As a company grows, it can implement several systems to collect and store critical data to run correctly. Unfortunately, they tend to adopt new platforms on an as-needed basis. In addition, they are not connected with one another, resulting in discrepancies across data platforms and entering information into each system manually. Inconsistent or inaccurate data may not seem like a big issue when done in smaller amounts. Still, as an organization grows, minor inconsistencies between systems can become huge roadblocks in data verification, retrieval, and reporting. Because data inconsistencies may cost an organization much money, it is in a business's best interest to look for big data integration solutions.
Cross-referencing measurements is tough when they're strewn all over the place. For example, let's assume your sales staff keeps track of their sales funnels with the help of an app. Once they make a transaction, all prospect information must be re-entered into the accounting system as customer data. It's also likely to be re-entered in the project management software and the invoicing software. Your productivity could be optimized if both systems were interconnected. Data Integration allows data to be input once and then correlated across all other programs, rather than entering the same data into each software program. Redundant duties lead to more mistakes and take time away from more essential activities.
Nowadays, multiple solutions in the market go from data warehousing, data governance, data transformation, data lakes, and data transfer. Most companies use Fivetran, Segment, or Stitch data to transfer their data from their different tools to data warehouses, where they can then perform data analysis by connecting various tools. No matter your preference below is the information you need to exploit your data in a data-driven world.
- FROM ETL to ELT
If your company has a data warehouse, you're probably using one of two data integration methods: extract, transform, load (ETL) or extract, load, transform (ELT). ETL and ELT are two of the most common methods for gathering data from numerous sources and storing it in a data warehouse that can be accessed by all users in a company.
ETL is the conventional technique of data warehousing and analytics, however, ELT has emerged as a result of technological developments.
ETL, which stands for "Extract, Transform, and Load," is used to get all of the data into one location. Data is taken from a variety of sources, such as ERP and CRM systems, converted (calculations are applied, raw data is turned into the appropriate format/type, and so on), and then uploaded to a data warehouse, also known as a target database.
Data is first imported into the target database and then transformed in ELT; data transformation takes place within the target datawarehouse.
Systems using ELT such as Datagran extract and compile different information, allowing management to gain a complete picture of their business. Then, it is feasible to identify strategies to enhance overall productivity by examining deep connections between measures. Otherwise, you're just making adjustments on the spur of the moment.
Here is a sample view of how Datagran allows you to centralize many data sources into a single system:
2. Data Integration Security
What happens when two systems are combined? Is the transfer of data from one place to another exposing and perhaps jeopardizing your data's security? What influence does data integration have on data security?
Is your organization more exposed to a data security breach as a result of data integration? What do you need to do to ensure data integration security?
Companies can handle data security in a variety of ways, depending on the solution. Data integration platforms provide a higher level of data protection than custom-made solutions. Many effective data integration solution vendors have given much thought to how to prevent data corruption and breaches. They do, however, generally have significantly different personalities.
Companies must focus on security as it is a day-to-day struggle for businesses. Over 300 million people were affected by publicized data breaches in 2020, according to the ITRC Data Breach Report 2020. This number has increased from the initial report in 2015. (169 million). In fact, in 2021, this number is anticipated to rise.
Let's assume I am a bank with a database that stores account balances and account numbers in table format. If I'm an IT working for this bank, how can I guarantee that there will be no breach in the data integration process? At Datagran, for instance, we guarantee the security of data integration by IP and SSL. IP is an address that a computer identifies and acts as a firewall. Then, during the integration process, the bank verifies if Datagran's IP address is registered and safe to access that said database. SSL is an additional security layer, Datagran provides SSL in this process as part of its data integration security– Secure Socket Layer (SSL) is an acronym for Secure Socket Layer. A transport layer protocol allows a secure connection to be established between a server and a client.
Our Integration tool walks you through a specific form to connect with your data storage system, ensuring that security is at the forefront of your business. Once integrated, a dashboard compiles information about the integration status, data rows retrieved, replication frequency, and more. Learn more about integrations here.
- Data Governance
The accuracy of data-driven insights and technology is only as good as the data that goes into the– "garbage in and garbage out," as the old saying goes. Therefore, maintaining data integrity throughout the data management and the analytics life cycle is crucial for delivering helpful business insights and establishing trust with customers, workers, and other stakeholders.
The people, methods, and techniques involved in data acquisition, storage, and usage are known as data governance.
Having this in mind, companies need to understand what types of data they extract during an integration process.
For a mass-production company in the food industry working with Datagran to centralize their data and run and put ML models into production to increase productivity, this is a huge factor to consider to understand shifts in their business successfully. One of our features nested in our integrations tool solves this problem.
As the name implies– these are flows of data that are extracted from a data source. Let's take the above company as an example. They currently have a data warehouse storing customer and product information. With streams, they can choose which information to extract. This customization gives them complete control over which information to use to run models. Furthermore, they can start building a model with an incremental widget by adding the said data source. Then, an SQL operator can customize the information brought in even more, like, for instance, customers with an email address or a specific ID.
Business systems must be smoothly linked to developing a successful big data system. For example, when a corporation utilizes a combination of old systems and newer platforms to gather and store diverse data sets, big data plans fail because the web service API connections do not easily combine multiple platforms. Hiring integration specialists to build bespoke APIs for each system to allow those systems to transfer data across systems in real-time and database administrators to monitor the data interchange and manage the databases to keep the systems operating as intended helps alleviate this difficulty.
Replication is another widget within Datagran's Integrations tool which controls the frequency to extract streams from the database.
When companies transfer data from one company to another, many times there’s no information on the streams and rows transferred. It is very important in the Data governance cycle to keep track of this information and metrics to make sure the data is there besides the traditional manual work to try to ensure data integrity is maintained.
In Datagran we provide the tools to track the streams replicated, times, and the amount of data transferred per row to give users additional information.
Data integration provides many advantages to businesses to increasing productivity and boosting business analytics. Organizations that leverage their data have the resources they need to keep ahead of market trends, maintain consumer loyalty, and boost income by combining data from many systems into a centralized location.
To learn more about how you can get started in data centralization, contact us for further information.