![]() ![]() From rigid data models toward flexible, extensible data schemas.From an enterprise warehouse to domain-based architecture.From point-to-point to decoupled data access.From pre-integrated commercial solutions to modular, best-of-breed platforms.From batch to real-time data processing.From on-premise to cloud-based data platforms.Here is the short version of these six changes: It highlights the older architectural components, and how it has been updated to the distributed, agile architecture for today’s companies. McKinsey published a great article about six important changes to consider when building a data architecture in today’s world. Anonymizing data to decrease the value of the information upon receipt by receiving party.These can be visualized in the architecture and schema by showing what data gets passed where, and, when it travels from point A to point B, how the data is secured. ) Data securityĭata standards also help set the security rules for the architecture. (Explore data storage from database to warehouse to lake and from hot to cold. The ability to ask when a data was where.Versioning the data schema helps standardize: Plus, these relational databases can grow much larger and handle adding data dynamically to the database, where traditional SQL databases could not (or was strongly advised against). Relational (NoSQL) databases allow you to easily add data and piece data together more like a network of entities rather than a strict hierarchy of entities. As data becomes increasingly pervasive, companies will begin using relational databases over more traditional SQL databases. Most companies will version their data schema. The relationship of that entity to others in the database, such as where it comes from and where it’s going.For example, name is text data, phone number is integer data, email is text data, place of work is text data. The type of data each piece should be.Schema for contact info, for example, might include name, phone number, email, and place of work. These standards can be achieved by creating a data schema. The architecture is responsible for setting the data standards that define what kinds of data will pass through it. The architectural components of today’s data architectural world are:ĭata standards are the overarching standards of a data architecture, which you apply to areas such as data schemas and security. Grants all teams the ability to make data-driven decisions.Ensures a system is in place to secure the data.Offers protocols by which data moves from its source to being analyzed and consumed by its destinations.Creates a better understanding of the company’s data.Gives a fuller picture of what is happening in the company.If agility is what is needed to avoid collapse during slow seasons or to capitalize on the spontaneous popularity of a new product, the more advanced the data architecture is, the more capable the company is to take action. The data architecture is 100% responsible for increasing a company’s freedom to move around the world. Streaming data from a set of point-of-sale registers to accounting is another kind of architecture.csv on a local hard drive and reading the file into Tableau on a person’s computer for analysis is a very simple kind of data architecture. Picture these different data architectures: An advanced architecture can make that pawn a queen. If a company were a chess piece, the data architecture defines the moves the company can make on the board.Ī primitive architecture allows your company to move like a pawn. What is data architecture?ĭata architectures will define a company’s livelihood. The writings and how-to’s and best practices are there to share the architecture and help get organizations moving towards it. The world is jumping aboard this framework. A particular kind of data architecture can actually enable agility, so you can meet these business demands.ĭata architecture is critical to the success of a business (and why we’ve written extensively on each data component). Data architecture supports agilityĪgility allows your company to adapt quickly to the business environment and industry. ![]() It is the “how” when implementing a data strategy. The goal of any data architecture is to show the company’s infrastructure how data is acquired, transported, stored, queried, and secured.Ī data architecture is the foundation of any data strategy.
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