This article will cover the critical challenges that come across while implementing data unification for any business.
Unified Data is the process of blending data sources to present a single view of enterprise data. At least partially Unified - The majority (but not all) of data in your system is transformed into two or more different components. One is a Data source, and the other is a destination store, which can include separate database entries that you refer back through multiple databases for some reason. Unified Data can have various dimensions, such as in-data - A set or list that contains all kinds of information stored and retrieved between multiple periods.
Before starting data unification, we must first identify the multiple data sources where the enterprise data is distributed. For example, one of the data sources can be e-commerce data that resides at Shopify, whereas the Customer Data lies in in-built Customer Relationship Management storage. Most of the time, we don't know all the data sources that lead to data mismatch. So, we need to make sure we list down all the data sources we use and proceed further.
Now that we have all the data sources for our organization, we need to gather the data next. Here majorly, we need to consider the size of data and the data collection date ranges that need to be matched with all data sources. As the Data is not small, it is a time-consuming process for collecting data from multiple sources. Once all data collection is done, we can move forward to the next hurdle.
Connecting Data from multiple sources is the next job to be done. Data integration is challenging as every source of data does not have direct integration with other sources. Hence, it isn't easy to join the data sources, so we often need to use a data connector tool or a data source joiner tool available in the market. Actual Data can be unified only when the data sources are connected, and data can flow easily once Data transformation completes.
Here is the next level upgrade to data unification where we do not restructure the multiple data sources and turn them into a single data source. Lack of technical understanding of the data transformation or data blending to a single source is the challenge at this stage. To overcome using a data unification tool where with the help of artificial intelligence, the product tool understands and binds the data in a single source just with few clicks.
Data cleaning is identifying errors or manually processing data as needed to prevent the same mistakes from occurring. Performing structural changes in data as per requirement. Data validation is essential to our findings. We tested whether the results of this data-validation approach were reflected in the general pattern of age differences between subjects and their parents, regardless as they came from different domains.
A data silo is referred to as a collection of data held by one group that is not quickly or thoroughly accessible by other groups in the same organization. For example, an executive office has administrative offices and staff with different duties; therefore, individual records are assigned to each person's account within these departments. Data silos allow organizations both access their information without sharing it outside of this single environment and enable management to understand better how employees use personal details across various levels of organizational decision-making. Removal of data silos is the last challenge that we come across before achieving the unified data.
In order to achieve unified data, challenges mentioned above need to be overcomed, due to which we can get better organised data. Using a data unification tool like propellor is great to try out.
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