Gathering and analyzing data underpins any company’s business strategy and decision-making. Every improvement a business makes these days - anything from introducing new sales strategies to adding new partners in the existing ecosystem to transforming IT and so on - is backed by data that tells a story. Businesses need to employ different strategies to manage this data in order to drive value and create new revenue streams.

With the surge in big data, businesses have access to huge volumes of data, both structured and unstructured. Now, the technical teams and other business users need to come up with ways to use this diverse data to its full potential. And in order to unlock its true power, data must always be available in any format, at any time, in any place, and it must be governed or monitored to ensure quality and trust. Data has to be ingested correctly. It, then, should go through an ETL (extract, transform, load) pipeline to be reliable and, if any error remains in the initial stages, they impact the quality of the output at the end of the process.

Data ingestion is a process that takes place first in the data pipeline. It moves data from the place of source into a place where it can be safely stored, analyzed, and managed.

As businesses adapt to the big data revolution and Internet of Things (IoT), they must learn to manage the large amounts of data and different sources, which make data ingestion far more complex. It is important not only to be prepared for the present state of data ingestion but to look ahead as transformation gains momentum. Here are a few best practices to consider as you reflect on your data integration strategy:

1. Decide Whether You Need Batch-Streaming, Real-Time Streaming, or Both.

Data ingestion can be carried out using a batch data source, streaming data source, or a hybrid. Based on whether a company needs to ingest data in batches, in real-time, or both, their data ingestion will differ accordingly. This will greatly depend on the kind of data being handled, whether the data is on-premise or cloud-based, or hybrid.

2. Trust the Process and Manage Your Time Wisely.

As per Forbes, data scientists and analysts spend almost 80% of their time preparing as well as managing data prior to analysis. This time spent in preparing and managing data could otherwise be used to streamline analysis. But the need to prepare and manage data cannot be ruled out either. As per the IDC report, companies will start investing in data preparation tools greatly (almost 25% faster than conventional tools). These solutions will aim to simplify and speed up the data ingestion process.

3. Rely on Artificial Intelligence and Machine Learning-Powered Technologies

There was a time when data ingestion was performed manually. One individual defined a global schema and assigned a programmer to each data source. The job of these programmers was to map and clean the data into a global schema. But, with the growth in the volume and number of sources of data, managing these steps manually is almost impossible. Artificial Intelligence-enabled technologies can help companies automate the data ingestion process and ultimately save the day.

Artificial Intelligence (AI) is rapidly transforming the way companies process large amounts of information. It enables even non-technical teams to identify patterns, patterns, definitions, and grouping that are often lost to human error and oversight. While non-techies manage data ingestion, IT teams can focus on other innovation-driven or strategic tasks.

4. Data Governance is Key

Maintaining data quality is important to ensure the reliability of the insights and accuracy of data analysis. Teams must make continued efforts to maintain the quality. A data steward has a big role to play here. Not only does it determine the schema and cleansing rule but also decides which piece of information should be injected into each data source. Along with that, it manages the cleansing of dirty data. No doubt data governance is not limited to clean data. It must include security and regulation compliance.

Maintaining data governance is essential to unlocking opportunities. It is, however, necessary to plan the governance process ahead of time, so it is ready to manage data after ingestion.

Final Word

Data ingestion is indeed the first step in the data pipeline and it is extremely important. Businesses can employ these strategies to perform data ingestion properly with the help of Adeptia and pave a path to better decision-making and faster value generation.