Finding out the difference between data scientists, data engineers, software engineers, and statisticians can be confusing and complicated. While all of them are linked to data in a way, there is an underlying difference between the work they do and manage.
The growth of data and its usage across the industry is hidden from none. During the last decade in general, and the last couple of years in particular, we have seen a major distinction in the roles tasked with crafting and managing data.
Data Science is without a doubt a really growing field. Organizations and even countries from across the globe have experienced a drastic rise in their data collection endeavors. With numerous complications associated with collecting and managing data, this field is now host to a wide array of jobs and designations. We now have data scientists who are grouped into more specific tasks of data engineers, data statisticians, and software engineers. But other than the difference in their names, how many of us can comprehend the diversity in the work they do?
As I guessed, not many people can guess the job that these data experts are up to. Many of us eventually come to the conclusion that all of them do the same job and are grouped differently for the sake of it. There is nothing more mistaken then this myth and for this purpose I have turned up as a myth buster today to put an end to the conflict in understanding the role of these jobs present in the data industry. While all of them help propel the movement towards authentic data creation by architecting the growth upwards, there is a major difference in how and why they come into the perspective.
Here I have outlined some of the major attributes of these four subcategories that come in the bigger picture of managing and looking over data. They say ignorance is bliss, but it is always better to know the real picture than to shy away from it.
Statistician
The statistician sits right at the forefront of the whole process and applies statistical theories to solve numerous practical problems pertaining to a plethora of industries. They have the leverage and the independence to determine the method deemed feasible for finding and collecting data.
Since statisticians are deployed to collect data through meaningful methods, they design surveys, questionnaires, experiments, etc., to collect data.
They analyze and interpret the analyses from the data and report all the conclusions that they find through their analyses to their superiors. Statisticians need to boast of analytic skills along with the ability to interpret data and narrate complex concepts in a simple, understandable manner.
Statisticians understand the numbers that are generated through research, and apply these numbers to real life issues.
Software Engineers
A software engineer sits at an important front of the data analytic process and is responsible for building systems and applications. Software engineers will be part of the process of developing and testing/reviewing systems and applications. They are responsible for creating the products that ultimately lead to the creation of the data. Software engineering is probably the oldest one of all these four roles and was an imperative part of society way before the data boom began.
Software engineers are responsible for developing frontend and backend systems that help collect and process data. These web/mobile applications lead to the development of the operation system through a flawless software design. The data that is generated through the apps created by software engineers is then passed on to data engineers and data scientists.
Data Engineer
A data engineer is someone who is dedicated towards developing, constructing, testing, and maintaining architectures, such as a large scale processing system or a database. The main difference between a data engineer and its often confused alternative data scientist is that a data scientist is someone who cleans, organizes, and looks over big data.
You might find the use of the verb “cleans” in the comparison above really exotic and inadvertent, but in fact it has been placed with a purpose that helps reflect the difference between a data engineer and data scientist even more. In general, it can be mentioned that the efforts that both these experts put in are directed towards getting the data in an easy, usable format, but the technicalities and responsibilities that come in between are different for both of them.
Data engineers are responsible for dealing with raw data that is host to numerous machine, human, or instrument errors. The data might contain suspect records and may not even be validated. This data is not only unformatted, but also contains codes that work over specific systems.
This is where data engineers come in. Not only do they come up with methods and techniques to improve data efficiency, quality, and reliability, but they also have to implement these methods. To manage this complication, they will have to employ numerous tools and master a variety of languages. Data engineers actually ensure that the architecture that they work upon is feasible for data scientists to work with. Once they have gone through the initial process, the data engineers will then have to deliver or transfer the data over to the data scientist team.
In simple terminology data engineers ensure the flow of data in an uninterrupted way through servers. They are mainly responsible for the architecture needed by the data.
Data Scientists
We now know that data scientists will get data that has already been worked upon by data engineers. The data has been cleaned and manipulated and can be used by data scientists to feed analytic programs that prepare the data for its use in predictive modeling. To build these models, data scientists need to do extensive research and accumulate high volume data from external and internal sources to answer all business needs.
Once data scientists are done with the initial stage of analysis, they have to ensure that the work they do is automated, and that all insights are duly delivered to all key business stakeholders on a routine basis. It is indeed noticeable that the skill set needed for being a data scientist or a data engineer as a matter of fact is slightly similar. But the two are gradually becoming even more distinct within the industry. Data scientists need to know the intricate details related to stats, machine learning, and math to help build a flawless predictive model. Moreover, the data scientist also needs to know details pertaining to distributed computing. Through distributed computing, the data scientist will be able to access the data processed by the engineering team. The data scientist is also responsible for reporting to all business stakeholders, so a focus on visualization is necessary.
Data scientists use their analytical capabilities to find out meaningful extracts from the data that is being fed to the machine. They report the final results to all the key stakeholders. The field of data is a growing one, and encompasses way more possibilities than what we had imagined before.
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The concept of recruiting and hiring has always come with a certain degree of risk attached to it. All too often, hiring managers are drawn in by an impressive resume, fancy dress clothes, an upbeat and confident demeanor, and a handful of positive interactions.
While the initial meetings and interviews may set high expectations, companies never really know what they are getting with a new hire until months later. A single bad hiring decision can do a lot of damage to a company’s budget and reputation. From a financial standpoint, companies lose an average of $14,900 on every bad hire, according to CareerBuilder.
Fortunately, the rapid advancement and sophistication of big data have made its presence known in the hiring process. The results of this can do a lot to cut down on turnover. However, understanding how to make these algorithms work for your specific organization will require a good deal of time and commitment. Here is how to do it.
Know the data sources
For companies that are new to the whole concept of big data, one of the toughest parts can simply be knowing where to look for the most pertinent information.
In terms of hiring, these algorithms normally work within a relatively narrow scope of information. Many HR departments utilize three major source categories of data. These include:
Publically available data can come from a wide range of sources. These can include social media, demographic information of the area, pay scale, employment rate, and much, much more.
The background information is what hiring managers see on a resume, or any other credentials submitted by the applicant. These typically relate to skills, qualifications, and experience.
Interaction data refers to the small insights gleaned from how an applicant communicates with a company. These insights come from things like keystrokes, word choice, and answers to questions. The metrics can have a strong correlation with future job performance.
Once you have identified the data sources necessary for the hiring process, your data mining tool will be able to run analyses to find the context you need to make more informed choices.
Understand key variables for each position
Upon finding the ideal sources, one of the biggest data-related challenges companies face is knowing exactly which metrics pertain to their goals, and how to apply them. According to IBM, about 2.5 quintillion bytes of data are created every single day. That being said, locating the right information can seem like finding a needle in a haystack.
Depending on the position you are recruiting for, there will likely be a wide range of data variables that play into the equation. This is one of the areas where there tends to be a high margin of error. Keep in mind, algorithms can only work for you if you have all the information necessary.
Therefore, you need to have a crystal clear objective in mind for the exact variables that pertain to the job, as well as how you can leverage them to eliminate the guesswork. These may include college GPA, certain buzzwords from previous jobs, soft skill proficiency, certain personality traits, etc.
Fortunately, there are plenty of tools to help you with this part of the process. AI-driven “smart” recruiting tools like Harver are designed to automatically screen applications and background information to identify the ones with the strongest correlation to the open position. From here, it runs a number of specialized assessments to gauge the applicant’s interaction data related to problem-solving, communication skills, situation judgment, and more.
Once the candidate has completed the assessments, the system uses smart algorithms to determine the strength of each candidate and how well they fit the mold for not just the open position, but the company as a whole.
Even though big data can work wonders in making smarter hiring decisions, it’s important to remember that there will always be a good amount of human intuition and iteration involved as well. Big data algorithms are simply there to guide you.
Use each interaction as a predictive data point
Big data, in general, can best be described as a constant work in progress. Datasets are continuously building off of each other to become smarter and more precise.
As you begin to develop a bank of data relating to your hiring process, there will almost certainly be a number of patterns that will emerge. These patterns should serve as a reference to how people mesh with your company. For example, in terms of communication, the datasets might show that the best workers in your company were the ones who responded to messages from the hiring managers within one hour. Or, perhaps the ones who sent shorter and more concise emails had a better success rate in the company.
BI tools like Dundas make the concept of predictive analysis simple. The browser-based solution allows you to input any data source and view the trends in customizable, interactive reports.
From here, you can draw on previous datasets to justify decisions for the future. The goal of hiring managers is to stay one step of head of common issues like poor productivity, employee turnover, bad cultural fits, and more. If you use every single interaction as a predictive data point and keep a close eye out for trends, you are in a much better position to avoid mistakes and misjudgments down the road.
Over to you
Turning your company into a data center has many benefits. In regards to the hiring process, managers need to do everything they can to make smarter decisions and avoid the dreaded high turnover rate. In the age of constant-connectedness, a high turnover rate isn’t just bad for your budget; it’s a huge red flag for new talented candidates.
While there are very few guarantees in the business world, one of the safest bets is that big data is here to stay. The sooner you can get the algorithms working for you, the better you will be in the long run. Always remember, a business is only as good as the people it brings on board.
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Like many of the technological shifts of the past two years, the world of big data has marked a paradigm shift in how information is collected and stored across the world. Not surprisingly, legislation has fallen behind technology in this regard, but it’s aiming to catch up with the latest round of EU regulations, which are set to change the way client data is being handled not just across Europe, but in every significant market on the planet.
As currently drafted, the new legislation will force companies to require consent and be transparent with regards to their intentions when it comes to collecting data from consumers. While this law is necessary for many respects, it will certainly result in extra costs and will require each company to invest substantially in their data collection and consumer abuse departments. What’s more, the many gray areas that still remain in today’s legislation will make it hard to tell if a company is playing by the rules or bending them to their advantage.
Given the situation, it’s clear to see why the need for a better and transparent system has emerged. Luckily, that buzziest of today’s technologies – the blockchain, may provide significant aid in overcoming some of the most glaring issues plaguing big data management in the present. To that end, here are just three of the main ways through which blockchain technology can make a positive impact:
1. Decentralization
At its core, blockchain technology revolves around the idea that a decentralized, trustless system is not only inherently incorruptible, but also faster and easier to maintain than a traditionally centralized one. By putting big data on the blockchain, you’re ensuring its ultimate transparency for all parties involved. Shady behind-the-curtains dealing is completely eliminated, as is the need for the kind of costly maintenance that a centralized system typically requires.
2. Immutability
Another defining characteristic of blockchain technology is its inherent immutability. This means that once a transaction or an operation has been made, it cannot be rescinded or returned. While this principle may have its drawbacks in some areas, in big data it leads to more confident levels of testing data and creating models that work.
3. Fairness
Finally, and perhaps most importantly, the democratic nature of a blockchain will help shift the power of personal data back to consumers. Nowadays, people are unaware of just how much their data is worth, since it’s mostly being controlled by large corporations with little to no incentive in sharing the wealth. However, on the blockchain, a person can choose whether to share their data and with whom, and that may very well help users earn an income through the sharing of personal data alone.
As you can see, blockchain technology holds much promise with respect to big data, especially in the face of stronger restrictions, the kind that will likely become the new norm within the next several years. Still, taking full advantage of this fairly new and as-of-yet not all that developed technology will require enterprises to take the time to adequately gather the resources they need in order to make the transition as smooth and as graceful as possible.
To that end, hiring a quality software development company with a proven track record in the blockchain niche is a good start. However, finding one is not an easy task – Google only lists one blockchain developer on the first page, rest are informational and news resources. Good developers who are fluent in blockchain-adjacent technologies are few and far between, and may come at a high price. Likewise, finding insightful people to enlist as advisors may also prove to be a challenge, since the biggest players in the industry are highly sought-after for their consultation skills. Lastly, building a strong enough community to generate interest in any given blockchain-related project and help educate the masses is also essential.
No matter the struggles and hurdles that are sure to materialize, it appears that blockchain technology is here to stay. Whether we’re talking healthcare records or property deeds, the correct handling of data will be paramount in the coming years if one wishes to prevent any unpleasantly dystopian scenarios from coming true. Blockchain technology is not perfect, and still has ways to go before it is completely applicable, but it has so far shown immense promise for a variety of big data concerns, and is definitely deserving of further study on a global scale.
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Suresh Shankar, founder of Crayon Data, talks about how entrepreneurship is all about persistence and perseverance, through one of his favourite anecdotes on the Chinese bamboo tree. One that will get all you budding entrepreneurs fired up and ready to make a change! Catch him in conversation with the University of Oxford and Said Business School.
Suresh Shankar also discusses ‘obvious’ opportunities in the digital banking space and the financial disruption sphere. With burgeoning amounts of unstructured data, Suresh says the way forward is to utilize this and build products that will herald change.
Suresh Shankar is a big data and analytics evangelist, entrepreneur and innovator; he established his second start‐up, Crayon Data in Singapore in 2012. Recognized today as one of the world’s top big data companies, Crayon is on a mission to simplify the world’s choices with its flagship product, MAYATM.
Suresh spent the first 15 years of his 30‐year career in sales, marketing, advertising, media and banking. He has witnessed the transformation of marketing from a right to a left-brained pursuit. His expertise in customer analytics was the foundation for RedPill Solutions, set up in 2000 in Singapore. Business leader IBM acquired RedPill Solutions in 2009.
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The rapid pace of technological innovation and the sudden emergence of Big Data has left a lot of marketers feeling left behind. In less than a generation, the marketing industry has shifted in a way nearly unprecedented in its entire history. It has gone from a largely intuitive or psychological art to being heavily defined by data, analysis, and science.
This has created numerous challenges for marketing agencies, both new and old. Getting a handle on their data, using it properly, and finding new ways to reach out to consumers are challenges facing every marketer at work today.
What are some of the biggest problems faced by modern brands and marketing departments? And what could potentially address those problems? Here are some answers.
Problem 1: Getting a Handle on Big Data
One of the biggest key challenges simply involves the collection, storage, and access to data. Some organizations still find themselves struggling to get the information they need flowing in. Others have opened too many pipelines, and find themselves drowning in an ocean of data without clear ways of organizing it.
In either case, what’s called for is a data-collection plan. Don’t collect data for its own sake. Have clear goals in mind for what the data will be used for, then act accordingly. If you know what the data is for, it’ll be much easier to collect and sort it. Be forward-thinking, and focus on laying the good groundwork now that will pay off in the future.
Problem 2: Market Disruptors
If there is an industry that existed prior to the 21st century, it’s probably now seeing digital disruptors arise and create large changes to that industry. Retail is a perfect example: Amazon is putting retailers out of business across the country, and even causing problems for some of the biggest names like Wal-Mart, yet Amazon has (almost) no physical stores. Similar examples are arising constantly, such as the sudden boom of Uber and Lyft and their threat to traditional taxis, or the way Netflix is cutting into cable company profits.
The best solution here – if possible – is to become the disruptor. Go on the offensive. Read your data, look for trends, and ask “is there a digital solution to this problem?” Look at processes related to your industry where a long-established solution exists, then think of a better one. Re-invent the mousetrap.
If you don’t, someone else will. Don’t be on the defensive.
Problem 3: Consumer Distrust
If there’s one problem with marketing that a lot of companies really don’t want to address, it’s this: Most buyers and consumers don’t like us. Some outright hate us. Particularly when talking about younger buyers – those under 40 – there has never been an era when marketers have been more distrusted. And that’s a BIG problem.
Much of this has to do with how much information the public now has about businesses and their day-to-day interactions with customers. It’s easier than ever for buyers to learn of questionable behavior and organize themselves against it. Many have become so cynical that they simply distrust anything and everything that comes from marketing departments.
The solutions here are, broadly, twofold: First, spend more time cultivating brand ambassadors online. Find friends on social media, and YouTube, and other online outlets who genuinely support your product/services. Word-of-mouth is more powerful than ever in this age where advertisements are seen as propaganda. Use research and analytics to discover who your buyers trust, and get those people on your side.
The other solution is honesty. When you must openly market, be as transparent as possible. Cite sources. Don’t overstate facts or capabilities. Do not ever get caught in a lie. It is possible to build consumer trust on a brand-by-brand basis, but that trust must be earned through trustworthy behavior.
Problem 4: The Sales and Marketing Split
For too many decades, sales and marketing were treated as wholly separate entities within a business. In worst case scenarios, they even had something of an adversarial relationship, with each tending to blame the other for failures.
This simply does not fly today. Sales and marketing must be working hand-in-hand. They need to know what each other is doing, and they need to be sharing data – particularly since each will likely have access to key insights the other lacks. This is where a strong CRM-style solution can be extremely useful. By centralizing data where both sales and marketing can access it, they can form closer links and develop initiatives jointly.
Better yet, get product development in on the data-sharing too. In particular, this can eliminate the perennial problem of sales or marketing over-promising, and then getting stuck with a product which disappoints its buyers. Use smart data sharing to keep sales, marketing, and/or R&D on the same page.
Problem 5: Security
If you’re keeping data, you have to keep it safe. Unfortunately, as we’ve seen from many many headlines over the past few years, this is easier said than done. It seems like hardly a month goes by without another high-profile name turning into a high-profile embarrassment due to data breaches, ransomware attacks, or other cyber-criminal activity.
Unfortunately, there’s no magic bullet solution here. You simply have to be willing to spend the time and the money remaining abreast of the latest data security ideas and keeping your security systems up-to-date. If management balks at the cost of updating security, remind them that -according to IBM- the average cost of a data breach is between $3 and $4 million dollars. And it can be much higher.
The “it can’t happen to us” mentality has to be overcome because it can happen to anyone regardless of size. Be prepared.
Always Keep Informed
If there’s one unifying factor in this, it’s simply that knowledge is power. Both in terms of data and your own insights, keeping your eyes and ears open is the best way to ensure you’re aware of potential data challenges before they become major data problems.
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Although cognitive computing, which is many a times referred to as AI or Artificial Intelligence, is not a new concept, the hype surrounding it and the level of interest pertaining to it is definitely new. The combination of hype surrounding robot overlords, vendor marketing and concerns regarding job losses has fueled the hype into where we stand now.
But, behind the cloud of hype that is surrounding the technology currently, there lies a potential for increased productivity, the ability to solve problems deemed too complex for the average human brains and better knowledge based transactions and interactions with consumers. I recently got a chance to catch up with Dmitri Tcherevik, who is the CTO of Progress, about this disruption and we had a healthy discussion which led to the following insights.
Cognitive computing is considered a marketing jargon by many, but in layman terms it is used to define the ability of computers to replicate or stimulate human thought processes. The processes behind cognitive computing may make use of the same principles as AI, including neural networks, machine learning, contextual awareness, sentimental analysis, and natural language processing. However, there is a minute difference between both of them.
Difference between Cognitive Computing and AI
Both AI and Cognitive Computing may look extremely alike, but like we mentioned above there is a small difference between both methods.
Firstly, artificial intelligence does not work at mimicking human thought processes. The concept behind AI is to not mimic human thought and processes, but to solve a problem through the use of the best possible algorithm. This can be illustrated through an example of a car, which stays on course and avoids a collision. The processes in AI are not looking to process data in the same way as it would be processed by humans, but they’re looking to process it through the best known algorithm present. Processing data the way humans do it is a far more fault-prone and complex algorithm. And, we all know that a self-driven car isn’t giving suggestions to the driver, it’s responsible for all the decisions in driving.
Secondly, cognitive computing is not responsible for making decisions for humans, instead it is responsible for complementing or supplementing our own cognitive abilities of decision making. AI in medicine would be all about making the right decisions pertaining to a patient or the preferred mode of treatment, and minimizing the role of the doctor. Cognitive computing, on the contrary, would be more focused on achieving evidence that could supplement the human expert into making more flawless medical diagnoses.
Emerging Use of Cognitive Computing in Industries
We can gauge the success of cognitive computing and the development through the opportunities it has across industries. Cognitive computing is currently in a research phase, where research is going into properly implementing the technology in the fields deemed appropriate for its use. One can assess the opportunities for cognitive computing by looking at industries and industry specific scenarios where cognitive computing could make a big difference.
Customer services
Companies offering customer services deal with a lot of data which they have to accommodate with large processing requirements and are required to be efficient and flawless in advising customers to the right outcome. With so much happening, one can think about the opportunities for cognitive computing in this specific industry. At a consumer level, we can take the aid of robo-advisors that assist staff in advising new customers about what they can do and how they can go about creating a new account. There is also the concept of automated document processing that will limit human involvement and the flaws that come with it to a large extent. According to Dmitri: ‘Customer services are up for disruption, and the use of chatbots while booking airplane tickets or checking your insurance claim will go a long way in the future.’
Healthcare
Whenever we talk about Big Data, Machine Learning, AI or Cognitive Computing, the services that will be rendered through these technologies in healthcare always spring to mind. Human healthcare is certainly not at 100 per cent efficiency nowadays, which is because of the fact that there are certain flaws in the process. These flaws can be eradicated by giving machines the cognitive abilities required for going through a report and forming a basic judgment regarding the condition of any patient. The results can then be communicated to humans through a virtual display.
Industrial IoT
Most of the Industrial IoT giants that we have in industries such as car manufacturing, transportation, etc., have implemented exemplary data collection methods. These data collection methods do their job well, and hand over the necessary input to their patron organizations. Now, when the data is collected and stored off, the real challenge of anomaly analytics arises. Despite having stringent data collection and storage facilities, these firms don’t know what to do with their data and how to find actionable results.
The biggest problem facing businesses in today’s myopia is that only 20 percent of all problems or anomalies that occur are predicted and understood beforehand. This means that around 80 percent of the problems that businesses face are unpredicted, and the business is not prepared to handle them because of below par anomaly detection.
The Cognitive Anomaly detection is different from the traditional method, as it is a machine and data-first solution. The future for cognitive anomaly detection is seemingly bright, and it is now the time to move from a research phase to deployment.
How to Move to Deployment
The deployment of cognitive computing requires adhering to a certain set of levels for achieving the desired aims. The levels that should be used for proper deployment of the technique include:
With cognitive computing gaining center stage, it is expected that the concept will develop over time and will be implemented over numerous industries. Industrial IoT is expected to benefit a lot from cognitive computing as it can be used for deriving meaning out of the data they work with. In short, cognitive computing is currently leading the wave of the future as it holds the key to not only making healthcare, AI and Industrial IoT better, but also providing human thought processing and behavior that was needed here.
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