Data ingestion is the fuel that powers an organization’s mission-critical engines, from predictive analytics to business intelligence; machine learning to data science. In order to be useful, like any fuel, data must be of high-quality and abundant. Most importantly, it needs a place where it can be stored safely for further usage. Data ingestion is used by enterprises to obtain and import data for immediate use or storage in database.
While organizations find it easier to extract and load data, many are grasping at straws to deal with the “data transformation” part of data ingestion cycle. This challenge has the potential to disable efficient business systems and break business continuity, leading to delayed service delivery and slower revenue realization.
In good old days, when the size and diversity of data were limited to a few-dozen tables, manual data ingestion was a befitting solution. An operational user-defined a schema (that can be used manually), which was then assigned to a developer or a programmer for mapping. Mapping and cleansing routines were written and run accordingly.
In the current generation, the size and variety of data has increased remarkably and curating it with the help of manual ingestion techniques is folly. Essentially, companies need to adopt automated data ingestion techniques to utilize data. Automation not only alleviates the burden of data ingestion but also eliminates ingestion bottleneck.
It’s easy to get swayed by manual ingestion techniques that seem advantageous at first to business users. Truth is, automated data ingestion benefits the consumer and business user in the longer run. Automation helps companies ingest data systematically and efficiently, and as a result, the quality of insights extracted improves a hundred-fold, thus helping consumers and business users both.
Take a look at the reasons to find why automated data ingestion fits the bill.
Faster Time-to-Market: Almost 55% of enterprises informed that their inability to combine data from myriad sources has been holding them back from accomplishing their end-goals. In the absence of proper techniques to ingest data, data analyzation suffers and ultimately delays projects. Automated data ingestion methods allow enterprises to store and use data effectively, hence improving their time-to-market and giving them a competitive advantage.
Increased Scalability: The world of automatic data ingestion may feel mind-boggling, especially to a newbie who is trying to adapt to machine learning and data science. The good news is automation does not have to be a revolutionary process. It involves an evolutionary approach where companies can pick one of two data sources and automate it using industry practices. Once comfortable, companies can scale up data automation over time.
Minimized Risks: Data is the foundation on which an enterprise’s success, value, and growth stand. Any glitches in this can sabotage an organization’s reputation and demolish customer faith. Automated data ingestion mitigates this risk. The risk of human error introduced during the extraction, transformation, and loading process is eliminated. The risk of delays and falling behind the curve is also minimized.
Data Scientists Unburdened: Data scientists and IT experts have repeatedly reported that the least challenging and most time-consuming part of their work is data preparation, and automated data ingestion can take over this part immediately, giving IT experts the breathing room they need.
Instead of spending time on extracting data from sources, transforming data formats with valid rules, and loading data back into the warehouse, they can focus on other tasks of higher strategic importance. This removes the additional burden on data science experts and improves their efficiency.
The bottom line is introducing automated data ingestion can revolutionize companies’ ability to use data effectively, accentuate time-to-market goals, minimize risks as well as structure their data scientist teams to focus on growth-oriented tasks.