Decoding Data & AI: A deep dive into central data warehouses

Before diving into the explanation of a Data Warehouse, it’s important to understand what a database is.

What’s a central data storage?

All your IT systems, as well as most of your machines, constantly produce data. Your ERP records all processes within your organization, your machines record input, output, and downtimes, and your marketing systems record your customer's touchpoints and responses. And so on. Over time, this becomes a lot of data, so these systems usually have some way of storing this data internally, in case you might want to look at it to make some crucial data-driven business decisions.

However: when data is scattered across different systems, it can create isolated silos that hinder data accessibility and integration. If you wanted to analyze your data, you would have to log in separately to all your systems, rendering comparisons nearly impossible.

Centralized data storage is the concept that data from various systems is integrated into a single place, providing numerous benefits, particularly in the context of AI, as AI systems thrive on large volumes of diverse data to train models and generate accurate predictions. The centralization of data allows organizations to:

• Ensure Data Consistency: Data from disparate sources can be made comparable and cleaned, reducing inconsistencies and errors.

• Enhance Data Security: A centralized system can enforce robust security measures and access controls more effectively than disparate systems.

• Facilitate Better Decision-Making: Unified data provides a holistic view of your business and how different departments, processes, and initiatives interact, enabling more informed and strategic decision-making.

• Support Advanced Analytics: Centralized data storage is crucial for AI and machine learning, providing the comprehensive datasets needed for accurate model training and predictive analytics.
   

Choosing the Right Type of Data Storage

You might’ve heard about the different types of data storage, such as data lakes, data warehouses, or even data lakehouses. Do you know the difference between them, and would you be able to choose what you need for your purposes? If not, here is our cheat sheet:

Data Warehouse

A data warehouse is designed to store data in the form of tables (like Microsoft Excel, which you probably know). That means so-called structured data: every row has the same columns, and data can be letters, dates, numbers, and so on.

Pros:

1. Strict rules define the relationship between tables, which - if done right - ensure very high data quality and consistency. That also means you have to enforce these rules!
2. Given this structure, I can easily extract and crunch this data for analytics.

Cons:

1. Warehouses are very inflexible: if your data inputs change, this can be a real problem to accommodate! 
2. You have to adapt your processes to the data storage systems rather than the other way around. Be ready to think seriously about data governance!

So: if you have data mostly in tabular form (e.g. financial data), you plan to run lots of calculations, and are happy to define a fixed data structure for the foreseeable future, this data storage type is for you!

Data Lake

A storage repository holding a vast amount of raw data in its native format, including structured, semi-structured, and unstructured data.

Pros:

1. This can hold non-tabular data: pictures, videos, longer pieces of text (tweets, e-mails), or data with different formats (lists, networks); meaning you can use all of your data for AI and analytics, and not only your tables
2. Cost-effective as it can use highly scalable storage methodologies
3. Very flexible even if your requirements change!

Cons:

1. Requires strong data governance to avoid becoming a "data swamp."
2. It is a lot more effort to extract data for analytics, as data management of all these formats can become extremely complex.

If you want to ensure maximum flexibility for future AI and advanced analytics applications, and you need to keep all your data (including messages, pictures, and more) readily available, a data lake may be the ideal solution for you.

Data Lakehouse

This data storage type combines the features of both data warehouses and data lakes, providing the ability to store diverse data types while keeping a strict data governance layer with rules on top of it.

Pros:

1. Balances the scalability and flexibility of data lakes with the structured data management and performance of data warehouses.
2. Enables advanced analytics and machine learning on all types of data.
3. Simplifies data architecture by consolidating different storage solutions.

Cons:

1. Still an emerging technology, so while the idea sounds great, there may be some unforeseen kinks to iron out.
2. Higher complexity in implementation and management.

A lakehouse sounds like the best of all worlds, and if you are ready to invest in your in-house capabilities to set up and manage such a system, this is definitely a solution with extremely high potential for you.

Data Mesh

A relatively new concept that refers to a central warehouse where responsibility for the data lies with the different departments rather than a central data team. A central storage with decentralized ownership.

Pros:

1. Data is managed by domain experts (sales, marketing, procurement) rather than a data team removed from the day-to-day work, there is an incentive to keep data high-quality and useful.
2. Insights can be generated more quickly by the domain experts themselves rather than relying on a centralized team.

Cons:

1. It can be challenging to manage consistency due to decentralized responsibility: you need a powerful data governance process.
2. This is not only a technological solution, you need to invest in a restructuring of your internal processes to some extent.

If you organziation is large enough and you really want to drive forward the reliance on data across the business, the step towards a data mesh may be for you.

In summary, centralized data storage is pivotal for leveraging AI and advanced analytics, ensuring data consistency, security, and efficiency. Choosing the right type of storage—whether a data warehouse, data lake, or data lakehouse—depends on an organization's specific needs and the nature of its data. Data warehouses remain a robust choice for structured data and high-performance analytics, with versatile deployment options across major cloud platforms.

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