Data Engineering

The art of unlocking companies silo data, by using automatic or manual connections, by transforming system data into business features or parameters, but also cleansing and consolidating data into one or more meaningful data repositories.

What we do

data collection

Data is typically stored in different systems; in most cases fast & connected data needs aggregating with slow & disconnected sources in order to research specific questions or to set-up workflows.

 

data model design

Not all research questions can be answered using the provided data models; therefore new models might need to be designed in order to start researching the data.

data transformations & cleansing

Data is stored in systems for a specific purpose; in order to research cause-and-effect relationships, data needs transformation and cleansing.

 

feature design

Typical transactional and/or business specific data needs to be translated into parameters and features that will be used in the statistical data modelling.

Data Analysis

The power of understanding our customers business model and analyzing the available data sets in order to optimize day-to-day operations.

What we do

Field research

In order to optimize a situation or research a cause-and-effect relationship, we start by understanding the business process and research how the current activities are driving the data creation. We perform field research, interviews and workshops with end-users for getting the current situation clear.

 

AS-IS analysis

“Bringing it all together”; in many cases, data resides in different silos withing different systems and / or departments. An AS-IS analysis matches the field research and the qualitative insights with the reality of the data.

Impact assessments

Scenario thinking is important when proposing optimisations; when formulating alternatives, the “what - if “ scenario thinking is crucial. Sometimes, this is descriptive analytics, but there are also situations where predictive data science kicks in.

 

Implementation

When the rubber meets the road”. Analysing and proposing alternatives are a first step, but working with the people & systems to implement a change is crucial. The field research performed at start is a major enabler for making the implementation work.

Data Science

The science of generating insights with statistical modelling, machine learning, artificial intelligence, etc. Using different data sources and business rationale we correlate data, perform predictions and support our customers business with data science.

What we do

Machine learning and A.I.

Using statistical analysis to predict an outcome, based on inputs, while updating the outcome when new input become available. Machine learning is used in recommender systems, predictive maintenance, etc.

 

Text mining

Using BigData technologies to automatically derive information from text and hereby optimising the workload or even discover a wealth of information that we previously undiscovered.

Deep learning

Deep learning is part of a bigger family of machine learning; it can be applied in image processing, natural language processing (NLP), computer based automatic translation of text, etc.

 

Neural networks

Mimicing how humans think and recognize relationships between large data sets, neural networks help us to forecast, predict fraude, etc.

Data Visualisation

The skill to translate data into images, by building the right charts for the right purpose, generating meaningful dashboards and achieving the fact that a picture paints a thousand words. 

What we do

Dashboarding

By using different styles of charts, graphs, maps, etc we propose and test the most optimal representation. In the end, data needs to speak and an image says more than a thousand words.

 

Technology

With recent technology, different visualization tools are available (and can even be integrated into 1 visual representation). At Cropland, we have experience with the typical dashboarding tools (PowerBI, DataStudio, Tableau, etc.), but also with the different visualisation methods of the typical data science stack (e.g. Plotly, Dash, Shiny, Rmarkdown, etc.)