Data Ingestion: Where Every Healthy Data Ecosystem Starts
Businesses accumulate data in volumes beyond comprehension. However, gathering raw data makes little sense in terms of future perspectives and company growth. To make use of data, companies have to understand how to store data properly, transform it into actionable insights, and analyze the acquired information. The first step in turning your data into a revenue driver is to properly extract it from all sources and transfer it to a data warehouse for further analysis. And that’s where data ingestion comes in.
Analysts should ingest data before the actual analysis. The key to valuable analysis is to understand how data can be transferred and arranged in the database. In this post, we’ll break down the data ingestion concept, go through common data ingestion challenges, and take a look at its use cases.
What is Data Ingestion?
Data ingestion is the process of streamlining large datasets from external data sources to the required destinations. Destinations vary based on your objectives. It may be your internal database, external data warehouse, and other options.
Businesses ingest raw information to their warehouses for further organization and analysis. Cleansed and arranged data makes much more sense to analysts. Analysis specialists don’t have to waste time preparing insights each time they need to update dashboards and create reports.
Where Analysts Get Their Data From
Businesses usually accumulate user-generated data. Depending on the company’s requirements, analysts gather data from:
- Hardware, various equipment
- Desktop software and mobile applications
- Social media, websites, search engines
- IoT devices
- Other sources
The amount of data that can be ingested is limited only by the volume of your storage. Over time, organizations performing data analysis expand their list of sources. Companies accumulate more data as the number of clients or app users increases.
Data ingestion itself is a part of a bigger process called ETL (extract-transform-load). Skipping the transformation part, data ingestion deals with data extraction and loading it to the storage. The information is transferred through the data pipeline across different destinations and stages. Let’s take a closer look at data ingestion as a part of a marketing ETL system.
Marketing Data Sources
When dealing with omnichannel campaigns, marketers always process large clusters of data that contain metrics, customer journey information, user behavior patterns, and other types of marketing data. The main issue is that these data come from disparate sources. Generated insights from Facebook Ads, TikTok Ads, Google Ads, and a variety of organic sources may clutter your storage and fail to produce the desired result due to their chaotic structure. Moreover, if a marketing team extracts all data manually, they waste too much time on repetitive operations.
Data ingestion solves the organization problem. With well-designed algorithms, ETL systems can automatically extract data from multiple sources, cleanse it, then arrange it in the storage. For example, Improvado provides prebuilt data extraction templates that allow users to set up the connection with 200+ marketing data sources in a matter of seconds. Then, users can push raw data into the warehouse or transform it as necessary to operate with analysis-ready data in the future.
After the data ingestion process, the pipeline streamlines data right to visualization tools. With prebuilt dashboards, analysts can get fresh updates on their marketing performance at any time. The normalization framework removes the need for manual data preparation each time analysts need to check on metrics or monitor new trends.
SEO Dashboard Example
The Data Ingestion Process in a Nutshell
Now that we’re clear on the concept of data ingestion, it’s crucial to understand how this process is arranged and what actions take place in the background.
Modern data ecosystems offer two ways to ingest your data:
- Data ingestion in real-time mode
- Data ingestion with predefined intervals (batch data ingestion)
Both of these approaches have their own benefits and drawbacks. Let’s dive into each of them.
Real-time Data Ingestion
This method involves continuous data extraction from all required data sources. Real-time data is typically used in systems that need a constant flow of data to operate. For example, medical systems that track the patient’s heart rate and oxygen saturation. If the data infrastructure malfunctions, physicians can’t get a holistic picture of the patient’s health conditions, which may lead to undesirable consequences. It may also apply to IoT devices that continuously track the condition of devices or valuable assets. Losing sight of this information for even a minute may cost businesses a fortune.
The main benefit of this approach is evident. You get an uninterrupted flow of fresh data that allows you to constantly stay on top of the ongoing performance of your system. However, when implementing real-time data ingestion, you should analyze whether you really need this flow.
The thing is that data warehouses like Amazon S3 will charge you for the amount of data you’re storing. It’s not a big deal when you’re storing small amounts of data gathered from several sources. But if you promote your brand in different markets across a variety of channels, the storage price may become too high. The same thing applies to marketing agencies that work with a lot of clients. For example, Amazon S3 storage for 100 TB will cost you around $3,000 per month, not including queries, which are charged separately. Here’s a calculator that allows you to estimate expenses for your exact use case.
That’s why with real-time data updates, you’ll have to clean your existing data from time to time, which is a time-consuming and repetitive process. If you need to load historical data, you’ll have to increase your budget for data storing.
Interval Data Ingestion
Interval data ingestion implies extracting and updating data in your warehouse according to predefined time intervals. That means you’ll get fresh data once an hour/day/week and so on. This approach is a good fit for marketing analysis. Usually, analysts don’t need instant updates on the number of clicks generated by a new creative or marketing campaign. The banner itself generates clicks after a while. That’s why analysts can simply update data the next day or after a week to see the impact and assess the performance of their efforts.
At Improvado, we provide a one-hour data synchronization interval that suits all of our customers. Users can also set a custom data update interval or get new data on demand. By using this approach, we eliminate excessive costs related to ineffective data stockpiling and reduce the level of warehouse clutter for our customers.
It’s also important to remember that external warehouses like Google Big Query charge for each query analysts make. With less clutter in the warehouse, analysts will write more precise queries, thus utilizing the budget more effectively. Here, you can find a GBQ pricing calculator to estimate the expenses for data spring and analysis.
Why is Data Ingestion Important for Businesses?
Today, businesses utilize data in every field of activity. Companies use data to build effective marketing strategies, conduct market research, and choose their future development vector. Information enables companies to create a better product, provide a quality service, and make correct decisions faster.
Data ingestion is a vital part of the whole data analysis process. It’s crucial to start the data extraction process correctly as it is the first step toward achieving actionable insights. A common problem for marketing agencies is the manual configuration of data connectors. In the worst-case scenario, analysts have to ask developers to write queries that trigger the API. Even if that’s not the case, they still have to handle a manual setup, during which a lot of human error can occur.
Data extraction templates provided by ETL systems like Improvado eliminate the possibility of human error, therefore increasing the quality of the extracted data. Moreover, users can create custom templates tailored to their analysis needs.
Data extraction templates example
With correctly ingested data, the ETL system takes over the transformation and streamlining of data to destinations. The only thing that analysts have to deal with is the analysis itself. For example, in marketing, users utilize business intelligence tools that display insightful charts, dashboards, and graphics about the performance of each campaign. If clients don’t have enough time or resources for the analysis, the ETL vendor may allocate offsite employees under its professional services package. The external team will then analyze the partner’s data infrastructure and set up beautiful dashboards that reveal new intelligence.
Data Ingestion Pitfalls
Data ingestion, as a part of a complex ETL organism, has its own pitfalls and slippery spots. To correctly ingest your data, you have to be aware of these spots. It’s our task to walk you through them. So, let’s get started.
It Takes Time
Data ingestion takes time. Especially when it’s done manually. When streaming data from multiple sources to a single destination, it may come in different formats. Moreover, the same data fields from two different sources may have different names and syntaxes. The data is nonuniform. Even when it seems like you’re working with two similar datasets, they may have particular differences. That’s why all data should be converted into a common format that won’t cause any confusion during the analysis process.
However, extracting and transforming data manually not only causes human mistakes, but also takes too much time. This is particularly true for businesses that deal with data from multiple accounts. For example, we’ve helped a large marketing agency to optimize its data ingestion processes. With manual extraction, our partner spent five days drawing up a report for their high-spending clients. With Improvado’s ETL system, they’ve reduced reporting time down to one day. That’s a 500% time-saving , along with more accurate data.
The advice here is to find ways to automate the data ingestion process. Whether you’re building your own ETL ecosystem or hiring a third-party vendor, automating manual operations will significantly improve your analysis results.
Another critical downside of manual data ingestion is that your team may not have the required expertise to connect data sources or convert data to the required format. The thing is, if your analysts aren’t crackerjacks in programming, database queries, and analysis at once, you’ll have to find the right people to handle some of these tasks.
When it comes to automated data ingestion, ETL systems are designed in such a way that even junior specialists can take care of all processes on their own. Data extraction templates, predefined extraction intervals, and smooth data upload to multiple destinations allows for the seamless setup of new data sources.
Data source connection in Improvado
Data discrepancy is a major issue when trying to build a comprehensive picture of the company’s efforts. Disjointed data from multiple sources may ruin analysts’ workflow and lead to unclear and sometimes distorted results. To unify data based on a single data field or metric, specialists should manually go through each row and column in the table in order to put it in a uniform format. However, due to a lot of manual manipulations, analysts can overlook some inconsistencies or even create new ones.
To standardize data automatically, ETL systems are equipped with normalization features. For example, at Improvado, we offer an MCDM (Marketing Common Data Model) normalization framework. This solution unifies a disparate dataset based on parameters like age, geography, gender, device, and a range of other custom metrics. The software addresses all normalization issues on its own. At the very beginning, users create a normalization pattern and choose how exactly they want their data to be standardized. Further on, everything is automated on your behalf by the system.
An automated normalization approach reduces the number of errors that could be potentially made by analysts and leaves more time for them to work with ready-made insights.
Data Ingestion Use Cases in Marketing
Data-driven marketing campaigns are the future of marketing and have already taken their place. Without precise information about the performance of your advertising strategies, marketers can’t clearly identify points for improvement.
The first and the most important data ingestion use case in marketing is metrics monitoring. Marketing analysts pay a lot of attention to indicators like ROAS, ROI, CPA, CPC, and more. At the initial levels, marketers can monitor metrics directly in personal ad accounts. However, at a larger scale, when you’re dealing with tens of paid and organic marketing channels, going through them manually becomes exhausting and confusing.
CPC and ROAS marketing dashboard template
Data ingestion allows advertisers to extract all required metrics from their marketing accounts, gather them in a single data warehouse, and unify them on an illustrative dashboard. In this way, advertisers will have a complete vision of their omnichannel marketing strategy, understand which channels perform better, and will be able to identify the campaigns that could be improved.
Marketers use attribution modeling to understand which touchpoints and marketing channels are more likely to convert a prospect into a client. Different attribution models allocate the value of a conversion across channels in unique ways. With data ingestion and visualization, marketers can see the full path of their potential customers and analyze their interactions with each touchpoint across the customer journey. Stacking related insights in a data warehouse will accumulate historical data and bring more understanding from a long-term perspective.
Brand Promotion Across Different Regions
Large brands dealing with cross-market and cross-region marketing accumulate tons of disparate data in different languages, currencies, and cultural characteristics. When marketers get to analyzing data from different regions, scattered data may turn the whole process into a disaster. Not to mention additional activities like market research, A/B testing, and other actions that generate even more raw data.
Data ingestion helps marketing teams streamline their data flow and arrange information in the chosen warehouse. ETL systems, such as Improvado or Xplenty, supply advertisers with fresh, precise, and normalized data gathered from each and every marketing channel. With automated data ingestion, businesses get detailed marketing reports and all their datasets are just a few clicks away.
How can Improvado Help?
Data ingestion is a booming trend in data analysis and big data that should be taken seriously by every data analyst. An automated data flow opens new horizons for analysts and allows them to approach the same information from a completely different angle. With less time spent on aggregation, data scientists can finally concentrate on the development of valuable insights.
If you need help with data ingestion for your marketing data, Improvado is here to help. We create optimized data pipelines tailored to your business requirements and existing marketing ecosystem. We use every last drop of data to create the most actionable insights that will drive your company’s growth and increase your revenue.
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