Table Of Contents
People prefer manual data entry and filing tickets to request data. Nobody wants to build reports or fulfill data requests. It raises many questions: Who made the request? Why are they asking for it? Does it comply with our governance policies? Where do I put the data?
Data automation solves these problems by automating data-related tasks that would otherwise require manual efforts. Data entry is time-consuming and error-prone. Plus, manually segmenting and analyzing data takes a lot of work.
The importance and value of understanding your business’ data can’t be overstated. Data automation helps to predict future outcomes and make well-informed decisions based on what happened in the past. Data automation can identify trends and impact factors humans often miss. Plus, it can save countless hours (and dollars) troubleshooting manual issues like fat-fingering and human error.
Organizations not implementing data automation to streamline business processes are working harder, not smarter. Companies lose out on potential opportunities and incur more expenses when they fail to automate their data. Having better access to and understanding their data could have revealed such opportunities.
A successful data automation strategy can only be fulfilled by implementing the best tools, methods, and solutions.
- What is data automation?
- Data Automation Techniques
- Examples of Data Automation
- Challenges of Data Automation
- Data Automation Tools
What is data automation?
Data automation uses technology to automatically collect, process, or analyze data without manual intervention. Tasks like data entry, extraction, modeling, mining, visualization, loading, and transformations can be automated. This helps to save time and increase accuracy.
Businesses use data automation to enhance workflow efficiency across departments and improve resource allocation. Automating tedious tasks reduces errors caused by manual data processing. This frees team members to focus on more strategic initiatives requiring human input.
Data Automation Techniques
The data automation process has several stages, each with its challenges and related solutions. The ETL/ELT portion of the data automation process lays the foundation for an organization’s data strategy. These techniques include web scraping, APIs, file parsing, and querying databases using SQL. Optical character recognition (OCR) is also used. OCR involves using software to scan and extract text from images or documents. How can we get the data? How can we clean the data so it is usable? How can we get the data to where it needs to go?
Additionally, how data is stored and managed in the short and long term is pivotal. Data storage and management also come with compliance and regulations standards that companies must adhere to.
Standardizing best practices and employing proven data automation techniques and methods help teams ensure success across all process stages. It can save money on infrastructure costs by standardizing storage types, locations, and access frequency.
Extract, Transform, Load (ETL)
Extract, Transform, and Load are three central components of data automation and encompass the beginning stages of the process.
- Extract: Extracting data from single or multiple source systems.
- Transform: Adapt data into a defined structure, like a CSV flat-file format.
- Load: Data is transferred from the source system to the data automation tool.
Data extraction involves gathering data from one or multiple sources. Automated data extraction uses software to extract data from files, databases, and websites.
Organizations use various methods to retrieve data from diverse sources. These include web scraping, APIs, file parsing, and querying databases using SQL. Another method is optical character recognition (OCR), which uses software to scan and extract text from images or documents.
Before data can be loaded and analyzed, it must first be transformed. This requires cleaning up the data in an orderly manner. Doing so will enable the data automation tool to maximize its value.
Data transformation can include combining datasets to make the analyzing process most efficient. When working with big data and large datasets, data can be transformed into a single table before analysis. This saves time and makes it easier to perform calculations during the analysis phase.
There are many data transformation strategies. They include data cleaning, data normalization, data aggregation, data merging, data pivoting, data discretization, and data imputation.
While it may sound simple, data loading involves more than just transferring information from one system to another. Loading refers to putting a clean, transformed dataset into a data management system that can be easily accessed.
Data loading enables teams to work with large datasets and scale data-related capabilities without taking up memory on local machines. This makes it easier to use computationally complex data mining algorithms or other strategies that require extensive processing times.
Deciding whether to use ETL or ELT (or both) for your organization is important. However, the most essential factor is that the data is valuable and reliable after it reaches its destination. In addition to freeing up memory, this streamlines internal collaboration and improves overall business processes.
Extract, Load, Transform (ELT)
Many data integration providers are moving away from ETL pipelines and toward ELT. There’s a valid argument for moving the computational load of transformation to the end of the process. This is particularly useful for scalable cloud-based infrastructure. Whether your organization goes ETL or ELT (or both), the biggest concern should be how valid and resilient the data is once it’s been moved to its destination.
Data Storage and Management
Organizations have several options to choose from when it comes to data management and storage. Relational and NoSQL databases, data warehouses, data lakes, and simple cloud storage methods are popular for data storage.
Relational databases organize data into tables with predefined relationships, which allows for queries and data manipulation. NoSQL databases are non-relational and can handle large amounts of unstructured data, including documents and social media posts.
Data warehouses are used as central repositories that consolidate information from multiple sources. Similarly, data lakes centralize both structured and unstructured data in its native form. Data lakes provide more flexible data processing and analysis.
Popular cloud-based storage solutions like Amazon S3 or Microsoft Azure are convenient for enterprise data storage. This is especially true for teams already using ETL or other data automation tools that integrate easily into those systems.
Data Management and Governance
Because of increasing privacy concerns, companies must be more diligent than ever when storing and managing data. Proper data governance is essential. This requires defining policies and procedures for managing and using data. Policies should include strict rules for data security, data privacy, and related compliance standards.
Examples of Data Automation and Use Cases
Data automation is used across industries to process and analyze quickly. With that insight, teams can consistently make data-informed decisions. They can also improve sales strategies and customer experience to increase profits.
Banking and Finance
In banking and finance, data automation is used to prevent fraud, manage risk, monitor compliance, analyze investments, and provide customer personalization.
Data automation can detect and prevent fraud attacks in real time. Automated data models can predict potential risk factors and run credit risk assessments. With compliance monitoring, data automation can monitor and analyze customer transactions. This helps ensure compliance with anti-money laundering and know-your-customer regulations.
Automated algorithms can create informed insights and predictions by quickly analyzing financial market information like stock prices, economic indicators, and news events.
Some exciting data automation capabilities in the healthcare space are revolutionizing patient care. At the same time, because healthcare companies are working with such sensitive personal information, data governance has become increasingly important.
Electronic health record (EHR) management is sufficiently streamlined using data automation. Digitizing patient records enables providers to integrate EHR data with other systems and apps, like medical billing and coding software and clinical decision support tools. This improves care coordination between multiple providers and facilitates better data exchange.
Machine learning models and automated algorithms can analyze patient data. In turn, it can provide insights and suggestions for optimizing patient care. Predictive analysis can also anticipate patient outcomes. Data automation techniques can identify potential patient risks and complications, then provide treatment suggestions.
Automating administrative tasks like appointment scheduling and patient refills improves the experience for both the customer and the employee. Other tedious tasks like billing, patient coding, and submitting claims to insurance companies can be automated. This improves efficiency and reduces manual errors.
Retail and e-Commerce
Retail and e-Commerce operations often involve large volumes of data. That data must be constantly processed and analyzed—quickly. The faster transactions are made and orders fulfilled, the higher the profits.
Data automation can optimize supply chain and inventory management. Predicting demand and coordinating stock levels saves businesses from expired or obsolete products before they can sell them. Machine learning models analyze sales data and customer behavior. The models account for external factors like weather and holidays to forecast demand and optimize supply chain management accurately.
Retailers frequently use data automation to dynamically evolve and adjust pricing strategies on market trends, customer demand, and competitor activity. Based on recent behavior, these methods provide dynamic pricing and promotions to individual customers or segments.
Everyone loves a personalized customer experience. Customers who feel like a business understands their preferences and needs are likelier to buy, recommend, and repurchase. Data automation can tailor product recommendations, offers, and content based on customer browsing and purchase history, demographics, and location.
Challenges of Data Automation
Data automation presents numerous challenges that primarily involve the data itself. Data automation is only effective if the data is accurate, timely, and usable. Data quality can lead to good analysis and good decisions. Complex data or large volumes of information can negatively impact data automation scalability and performance.
Data Quality and Integrity
Poor data quality is a common challenge when cleansing and validating data for automation. Raw data is often incomplete, inaccurate, inconsistent, or sometimes missing.
Data cleaning and validation must comply with the organization’s data governance policies. This is especially true with data retention and deletion policies. Tracking multiple compliance standards can make efficient data management challenging. Additionally, ensuring data privacy and security can be difficult during validation. Data must be protected from unauthorized access, theft, or misuse during transit between systems.
Scalability and Performance
Scalability and performance can become a challenge for data automation. As data becomes more complex or increases in volume, the speed and interpretation of data automation and analysis are negatively impacted.
Businesses must choose the right tools and platforms to meet their data strategy, requirements, and solutions. Tools should also be able to adapt to changing business needs.
Data Automation Tools
The benefits of robust data automation tools are far-reaching. Data automation tools perform and automate data extraction, transformation, and loading tasks. They can also safely and effectively store and manage data.
The long-term benefits of using powerful data automation tools include:
- Reduction in human errors
- Heightened productivity across departments
- Better employee and customer experiences
- Cost savings
- Improved resource allocation (time and talent)
- Faster data processing
- Expanded access to valuable data when making business decisions
- Increased sales and higher profits
Revelate supports data-driven innovation, enabling businesses to generate valuable insights and speed up decision-making. Teams can integrate Revelate into the BI, analytics, or data science tools they already use. More data can be accessed quickly and easily from an internal data marketplace. This data can expand models or reports.
Break down silos across supply chains, partner networks, or internally between departments by providing insights across the entire organization. With Revelate, data can still reside at the source and be shared among groups honoring related data rights and privileges.