Data management automation is a high priority for startup businesses. The biggest challenge any startup business faces is making sure that its data is safe. A data breach is not only worrisome — it can also cause businesses to lose potential clients.
Data management automation helps ensure that your data is safe and secure. Working on this front ensures that all your data is safe and at the same time, saves you from the hassle of having to manually manage this data.
Besides data protection, the exponential growth of data has made it difficult for startup businesses to handle data manually. Collecting, cleaning and organizing data can be an enormous undertaking.
Automating data management helps you to make the most out of the data you have.
What exactly is data automation?
Data automation is defined as the process of uploading, managing and processing data using automated technologies rather than completing all of these processes manually.
It consists of three components: extract, transform and load (also called ETL). The ETL procedure is divided into three easy steps:
- First, extract data from the source.
- Then, transform the data into the desired format.
- Lastly, load the data into the target system.
Collecting critical business insights from your data is crucial for your startup to move forward. As a result, using automation in data analytics allows business users to concentrate on evaluating data rather than organizing it.
Data automation saves time and money while increasing corporate efficiency. It also helps to decrease mistakes by validating data and ensuring that the loaded data is organized.
Data management automation can help improve the following:
- Data quality — the process of profiling, cleaning, connecting, and reconciling data with a master source, a standard for structuring master data, or the data to do business. In addition to correcting the data, automating this process guarantees that businesses follow well-defined standards and maintain logs outlining the methods required to make the data digestible.
- Metadata management — metadata describes information about the material at hand and its context, which is critical for searching through millions of entries. To comply with legislation, automated cloud archiving tools index data and allow you to rapidly identify and access the information you need. They also improve data asset monitoring while in use, making data quality and data management easier.
- Management of master data — master data defines the important data about people, locations, and objects required to do business. Through a centralized reference point, standardization keeps master data reliable, ensuring that one person's record is actually unique. Master data management automation ensures that this type of data is consistent, trustworthy, and up to date across systems.
- Data integration — many businesses have to deal with several database systems that use different standards. The end-user, whether human or AI, requires a uniform flow of data via the pipelines. When a user recalls this information, integration mechanisms ensure consistent data quality.
Benefits of data automation
Data automation can provide tremendous benefits to startups. It is a productive and cost-effective option for a company since it increases work efficiency and reduces costs.
Here are some examples of how automated integration may transform company processes:
It boosts productivity and saves time
Let's face it: analyzing massive amounts of data from different sources is a difficult task. The format of data gathered from various sources varies. As a result, it must be standardized and approved before information can be fed into a unified system.
Automation saves a significant amount of time when handling jobs that are part of the data flow. Furthermore, it reduces manual involvement, resulting in lower resource use, time savings, and enhanced data dependability.
Furthermore, automation benefits employees by allowing them to focus on more demanding and engaging activities rather than tedious duties.
It improves scalability and performance
Data automation improves your data environment's performance and scalability. By activating change data capture (CDC), for example, any changes made at the source level are propagated across the business system via triggers. Manually upgrading, on the other hand, takes time and needs substantial experience.
Loading data and managing CDC is as simple as dragging and dropping things on the visual designer using automatic data integration tools.
It ensures consistency and eliminates human errors
Additionally, automatic data management assures consistency. Maintaining job quality is critical for firms, and manual methods may jeopardize this. By automating your data flows, you are assured that the generated results are always correct.
Data errors are costly. They can undermine your marketing campaigns and strategies. Additionally, inaccurate data may ruin your company's reputation and even cost you consumers' trust.
Data automation eliminates manual input errors, which increases accuracy.
It provides centralized access to data
Furthermore, automation aids in the integration of data from numerous sources into a single one.
Automation tools can help establish an enterprise data warehouse (EDW). EDWs centralize data from multiple sources into a single, integrated and easily accessible location, allowing companies to view data at a single source.
It improves data security and compliance
Data integration tools use encryption during data transfers, and this offers improved data security. Additionally, data automation tools can help you meet regulatory requirements.
How to get started
Before you get started with data management automation, here's a list of questions to assist you to identify which types of data are suitable for automation:
- Is it necessary to update the data on a regular basis?
- Is it necessary to alter it before uploading/processing?
- Is there a lot of data?
- Is the data coming from a variety of sources?
If the answer to these questions is positive, that data set is a good candidate for data automation.
But where do you get started? Here’s a step-by-step guide.
1. Sort data into categories
The initial stage in this procedure is to organize source data by priority and ease of access. Refer to your source system inventory to determine which sources you have access to.
2. Identify key transformations
The next step is to determine the changes required to convert the source data to the desired size. It might be as simple as turning tricky acronyms into full-text names or as sophisticated as converting relational database data into a CSV file. Identifying the necessary transformations to get the intended results during data automation is critical, or else your entire set of data may be incorrect.
3. Create the ETL Process
Using information that you gathered during data sorting, build an Extraction, Transformation and Load (ETL) process.
Remember, ETL is a process that extracts data from the source system, transforms it, and loads it into the target system. It's completely different from ELT which is the data integration process that moves data directly from source to destination. Determine which data is the most critical, which transformations are required for organizing the data, and which data should be loaded to the target system.
Select an ETL tool with all of the important capabilities required for processing or updating data while maintaining quality based on the data automation needs specified in the previous two phases.
4. Plan for Data Updates
The next step is to schedule your data so that it is updated on a regular basis. For this phase, choose a reverse ETL with process automation capabilities such as task scheduling, workflow automation, and so on. It guarantees that processes are carried out without the need for manual intervention.
Types of Data Management Tools
Keep in mind that ETL solutions are only one type of data management automation tool you should use.
With the rise in data volume, high-quality solutions are required to accomplish the finest data management techniques. The four types of Data Management Tools are as follows:
ETL and data integration tools
The already mentioned ETL tools assist businesses in defining complicated and automatic data transformations, loading data from many sources, and continually loading data to a destination database or data warehouse.
Cloud data management Tools
These tools are designed on and for the cloud, allowing them to connect to and integrate a wide range of data sources via APIs, webhooks, or direct database connections.
Master data management tools
These data management tools assist a company in visualizing complicated collections of master data across the organization.
Data visualization and data analytics tools
Data visualization and data analytics tools assist businesses in visualizing, analyzing, and exploring huge datasets, as well as creating reports and dashboards on top of them to extract insights and influence critical business decisions.
Over to you
Data is a valuable asset for any company, and it is growing at a fast pace. With growing data volumes, companies are also facing challenges in data integration, storage, and transformation.
Automating these processes allows businesses to manage data more efficiently and securely.
Data management automation is becoming increasingly important for startup businesses. It assists you in solving your data issues by allowing you to control your growth and scale, improve data quality, and ensure data consistency.
Utilizing data automation tools simplifies the process of data management and helps you to achieve results faster.