Barriers to a Solid Data Foundation
Posted: Sun Feb 09, 2025 4:55 pm
It’s a significant challenge for many enterprises to determine where their data is stored and how available it is. Do you know what kinds of data exist in your business? Do you know where it is and what rules govern that data? That’s a great place to start. Frankly, many organizations don’t know these things, but they are essential.
Having data doesn’t always mean rcs data malaysia having ready access to it. Data may exist in multiple systems and silos. Enterprises are especially known for having rather complex data landscapes. They tend to lack one curated database that features all that the model needs, laid out in rows and columns, waiting to be retrieved.
In addition to data being spread across many systems, it’s also in multiple formats: data lakes, graph databases, SQL databases, NoSQL databases. In some instances, you can only access data through proprietary application APIs. Some data is structured, and some isn’t. Some data is coming in near real time from an IoT sensor, some is stored in files and so on. Gathering all this data is a challenge, as most companies don’t have systems or tools that can do it.
Let’s say you find all your data and translate it into one common format that your business can understand. That’s the canonical model. The next step is to consider the quality of that data. It may look great from afar, but close up, you find the duplications and errors that are unavoidable when data comes from many sources. Data in this shape is not fit for purpose.
Having data doesn’t always mean rcs data malaysia having ready access to it. Data may exist in multiple systems and silos. Enterprises are especially known for having rather complex data landscapes. They tend to lack one curated database that features all that the model needs, laid out in rows and columns, waiting to be retrieved.
In addition to data being spread across many systems, it’s also in multiple formats: data lakes, graph databases, SQL databases, NoSQL databases. In some instances, you can only access data through proprietary application APIs. Some data is structured, and some isn’t. Some data is coming in near real time from an IoT sensor, some is stored in files and so on. Gathering all this data is a challenge, as most companies don’t have systems or tools that can do it.
Let’s say you find all your data and translate it into one common format that your business can understand. That’s the canonical model. The next step is to consider the quality of that data. It may look great from afar, but close up, you find the duplications and errors that are unavoidable when data comes from many sources. Data in this shape is not fit for purpose.