Driving business growth rests on knowing how to approach data integration

Businesses today have a lot of data on their hands. In India alone, there are over half a billion internet users, which means that businesses have access to a vast amount of consumer data. This data can be used to create a 360-degree view of customers, which can help businesses to understand their customers better and to drive growth.

However, businesses also face a challenge in that they often have difficulty making sense of this data. The data can be siloed and difficult to access, and it can be difficult to integrate data from different sources. This can make it difficult for businesses to get a complete picture of their customers and to use the data to make informed decisions.

Therefore, businesses that want to gain the upper hand need to be able to bring together information on customer profiles, transactional data, web analytics, and so on. This can be challenging since data is often spread out across the organisation, involving multiple sources and applications and typically mired in complexity.

Amid this, more organisations are looking to data integration as a means of removing the friction in their digital transformation efforts. According to Verified Market Research, the value of the data integration market could surpass USD 30 billion by 2030. However, it would be a mistake to assume that data integration is some generic cure-all that can be simply plastered on with little thought.

Can There Be Too Much Data?

Managing the flow of data to gain crucial insight into business operations is a complex and time-consuming process. The sheer volume of data held by the average business alone exhibits this. That information is more often than not chock-full of insights, and demystifying it could propel the business to uncover new opportunities and growth drivers.

Paradoxically, however, the amount of data organisations typically hold today also poses one of the biggest data integration challenges, as the amount of unstructured data becomes more of a hindrance than anything. Then, there is also the question of data quality, accuracy and security. Each data source has its own unique structure and format that can hinder the creation of a single unified view.

This not only has repercussions for enterprises' customer offerings, but also impacts their ability to navigate regulatory compliance. 

Hence, having a single source of truth facilitates the flow of reliable and actionable data, which is ultimately critical to ensure the accessibility of the entirety of information held by the business. The big question, however, is how do organisations go about establishing this, and what approach should they take to put an end to data being spread out across different platforms.

The Building Blocks of Success

While it may seem overwhelming, data integration can actually be achieved by sticking to some fundamentals. The first port of call should be to decide whether the business would most benefit from extract, transform, load (ETL) or extract, load, transform (ELT). Both have their merits, but the latter has the edge when it comes to usability and accessibility to business as well as customer data.

Organisations should also assess their integration needs, especially in terms of whether they want to outsource data operations to a third party or a cloud. Smaller organisations may not necessarily want to do this.

Sizing up a modern data stack also requires the business to gauge if it meets certain performance or regulatory compliance standards. Matters like latency and so on need to be looked at thoroughly, to meet service level agreements or steer clear of regulatory retribution.

Meanwhile, to position itself for integration success, businesses should weigh the feasibility of leveraging automated data integration solutions. Although there is no shortage of options in the market, businesses can pick the solution that best fits their goals by evaluating their value-add credentials. 

For instance, what time, money and labour savings does the solution bring to the table. A modern data stack should dramatically reduce data engineering costs by eliminating the need to build and maintain data connectors. This also ties into whether a modern data stack expands the capabilities of data teams by making more data sources available without  incurring more costs. 

The organisation should also assess how the modern data stack impacts reliability. Simply put, the right solution will shorten downtime and virtually eradicate maintenance burden. At the end of the day, businesses that want to thrive and differentiate from their peers will need a modern data stack that enables new data models to be built, including those that track the same entities across multiple data sources. Armed with such a tool, enterprises can leverage integration to drive analytics and become more data driven in their business decision-making.

The article has been written by Viswanath C, Sr. Director of Engineering, Fivetran India