How to turn raw data into an asset for your business
Many companies have lots of data but don’t know how to use it. These data could include information about customers, telephone numbers of companies, data from GPS trackers and so forth.
It becomes useful when data is organized and analyzed. The company delivers parcels and cargo to people and businesses, for example.
Managers receive daily information about the weight and cost of the packages, as well as the distances that drivers have to travel. Without analytics, all this information is useless.
A little statistical analysis can help you determine when there is an increase in shipment, the distribution of traveling distances, and what items are most commonly ordered.
The company’s advertising department can then create targeted campaigns based on the data.
This can be helpful, e.g. Preparing drivers for heat seasons, such as at the beginning or end of autumn, hiring more of them, advocating for new trucks, and so forth.
Another example. Another example is that a company transporting goods collects daily data from drivers. Telematic systems track fuel consumption and miles traveled.
This information can be used to make trips more efficient and calculate safer and more cost-effective routes. Software vendors that specialize in logistics such as Twinslash are doing this.
Useful data can also be a huge boost for healthcare operations.Doctors can use machine learning algorithms to detect patterns and tendencies that humans don’t notice and process and analyze health records and laboratory data, especially imaging data.
Data is a valuable asset for any business. Data is an asset that can help improve a business’s competitiveness and to redefine business strategies.
But, raw data, which is just the unstructured and unorganized values that your system collects, cannot be used to make business decisions.
Use the ETL Pipeline to Make Sense Of Raw Data
ETL (Extract-Transform-Load) is a technology designed to collect and transform data from different sources and transfer it to an intermediate storage base.
This storage base can then be used to create a data warehouse/datapool, from which data can be fed into machine-learning/AI algorithms for analysis and forecasting.
How does the ETL process work? Data is pulled from many sources, including web pages, CRM, SQL, NoSQL, SQL databases, emails and so forth, depending on the company’s data.
Next, the data are converted and sorted. Automated algorithms, or manually sorting people, can get rid of duplicates and junk data.
ETL is ideal for processing legacy data and revealing insights. This is why ETL can be so beneficial for use in the travel industry, healthcare and fintech.
Next, the data are loaded into the target software — either manually or automatically.
ETL can be used:
- If all data is from relational databases or if the source data needs to be cleaned up before being loaded into the target system
- When you are working with relational databases and legacy systems
- When a company must protect data and comply with various compliance standards like HIPAA, CCPA or GDPR (another huge plus for the healthcare and fintech industries).
Although the ETL pipeline has been proven reliable and is well-established, it can be slow and requires additional tools such as Informatica Cognos Oracle and IBM.
Data Engineering is now Faster with the new ELT Pipeline
Information is growing in volume. The ETL methodology is not always able to handle large data sets that are needed for business intelligence purposes.
Therefore, a new, more modern method has appeared – ELT (Extract-Load-Transform). It also involves collecting, organizing, cleaning, and loading data.
It differs from ETL in the fact that data is sent directly to the warehouse where it can be checked and structured. There is no limit to the amount of information that can be kept there.
ETL is therefore more flexible and quicker. You will need the following tools to complete such a process: Kafka and Hevo data.
When to use ELT
- When you have to act quickly ELT is a great tool for making decisions and collecting data to reach your business goals. scaling startups/re-positioning business.
- When a company receives an inordinate amount of unstructured data;
- You are working with hybrid architectures or cloud projects.
ELT is an alternative method to ETL that is slowly replacing it. This allows you to rapidly scale up projects in competitive markets.
ELT is flexible and cost-effective, with minimal maintenance. It can be used by companies of all sizes and industries.
Example of Data Pipelines to Make Better Decisions
Large companies have shown that data analytics can be used to reach various business goals when supported by a solid data pipeline.
The recommendation engine from Amazon is a great example of how to use a data pipeline in eCommerce. Amazon has implemented a dynamic, unique recommendation model in their e-commerce products.
Amazon’s recommendation engine communicates with buyers at every stage of their journey through the site, suggesting the target product and motivating purchases.
An algorithm was developed by the company that matches products purchased in the past and is rated by users with similar trading positions.
The engine combines them into a recommendation list. The system uses a lot explicit and implicit data, including user’s product ratings and purchases.
It also allows for the system to generate personalized recommendations.
Otonomi’s predictive engines are used in transportation and travel. Otonomi, a freight company, developed its parametric solution using OAG data.
Otonomi can use OAG travel data to predict and forecast delays, price more accurately, and calculate potential risks.
The company was able to significantly reduce its administrative and operational expenses due to its ability to quickly process data and generate insights to facilitate disruption management.
We have already discussed how good utilization health data can positively impact patient outcomes. To improve harvesting, agricultural firms can use data about the weather and the prices of agricultural machinery components.
To detect fraud, insurance companies can access customer claim histories. Anonymous customer data can be used in the media to determine user behavior patterns and to find areas where UX can improve conversions.
Final thoughts: Don’t forget about data literacy and Accessibility
Every employee in the company must be able to see the benefits of data analytics. Let’s take, for example, a data pipeline that you have built into your transportation company.
Drivers, managers, customer service specialists, and others who aren’t data scientists need to be able see the data and understand where it came from.
Data analysis can only be useful if it is simple to understand and find. Data tools that are not understood by data scientists are useless as business intelligence tools.