Data Engineering Consulting


We equip your business with modern analytics, BI, architecture & data modeling to capitalize on opportunities, minimize business risks, and amplify customer engagement.

Trusted by Industry Leaders

Data Engineering Consulting

Unlock the true potential of transformation with the right data approach

From operational efficiency and product innovation to technology investment decisions, our data engineering practice brings a holistic view to data, data flow, and data architecture to create a clear sense of where the business is going and how to get there. Then we utilize advanced techniques like computer vision, NLP, AI, RPA and ML to create new opportunities and experiences.

From XpressSpa to CSCS, we collaborate with marketing, IT, and product development departments to deliver unmatched experience, flexibility, and possibilities. Our mix of services and solutions always comes with the freedom, choice, and agility that a modern business needs.

Data Engineering Consulting

    Understanding what data can do for your business. Then, defining how your organization can learn, store, process and govern the data you have available and the data you need. We enable our clients to become truly data-driven through a wide range of modern technology and tools.


    We build robust, accessible data blueprints that map out how data flows, is collected, integrated, governed and secured. Icreon's data architecture solution assures that you have the most updated and cross-silo data for your enterprise.

  • AI / ML

    We put data to work. By using the data architecture and stack, we use artificial intelligence to process and analyze data. Then, we use machine learning to execute programs or actions. Get game-changing performance from our AI and ML solutions that helps your business to gain a robust foundation that is insights-driven.


    Moving beyond text communication and getting into the nuance of voice and regionality, our NLP engineering lets our clients decipher data in new ways – whether you're training a chatbot or doing the financial analysis algorithms, capture the subtleties of NLP with our solutions.

    Talk To Our Data Consultants


    Every action in the physical space now has a reaction in the digital space. We help clients track sensorial data to add to their stack and architecture to learn more. We bring together an expert team of developers, and engineers with the shared goal of developing AR and VR across the spectrum.


    Having the data is one thing. Acting on it is another. We help clients make sense of their data by connecting their data to their business and customer KPIs – increasing organizational intelligence.


    Power comes to those who can predict the future. We help our clients use data intelligence to activate statistical models to predict what’s next. Our predictive modeling solutions are packed with advanced capabilities to make better decisions in the future. Discover the risks and opportunities for your business.


    Acting on data goes beyond reporting and analytics. We use data to power algorithms and AI to bring user experiences to life through an interface or interaction. We can help you right from adopting a data-driven approach to conceptualization, design and development of the intelligent user experiences across multichannel and touchpoints.


Trusted by the world's leading brands

"I highly recommend Icreon to everyone. Icreon is a great balance of a large enterprise that really knows its stuff from soups to nuts. Whether its infrastructure or building software or design or product lifecycle, Icreon is a perfect match."Ross Goldenberg, SiteCompli Co-Founder

Construction Skills Certification Scheme (CSCS)
Putting data at the core of CSCS

The Construction Skills Certification Scheme (CSCS) is UK's leading construction skills certification program. CSCS provides proof that the individuals working on job sites have the essential qualifications according to the type of work. CSCS keeps a complete database of individuals working in the construction field to track who have accomplished or are going to achieve a recognized construction-related qualification. Over the last two decades, having delivered application documentation via traditional methods of paper and mail, they wanted to modernize the entire business operations.

Icreon worked with CSCS to redefine their business digitally and bring efficient workflows to make the entire experience smoother and more efficient.

Read the full case study here


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Data Engineering FAQs

  • What is data engineering?

    Data engineering is the process of extracting, transforming, and loading data, as well as making it available for analytic purposes. It involves creating a data lake or data warehouse, which is a place where all the data that is needed for analysis is stored. Data engineers are responsible for designing and building this infrastructure.

    Data engineers need to understand how the company wants to use its data to build the right architecture for it. They also need to know how to collect data from various sources and ensure it's reliable.
  • What is data analysis in engineering?

    In engineering, data analysis is converting raw data into information. This is done using a variety of tools and techniques to extract knowledge from the data. The goal is to take raw data and make accurate predictions about future events, such as the failure rate of a machine part or the amount of traffic on the road.

    Data analysis typically involves the following steps:

    Step 1: Data Preparation
    Cleaning up the raw data by removing errors or corrupted values. You may also need to transform your data into a format more accessible to analyze with specific tools such as spreadsheets or databases.

    Step 2: Data Exploration
    Visualizing your data using graphs or charts so you can spot patterns in the data that might otherwise go unnoticed. For example, this shows how frequently customers shop at different locations over time (e-commerce).

    Step 3: Data Modeling
    Using statistical methods such as regression analysis describes how variables are related to each other (e-commerce).

    Step 4: Model Validation
    Evaluating whether your model describes reality well enough for it to help make predictions about future outcomes (e-commerce).
  • How much does a typical data project cost?

    Data projects can be expensive and time-consuming, but you should consider them an investment. Data science is a powerful tool for improving your business or creating new business opportunities, and it's a field that won't be going away any time soon. It's also a good idea to consider how much it costs not to do data science because the alternative is often worse than spending money on the right tools and hiring the right people to get your data project off the ground.

    If you have a small dataset, you may be able to get away with doing the work yourself or hiring an intern or junior programmer. If your dataset is large, though, or if some specific things need to be done with it — like cleaning up messy data or making it machine-readable — then expect to pay more.

    A data project cost may vary from $2,000 to $3 million. The price depends on what you're doing with the data.

    If your project involves a lot of data or is particularly complex, it's best to hire an expert to help guide the process from start to finish.
  • What is data lake analytics?

    Data lake analytics is running queries against a data lake to discover and analyze trends. Data lake analytics is a way to perform unstructured data analysis without worrying about the database schema.

    Data lakes are repositories that hold raw data from multiple sources. They're often built on top of Hadoop. They are designed to store large amounts of unstructured data, including emails, spreadsheets, and images, to social media posts and customer service tickets. Because data lakes are designed for storing all types of data, they don't have a predefined structure or schema — instead, they use columnar storage (like Apache Parquet) to allow for fast reads and writes across different types of columns.
  • Does your team use any form of data visualization?

    Data visualization is converting raw data into visual representations that help people see how the data works together and how it relates to other forms of information. It makes complex information easier to understand and more accessible for people who are not statistics or computer science experts.

    As a result, businesses can maximize efficiency by making better decisions based on real-time information rather than relying on guesswork or gut instincts.

    We use data visualization in many ways at ICREON:

    Observe Predictable Patterns
    To simplify complicated concepts by finding ways to represent them visually. For example, we may use methods like clustering, skewness, linear trends, exponential trends, logarithmic trends, etc., to observe data patterns that are easier to interpret than if we just presented the raw numbers.

    Streamlining Complex Concepts
    To make complex concepts more understandable by simplifying them into bite-sized pieces that are easy to digest. For example, instead of showing all the data from a survey in one image, we might break it down into smaller parts so that each portion makes sense and forms a bigger picture when combined with others.

    As part of other projects such as infographics or presentations where there isn't enough space for text explanations but visuals are still needed.

    ICREON works with clients at all levels of their business to develop compelling data visualizations that will help them gain a deeper understanding of their business and how it operates.
  • What Are the Key Data Engineering Skills and Tools?

    Data engineers are responsible for managing and processing large amounts of data. They build tools and systems that process, store, analyze and visualize information at scale. Data engineers work with a wide range of technologies, including cloud platforms like Amazon Web Services (AWS), Apache Spark, and Google Cloud Dataproc, databases like MySQL or MongoDB, machine learning frameworks like TensorFlow and Apache SparkML, NoSQL databases like Cassandra or Neo4j and more.

    Data Modeling
    Data modeling is extracting and transforming data from various sources into a format used for analysis or decision-making purposes. Data engineers should have good knowledge of databases and SQL queries and the ability to understand business requirements and translate them into database tables and relationships.

    SQL (structured query language) is a programming language for managing data stored in relational databases. It allows users to create queries based on tables, columns, and rows. It's used by all major database management systems, including Oracle Database, IBM DB2 Database Manager, Microsoft SQL Server Management Studio, MySQL Workbench, PostgreSQL Workbench, Microsoft Access Desktop Database Manager, etc.

    Machine learning
    Machine learning is a branch of artificial intelligence (AI) that focuses on developing algorithms that allow computers to learn without being explicitly programmed. Data engineers use machine learning to analyze large quantities of records (such as customer transactions) to identify patterns that can help improve business decisions or predict future outcomes. For example, they might use machine learning algorithms to examine customer purchase histories to predict which customers are likely to buy certain products in the near future.

    Data Integration
    Data integration is an essential part of the overall data science process because it helps connect different systems, allowing them to exchange information seamlessly. It also enables users to access all their data in one place instead of having multiple applications open simultaneously on different devices. If you want your data engineers to succeed in this area, they need to know how to handle big data flows by using tools like Apache Spark or Apache Flink, which are open source libraries used for processing large amounts of unstructured data efficiently using parallel processing techniques such as map-reduce operations so that no single machine has too much work on its plate at once.
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