Data platforms

In companies, data gets easily siloed in various systems. The challenge, then, is to get a comprehensive picture of the business. A future-proof data platform and a centralized data repository solve this problem and make the data available to support management and decision-making.

Data platform enables the efficient use of data in the long run.

DATA PLATFORM

Clarifying things and making all data available

One of the underlying reasons for the siloing of data is that the “software as a service” concept is becoming more popular with the result that new systems can be quickly deployed as cloud-based services. For example, a company may have a Salesforce Customer Relationship Management (CRM) system, a Microsoft Dynamics 365 Enterprise Resource Planning (ERP) system, the Google G Suite for office applications, as well as other cloud services to manage accounting, projects, and time tracking. In addition, the company may, for example, have an online store on the Magento platform.

Typically, the information is managed on a per system basis. In practice, this can mean that the company’s customer register exists in multiple copies in different systems. If a customer has been created separately in several different systems, or if different systems need to be used to edit a customer’s data, you are dealing with siloed information. In other words, the different systems have not communicated with each other and the missing or inadequate integration results in a waste of time and increases manual work prone to errors.

Thus, data is distributed in different systems and there is no centralized view of the data. Data utilization is a manual process that does not allow access to real-time data at all.

The modern data platform solves precisely this challenge, as the data can be centralized in one place and, in addition, clear real-time dashboards can be created for different stakeholders, taking into account factors such as the different reporting needs of different businesses. The end user of the data platform may also be another system: the IT-owned data platform can be easily connected to the systems of different businesses using APIs.

Data warehousing enables intelligent decision making

The siloing of information is a problem when you want to get a comprehensive picture of your business with the help of business intelligence (BI) tools. Furthermore, applying machine learning to resolve business needs and problems is challenging if the information is scattered across multiple locations.

The solution is to collect the data from the different systems into one centralized data warehouse. This can be accomplished using a so-called ETL process, where the data is first extracted from the source system, then edited as required and finally stored. Such a data warehouse serves as a centralized source of information for both business intelligence tools and machine learning tools, thus facilitating data-driven management and decision-making.

Lamia’s data warehouse expertise is particularly profound in BigQuery. BigQuery is an enterprise data warehouse (EDW) system developed by Google. In addition, BigQuery is a service provided and managed by Google in the Google Cloud Platform (GCP). BigQuery can be easily defined as a data source for business intelligence tools such as Microsoft Power BI or Tableau.

In addition, Lamia has solid expertise in integrations and GCP, which are purely routine to us in our daily work in the development of online shopping.

Machine learning

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