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Automated Data Preparation for Higher Analysis Efficiencies

February 8, 2024
min read

In the data-driven world today, businesses need to be smart about managing and treating the everyday data that their systems generate and collect. The quality of analytics and output depends on the quality of raw data that travels through the internal business intelligence systems, which necessitates the process of data preparation.

Automated data preparation is the technique that enterprises employ to process raw data for suitability of inputting it into the analytical and intelligence systems. Not only does it improve the analytics quality, but it also enhances the streamline of the entire process and reduces the overall turnaround time.

The Significance of Data Preparation

According to Forbes, about 76% of data scientists agree that data preparation is the most dreaded aspect of their jobs.

Steps for data preparation

Data preparation involves following six key steps to prepare data for input into business intelligence and analytics systems:

  • Accessing data
  • Fetching data from the source
  • Cleansing data
  • Formatting and structuring the data
  • Consolidating the data
  • Data analysis

Data preparation can impact several aspects of decision-making, such as forecast accuracy, making it a necessary step for organizations. Good data preparation practices help enterprises with the following:

  • Data preparation helps identify and fix errors before they become bad business decisions. Before data processing, data preparation helps catch inconsistencies and correct them.
  • Cleaning, structuring, and formatting the data before it goes into business systems helps ensure high output quality.
  • Better data quality translates into better and faster analytics, leading to sounder and more reliable business decisions.

Moving automated data preparation processes to the cloud enables organizations to scale their analytics operations and become agile.

Challenges in Manual Data Preparation

Manual data preparation is a challenging job, which makes automation a better alternative. Let's have a look at some major challenges in manual data preparation.

1. Time-Consuming Processes

Manual processing is never as fast as automated processes. Manual data processing requires more time and effort. Moreover, it may not be as cost-effective as automated data preparation tools.

2. Proneness to Errors

Manual data preparation is prone to errors on account of distractions and disengagement from work. A loss of productivity also impacts other factors like output quality.

3. Inconsistencies in Data Quality

Manual data preparation lacks consistency. Professionals who are more productive and focused than others create better datasets, which creates quality differences from the outputs of other team members.

4. Limited Scalability

As data preparation needs increase with expansion in operations, manual methods cannot be scaled proportionately within the economies of scale. This makes manual data preparation a costly affair for larger data volumes.

5. Dependency on Human Expertise

The prepared data that comes as output from manual methods depends heavily on the individual data preparation skill of the associated human resources. The better you hire, the better your data will be. However, that places a dependency on the enterprise, which may be a risk to competitiveness.

Introduction to Automated Data Preparation

Automated Data Preparation (ADP) is a data preparation process that leverages machine learning prowess to identify issues with raw data and correct them before the data enters business systems.

Automated data preparation tools help enterprises with screening problematic data, derivation of novel attributes, adding semantic information in the form of metadata, and even improving the performance of analytics systems.

Automated data preparation tools can perform functions such as reformatting data, making corrections to erroneous or missing data, combining datasets, and consolidating information, etc. Automation helps eliminate human resources from handling data, thus improving the speed, efficiency, and accuracy of the output.

Advantages of Automated Data Preparation

Automated data preparation provides six key benefits to an enterprise:

1. Accelerated Data Preparation Process

Automation that is powered by artificial intelligence and machine learning operates at lightning-fast speeds. As compared to manual methods, automation provides significant time savings to enterprises.

2. Reduction in Human Errors

Humans are prone to making errors even with the highest levels of focus. With an automated data preparation process, errors can be reduced to a minimum or even eliminated completely based on the effectiveness of the chosen programming.

3. Improved Scalability

Automated data processing hosted on clouds provides endless scalability potential to an enterprise. Whether scaling up or down, cloud service providers can adjust the server allocation that lets your enterprise have access to robust computing power resonant with needs.

4. Enhanced Consistency in Data Quality

Automated data processing tools work based on set programming and specified parameters. This enables them to continuously deliver prepared data with consistent quality with each raw data provided. Machine learning algorithms learn from each iteration and keep enhancing the output.

5. Increased Productivity and Resource Efficiency

Automated systems can work at consistent productivity levels for as long as needed. Additionally, they are far better at smartly gauging resource efficiency and optimizing resource use based on requirements for the present and future.

6. Adaptability to Evolving Data Structures

As modernized data processing methods are invented, the data structures also evolve in tandem. Manual processes can’t keep up with the speed of innovation, and this is where automation provides the requisite adaptability.

Popular Tools in Automated Data Preparation

Automated data preparation is a complex process that requires robust tooling for enterprise-level applications. Listed below are three of the most popular tools in the industry:

1. Altair

Altair is one of the most popular tools on the internet that provides self-service data preparation modules to businesses. It provides powerful automation and streamlining capabilities for data from a variety of resources. It has extremely efficient data-sharing systems that remove organizational siloes and achieve smooth data flows.

2. Alteryx

Alteryx is a full-suite data solution that offers automated data preparation as one of the products. Enterprises can prepare, blend, and enrich their data using Alteryx’s low/no-code analytics automation. The tools are mostly visual and interactive, making the learning curve extremely flat. The tool has several data connectors that enable easy publishing of outputs.

3. Talend

Talend is a highly capable data preparation tool that is browser-based, enabling point-and-click self-service for data preparation. You can create reusable rules that can be exported across datasets to help identify errors and fix data structure.

Implementation Strategies for Data Preparation

Data strategies and steps for effective analytics

You can leverage the following six strategies to ensure that data preparation remains effective and accurate at all times:

1. Integration into Existing Workflows

When implementing automated data preparation tools at your enterprise, understand the workflow integration requirements beforehand. If plugins are required, you may need to consider API options or acquire SDKs for the tools you have selected.

2. Configuring and Optimizing Automated Processes

Every organization’s data requirements are unique. The data engineers need to create a configuration framework that defines their data preparation process. Optimization of automated processes enables better alignment of data preparation with business goals.

3. Data Security and Privacy Considerations

Organizations need to define the privacy parameters in their datasets for compliance with different regulatory and law mandates. Automation rules must also define the regional compliance requirements so that cybersecurity and consumer privacy measures are effectively met.

4. User Training and Skill Development

Automated data preparation tools require learning and sometimes certain coding expertise. It helps to have training and skill-building programs that help staff learn software use.

5. Documentation and Knowledge Transfer

Migrating from a manual to an automated process requires some level of handover from data professionals to ML model developers. Systematic documentation and knowledge transfer ensures a smooth flow of information during the transition.

6. Continuous Monitoring and Quality Assurance

It is important to ensure that data quality does not degrade over time as machine learning programs handle incremental data. Continuous monitoring and quality assurance measures need to be clearly defined for this purpose.

Future Trends in Automated Data Preparation

In 2023, according to Statista, there have been 120 zettabytes of data produced up until now. Organizations need to ramp up their data management operations because this volume is only set to increase as digitality penetrates further into the fabric of the world.

The key future trends shaping data management and preparedness hint heavily at agility, mobility, and scalability:

  • Keeping scalability and data mobility in mind, cloud-based data preparation systems are set to shape the data architecture.
  • Advanced language models that leverage the power of AI are estimated to enhance the usability of unstructured data in preparation.
  • Digitality puts data at risk, and this puts a higher emphasis on data security and privacy measures for the future.


Automated data preparation enriches raw enterprise data with a view to positively impact the quality of analytics for the organization. Where manual processes would require higher cost and effort inputs, automation can benefit enterprises tremendously.

Especially in the data preparation aspect, any room for error creates gaping holes in subsequent business decisions. MarkovML equips businesses with a robust AI platform on top of which organizations can build smart data preparation solutions.

MarkovML provides organizations with a data-centric AI platform where they can build custom data preparation workflows. Enterprises can experiment with custom-built ML models and datasets for explorative approaches toward data preparation. Additionally, the data quality measurement and reporting features of the platform enable the enhancement of the data preparation process with smart insights.

Connect with MarkovML today to explore data preparation capabilities further.

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