Infrastructure Setup

The right tools in the right place

The tool stack of companies often grows historically. As a result, many companies are “overtooled” or depend on organically grown structures due to existing processes. However, simply introducing new software is just as damaging as keeping tools that no longer comply with the current state of the art.

It is therefore important to carefully review the requirements for the tool stack and then optimize data sources and data flows in such a way that data quality is increased.

Our process

Purchasing Support

Conversations with tool providers on framework conditions such as data protection, SLAs, commercials, etc.
Requirements analysis to find the right tools together
Authorization of administrative access on components for consultants

‍

Infrastructure setup

Configure all agreed infrastructure components and provide ongoing maintenance support:
- Data integration
- Data warehouse
- Scheduling
- Data modeling
- Visualizing
- Reverse ETL
It is provided as infrastructure-as-code

‍

Handover

Providing a comprehensive documentation of system configurations, processes, and procedures
Handing over the infrastructure to the relevant stakeholders, including Walkthroughs and Q&A sessions
On request further infrastructure support and 3rd level support

‍

Why do companies need data architecture?

Improving data quality

Using data in a targeted manner, extracting it from the right sources and optimizing data flows ensures higher data quality throughout the company. This is essential for clean data models, decisions based on data and structures in which everyone can rely on the collected and processed data.

Adaptation to the latest technology

An old-fashioned data architecture has disadvantages in many areas. It hinders innovation and the ability to adapt to new circumstances such as artificial intelligence or machine learning. The topic of real-time processing is also often influenced by this. It doesn't always have to be the latest, but the tools should always be up to date.

Interoperability and Standardization

A modern data architecture enables greater data interoperability by minimizing the need for data transformation and degradation. This is achieved by standardizing data collection, which makes it easier to integrate and manage data and reduces costs.

Agile and flexible decisions

A well-thought-out data architecture enables companies to react quickly to market changes and develop new business models. It offers the flexibility to integrate and process data from various sources, which is essential for quickly testing and implementing new ideas.

This is how we go about building data architecture

From data audit to a strategic data architecture plan to implement the tools and, of course, enabling all employees — we do not advise to sell the next “cool” tool, but to generate the highest added value.

Phase 1

Status quo — the data audit with a focus on data architecture

The first step in our collaboration is to implement a comprehensive audits of the existing data architecture. This includes reviewing the current data infrastructure, data models, integration processes, and data storage solutions. The aim is to identify strengths, weaknesses, and potential risk areas and to assess compliance with business objectives and compliance requirements.

Phase 2

Strategic data architecture plan

Based on the results of the audit, we develop a detailed plan for optimizing and possibly expanding the data architecture. This includes the development of use cases that can be implemented at short notice as well as a long-term architecture strategy, which includes the roadmap for implementing new tools and adapting existing tools. The goal is always to optimize data quality data security and data integration.

Phase 3

Implementation of data architecture tools

Now it is time to implement new tools, switch off outdated ones and, of course, ensure a smooth transition. We are supported by a large team of experts who are thoroughly familiar with the respective tools.

Phase 4

Implementation of processes

Once new tools have been implemented, they must of course also be adapted to the company's processes. To this end, we jointly review existing processes and stakeholders, optimize them if necessary and ensure that processes — including through the use of new tools — work more efficiently than before.

Phase 5

Enablement and training

The best tools are useless if employees can't handle them. That is why we attach great importance to training the various stakeholders in how to use the tools and their new roles. For this purpose, we are also happy to call on experts from our network who know the respective tools inside and out.

Grafik des Frameworks mit dem Data Institute arbeitet.

Data architecture in our framework

We always work with the framework organization, culture and architecture.

In our opinion, these three areas are the most important factors for successfully anchoring data in the company in the long term.

Data architecture is therefore one of the three pillars that are inextricably linked and interdependent. As a company, you can opt for a focus that best supports the company's goals in the short term. However, it is important to always keep an eye on all areas and not to neglect any of the three pillars in order to generate long-term impact.

How does our framework work?

The Data Institute — the strong partner in building the data architecture

Using the right tools and ensuring that they fit into employees' work processes — that is our goal. In doing so, we never lose sight of the entire company and focus on ensuring that all actions focus on the corporate and data strategy.

Learn more about us

What is data architecture anyway?

In our opinion, data architecture describes what is being worked on in the company. This includes the various tools in the area of data, the quality of the data, the way data is collected and processed (data sources and data flows), the structures and also the models of the data.

Of course, data governance is also a large part of the data architecture in this context.

In summary, an effective data architecture ensures that companies make optimal use of their data resources because the data is accessible, consistent, reliable, and secure.

Data architecture is often located in the data department, but the IT department is also a major decision maker when it comes to choosing the right solutions. After all, the tools are then part of the company's IT infrastructure and play a decisive role in supporting data-driven decisions and strategies.

The data architecture also includes data organization as well as the data culture after all, the tools must also be used efficiently and sensibly so that they provide added value to the company.

Who actually needs data architecture?

The answer is simple: Data Architecture deals with tools, data quality, data sources, and data flows and is therefore necessary for all companies that deal with data.

Because it is the basis for efficient data management and data-driven decisions and is therefore indispensable in building a data strategy.

The larger the company, the more data sources there are often and the more roles arise that work with data. However, this is not just about IT and technology companies or Financial Service Provider, but also in healthcare, universities and the public sector, a healthy data architecture is necessary.

Even in eCommerce It is indispensable — just as in stationary stores that want to develop new business models and want to better address their customers through efficient use of data.

Who is involved in building the data architecture?

Building a data architecture in is a complex process that requires the involvement of various stakeholders and departments.

We primarily work together with the data department and the Chief Data Officer or Head of. But it is just as important to talk to data engineers, data analysts and data scientists in order to understand in depth where talents and skills lie and how they can best be used in the data architecture.

Of course, data protection officers and compliance managers are also important, as is IT, which helps implement the tools.

And data users must also be involved in the structure so that the solutions are used gladly and extensively.