The average startup with less than ten employees makes use of 26 apps on an average. Larger businesses with an employee count between 250 and 1000 make use of as many as 124 apps. This is according to a report published by SaaS management firm Blissfully.
Sometimes the reliance on third-party apps could be part of your business model. If you are a company that dropships products from a supplier or printing accessories on demand, then your website is integrated with third-party apps to perform its basic functions.
Such a sheer use of apps by businesses could be contributing to operational inefficiency.
Consider this – businesses use applications like Salesforce to reach out to prospects and engage them. In addition to this, applications like HubSpot are used to bring visitors to your site organically through inbound marketing techniques.
Yet, once the prospect is converted into a lead, businesses make use of tools like MailChimp to send newsletters to engage these leads and convert into paying customers.
Each of these tools operates in silos that are independent of each other. Capturing insights from each of these tools independently can be misleading while manually aggregating data from these tools for analysis can be time-consuming and operationally inefficient.
This makes data integration a vital aspect of the modern-day decision-making process. With the integration, data from one app seamlessly flows to the other and this enables holistic data aggregation and analytics process. In addition to improving operational efficiency, this also helps with a more accurate insights-gathering system.
Achieving data integration
Most data integration solutions are developed with the help of what is known as ETL methodology. ETL, or Extract-Transform-Load, relies on extracting data from the source server, processing them, and then loading into the destination server in the new standardized format.
Let us take a real-world problem to understand this integration better. An ERP service provider uses Salesforce to reach prospective customers and engage with them in order to convert them into the lead. The company also runs a website built on LAMP to acquire new leads via online ad campaigns. The data captured from both these sources need to be stored in your enterprise server that runs on a different architecture like PostgreSQL.
A large organization will have dozens of such lead acquisition sources and a number of different use-cases for the acquired leads (for instance, you may need to share a part of this acquired data to a third party agency without violating GDPR).
Traditionally, organizations have IT teams that focused purely on enabling these integrations. This can be messy today given that different teams within an organization use their own app for outreach and communication. The workload on your IT could be huge, not accounting for the subsequent lag in deployment.
It is now typical to make use of third party integrators to execute these needs. For instance, you could use such an integration instance to pull data from your MySQL database and use a similar instance to translate information to your PostgreSQL server – this is done without a lot of development resource.
Coming up with an implementation strategy
A CapGemini report looked into the silos around organizational frameworks and determined how it affects their ability to innovate. A large part of this stems from their inability to make use of data that is available at hand to analyze and interpret information. Another report from the Economic Intelligence Unit found a direct correlation between financial performance and data strategy.
These studies underline the importance of establishing a clear data integration strategy for your business. Doing so allows businesses to plan and execute their data analytics strategy that helps maximize impact on the bottom line.
Does your organization have a data integration strategy in place? How has it impacted your performance? Share your thoughts in the comments.
