What is data preparation?

Why does an organization need data preparation?

The main reason that data preparation processes are applied to raw data is to ensure that the information is of good quality. Processing data with business intelligence and other analytical applications will result in quality output. Raw data is often riddled with missing values, inaccurate entries, and other mistakes. When multiple data sets are in play, varying formats can duplicate or ignore values. Therefore, correcting all these errors, validating their quality, and consolidating data sets is the first step to data processing.

During data preparation, ensuring that data is ready for analytics serves as a starting point. This is a base to derive actionable insights necessary for intelligent business decisions.

An organization’s raw data can be enriched to be more useful in several ways. For example, it can be done by merging internal and external data sets, or data sets can be balanced. Data preparation is regularly used by business intelligence and data management teams to streamline the process of analysis and ensure seamless self-service with business intelligence applications.

Benefits of data preparation

For data scientists, the most significant benefit of data preparation is that they can spend their time doing their job of analyzing and mining instead of cleaning and structuring data. Prepared data can be fed instantly to multiple users for deployment in various recurring analytics procedures. There is a range of other benefits of data preparation:

• It ensures that data utilized in any analytical application is clean and capable of providing reliable output.
• Data preparation helps identify potential problem areas with data that would otherwise go undetected.
• It helps management-level employees, executives, and operations-related professionals make better business decisions.
• It brings down the cost of analytics and data management.
• It prevents any repetition of data when preparing it for use across applications.
• It helps a business ensure a better return on investment from business intelligence and its analytics initiatives.

Data preparation in the cloud

Data preparation should be the norm, particularly in big data environments where information is stored in multiple ways. These storage facilities, such as data lakes, can store data in structured, unstructured, or semi-structured forms. This means that data often remains in its natural condition until it is used for a specific analytical purpose. These can be predictive analytics or machine learning or can even be further advanced methods that require large swathes of data.

With data and its processes increasingly moving to the cloud, data preparation also makes a move simultaneously. These benefits can be huge.

Better scalability

When data preparation moves to the cloud, it scales with the business. A business need not worry about scaling up its infrastructure or planning for the long-term evolution requirements of the company.

Future-ready

With cloud-based data preparation, any upgrades to capabilities and patch fixes for bugs are incorporated and functioning when released into the market. For businesses, this means staying ahead of the curve with innovations. It also avoids delays in going to need and any additional costs that may be incurred.

Faster access and collaborative use of data

When data preparation happens in the cloud, it remains an ongoing process. It does not need technical installations and facilitates better teamwork for quicker output. With quality, cloud-native data preparation tools, a business can also benefit from intuitive graphical user interfaces that ease the process of data preparation.

How to get started implementing data preparation

Here are six points to ensure that the organization has the ideal start to data preparation. An organization will likely need to hire data scientists or expert analysts to help them with this step.

1. It’s best to look at data preparation and analysis as closely related. An organization cannot prepare its data well unless they know what kind of analytics it is prepped for. Knowing this from the start sets a suitable base for data preparation.
2. Set goals for data preparation. When users know what accuracy levels are needed and what quality metrics are desirable, management can arrive at a projected cost for the project. This will help create a plan for every use case the organization has.
3. Prioritize data sources based on the analytical processes that are planned. Ensure that all differences are resolved when multiple sources bring in all data. This forms an important starting point for preparing data.
4. Evaluate the skills and tools on hand for the job of data preparation. Self-service data preparation tools are often believed to be the only available option. Several other devices and technologies work in tandem with existing skill sets and data requirements.
5. Always anticipate failures during data preparation. It is critical to building in the capability to handle errors when setting up a data preparation process. This will reduce the chances of going wrong or any downtime should a problem arise.
6. Keep a close watch on data preparation costs. There is much expense involved in obtaining licenses, processing data, and storage resources an organization will need. Knowing estimates and providing a workable leeway is essential to keeping the process within a company’s budget.

Steps of data preparation

There are several steps to produce the best possible outcomes for the data preparation process.

Access

Every organization has various sources of business data. Some of these sources can be data from endpoints, customers, the marketing department, and other sources that are associated with these domains. The first step to data preparation is identifying all the required data and their related repositories. The identification must include all necessary sources for the kind of analysis an organization has in mind. The organization must have a plan that outlines all the questions that require answers from the planned data analysis.

Ingest

When the data is identified, it is introduced into the analysis tools. The information on hand will probably be a mix of structured and semi-structured data located across different repositories. An important step is bringing all of the data from various repositories into one. Access and ingest are flexible, as steps vary mainly depending on the requirement. These two data preparation steps require business and technological expertise and are ideally handled by a small, efficient team.

Cleanse

Cleansing data is done to ensure that the data set that is being worked with provides accurate answers when it is analyzed. Small data sets can be analyzed manually, but larger ones will require automation, the readily available software tools. Data engineers use applications coded in the Python language where custom processing is needed. Ingested data can have its share of problems–values can go missing or out of range, and there can be nulls or whitespaces where there should be values.

Format

Cleansing of data is followed by formatting. At this stage, issues such as varying data formats and abbreviations are addressed. Any data variables deemed unnecessary for analysis will be deleted from the data set. This is the stage of data preparation that is best automated. Both cleansing and formatting are ideally saved as a repetitive formula that data scientists and professionals can apply to similar data sets any time they need. For example, if a company requires a monthly assessment of marketing and support data, the sources are most likely to remain the same and require the same kind of cleansing and formatting each time. Having a saved formula helps move things faster.

Challenges of data preparation

As a process, data preparation is complicated. Data sets created from several source systems will have multiple quality, accuracy, and consistency issues that need to be addressed. The data will also have to be reworked to make it user-friendly and remove all irrelevant data. This can be a long-drawn process.

Here are seven main challenges that are often seen with data preparation:

1. There can be insufficient or inadequate data profiling. When data is not profiled correctly, it can lead to several errors, anomalies, and issues that can result in poor results during analytics.
2. Without proper data profiling, an organization can have missing or incomplete data. Missing values is just one form of incomplete data. There are several more that need to be addressed right from the beginning.
3. Data sets can also contain invalid values. This can result from spelling errors, typos, or the wrong numbers input. These invalid entries must be spotted early on and fixed to ensure analytical accuracy.
4. When data sets are brought together, name and address standardization is a must. Often these details are stored in various systems in different formats. If not corrected, they can affect the way the information is viewed.
5. There are many other inconsistencies in data that users will find across enterprise systems. These inconsistencies can happen from any one of the multiple source systems that are worked with. Differences can be related to terminology, specific identifiers, and the like, which can challenge data preparation.
6. While data enrichment is needed, knowing what to add to it can be complex and will require solid skills and business analytics skills.
7. Setting up, maintaining, and enhancing data prep processes is necessary to standardize the process and ensure that it can be used repetitively.

Data preparation principles and best practices

Interestingly, several functional programming principles can be applied to data preparation. While it is not a rule that available programming languages be used to automate data preparation, such languages are the norm. These are some of the data preparation principles and best practices to follow:

• Always ensure that the organization has a clear understanding of the data consumer. Who is the end-user of the data, and what information are they looking for with these sources?
• Know where the data is coming from and the sources that generated it.
• Never get rid of the raw data. With the raw data, a data engineer can always recreate data transformations. Also, never move data or delete it after it has been saved.
• If possible, always store all data and their raw and processed results. Also, know the compliance laws of the region the organization operates in.
• Document every stage of the data pipeline. Make versions of the data, the analysis codes, and the application that transforms the information.
• Always make sure that there are clear demarcations between online and offline analyses. This is to prevent the ingest step from impacting any user-related services.
• Constantly monitor data pipelines for any inconsistency found in data sets.
• Bring in a proactive form of data governance. Because information technology constantly requires security and compliance, ensure the presence of governance capabilities such as data masking or retention, lineage, and any role-based permissions.
• Work on creating a data preparation pipeline. The best way to do this is to understand the data well with consumer needs and then create a workable data preparation pipeline.

With quality data preparation, organizations can be confident that their data for any process or gathering insight will help. It instills confidence in the process, the system, and the output, which can be highly beneficial for any business.