Fluctuating technology landscape and variable customer-market policies underpin today’s economy. The changing dynamics of business and rising competition have elevated the importance of data, making it one of the biggest assets.
Data is not only responsible for reinforcing and sharpening organizational agility to capture business opportunities, but also essential in helping businesses create value. To gain a competitive edge and ensure continued success in future, companies need to adhere to a robust data management tool.
Though businesses rely on data to attain their ambitious objectives, a majority of them still lack access to effective data management technology. Companies are unable to use this voluminous information to their benefit. The first step to making sense of this humongous data is understanding what a Data Lake is.
A data lake is a centralized repository that allows businesses to store, manage, and exploit their disparate, structured or unstructured, and external as well as internal data at any scale. Companies can store data as it is, without having to pursue the need for structuring the data first and running different types of analytics – from big data processing, real-time analytics to dashboards and visualizations. Organizations relying on data lakes enable users to collaborate and analyze data in different ways for better, faster decision making.
With so many benefits, data lakes are deemed essential for enterprises to grow. However, some data lakes fail to serve their purpose owing to their inherent complexity. Many factors can catalyse this complexity, one of them being improper data digestion. Companies need to establish a sound data ingestion strategy to use data lakes effectively, and here are the top 5 practices that help you do that.
Many businesses build data lakes for the sake of it. The respective IT teams are indulged in finding ways to transform their data lakes into science projects. However, this path will only result in problems. It is important to identify business problems for organizations to use data effectively, and how data lakes will solve that problem. If building a data lake seems the right solution after this analysis, only then organizations must forge ahead.
Typically, data ingestion process flow consists of three distinct steps: data extraction, data transformation, and data loading. With the increase in data volume, data ingestion process becomes highly complex and demands substantial time.
As a result, methods like data ingestion automation are used where the incoming data is converted into a single, standardized format automatically. Automated data ingestion platforms allow organizations to ingest data efficiently and quickly.
Tools used for automated data ingestion assure success to a certain extent. However, they fail to conduct root cause analysis themselves in case a failure arises. Therefore, a holistic solution that not only automates data ingestion, but also conducts supportive tasks, such as quality checks of incoming data, managing the data lifecycle, and automating metadata application, deliver better value for organizations.
Large files are data integration sphere’s biggest pain. Processing of such large volumes of data often leads to application failures and enterprise data flow breakdowns, resulting in incomprehensible information losses and painful delays in processing mission-critical business data.
Companies need access to a robust data ingestion solution to process, ingest, and transform large volumes of data. The data ingestion platform chosen needs to be elastic and agile to survive the ebbs and flows in data volume. Besides, carving out soundproof data retention strategies that include points like where data will be stored and how long will it be stored, etc. will help organizations in the long run.
Ignoring streaming data as the primary information source can sabotage organizational efforts to use data effectively in this generation. B2C companies use data streaming to analyze their customer behaviour better, thereby helping them offer delightful customer experiences. To conclude, while designing a data ingestion strategy for data lakes, it is important to think of different types of data one may receive, including streaming data, files, or batches of data coming from different sources.
Since data ingestion involves a series of coordinated processes, notifications are required to inform various applications for publishing data in a data lake and to keep tabs on their actions. With the help of notifications, organizations can gain better control over the data lake and improve transparency and traceability.
Automated data ingestion in a data lake calls for careful planning, strategic building, and skilled resources. Companies that devise an effective data ingestion strategy based on the factors mentioned above are likely to accelerate their revenue and deliver a fantastic experience to their customers.