Data Activation

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People argue about this, but what does it really mean?

Not many years ago, data was considered to be a useful weapon in a company’s arsenal, one that could generate benefits (albeit rather intangible) against its competition. Fast forward to present times and businesses without a complete data strategy and correct infrastructure find it hard to keep up.

Why? Because automation is already here, replacing a lot of human effort. Speed and precision are not considered to be advantages - they are rather necessities. The vast volume of data and events set the pace for data’s new role in today’s economy.

So, where are we heading to? The main principles around data’s new role are summarized as follows:

  • Lightning - fast execution and replication
  • Complete and accurate tracking of data and event changes
  • Wider understanding of internal (processes) and external (consumer/competition) data
  • Process automation
  • Data Activation for internal and external processes (such as Customer Relationship Management and Business Process Optimization).
  • Improved, machine-learning based feedback loops, in order to further improve outputs.


  • We start from clear business end-goals - analyzing anything without a business end-goal consumes time and resources.
  • We try to be as exhaustive as possible when it comes to the correct approach, as well as clean complete tracking of all data capturing points.
  • We favor scalable, bullet-proof infrastructure, with clear processes and security layers.
  • Coming from a business background, we utilize a combined team of engineers, architects, data scientists, and CRM experts, who work together, as one. The integration between these disciplines into one team, where different expertise fields are combined, ensures that our focus is on one single target - business results.



Ensure that what needs to be measured can be measured.

In the measurement part we focus on mapping the front and back end processes of a business, which allows us to identify the factors that need to be measured as well as the measurement sources and data typology.

In that sense, Measurement is the part where we identify what needs to be measured (in many cases the result of a Consumer Journey Mapping process), but also the part where we agree upon how we capture data (i.e. what is the process of capturing offline data at the point of sale and ensuring the quality and adherence is up to our the expected schema), naming conventions and ways to create a scalable measurement structure for online events (i.e. through the use of a tag management solution).

Measurement is the backbone of all data activities, acting as the strategic methodology, upon which we build the rest of the building blocks.

Store & Retrieve.

Structure your available data in a clear and sustainable way.

Following the Measure part, Store & Retrieve are related to Data Infrastructure. Starting from structural and storage decisions (cloud/on premise, ERDs, table structure & schemas) we design an infrastructure able to provide speed, size, clean data, simplified repetitive routines and beneficial costs.

At the same time, we work on connecting the single-source-of-truth infrastructure to other 3rd party applications within the organization, while ensuring that our data stays clean and in an easy-to-query format.


Reach to conclusions that drive decisions

Of course, storing data is not the end result itself. In the analysis part we focus on a wide spectrum of methodologies, depending on the consumer objective, including Customer Clustering & RFM, Forecasting applications, User Preferences (such as Market Basket analysis, Price Elasticity) and Financial Applications.

Visualization is another important aspect of analysis, focusing on creating dashboards and visualizations that provide useful insights and enhance decision making. In several cases, we provide consultation based on these insights, in an attempt to help the customer turn this information into actionable insights.

Finally, in several analysis cases where overtime improvement is a necessity, we implement machine learning methodology that allows the system itself to train and improve overtime.


Use the available data for user engagement and process optimization.

On the last part, data is used as activation points for front-end and back-end processes. At the back-end, data is used in purchasing/manufacturing decisions, product development and custom applications such as risk/churn calculation and fraud detection.

At the front-end part of applications, data is used to power CRM and Marketing automation tools (such as EMS and website integrations), advertising platforms (such as DMPs), product/pricing/channel applications and customer support channels.