Automated Data Preparation: A Step-by-Step Guide for Efficient Analysis

In today's data-driven world, 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 data preparation.

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

The Significance of Data Preparation

Data preparation is often the most daunting part of a data scientist's or data analyst's job, as it requires a significant amount of time and multiple iterations.

Data preparation involves six key steps to prepare data for input into business intelligence and analytics systems. Here's how the below steps come into play for customer sentiment analysis:

Step 1: Accessing data‍

Identify and obtain the relevant data sources, such as customer feedback, reviews, and social media comments.‍

Step 2: Fetching data from the source‍

Extract data from various platforms and repositories to compile a comprehensive dataset.

Step 3: Cleansing data‍

Remove any inconsistencies, errors, or irrelevant information to ensure data quality and accuracy.

Step 4: Formatting and structuring the data‍

Organize the data into a consistent format, making it easier to analyze and interpret.

Step 5: Consolidating the data‍

Combine data from multiple sources to create a unified dataset for comprehensive analysis.

Step 6: Data analysis‍

Apply analytical techniques to the prepared data to uncover insights, trends, and patterns in customer sentiment.

Good data preparation practices

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:

A. Identify and fix errors

Data preparation helps identify and fix errors before they become bad business decisions. Before data processing, data preparation helps catch inconsistencies and correct them.

B. Data formatting

‍Cleaning, structuring, and formatting the data before it goes into business systems helps ensure high output quality.

C. Data 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 challenging, which makes automation a better alternative. Let's examine 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 screen problematic data, derivate novel attributes, add semantic information in the form of metadata, and even improve 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, driven by artificial intelligence and machine learning, operates at high speeds, providing enterprises with significant time savings compared to manual methods.

2. Reduction in Human Errors

Humans are prone to 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, depending 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.

4. MarkovML

MarkovML is a no-code platform that enables you to visualize and analyze your data. It also allows you to perform data preparation or preprocessing using easy-to-use drag-and-drop workflows. You can use existing templates and customize them, or create your own custom workflows.

Implementation Strategies for Data Preparation

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 ensure 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

According to Statista, there have been 120 zettabytes of data produced up until 2023. 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 risks data, which puts a higher emphasis on data security and privacy measures for the future.

Conclusion

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

Any room for error, especially in data preparation, creates gaping holes in subsequent business decisions. Platforms like MarkovML equip businesses with a robust AI platform on which organizations can build smart data preparation solutions.

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

Contact us today to explore data preparation capabilities further.