There is a reason why an enterprise data storage mechanism is also called a vault; the data contained inside it is as valuable as gold to the company. But much like gold, the good stuff comes mixed with a lot of unwanted waste material that needs to be removed to gain the material that matters. For data, the refining process takes the form of multiple stages of data management processes. Data standardization services providers, as an example of those processes, transform that data to make it conform to the required standards. 

Multiple such processes are needed to prepare your company’s data for the various use cases down the pipeline, like analytics, business intelligence, visualization, etc. Feed them raw data, with its multitude of inaccuracies, missing values, errors, and other issues, and you end up with results that can take your business down the wrong path. But feed them prepared data, and those results can help you make business decisions that can bring the expected conversions and brand value. 

Read on to learn about the many data preparation processes involved and the best ways to implement data preparation for your business. 

The Steps Involved in Data Preparation

Below are the many steps that go into data preparation. While some apply to all businesses, others may or may not suit yours. You may select them based on your needs during implementation. 

  • IT Infrastructure Setup

To have data, you must first decide where you can store it. For that, you should prepare your IT infrastructure first. Going with third-party cloud service providers is your best option, as you get instant access to the necessary amount of space and tools. It also saves you the high upfront and continued maintenance costs of having an in-house system setup. 

Simultaneously, delegate the various tasks that are associated with the process to applicable team members such that there is coordination between the outsourcing agency, your company, and the cloud operator. Ensure that the agency you outsource data entry services and other data prep functions to works on your choice of cloud service provider and is familiar with the technological tools in use. 

  • Data Source Selection

The more the number of data sources, the merrier may be the mantra for the digital age. But that also comes with the problem of redundant sources that may not be worth maintenance effort. An example is gaining customer feedback through a physical form and a digital version from the same customer base. 

You get the same data through two sources, and you have to prepare them both but, eventually, discard one for duplication reasons. You must also review data sources periodically for the value they are adding to your company. Some sources may not continue to add the same value they once did, leading to data bloating that only makes the prep process harder and prolonged. 

  • Data Collection

Once the necessary data sources have been ascertained, you can begin collecting the data from them. This may occur in the form of continuous data streams or periodic inputs of large data quantities. One of the functions of data preparation - categorization - can be executed at this stage crudely. Data scientists, BI team members, and other applicable stakeholders can set some conditions using which the incoming data can be sorted into certain categories while storing it.  

Whether you manage the data-gathering process yourself or let the outsourcing agency take over it completely is up to you. If there is some sensitive information that you don’t want to be shared with the agency, then you can find and remove it yourself before forwarding the rest for preparation. Hence, have a data-sharing policy in place before you engage an external company for your needs. 

  • Data Discovery and Profiling

Once the required data has been gathered, it is subject to discovery. As the name suggests, this function is used to determine what exactly is present in the data storage. Professionals use various data exploration tools to run through the mass of stored data and log information about the data files, like their data type, size, date of origin, location in memory, etc. 

Associated with data discovery is data profiling, where data management professionals work to identify any patterns, connections, relationships, and other such attributes between the various pieces of data. This function is also used to recognize the various types of data errors present like inconsistencies, anomalies, missing data, duplicates, etc. 

Thus, at the end of this stage, you get a clear idea of the present quality and quantity of your raw data. It also tells you about the amount of preparation that needs to be done and the approximate timeline for the process. You can proceed with budgeting for the process based on this information. 

  • Data Cleansing

The various stages of data preparation thus far have been about getting and understanding the data. It is with the data cleansing stage that actions on that data begin. Data Cleansing is the general term used to refer to multiple functions that collectively work to eradicate the various types of data issues identified in the collected data. 

One of the processes involved is data deduplication, where the data pool is checked for pieces of data that could have multiple copies of itself. Once identified, the duplicates are either eliminated by deletion or used as a backup. This process helps reduce data bloating, thus saving precious memory space. 

It also prevents various versions of the same data from accidentally being used by different teams, resulting in coordination issues from mismatched data processing results. Other data cleansing functions include the removal of unwanted attached data, the addition of missing data, the removal of outdated data, condensing data, and categorization based on various conditions. 

Data cleansing is typically performed on data that is stored, but that’s not necessarily the case. It can also be performed in real-time on incoming data streams if there’s a need for it. Mission-critical applications use real-time cleansing, such as self-driving vehicles for the input data about their surroundings. 

For delayed cleansing requirements, however, you can choose the appropriate deadline. It also lets you outsource data entry services as professionals at such agencies help cut down the cleansing time and costs by cleaning some of the data during entry itself using their experience. 

  • Data Structuring

Data cleansing drastically improves the usability of your data by introducing high levels of accuracy. However, that data may not be present in the right places in the data warehouse. It requires modeling and organizing so that data identification and retrieval become easy. 

Data analytics especially insists on having such well-organized data since most of it gets done by automation and it cannot use the applicable data unless that data is structured according to its criteria. Data structuring requires that you have a predetermined structural blueprint to use, which should be prepared at the early stages of data preparation planning. 

  • Data Transformation 

Not all of the data present is in a file format that is palatable to the various data processes to follow. Thus, it needs to be transformed into the file format that an application can use. For example, a text file needs to be converted to its HTML version for a web browser to be able to access it. You may even have a custom format for your company that the data needs to comply with and that needs transformation. 

Data transformation finds true value when it comes to compliance requirements. Government agencies may require that data files shared with them should follow a prescribed format only. Then, you would have to transform your data to fit those requirements. Hiring professional data standardization services for such situations as they will be familiar with all applicable standards in the market. 

  • Data Validation and Enrichment 

Finally, the transformed data is verified and validated. These functions are vital last-stage measures to ensure that the previous processes have provided the kind of accurate, consistent, and complete data expected of them. The present data is checked against set standards to know its quality. 

There may even be test runs conducted using samples of this data to see if it produces the expected results. Validation helps to determine if a particular data set is useful for a given data processing function or not. For instance, an analysis may require only a portion of the entire relevant data. Then the necessary data set can be validated to see which part can be used. 

Data enrichment is applicable whenever there is a need to add new data to existing ones. It helps keep the data relevant and up-to-date, preventing problems from using outdated data annotation services. An example is changing the address or other contact information of a client or customer. 

In Conclusion

With more data expected to be generated and consumed by enterprises everywhere, it becomes ever more important to maintain company data at optimal quality. You don’t know when a need may arise to work on such data and keep your company afloat or push it to new heights. Data preparation enables you to be prepared for such opportunities with enterprise data the way it should be. And you gain more advantages if you outsource data prep functions like data standardization services to a professional agency. They can prepare your data using the best practices and give you high ROI along with high-quality data.