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In the InsideView Alignment Report 2020, more than 70 percent of revenue leaders rank data management as the highest priority. Although many organizations have implemented a system for data collection and analysis, their biggest concern is now maintaining the integrity of their data.
The term "data integrity" is sometimes used as a process or a state of data. Either way, it refers to data being accurate, valid, and consistent across all data sources.
ERP implementation is an immense undertaking. This central system is vital for growing businesses looking to scale their workflows, processes, and functionality. After all, as a business grows, there's more to manage.
And with that responsibility comes risk.
Information is no power unless it is equally accessible to all. Data can be the new age oil, but it cannot be used to fuel the world unless it is equally available, accessible, and affordable by all the stakeholders. More so in a day and age where, under the weight of the global pandemic, enterprises worldwide have embraced “Virtual Agile Delivery” as the new accepted norm of working.
Surveys have shown that employees on an average end up spending ~40 minutes a day in trying to discover a document. While 71% of the people keep asking around, around 46% of them choose to use the company directory, roughly 34% use the intranet and 30% of the people eventually send a company wide mail to find the information they need.
Most databases (old and new) recommend, and in some cases force, an application to model its data in either a fully normalized or fully denormalized model. However, as we will see, matching the complexity of enterprise applications with modern-day data requirements cannot be achieved by only either end of that spectrum.
You may also like: Pros and Cons of Database Normalization
Normalization is defined as the process of reducing or eliminating redundancy across a dataset.
The NoSQL databases are famous because they are schemaless. That means a developer does need to care about a pre-defined type as it does on a relational database; thus, it makes it easier for developers to create an application. However, it has a considerable price once there is no structure that is not trivial to validate it on the database. To ensure integrity, the solution should work on the server side. There is a specification and framework in the Java world that is smoother and annotation-driven: bean validation. This post will cover how to integrate bean validation in a NoSQL database.
Bean Validation, JSR 380, is a specification which ensures that the properties of a Class match specific criteria, using annotations such as @NotNull, @Min, and @Max, also create its annotation and validation. It is worth noting bean validation does not, necessarily, mean an anemic model; in other words, a developer can use it with good practices to get data integrity.
First, let's understand what is ETL. This notation stands for Extract-Transform-Load. For large-scale firms, initially, the data is extracted from the source systems and then transformed into specific data types and, ultimately, loaded into a distinct repository. And this process should be tested efficiently to make sure that the data is managed properly in the warehouse.
It is a procedure that tests the withdrawal of data for further transformation, authentication of data during the transformation stages, and loading or filling of data in the endpoint.
Data integrity is the assurance of accuracy and consistency of data over the course of the data life cycle (from when the data is recorded until it is destroyed). In simple terms, data integrity means that you have recorded the data as intended and that it wasn't unintentionally changed over the course of its life cycle. The concept is simple, but the practice is not. Data integrity is a critical component to creating or designing any software system that will store or move data.
Data integrity is important because just about every critical business decision is based on a company's data. With good data integrity, you can analyze your company's data to answer questions like: what were your business achievements? What were your business expenses? How are your sales in different regions? Are there areas of your business where expenses are growing faster than income? What is the productivity of different divisions of your workforce? Are you meeting your benchmark goals? Can you forecast your expenses for the upcoming fiscal year? If you don't have good data, you can't answer any of these questions accurately.