What are data silos?

Data silos, also often referred to as insular solutions, are collections of information (data) owned by one department that other departments in the same company cannot access easily or only to a limited extent. The individual departments, such as finance, administration, human resources or marketing, usually store the information, i.e. data, they need for their work in separate locations. This is promoted by the separate work processes and a closed corporate structure. At first glance, these data collections don't seem to be a problem, but they become so when corporate processes change and departments need to collaborate more. Then data silos make it difficult for departments to collaborate and share data. One problem is often data quality due to inconsistencies between stored data from different departments (e.g., different addresses for the same person). In addition, it is difficult for executives to get a holistic overview of the company's data if the data is arranged in silos.

Improved data management with firmly integrated data processing methods helps companies to get a better overview and thus to be able to take the next step in optimization.

What is Data Processing?

"Data Processing" is the collection of data and its processing into the desired and usable form. This is nothing more than data processing performed either manually or automatically in a predefined sequence of operations. In the past, processing was done manually. However, this was very time-consuming and error-prone. Today, digital processes enable automatic and time-saving processing and a correct result.

Goals of Data Processing

The daily volume of data within tools and processes in the company is incredibly large and hardly manageable for the individual person or department. In the future, the amount of data will continue to grow. Data processing is part of every business and leveraging optimized data offers many benefits. Potential risks and opportunities to increase financial success, for example, can be tracked down, analyzed and processed through data processing alone.

Integrated tools and software also benefit from optimized data processing and customized data management. By seamlessly linking underlying databases, processes and interfaces, the potential can be fully exploited. The result is, for example, an improvement in the customer experience.

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Why is data processing worthwhile?

For enterprises, there are three challenges related to data processing: data growth, platform complexity, and lack of knowledge about how data is used and its purposes.

‍Improving and accelerating enterprise data processing helps address these challenges.

Advantages include:

Push Notification & Updates
Faster cross-platform processes
Location and time-based notifications
Performance boost through synergies
Location and time-based notifications
Overview for management of departments and dependencies
Location and time-based notifications
Automation of data warehousing
Location and time-based notifications
Creation of a foundation for data analytics

The importance of data processing for marketing

Well-functioning marketing is based on a good understanding of the customer base or target group and product data.

These include the following factors:

A high processing speed of this information, in order to be able to react quickly and in a personalized way to interactions with customers when required, is of crucial importance in order to use the information to its full extent. This is a clear advantage over manual cross-departmental analysis processes that require more time.

However, fast processing alone does not lead to success. To be effective, retailers need a unified customer data platform that responds to the activities of a single customer or shopper across all channels.

Apart from that, data processing methods help in the following areas:

An improved "Data Process" almost always goes hand in hand with the revision or renewal of the already existing system architecture architecture. For each specific use case, there needs to be an individually tailored solution and a system that fits the requirements of the respective company.

What is the best way to implement data processing?

Analysis of the actual state
Definition of the goals
Stakeholder requirements analysis of the result
Prototype implementation
Adaptation and expansion through iterative development cycles

Implementation example

Our conclusion

Companies are confronted with an ever-increasing amount of data. Big data can quickly become confusing, but it offers many opportunities.

Want to use your data with analytics tools to gain actionable insights that drive your business forward? Whether you want to analyze customer data to make your marketing more customer-centric or collect data from multiple sources to build predictive models, whatever form you want to use the data in, it requires knowledge, time, and a high level of diligence. Unfortunately, it's often not enough to simply enter data into a business intelligence system and then expect high-quality results.

To successfully implement data processing methods, don't just follow the trends. Instead, focus on where they add the most value to your business and where they are needed most.

Whether it's in intelligent Portal solutionsPIM and MAM solutions or the integration of analyses, automatisms or marketing evaluations, we will be happy to advise you from the first step through to support during implementation and operation. Profit from our experience!

How can customer data change your marketing?

In online marketing today, almost everything is measurable. In order not to get lost in the KPI overload, it is important to identify the individually relevant key figures. Learn more about important KPIs and their role in data-driven marketing in our white paper!

Download Whitepaper now!

Interested? I look forward to your questions and exciting conversations about data processing!

Erim Kansoy
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Our contact person around Chili Publish - Patrick Tosolini