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Building Data Warehouses

Most of the time, the data needed to do the job is located in different places in an organisation. For example, financial data is in the accounting system, detailed sales data is in the treasury system, and personnel data is in a third system or in an Excel file on the HR specialist's computer. The purpose of a data warehouse is to bring all this business-critical data together in one place and update it with sufficient regularity to ensure that everyone in the organization has the same truth.

What you’ll get

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    Business-critical data aggregated in one place

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    Automated movement of data from primary sources to data warehouses

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    Data quality control through automatic interfaces.

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    Usaldusväärne keskkond, kuhu anda kontrollitud ligipääs analüütikutele ja mille pealt aruandeid luua.

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    Võimekus integreerida ükskõik millises formaadis digitaalseid andmeid, mida ettevõte vajab.

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    Lihtne andmete transformatsioonide tegemine sisemiseks ärianalüüsiks.

Why do we need a data warehouse?

Are you familiar with the situation where a meeting finds that different people have different data on last month's result and nobody knows where the differences come from? This cannot happen if a data warehouse is in place and all reporting and analysis is done through it.

We have the experience and skills to bring together all your disparate data sources and databases into a single data warehouse so that they can be used effectively in your day-to-day work and reporting. Through the data warehouse, your data becomes the information from which you can make informed decisions and see connections you may not have noticed before.

Automated data traffic

Data warehousing significantly reduces errors that occur when processing data. Data moves automatically from the source systems to the data warehouse and is subject to a set of rules that prevent erroneous data from moving into the reports. For example, the data warehouse does not allow text to be entered in columns that should always contain numbers.

Analysts will be able to access the data warehouse when needed to perform various analyses. Generally, the output of the data warehouse is used by the end-users through the data model and regularly updated reports.

✺ Frequently asked questions ✺

  • Our main technology partner is Microsoft, so we recommend using Microsoft SQL as the data warehouse platform. This can be deployed through cloud technology solutions from Microsoft Azure or through the classic MS SQL Server. In addition, we have also developed data warehouses on PostgreSQL and Vertica platforms.

  • It is possible to import data from virtually any digital data source, including Excel spreadsheets. To do this, the Excel file must be available for the data warehouse query, either in a shared directory or in the cloud.

    If possible, we recommend using a medium other than an Excel spreadsheet as a data source. The main risk with Excel is the potential errors that can occur when modifying the data, as Excel does not usually have any data quality rules.

    Nevertheless, Excel spreadsheets are sometimes a necessary source of information to ensure the integrity of the data in the data warehouse.

  • Most of our clients use Microsoft's business analytics platform, and according to the client's needs, this generally means:

    -> Azure cloud services

    -> MS SQL Server with SSIS and SSAS services.

    -> on-premise solutions with Power BI Report Server

    -> Microsoft Power BI business analytics platform for data visualization.

    As our solutions are designed according to industry best practices, they can be made compatible with other BI platforms, for example, we have customers running Power BI and Tableau in parallel, as well as Power BI and QlikSense.

    But Microsoft's comprehensive analytics platform is rated the best in the world (see, for example, Gartner's Analytics and Business Intelligence Platforms analysis). As it is also better priced than other platforms, we have generally focused on Power BI-based solutions.

  • We've done so many different business analytics solutions that we have a pretty good idea of how much work needs to be done to create different solutions. First, you need to describe the data source (input data) and the desired end result (output). From this, we can give a pretty accurate volume estimate.

    For more complex solutions, it is sometimes wise to do a little analysis and identify the key nuances, but we will approach this as appropriate.