Frequently Asked Questions

Asked Questions

Frequently Asked Questions

  • What is Artificial Intelligence? AI technologies and Real-world applications.
    A simple definition of Artificial Intelligence is: an area of computer science which emphasises human intelligence in machines programmed to think and act like humans. The most common AI technologies in use today are: Computer Vision (such as image recognition, object detection or semantic segmentation), Speech Recognition (Speech to text) and Natural Language Processing (such as Chatbot or Language Model).

    AI technologies offer a myriad of real-world applications: Prescriptive Analytics can be used in Healthcare to make better and faster diagnosis (ie. IBM Watson), Chatbots are being incorporated into websites and e-companies to provide immediate responses (ie. Facebook chatbot), Deep Learning in LIDAR technologies in Automotive (ie for self-driving car-sensors to understand the world around them, process and act based on this information), Computer vision, OCR to support financial approvals in real time (ie process instant uploaded documents for loan requests)
  • Is my business ready for Artificial Intelligence?
    Tough as it may be to reason with, this is not a question that can be answered with a simple Yes/No answer. In our experience as an organization, before moving further and submitting proposals for AI-specific projects to our clients, we always analyze their business core and we address this possibility intensively with them. There may be steps companies need to take in order to prepare their systems for AI projects and we want to make sure that our clients have all the facts to make the best-informed decisions for their businesses.
  • What is digital transformation? Why is digital transformation important and how can I implement it in my company?
    Being ready for digital transformation is similar to being ready for AI. In some cases, the two are complementary in the sense that digital transformation can be achieved through AI. Nevertheless, once again we suggest an analysis of your business core, correlated with a company stability status analysis, an analysis on your digital targets, cost-efficiency plans and long-term expansion plans. Based on this research, you can choose to start with streamlining operations to increase business efficiency, delivering new digital products or enhancing customer experience processes by using data to your advantage.
  • What is Big Data Analytics? When does it use AI and what can I do with it? Big Data Analytics examples across industries.
    Big Data is defined by the extraction, transformation and transfer processes related to high-volume collections of diverse datasets. Big Data Analytics (BDA) is the activity of using advanced analytic techniques or software, against these datasets in order to reveal trends, patterns and insights. Split in 4 major categories, answers 4 different questions:

    • Descriptive BDA answers “What happened?”; it’s low-level complexity, used by most organizations, rarely uses AI.
    • Diagnostic BDA answers “Why did this happen?”; it’s of medium complexity, used by many organizations to understand historic processes, sometimes uses AI.
    • Predictive BDA answers the question “What is going to happen in the future?”; it’s high-level complexity, used by a few organizations and gaining more and more traction, most of the times uses AI.
    • Prescriptive BDA answers the question “What should I do in the future?”; it’s of very high complexity, used by a limited number of organizations, based 100% on customized AI.

    Big Data Analytics offers applications in different sectors. For example, Healthcare & Pharma organizations store massive amounts of difficult to process data and use Big Data Analytics to lower patient costs, identify which actions improve patient care, maximize resources, optimize financial management, detect insurance fraud, predict patient health requests. In a similar way, Retail, FMCG & Wholesale businesses leverage Big Data Analytics to quickly and continuously enhance customer experiences through customer insights, price optimization, inventory management, product planning based on customer buying habits.
  • Data Strategy vs Data Consumption Strategy vs Data Producer Strategy. What are these and do I need to implement one in my organization?
    In order to grow and develop, every organization has in place a business strategy, directed towards specific business outcomes such as: scaling up the company, expanding across multiple verticals, launch of cross-selling products, preparing for an IPO. Data initiatives can support such outcomes by streamlining operations, increasing productivity, creating predictive or prescriptive algorithms to increase market value, turning information into competitive advantage to stay on top of the market or get ready to go public, leveraging massive amounts of operational and transactional data to cut costs and improve customer interactions.

    If you want to implement two or more of the outcomes presented above, you do need a Data Strategy, as it enables your generic business strategy to work on multiple, interconnected initiatives, to draw insights from the collective data and make data-driven organization-wide business decisions (and predictions). Data Producer Strategy and Data Consumption Strategy are actually parts of the Data Strategy Implementation Process (How to?) and they are used when a company produces and consumes massive amounts of data ( ie. Retail, Healthcare, Pharma, Travel, etc). Implementing a Data Consumption Strategy means keeping track of what data you do have, where did you get it, how you use it and furthermore, what happens with your data after it creates new or transformed data.
  • Do I need to rearrange my software engineering team to make room for data engineers or machine learning engineers?
    No, most likely your existing software engineers work on developing your base-code or they are very engaged with deploying new functionalities. Data engineers and machine learning engineers complement your software development processes and sometimes interact with it in order to integrate their work with the existing platform.
  • What is the difference between a Data analyst, a Data scientist, a Data Engineer and a Machine Learning Engineer?
    The roles work towards achieving different sets of outcomes and are using different day-to-day processes, frameworks and programming languages to do so:
    • Data Analyst: A Data Analyst analyzes data and seeks to identify trends by looking at numbers and building data visualization dashboards. A Data Analyst generally requires limited mathematical and computer science background
    • Data Engineer: A Data Engineer designs, tests and maintains complete data architectures and creates complex APIs. A Data Engineer usually requires a strong, technical background
    • Data Scientist: A Data Scientist analyses and interprets complex data through a series of different skills, including statistical analysis and programming. A Data Scientist generally requires an advanced Degree
    • Machine Learning Engineer: A Machine Learning Engineer bridges the gap between a Proof of Concept and the release of a system in production environment. A Machine Learning Engineer requires mathematical/statistical skills, coupled with advanced Computer Science knowledge
  • I don’t have budget to hire a Data team. Can I invest in training my existing software engineers to take on Data and Machine Learning projects?

    You can invest and train basic principles, yes. If you want to develop an internal POC to prove the utility of a solution and you think it’s a good time your team learned the basics of Data Engineering and Data Science, you can find multiple Machine Learning tutorials and Artificial Intelligence Courses your team can take and highly qualified software partners willing to offer workshops or training (we also provide onsite trainings, take a look of what this means)
    However, if you don’t want your software team to lose focus, you can turn to a mix of already trained engineers, Outsourced Dedicated Teams, Project-based Teams (T&M) & Contractors. This way, you can scale up your own team at your preferred pace or scale up a dedicated team due to this high demand, with minimized cost.

  • I need to cut costs in the technical department. What are my options?
    Most software and data solutions are based on solid, long-term strategies. Cutting them altogether could severely affect your overall business plans. The first approach we recommend is to conduct a swift cost-analysis for internal functions and external licences, to determine which is draining your budget without providing ROI. A second, more drastic method, which we do not support as a specific cut cost method, is to conduct employee assessments and determine departmental restructuring.

    However, there is a third solid approach and a business-focused approach to cost cut: cost optimization. This method supports business leaders to drive spending and cost reduction, while maximizing business value: starts with obtaining the best pricing and terms for all business purchases (ie. SaaS, Cloud licences, third party providers) and ultimately focuses on standardizing, simplifying and rationalizing services. For example, you can use your company’s data initiative to your advantage to streamline operations and drive further increase in productivity (take a look at our data science consulting to see how we take a similar approach to support our clients)
  • What are the benefits of outsourcing Data projects?
    • Quick access to unique or rare skills and greater talent retention: Big Data, Machine Learning and Data science skills are very challenging to recruit for and are the hardest skills to retain, therefore outsourcing gets you access to specialists covering all data roles, skilled professionals with a proper knowledge of data analysis
    • Scalability of your analytics capacity, on-demand
    • Possibility to leverage the value of your data to make profitable business decisions
    • Fresh insights into industry knowledge, functionalities or requirements: apart from not having to deal with a difficult and long hiring process, you also benefit from dedicated teams of experts with knowledge of technical applicabilities in your specific industry
    • Saving time: quick access to a team of professional experts who know very well what to do, how to do it and how to manage their time - an experienced data team will take quicker ownership and will have the ability to prioritize better and deliver results faster
    • Saving money: access to professional experts at lower costs. There are no HR expenses, no administrative expenses and while the hourly rates for Data Engineers and Data Scientists in the DACH region or the NORDICS, Australasia and the US range from 50 EUR -140 EUR or more (parts of the US), the hourly rate in EEU ranges from 40 EUR to 80 EUR (for comparison, ask our team to design a custom pricing model based on your specific need)
    • Innovation: access to advice on a technology stack to achieve much-needed innovation with minimal risks, as outsourcing partners are not limited to a single set of traditional approaches and have more opportunities to check out the latest tech innovations
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