Transformation

Are your datasets ready for use? Can you say with certainty that your dashboards are correct, that your charts reflect reality? If you have to answer ‘no’ to one or more of these questions, read on.

Every company receives a daily stream of data, often in large quantities and from disparate systems. Those who know how to make good use of it extract valuable insights: for reporting, steering, or strategic choices. But how do you turn raw information into insights you can trust? That’s where data transformation comes in.

What is data transformation

Fundamentally, data transformation is the process of converting raw, unstructured data into usable, well-organised information. This usually happens after the data has been loaded into a database, and before it is made available for analysis, dashboards or reports. After the data is loaded into a database, you go through a number of steps: you clean the data, put it into a suitable format and make it available to end-users.

The goal? To reveal insights and patterns that would otherwise remain hidden. With this polished information, you can better steer your organisation and make informed decisions.

Data transformation has different types of interventions, including:

• Data cleaning: Removing or correcting errors, duplicate rows, missing values or anomalous inputs so that the data becomes reliable for analysis.
• Data aggregation and summarisation: Combining multiple data points into one clear whole, for example by calculating totals, averages or trends by period or category.
• Data enrichment: Adding additional information to a dataset, such as derived columns or external sources, to gain new insights.
• Structure transformations: Changing the shape or organisation of your dataset, such as rearranging columns and rows, or merging and splitting tables.
• Data normalisation: Returning values to a common scale or structure so that variables are easily comparable.
• Integrating data: Merging data from different sources into one coherent dataset, aligning structure, types and meaning.
• Anonymising data: Protecting sensitive information by encrypting, masking or deleting personal data, without losing the usability of the data.
• Data mapping: Matching fields correctly between different systems or data sources, so that data can be unambiguously combined and interpreted.

Why is transformation important?

The way you transform raw data into actionable information directly affects the quality of your analyses and the reliability of your dashboards. Data seems objective, but without proper transformation it is anything but reliable.

And therein lies the risk. Because if you are not keen on this, all kinds of errors can creep in. Sometimes small and invisible, but with major consequences. Think of:

• Slow reports and frustration in the team because the data processing process is unnecessarily cumbersome. Each step takes time, and that adds up.
• Dashboards that mislead because filters are missing, values are automatically rounded, or data sources are not properly merged. Everything seems logical, until someone asks a critical question.
• Small exceptions that have big consequences: one unprocessed field or missing value, and an analysis shows incomplete or distorted results. Without anyone realising it.

Without a careful transformation strategy, there is a high risk that errors in your data will go unnoticed. Until a colleague, customer or stakeholder asks a critical question. Small inaccuracies, such as misinterpreted dates, missing values or inconsistent calculations, can lead to wrong conclusions and bad decisions.

When you do get the translation of raw data into actionable insights right, it gives you peace of mind and confidence. You know where your data comes from, how it has been processed and what you can do with it. Reports become faster, the quality higher and decisions are made based on trust rather than assumptions.

We help you get the most out of your datasets

Good data transformation starts with the right questions:

• What information is really relevant to your goals and what would be better left out?
• Which data sources do you want to combine and how do you connect them logically?
• Do you work with real-time data or with batch processing?
• How do you make sure your transformations remain scalable and maintainable?
• Which intermediate steps do you materialise? For instance, for performance or reuse, and which ones do you keep virtual?

These are strategic choices that impact the quality, speed and reliability of your insights. At Blenddata, we help you focus on these choices and implement them technically. We help you think about the right architecture, build a future-proof solution together and ensure that your data forms a solid basis for decision-making.

Do you have a question or would you like to spar about your data structure or transformations? Feel free to contact us. We will be happy to help you further.