How can we help you?

Concrete steps that need to be taken to clean data are highly dependent on the problem. In the following list, we present the most common data issues and how to resolve some of them.

What are the common problems with data?

Data quality issues, such as incorrect or outdated information, missing data, unstructured text entries, and duplicate records, can significantly compromise the accuracy of analysis and decision-making. Problems like outliers, inconsistent data formats, and challenges in integrating data from multiple sources further complicate the process, leading to potential misalignment and faulty outcomes. Proper understanding and management of these issues are crucial to ensure reliable and accurate data analysis.

• Incorrect and Outdated Data, Processing Mistakes

  Datasets are often incorrect for various reasons, such as human errors, misspellings, outdated inputs, poor accuracy, incorrect measurement techniques, or processing mistakes. This leads to inaccurate analysis and decision-making.

• Missing Data and Null Fields

  Data that are either not entered, unprocessable, or inaccessible present a serious problem for analysis. Data processing with missing data leads to biased results and reduced statistical power. Fortunately, there often exists a remedy (interpolation and extrapolation).

• Unstructured Data and Text Entries

  Frequently, important data are stored as free text, with little or no structure, requiring elaborate preprocessing to make any use of such data. This often leads to important datasets being left unprocessed and skews other analyses.

• Duplicities and Redundant Data

  Data often occur multiple times in the system, either exactly the same or with small modifications. Such duplicities can cause serious problems for analysis. Similarly, when data are stored multiple times, it causes additional costs.

• Outliers and Data Out of Range

  During statistical analysis, there often is a certain percentage of values that clearly do not match expected values (deviate from the standard value). These values are called outliers, and if left untreated, they can distort statistical analyses and predictions.

• Data Integration and Linkage

  Various challenges emerge when multiple data sources are brought together for analysis, related to different formats, structures, encodings, and semantics. This can cause misalignment, inconsistencies, and redundant data, leading to faulty outcomes.

• Inconsistent and Incorrect Format

  There are often problems with the formats of data, typically including encoding, tags in free-text entries, invalid JSON or XML, inconsistent representation of dates and times, or floating-point values. Each of these issues can make further analysis incorrect or impossible.

• Dependencies in Data

  Common problems include geographical relationships, such as postcode and address, location and GPS coordinates, as well as mathematical (statistical) dependencies between variables. If these are improperly understood, they can lead to incorrect conclusions.

How to solve common problems with dirty data?

We offer comprehensive data solutions, including identifying and managing relationships between entities using algorithms for tasks like product recommendations, targeted marketing, and network optimization. We also specialize in text processing and entity recognition, utilizing NLP tools for tasks like medical text analysis, customer feedback processing, and text anonymization. Additionally, we assist with missing data interpolation, making predictions using machine learning techniques, and applying statistical methods. Finally, we optimize data processing by cleaning and preprocessing datasets to improve performance, reliability, and cost efficiency, ensuring smooth and effective handling of large datasets.

• Relation and Dependencies Identification and Management

  We can help you identify and manage relationships between entities (such as users) in your data. Our algorithms can be applied to many tasks, such as:

  • Optimal product recommendation for clients (Collaborative Filtering).
  • Identification of sub-communities of clients (Clique Detection).
  • Modeling the impacts of random processes like outcomes of certain decisions (Random Walks).
  • Deciding optimal groups for targeted marketing (Partitioning).
  • Identifying the most influential individuals in a social network or community (Centrality Measures).
  • Optimizing the flow of goods between suppliers (Network Flow Algorithms).
  • Grouping clients based on similar behaviors, preferences, or transaction histories (Community Detection Algorithms).
  • Matching buyers and sellers or job seekers to opportunities (Graph Matching).
  • Determining the fastest or least costly route for deliveries (Shortest Path).
  • Understanding behavior patterns of clients and optimizing targeting strategies (Graph Traversal).

• Text Processing, Entity Recognition and Anonymisation

  Extracting information from unstructured text can be difficult, but we can help you implement and deploy machine learning tools for named entity recognition. Using a range of Natural Language Processing (NLP) libraries, we handle tasks such as medical text analysis, customer feedback processing, text anonymization (removing names and sensitive information), and more.

  We tailor these tools to meet your specific needs. The process begins with unstructured text (like customer feedback) and produces structured data as output (e.g., whether the feedback was positive or what the customer required).

• Interpolation and Extrapolation (Predictions)

  Fixing missing data or making predictions is often feasible when we take the data's context into account (for example, in time series analysis). We can help you choose the right technique for your specific problem.

  Common solutions include machine learning techniques, such as data classification and scoring, which are trained on similar examples to predict or fill in missing values. Alternatively, analytical methods like inserting the mean value, using regression techniques, or applying statistical approaches (e.g., Expectation-Maximization) can be effective.

• Cleaning and Optimization for Further Processing

  We can help you preprocess your datasets to make subsequent processing faster, more reliable, and cost-effective. This includes tasks such as identifying and removing duplicates, normalizing datasets to achieve an optimal structure (either splitting large datasets or combining small ones based on your needs), and introducing indices into database structures to speed up basic operations.

  We also assist with selecting the best compression methods to minimize transfer and storage sizes, dividing large datasets into smaller chunks, and transforming data (e.g., converting strings to numeric values or standardizing date and time formats). Additionally, we optimize algorithms by using caches, creating auxiliary data structures, reducing dimensionality (especially for large image sequences), and designing data partitioning strategies.

How did we solve our customer problems?

The client had encountered a series of problems which we were able to address effectively. The first issue involved the incorrect ordering of coordinates in satellite images, which significantly slowed down their algorithms. The second challenge arose from noise present in the images. Resolving the first issue was a largely analytical task, requiring only minor interventions from our developers. The second problem, however, was addressed using advanced generative AI techniques that allowed us to correct time series data by predicting the most accurate matches for missing or outlier values. Our performance-optimised solution also contributed to cost reductions when running the application on their cloud provider.

Through our interventions, the client not only overcame their immediate difficulties but also enhanced the efficiency and accuracy of their processes, leading to improved financial performance and customer satisfaction. The streamlined operations and reduced costs have positioned them well for future expansion and success.