--- title: "Big Data: only by making it smaller you can create value" description: Big Data is happening, everybody knows that you have to do something with it, however not everybody has taken into account what that something might be. image: https://blogs.itility.nl/hubfs/Visuals_featured-big-data-small-maken.png --- [![itility](https://blogs.itility.nl/hubfs/blog/img/itility%20logo-01.png)](https://www.itility.nl) - Your industry - [Agriculture](https://www.itility.nl/agriculture) - [Energy](https://www.itility.nl/energy) - [Manufacturing](https://itility.nl/manufacturing) - Our expertise - [Business transformations](https://www.itility.nl/business-transformations) - [Software and data](https://www.itility.nl/software-development/) - [IT foundation](https://www.itility.nl/it-foundation) - [Managed services](https://www.itility.nl/managed-services) - [(Generative) AI](https://www.itility.nl/ai/) - [Who we are](https://www.itility.nl/who-we-are) - [Working at Itility](https://careers.itility.nl) - [About](https://itility.nl/about) - [About Itility](https://itility.nl/about) - [Offices](https://itility.nl/about#office-locations) - [Tech partners](https://itility.nl/tech-partners) - [Responsibility](https://itility.nl/responsibility) - [Contact](https://www.itility.nl/#contact) - Your industry - [Agriculture](https://www.itility.nl/agriculture) - [Energy](https://www.itility.nl/energy) - [Manufacturing](https://itility.nl/manufacturing) - Our expertise - [Business transformations](https://www.itility.nl/business-transformations) - [Software and data](https://www.itility.nl/software-development/) - [IT foundation](https://www.itility.nl/it-foundation) - [Managed services](https://www.itility.nl/managed-services) - [(Generative) AI](https://www.itility.nl/ai/) - [Who we are](https://www.itility.nl/who-we-are) - [Working at Itility](https://careers.itility.nl) - [About](https://itility.nl/about) - [About Itility](https://itility.nl/about) - [Offices](https://itility.nl/about#office-locations) - [Tech partners](https://itility.nl/tech-partners) - [Responsibility](https://itility.nl/responsibility) - [Contact](https://www.itility.nl/#contact) [![Picture of Marianne Faro](https://blogs.itility.nl/hubfs/Marianne%20Faro%20rond.png)](https://blogs.itility.nl/en/author/marianne-faro) Written by:   ## [Marianne Faro](https://blogs.itility.nl/en/author/marianne-faro) # Big Data: only by making it smaller you can create value **Big Data is trendy, research has proven that Big-Data-initiatives are on the agenda of nearly all enterprises. Big Data is happening, everybody knows *that* you have to do something with it, however not everybody has taken into account *what* that something might be.** The key question: what to do with Big Data, how to deal with it? Compare it with eating an entire elephant: you will eat it in bits. Every day a tiny bite. Not too large, otherwise you will choke, and never to skip a day – if you want to eat an entire elephant you cannot afford to miss a day. Big Data is basically the same: make it small and tangible, start with a tiny bite and continue evenly*. * Don’t get carried away in defining Big Data processes. Don’t be seduced into designing a firm, future-proof, scalable, and flexible target architecture – before you even get started. This would be like “solving world hunger”; too complex to easily solve it, too broad to make it tangible. Please note: a good design is absolutely necessary. However: don’t let it be your first bite. The same goes for data-ownership issues or data-governance arguments. Those are topics to discuss, in order to be able to provide answers to the many questions raised. But again: don’t let it be your first bite. ## Where to begin? Let the data speak foritself right as from the start. So: immediately upload that set of data in “any tool”. Goal here is to directly explore these large amounts of data – in order to gain new insights through statistic methods, correlations, visualization and machine learning. Explore data step-by-step*: *compose a small team, one that at least consists of a data scientist and a domain expert. The data scientist is necessary to do his magic with algorithms – to find a correlation, a trend line or an anomaly within the data. The domain expert is necessary to add business-process knowledge that will bring the value of data to life. And the bites? Research one hypothesis every day, reveal a tiny bit of added-value every day. And get added value from the business process. 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