Many companies have a lot of data at their disposal but don’t know what to do with it. This can be information about customers, phone numbers of organizations, data from GPS trackers, and so on. When data is collected, organized, and analyzed, it becomes useful.
For example, the company is engaged in the delivery of various parcels and cargo to individuals and businesses. During the processing of orders, managers receive daily data on the size/weight of packages, their costs and delivery distances drivers travel. All this information without analytics is of no value.
With a little bit of statistical analysis, you can determine when there’s an increase in shipments, what the traveling distance distribution is, what items are ordered most frequently, and more. Based on this data, the advertising department of the company can create personalized campaigns for the target audience. It can also help, e.g. prepare drivers for the heated seasons (like at the beginning of autumn) or hire more of them, advocate for buying new trucks, and so on.
Another example. A company that transports goods collects daily data from car drivers — telematic systems track the number of miles traveled and fuel consumption. By systematizing this information, you can make trips more efficient, calculate more economical and safer routes, and so on — software vendors in logistics, like Twinslash, are doing that.
In healthcare, useful data also can give an immense boost to operations. The processing and analysis of health records and lab data (especially imaging data) allow doctors to devise strategies that’ll lead to more accurate diagnoses and new ways of treatment, with machine learning algorithms noticing patterns and tendencies humans overlook.
Undoubtedly, data is an asset for a business. It helps to improve competitiveness in the marketplace and redefine business strategies. However, before raw data — just various values your system collects, unstructured and unorganized, — becomes useful, it needs to be processed.
Make Sense of the Raw Data via ETL Pipeline
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 then can be used as a data warehouse/data pool, and data within them can be fed into machine learning/AI algorithms for analysis, forecasting, etc.
How does the ETL method work? For starters, data is pulled from various sources: web pages, CRM, SQL and NoSQL databases, emails, and so on — depending on what data a company has.
Then the data is converted and sorted. During sorting, automation algorithms or people who do sorting manually get rid of all duplicates, junk data, etc. ETL is perfect for processing and revealing insights from raw data in legacy systems, which is why ETL is so useful for utilization within the travel industry, healthcare, fintech, and other fields that are siloed and often resist digital transformation.
Then the data is loaded into the target system — again, manually or automatically.
You can use ETL:
- if all source data comes from relational databases, or if it needs to be thoroughly cleaned before loading into the target system;
- when you work with legacy systems and relational databases;
- when a company needs to carefully protect data and adhere to various compliance standards such as HIPAA, CCPA, or GDPR (another big plus for healthcare and fintech industries.)
The ETL pipeline is proven and reliable, but it is quite slow and requires additional tools: Informatica, Cognos, Oracle, and IBM.
Do Data Engineering Faster with New ELT Pipeline
The volume of information is constantly increasing. And the ETL methodology cannot always meet the needs of processing massive data sets for business intelligence purposes.
Therefore, a new, more modern method has appeared – ELT (Extract-Load-Transform). It’s also about collecting, cleaning, organizing, and loading data. However, it differs from ETL in that the data goes directly to the warehouse, where it can be checked, structured, and transformed in various ways. Information can be stored there indefinitely. Therefore, the ETL method is more flexible and faster. To carry out such a process, you will need tools: Kafka, Hevo data, and Talend.
When to use ELT:
- when you need to quickly (!) collect data and make decisions to achieve your business goals, which makes ELT very good for making choices from marketing data when e.g. scaling startups/re-positioning business.
- when a company constantly receives a large amount of unstructured information;
- you are dealing with cloud projects or hybrid architectures.
ELT is a more modern method that is gradually replacing ETL. It allows you to quickly scale projects in competitive markets. The ELT is economical, flexible, and requires minimal maintenance. It is suitable for companies of various industries and sizes.
Examples of Using Data Pipelines for Better Decisions
Many large companies have proven that data analytics, enabled by a well-established data pipeline, can be successfully used to achieve various business goals.
A great example of utilizing a data pipeline in e-commerce is Amazon’s recommendation engine. Amazon implemented a unique, dynamic recommendation model into their e-commerce product. The Amazon recommendation engine interacts with the buyer at all stages of the journey through the website, thereby suggesting the target product and incentivizing purchases.
The company has developed and implemented an algorithm that matches the products that are already purchased and rated by the user with similar or related trading positions. Engine assembles them into a list to be recommended. The system relies on a lot of explicit and implicit data: user’s purchases, product ratings, on-website browsing history, and adding to the cart which makes it possible for a system to generate accurate personalized recommendations.
A use case for travel and transportation would be Otonomi’s predictive engine. A company within the freight industry, Otonomi developed its parametric solution based on OAG data. It allows Otonomi to determine and predict aircraft delays in time, calculate pricing more accurately and calculate possible risks using travel data provided by OAG. Due to the rapid processing of data & generating insights for efficient disruption management, the company has been able to reduce administrative and operational costs to a large extent.
We’ve already talked about how good utilization of health data may positively impact a patient’s outcome, so that’s a benefit, too. Agricultural firms can use data on the weather, on the prices of goods and components for agricultural machinery to improve the harvesting process. Insurance companies can use customer claim histories to detect fraud. In the media, anonymous customer data can be used to identify behavioral patterns of users to figure out where UX can be changed to improve conversions.
Final Thoughts: Don’t Forget About Accessibility & Data Literacy
Everyone in the company has to understand the results of data analytics. For instance, let’s say you’re implementing a data pipeline into your transportation company. If you want your data analytics to be truly useful, drivers, managers, customer support specialists, and other people who are not data scientists must be able to see insights from the data and know where they have come from. You must remember that data analysis is useful when it is easy to find and understand. A data tool that only data scientists understand is worthless as a tool for business intelligence.
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