What are data connectors and why are they essential for businesses?

Each year, businesses become more reliant on digital tools, making increasingly data-driven decisions to further the success of their company – decisions which are informed by insights derived from that data.

However, with the increased insight that is able to be obtained from data analyses, the wealth of sources containing important, informative data has similarly increased, making it more difficult for businesses to effectively compile and analyse the data.

As a solution to this problem, data connectors are proving themselves essential for business, allowing them to get the most value from their data.

A data connector collects data from a variety of sources – which could include a host of different databases, files, softwares, CRMs, analytics platforms and more – and delivering it to one predetermined singular destination.

This ‘destination’ is usually a data warehouse – or a data lake – as these services are able to store large amounts of data which can be easily queried or transformed; when assimilated in one location, the data is more easily analyzed to generate insight.

In order to remain useful, data must be regularly retrieved from each data source so that any analysis that takes place is based on the most up-to-date, relevant data, to enable similarly relevant insights to be derived.

Data connectors can run on a schedule. Users can specify how frequently they want the data connector to run, whether it’s every minute, hour, or day. The frequency depends on the nature of the data and how current users want the data to be.

When the data is retrieved by the data connector at each of these intervals, this can be done using one of two methods: snapshot or incremental.

In snapshot retrieval, all the data from the source is recaptured by the data connector, including all old and new data.

When retrieving data incrementally, only the new data that has accumulated since the last ‘run’ by the data connector is retrieved.

In the majority of cases, incremental data retrieval is the better option as less energy, storage and costs are associated with retrieving only the newly collected data, compared to fetching all data from the source every time the data connector completes a run.

In the digitally-driven world of business that exists today, there are numerous ways in which a data connector can be used to improve the operations and success of a business.

Data connectors enable businesses to acquire a 360 view of their data.

In order to get the most complete view of the performance of a business, insights need to be based on as much relevant data as possible, in order to get a 360 view of how an area is performing, using all the data available to make that judgement.

By compiling data from the various databases, files and softwares that relate to a particular area of performance – such as customer engagement and satisfaction – businesses can gain a comprehensive view of that area from a variety of different angles.

Data connectors enable the seamless aggregation of data from multiple sources into one place – typically a data warehouse – so that analysis of data can take place with the whole picture in mind.

Data connectors can integrate with other Business Intelligence tools.

In addition to aggregating data into a centralized location for analysts to study and derive insight from, data connectors can also be integrated with BI tools to make the usefulness of this data extend even further.

Data connectors can integrate with reporting applications and dashboards, creating visualizations that enable the compiled data to be displayed in a way that’s easy to digest and understand. This is particularly useful when it comes to identifying patterns and trends over the long term that could be crucial to the business.

Data connectors improve productivity.

If a company decides to forgo the use of a data connector, and instead opts to manually compile data from multiple sources for analysis, it will be a huge drain on both time and human resources, since each data source will need to be accessed – and data will need to be transferred from each source – one at a time.

Not only is this practice more susceptible to human error compared to using a data connector – for example, someone may forget to include data sources or fail to compile all the relevant data from each source – but it’s hugely inefficient compared to the speed of transfer carried out by a data connector.

Because of the time-consuming nature of this task, it’s also likely that the data will be updated less frequently, otherwise it would dominate the schedule of the workforce, so insights are likely to be less relevant and up-to-date.

The time spent by the workforce painstakingly gathering data for analysis could instead be spent carrying out higher-value tasks while a data connector compiles the data.

Data connectors allow companies to make data-backed predictions.

Businesses should no longer make decisions based on subjective human opinion. Today, most major corporations make their decisions based on insights revealed by data analysis.

In particular, effectively compiling data that has been recorded over a long period of time can allow for the analysis of past patterns and trends in order to predict what may happen in the future.

This can help businesses prepare for events that are likely to affect the performance of a particular area – or all areas – of the business, to maximize success during these times, and to rectify any business operations that may be causing a decline in performance.

Data connectors are making it easier than ever before for businesses to easily connect to and start using their essential business data. If you’re looking for an easy way to compile all your business’ data efficiently, enabling important, up-to-date insights to be made quickly, then consider implementing a data connector to reap the incomparable benefits for your business.