Businesses today generate, process, and store vast amounts of data. Making smart decisions depends on the data within their databases and how they extract and use it.
Businesses work towards achieving data quality freedom by ensuring they have excellent data quality on all their platforms.
Achieving data quality freedom is a complex process that requires employing cost-effective and efficient ways of attaining innovative techniques of extracting, connecting, and engaging data for meaningful insights and smart decisions.
Automation is the solution to this problem. Let’s find out more.
What Is Automation?
Data automation encompasses handling, uploading, and processing information using automated techniques instead of manual execution. It involves replacing the slow, laborious manual processes with procedures done automatically and relatively faster by computers.
Data automation doesn’t require human intervention. Instead, it depends on intelligent processes of collecting, analyzing, storing, and transforming data to save money and time while enhancing organisational efficiency.
Automation is also advantageous because it eliminates errors commonly associated with manual processes. Moreover, it makes collecting insights and trends from the data faster.
Data automation comprises several elements.
Extraction
Data extraction involves sourcing information from one or multiple databases. In most cases, it involves retrieving different forms of data from multiple processes. Physical and logical extraction are two data extraction tools used.
Transformation
Data transformation involves modifying the extracted data into a particular structure, format, or value to facilitate data analytics. Data transformation can be:
- Constructive, which involves copying and adding data
- Destructive, which comprises deleting data records and fields
- Aesthetic, which consists in standardizing the information
- Structural, which involves shifting and combining various data forms
The data transformation process usually occurs via extract, transform, load (ETL) processes. You can use several tools to transform your data. Popular ones include domain-specific languages and scripting languages like SQL and Python.
Loading
Data loading involves moving data across systems, folders, or databases. Data loading is the last stage of the ETL process and involves altering the data format. For instance, it can involve changing the format from CSV to DAT or .doc to .txt.
The two primary data loading methods are full and incremental loading. Full loading involves bulk selection and moving of the data, while incremental load entails transferring the data in intervals.
How to Develop Your Organisation’s Data Automation Strategy
Your data automation strategy depends on your business strategy. Developing a data automation plan that works for your business involves the following steps:
Step #1: Problem Identification
This step involves examining your organisation and determining the areas that can benefit the most from automation. Data automation eliminates the money- and time-wasting manual processes.
Begin by investigating the time your operatives spend executing manual duties. You can also examine the data operations that are consistently slow and failing. Ensure you list all the areas that require improvement.
Step #2: Data Classification
Data classification involves sorting your data into various groups based on accessibility and importance. Begin this process by examining your inventory to determine the data you can access.
Automated data extraction makes this process much more manageable. But ensure the extraction tool supports formats that align with your business processes.
Step #3: Prioritize Operations
Prioritizing operations involves examining the importance of the process and the time it consumes in manual labor. Also, consider the resources and time required to achieve full automation. Prioritize processes that will result in the most gains for your business once automated.
Step #4: Outline Transformations
Outlining the transformations your business requires involves establishing the changes needed to alter your data to the desired size and format. These transformations involve translating raw data into valid, cleansed, and usable forms that you can feed into your data warehouse, operational systems, or repository.
Ensure you identify your system’s correct transformation to achieve your desired results. Using the wrong transformation can corrupt your dataset.
Step #5: Execute the Operation
Executing your data strategies is the most challenging phase of the process. It involves:
- Data identification
- Determination of data access
- Tools and platform selection
- Defining operations and transformations
- Testing the ETL process
- Scheduling the automation
- Delineating the test procedure
Step #6: Scheduling Updates
Once you’ve executed the operation, you’ll need to schedule the data to facilitate regular updating. Scheduling for updates ensures that the process will continue without manual interventions.
Automation and Data Quality Freedom
Automation has many benefits that will guarantee you data quality freedom. They include:
- Reduced data processing time
- Improved time and talent allocation
- Improved performance
- Cost-efficiency
Reduced Data Processing Time
Processing large business data volumes manually is complicated. It gets even more complex if you’re extracting it in different formats. You’ll need to standardize and validate the information before you can load it into your unified system.
Automation does all these tasks without intervention. It will also minimize resource utilization, increase data reliability, and save time.
Improved Time and Talent Allocation
Automation saves data scientists processing time which they can use to generate fresh ideas to support organisational decision-making. It also frees data engineers and analysts from business intelligence and fundamental reporting activities. They can use the extra time to broaden their scope of analysis and bring in additional data sources.
Improved Performance
Automation ensures improved data scalability and performance. For example, automation enables Change Data Capture (CDC), which allows you to propagate source-level changes through the organisation based on triggers.
Conversely, manual uploading and updating your organisational data requires considerable expertise and experience and is time-consuming.
Automation improves analytical speed since it requires little human input. Data automation allows computers to execute complex analytics and evaluate vast amounts of data.
Cost Efficiency
You can easily translate the time you save through automation to cost savings. Employee time is a considerable cost to many organisations—usually costlier than computing resources. Investing in automation will reduce your need for human resources while saving your organisation time and money.
Wrapping It Up
The benefits of data quality freedom can never be over-emphasized. The best way to achieve quality data freedom is to invest in automation. It will allow you to complete your data processing efficiently and fast while eliminating errors. It will also result in considerable cost savings.
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