are usually repeated. For example, customers who introduce data supported process analysis as part of business transformation initiatives will typically face challenges in harmonizing processes from fragmented sectors and regional locations. Here it helps enormously to base actions on data and statistics from the respective processes, instead of relying on the instincts and estimations of individuals.

With this, process analysis which is supported by data, enables a fact-based discussion, and builds a bridge between employees, process experts and management. This helps avoid siloed thinking, as well as allowing the transparent design of handovers and process steps which cross departmental boundaries within an organization.

In other words, to unlock future success and transformation, we must be processing… today.

Find out more about process mining with Signavio Process Intelligence, and see how it can help your organization uncover the hidden value of process, generate fresh ideas, and save time and money.

Accelerate your AI Skills Today: A Million Dollar Job!

The skyrocketing salaries ($1m per year) of AI engineers is not a hype. It is the fact of current corporate world, where you will witness a shift that is inevitable.

We’ve already set our feet at the edge of the technological revolution. A revolution that is at the verge of altering the way we live and work. As the fact suggests, humanity has fundamentally developed human production in three revolutions, and we’re now entering the fourth revolution. In its scope, the fourth revolution projects a transformation that is unlike anything we humans have ever experienced.

  • The first revolution had the world transformed from rural to urban
  • the emergence of mass production in the second revolution
  • third introduced the digital revolution
  • The fourth industrial revolution is anxious to integrate technologies into our lives.

And all thanks to artificial intelligence (AI). An advanced technology that surrounds us, from virtual assistants to software that translates to self-driving cars.

The rise of AI at an exponential rate has disrupted almost every industry. So much so that AI is being rated as one-million-dollar profession.

Did this grab your attention? It did?

Now, what if we were to tell you that the salary compensation for AI experts has grown dramatically. AI and machine learning are fields that have a mountain of demand in the tech industry today but has sparse supply.

AI field is growing at a quicker pace and salaries are skyrocketing! Read it for yourself to know what AI experts, AI researchers and any other AI talent are commanding today.

  • A top-class AI research laboratory, OpenAI says that techies in the AI field are projected to earn a salary compensation ranging between $300 to $500k for fresh graduates. However, expert professionals could earn anywhere up to $1m.
  • Whopping salary package of above 100 million yen that amounts to $1m is being offered to AI geniuses by a Japanese firm, Start Today. A firm that operates a fashion shopping website named Zozotown.

Does this leave you with a question – Is this a right opportunity for you to jump in the field and make hay while the sun is shining? 

And the answer to this question is – yes, it is the right opportunity for any developer seeking a role in the AI industry. It can be your chance to bridge the skill shortage in the AI field either by upskilling or reskilling yourself in the field of AI.

There are a wide varieties of roles available for an AI enthusiast like you. And certain areas are like AI Engineers and AI Researchers are high in demand, as there are not many professionals who have robust AI knowledge.

According to a job report, “The Future of Jobs 2018,” a prediction was made suggesting that machines and algorithms will create around 133 million new job roles by 2022.

AI and machine learning will dominate the tech world. The World Economic Forum says that several sectors have started embracing AI and machine learning to tackle challenges in certain fields such as advertising, supply chain, manufacturing, smart cities, drones, and cybersecurity.

Unraveling the AI realm

From chatbots to financial planners, AI is impacting the way businesses function on a day-today basis. AI makes the work simpler, as it provides variables, which makes the work more streamlined.

Alright! You know that

  • the demand for AI professionals is rising exponentially and that there is just a trickle of supply
  • the AI professionals are demanding skyrocketing salaries

However, beyond that how much more do you know about AI?

Considering the fact that our lives have already been touched by AI (think Alexa, and Siri), it is just a matter of time when AI will become an indispensable part of our lives.

As Gartner predicts that 2020 will be an important year for business growth in AI. Thus, it is possible to witness significant sparks for employment growth. Though AI predicts to diminish 1.8 million jobs, it is also said to replace it with 2.3 million jobs that will be created. As we look forward to stepping into 2020, AI-related job roles are set to make positive progress of achieving 2 million net-new employments by 2025.

With AI promising to score fat paychecks that would reach millions, AI experts are struggling to find new ways to pick up nouveau skills. However, one of the biggest impacts that affect the job market today is the scarcity of talent in this field.

The best way to stay relevant and employable in AI is probably by “reskilling,” and “upskilling.” And  AI certifications is considered ideal for those in the current workforce.

Looking to upskill yourself – here’s how you can become an AI engineer today.

Top three ways to enhance your artificial intelligence career:

  1. Acquire skills in Statistics and Machine Learning: If you’re getting into the field of machine learning, it is crucial that you have in-depth knowledge of statistics. Statistics is considered a prerequisite to the ML field. Both the fields are tightly related. Machine learning models are created to make accurate predictions while statistical models do the job of interpreting the relationship between variables. Many ML techniques heavily rely on the theory obtained through statistics. Thus, having extensive knowledge in statistics help initiate the first step towards an AI career.
  2. Online certification programs in AI skills: Opting for AI certifications will boost your credibility amongst potential employers. Certifications will also enhance your earning potential and increase your marketability. If you’re looking for a change and to be a part of something impactful; join the AI bandwagon. The IT industry is growing at breakneck speed; it is now that businesses are realizing how important it is to hire professionals with certain skillsets. Specifically, those who are certified in AI are becoming sought after in the job market.
  3. Hands-on experience: There’s a vast difference in theory and practical knowledge. One needs to familiarize themselves with the latest tools and technologies used by the industry. This is possible only if the individual is willing to work on projects and build things from scratch.

Despite all the promises, AI does prove to be a threat to job holders, if they don’t upskill or reskill themselves. The upcoming AI revolution will definitely disrupt the way we work, however, it will leave room for humans to perform more creative jobs in the future corporate world.

So a word of advice is to be prepared and stay future ready.