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Do smarter things with proprietary data

Every business has data, lots of data. But what do you do with it and how accessible is the technology? In this blog, guest blogger Clint Huijbers of MonkeyDWH shows that there are plenty of ways to get more value out of your data.

What is a data landscape?

Most companies have multiple types of databases: for example, their own on-premises SQL Server database for all operational processes and an online MySQL database for the website. In addition, there are often numerous other sources, think Google Analytics, AFAS Online, Zendesk or Twinfield, among others. All this together we call the data landscape, a rather broad term referring to both on-premises sources and cloud sources.

Data as an asset

But how do you report on all this data? For example, does the same ‘Jan Janssen’ appear three times in the CRM system with different customer numbers? Then how do we integrate all this data so that we only get one ‘Jan Janssen’ (as a customer and natural person)? Determining revenue per customer, requires you to have your data quality in order as well. In short, 2020 is the year to put “data” at the center of the business and treat it as an “asset,” allowing you to get more value out of your data.

Technology is getting closer

How and where do you start? These days, that’s a lot easier than you might think. Traditionally, business intelligence and data warehouse projects were very expensive and time-consuming. These types of projects why therefore only reserved for the big multinationals. However, technology has changed a lot.

For example, Microsoft Azure has matured as a cloud platform and is the market leader in terms of data science, machine learning, chatbots and virtual agents. By generating as much code and objects as possible using metadata (data that describes the data itself, such as tables, columns and data types), we are able to drastically reduce the number of development hours (and thus development costs) to days instead of weeks.


Getting started with your data within a week

MonkeyDWH ArchitectuurZo zijn we met behulp van het MonkeyDWH-framework in staat om binnen twee dagen een database toe te voegen aan het historische Data Warehouse (DWH). Het initieel inrichten van zowel het nieuwe datalandschap op Microsoft Azure, als het inrichten en gebruikmaken van Azure DevOps (CI&CD), neemt slechts drie dagen in beslag. Kortom, aan het einde van de eerste week kun je al met je eigen data aan de slag!

No comparing apples with oranges

After historically storing all source data in the DWH, the next step is to determine definitions (what is a “customer”?) and define metrics and KPIs (how many active customers do we have?). This makes it possible to use the same unambiguous definitions within all Power BI reports and Excel pivot tables. This prevents you, during an MT meeting, from comparing apples with oranges.

Really smart things

Slimme dingen doen met je DWH

Ready to start doing really smart things with data? Repetitive administrative activities can be automated. In addition, checks can be carried out within the operational process using extensive workflows with an if-this-then-that approach. Useful for AML, KYC, error checks and even reactivating customers, for example by making a selection, updating customer data within CRM or even emailing directly from Mailchimp or Office 365. This, of course, fully automatically.

Opportunities abound

In short, plenty of opportunities to get more value from your data! A first step is setting up a Data Warehouse and this can be done within just a few weeks. The MonkeyDWH will take center stage within your Azure data landscape. Using Power BI or Excel, end users or data analysts can tap directly into the common data model, this of course along with Two Factor Authentication (2FA) for maximum security.


Want to know more about Azure DevOps, DWH or how best to set up your data landscape? Feel free to contact us with no obligation!