Frequently Asked Questions

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.

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.

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.

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.

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.

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

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)

• 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