20 years of Cmotions: from predicting French fry sales to a better future using data

Cmotions is celebrating its 20th anniversary this year, and 20 years of Cmotions is also 20 years of professional history and development. This provides valuable insights for years to come. The increasing flow of data and the means to collect, analyze and access it creates a future of new opportunities and possibilities. With data, we make the future better. And we are doing it together! That's why I want to take you through the history of Cmotions in a bird's-eye view into the future.

Back to 1988: predict french fry sales

At a trade meeting of the then DDMA in Artis, a number of marketers looked at you pityingly when you said we were able to predict french fries sales on both rainy and sunny days.

At that time, The Postbank, later ING, was one of the first truly data-driven organization in the financial sector with the realization of a direct marketing database. With segmentation and customer value models, with churn prediction and first party data market research. This eventually resulted in the idea behind setting up Cmotions.

2002: Establishment of Cmotions

Ten years of (marketing) data analysis typified the rise of advanced analytics. That was also the focus within Cmotions. Not only in the marketing field, but beyond. Similar techniques had long been used in risk departments of financial institutions. But inflow and outflow modeling was mainstream and for a long time the emphasis was on that.

Data quality

In fact, that time also laid the groundwork for a trend that has never gone away: data quality. Modeling based on bad data is never a good idea. But what is bad data? Duplicates, errors, wrong data, incomplete data? The hot topic from 2002-2005.

Part of today's trend does include this question: what data are we actually talking about? Is the data we are talking about supportive of the goal we are aiming for? Or in terms of CRISP-DM: is the "data understanding" a logical consequence of "business understanding"? Do we understand what we are talking about and understand what needs to be done?

From data quality to data governance

A piece of this discussion shifted as a logical consequence to the governance of data. Who is responsible and who is allowed to do what with the data and who decides? And then the question: who should have access to what data? Data Governance is a trend that will certainly continue to play a role in the coming years.

It is no longer applications that determine data control, it is the processes with the various data domains that play a role in them. Value chains become value streams and the data in this stream will be value-determining.

From data governance to data compliance

The basis for compliance came about in 2001, when Harvard came out with a landmark article on the reversal: personal data should no longer be collected and used indiscriminately by advertisers. No, consumers own the data and must consent to its use by advertisers. The basis for privacy legislation was born.

A trend that has become a factuality. Data is no longer an abstract thing, but a reference to human reality. So this needs regulation and transparency. Do I as a consumer fit into this selection and why? And on what is being differentiated at all?

Specialization of the data profession

Starting in 2012, data analysis is being enriched by other disciplines. The data scientist makes his appearance and the "data profession" gains a new specialization.

Not only is there the data analyst, there is also the data engineer who builds pipelines, from extraction to linkage to new data set. But also the BI specialist who builds dashboards. The data manager who is the bridge between ICT and user and the data governance specialist who defines responsibilities and implements them organizationally.

Data science as a game changer

Data science is fortunately not suffering the same fate as "big data": creating such high expectations that reality can no longer stand up to it. Yet you can gradually see commodity thinking emerging about data science. So there is a need for a new hype: AI, say artificial intelligence.

The common thread does remain the question: as data continues to grow in form, type and quantity, how do we extract value from it that adds value for organizations? And even though we may all soon start calling data science AI, this remains the core.

Data science is still a relatively young profession that has made a lot of game-changing changes in different ways. Smaller specialist companies have emerged in the market in this field.

Many organizations have invested in data science specialists, in knowledge and in culture. But what about the ROI question? What do we earn from it and does it outweigh what we put into it? This will be the focus in the coming years and organizations will be more critical of this than ever. There is only one answer the data scientist can give to this question: you want to be good at your job and you want to get better.

In any case, we as Cmotions are totally ready for it! And that with a renewed brand identity:

By themselves, data and technology do not make your organization more successful. People do: you, your employees, and your partners. Cmotions puts you in the driver seat with the right insights, allowing you to make fact-based decisions and build a solid foundation to move forward. This way, you maintain your focus on a sustainable result for the future in which you realize your own ambitions.

Our promise for the next 20 years that we can only fulfill together: creating a better future with data!

On to a better future with data!