Technology and Data Scientist – Spectrum
There are many backend tools and solutions designed to extract value from data. The backend is the part that deals with hardware, efficient computing, and data storage infrastructure, or what is often referred to as data engineering.
The frontend data scientist(s) landscape is more challenging. We define the frontend as the part geared more toward data analysis and can be further divided into tasks performed by data analysts, machine learning engineers, statisticians, specialists in natural language processing, neural network, data visualization, and various roles such as data science software developers.
The data product lifecycle involves a team of data scientists with non-overlapping complementary skills
Data scientists use various forms of AI ingesting historical and present data to predict future data.
Data analysts wrangle, explore, quality assess, fit models to data, perform statistical inference, and develop prototypes.
Machine learning engineers build and evaluate prediction algorithms and make solutions scalable and robust. Often to finish a project, backend engineers integrate a final solution into a reliable automated pipeline.
Expertise trained in data science and AI
Using data science and AI to support business-critical decisions makes it vital to understand what AI is doing and why. Expertise trained in AI is needed to develop unique solutions but also mitigate a host of challenges and growing regulations.
The value of data tools relies on the hands that wield them. Have they been appropriately trained – not just in the tool but also in the underlying concepts?
Vendors often make sweeping statements, saying they have democratized data ingestion, data cleansing, mining, and machine learning by creating drag-and-drop tools. Or that they have democratized complex statistical and computational model development by automating the entire machine learning or data science process.
Key Trending Challenges across the Data Science & AI Landscape
AI’s Two Races
Presently, there are two races going on in AI.
The first is to win over the front-end data science expertise, and the other is to win over end-users, clients.
Part of the race is related to AutoML. AutoML is the process of automating the process of applying machine learning to real-world problems. AutoML covers the complete pipeline from the raw dataset to the deployable machine learning model.
Participating companies seek to win long-term commerce with less well-resourced companies. A pivotal question is, “How many end-users will actually build their own models, regardless of whether it is as easy as using drag-and-drop mechanisms?” Using data science and AI to support business-critical decisions makes it vital to understand what AI is doing and why. Expertise trained in AI is needed to develop unique solutions but also mitigate a host of challenges and growing regulations.
AutoML business models might rest on creating a ‘relatively easy to use’ – by definition – limited platform, and propose a premium subscription for better AI-related services (training, assistance, etc.) Hence the race to win over uncommon frontend data science expertise.
Large tech firms develop open-source or no-code applications because they wish to be the foundations on which other people innovate. By doing so, they will become able to gather all the most strategic data of many mid-size companies and SMEs.
Backend Data Science Challenges
Most of today’s AI solutions are used mainly by Data Analysts, IT wizards, and Data Scientists.
Data Warehouses and CRMs are examples of common data activation challenges that most companies face.
Only internal and structured data invariably provides no tangible benefit for a business user. Construction, administration, and quality control are examples of significant operational issues that can arise with data warehousing. Given this, a lot of data warehouses remain unleveraged and are not always useable.
Systems of record and under usage. CRMs are limited in functionality and pose more as systems of record rather than systems of action. Valuable client data is not gathered from client interactions – MMS/text, video calls, mobile calling, email reveal an incomplete picture.
The complexity of data systems grows daily leading to compounding data quality and activation challenges.
Recruiting Data Scientist - Landscape
Data scientists are a relatively new breed of analytical data experts who have the technical skills to solve complex problems and the curiosity to explore what issues need to be solved.
Many data scientists began their careers as statisticians, computer scientists, or data analysts. But as big data (and big data storage and processing technologies) have grown and evolved, roles have evolved as well.
As organizations invest heavily in digital transformation, integrating digital technologies into all business areas broad data science roles have emerged. From data architect to business intelligence engineer, data engineer, database administrator, and niche machine learning specializations such as NLP engineer or computer vision specialist.
Data Scientist Supply is Low
The field of data science is still relatively new even in 2021. Twenty years ago, it was impossible to learn data science because of slow internet connection and low computational primitive programming languages.
Traditional education was not ready to meet the needs of those who wanted to learn.
Demand for Data Scientists is High
Demand is incredibly high and in no sight of slowing down as more companies recognize the need to adopt data science and the use of AI.
According to LinkedIn, there has been a 650% increase in data science jobs since 2012. The demand is expected to continue. The U.S. Bureau of Labor Statistics sees strong data science growth and predicts that the number of jobs will increase by about 28% through 2026. That’s approximately 11.5 million new jobs in the field.
Recruiting Data Scientist Challenges
Data Scientists prefer a vibrant urban culture and a challenging intellectual environment; however, many companies are not in such places and therefore have difficulties recruiting data scientists or can only attract mediocre talent.
Good data scientists are scarce and thus very expensive. For a functioning data science team, a company typically needs about five data scientists to cover a broad area of expertise and produce useable results.
Most companies do not have enough interesting projects that can lead to an under-utilization of the workforce and retention challenges, a high fluctuation of employees, and costly knowledge loss.
Alephnet’s progressive culture, diverse project engagement opportunities across broad industries, and business activities are fundamental to attracting, retaining the frontend data scientist expertise, and domain experience essential to delivering impactful end-to-end data science services and solutions.
Alephnet’s Academic ties underpin access to both graduating and seasoned expertise. Flexible working arrangements, stellar benefits, learning opportunities, and career pathing through to partnership make Alephnet an attractive, compelling company for data scientists to achieve aspirational outcomes.
Key Trending Questions across the AI & Data Science & Landscape
Why wouldn’t we just choose to implement vendor business solution software?
The option of buying off-the-shelf technologies from AI vendors can work for smaller firms or where applications require minor customization. But as business complexities increase, the application of AI becomes progressively targeted and strategically important. Companies that rely solely on plug-and-play AI solutions jeopardize long-term value creation.
Companies benefit tremendously from the ability to develop data models from the ground up creating their own AI intellectual property and the advantages of independently scaling volume and quality.
Open source versus proprietary
The pitfalls of proprietary software versus open source can be dire. Proprietary software cannot be adapted to meet the needs of the user since only one code version of the software is distributed, which cannot be edited. This effectively ties the hands of developers who could have added improvements and customized the code to suit the company’s evolving needs. With proprietary software, it’s strictly using “as is”.
Not only is proprietary code unbending and unchangeable but it becomes increasingly rigid with added versions. For example, it is not uncommon for large companies to use various versions of the same product in different areas of the business. This is even more widely true if involved in mergers and acquisitions. The situation, while generally untenable, tends to continue unabated because of difficulties in integrating various versions or upgrading older versions without losing data files in the process. Issues are generally easier to resolve in open source than proprietary software by virtue of common computer languages and overall code accessibility.
For various, it has become clear proprietary code was actually a quagmire for both vendors and customers. The shift to open source has rapidly gained momentum. Developers and industry media like TechCrunch declared some years ago “how and why open-source software took over the world.”
Data Science Codeless or Code First Approach?
There are various no/low code tools offering pre-built algorithms and simplistic workflows with features like drag-and-drop modeling and visual interfaces that can easily connect with data and accelerate bringing services/applications to the market.
However, drag-and-drop tools look great if you only need to drag and drop a few things but the reality is not that easy. In order to scale and reach production, AI projects typically have thousands of tasks so front-end expertise is necessary.
No code, low code, and code approaches have short, medium, and long-term tactical and strategic implications and should be carefully considered and intentionally executed.
The advantages of a code-first approach?
- Flexible: No black-box constraints. Access and combine all your data, analyze and present it exactly as you need to.
- Iterative: Quickly make changes and updates in response to feedback, and then share updates with your stakeholders.
Reusable and extensible: Tackle similar problems in the future, and extend to novel problems as circumstances change.
- IP Rights: A growing and valuable source of IP
- Inspectable: Combined with version control – Track changes over time, discover errors, and audit the approach.
- Reproducible: Combine with the environment and package management, ensure that you can rerun and verify analyzes.
No-Code, or Low Code Machine Learning?
There is a natural high demand for data scientists at mid-sized companies who lack the breadth of talent required to build scalable AI solutions.
As a consequence software companies are developing backend no and low-code platforms for machine learning. The purpose of no-code/low code platforms is to enable business professionals with minimal or no coding experience to build machine learning data products and subsequent apps to fill the talent gaps in their organization.
AutoML – Automated Machine Learning
AutoML is the process of automating the process of applying machine learning to real-world problems. AutoML covers the complete pipeline from the raw dataset to the deployable machine learning model.
The underlying objective of AutoML business models is to harness the size and perhaps the international scope – of a largely untapped market – SMEs and mid-sized businesses with no, or limited data science teams.