As a quick recap on the concept of digital transformation, we understand that data is the crux of any digital transformation solution. And as we face the digital world now more than ever, data has become an integral part of how a business operates.
But in this rapidly changing digital world, data rises from a multitude of sources in various formats, making it difficult for businesses to gain valuable insights. To keep up with it, enterprises today are looking for ways to integrate data from such disparate sources and make sense of it.
Data pipeline could be a solution to your data related business concerns.
With a proliferating digital world comes a suite of tools & apps that businesses use to serve different functions. For example, the sales team might depend on salesforce or any other CRM tool to manage leads; the marketing team might rely on Marketo or HubSpot to suit their marketing needs; the customer experience team might count on Magento to help curate unique experiences for their customers. This leads to the disintegration of data across multiple systems & tools, resulting in data silos.
Gaining valuable business insights is even more difficult if your organisation's data is siloed. Even if you manage to put together data from multiple sources into an excel sheet, analysing it could be computationally taxing. It may end up with errors such as data redundancy.
The solution to such a problem? An efficient data pipeline.
Data pipeline: a brief overview.
In simple terms, a data pipeline is a series of steps that consolidates data from a multitude of sources, processes it into a destination, and enables quick data analysis for business insights. They also ensure access to real-time, accurate data and consistent data quality.
A use case of data pipeline.
Let's say you run an online financial services company having a range of offerings such as trading, investment, advisory services, etc. It is open 24x7. That means your customers can pay and use your services any time of the day.
Your business has a massive magnitude of data getting collected into a database, but it is not easily accessible. Different teams have to work on a subset of the whole data to gain insights out of it, which means the data is not timely and accurate.
Say a user is interested in investing in the stock market and has opted for your advisory service to support his investment decisions. Your advisory team needs to have access to real-time, accurate, correct, relevant data to advise him on the same. Given the financial data the organisation deals with, imagine the negative impact one wrong decision could have. For such a scenario, having multiple, optimal access points for the various departments as per their requirements can help the business make informed decisions.
Below is a simple example of handling a massive data set.
The case in point here solves a similar problem mentioned above.
Problem statement: It is a case of a financial giant facing issues with handling a massive magnitude of data getting collected into a database day-in-day-out and in multiple formats. The data is not easily accessible, making it difficult for different teams to make sense of it. The teams have to work on a subset of the whole data to gain insights out of it, which means the data is not timely and accurate.
To help resolve it, the data was processed into a data lake giving multiple access points to the teams so that they can derive timely, relevant insights out of it and in less time. Insights that used to take hours to appear from the chaos could now be seen in a matter of a few minutes.
Wondering how to set up a data pipeline for your enterprise without making it complex and time-consuming? Let's find out together.