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Friday, May 24th, 2019


Top five tips for data integration in the cloud

Data-driven digital transformation represents a huge paradigm shift for businesses, allowing them to become nimble and more competitive by leveraging the power of data. Across industries, the businesses that are leading are those using data to steal a march on their competitors - reacting more quickly to trends and demand, providing better services and finding cost-cutting efficiencies.

This is a serious issue for businesses who are not using data in the right way, or not using it at all. Such organizations need to recognise that the bar has been raised and embark on their own transformation journey as soon as possible.

The shifting of workloads to the cloud is a vital piece of the transformation journey, but this has proven to be a significant obstacle for many. To avoid some of the pitfalls and ensure the best possible outcome, here are some best practices for businesses to ensure a seamless transition to the cloud:

1. Know your data

Most major data integration mistakes can be traced back to failures around understanding what data exists in the source and target systems.  Knowing where the data lies is of paramount importance before starting any data integration strategy. This allows businesses to iron out any inefficiencies or poor practices in data management that are bogging the business down. More importantly, it helps businesses realise faster time to value, minimising back-end admin work and freeing up time to focus on nurturing their customers.

Metadata-driven data management strategies can also help ensure seamless data migration between platforms. Such strategies detail and record the context around the creation of data, meaning IT professionals can then analyse their data in greater detail to achieve its full usefulness and value. By cataloging data using its metadata attributes, users can better leverage data to perform a wide range of important business activities like data access, data exploration, query, virtualisation, analytics, governance, and curation. More importantly, this sets a good foundation to take advantage of emerging technologies like machine learning. Metadata cataloguing should be put in place at the start of any data integration plan, because retrofitting it later is risky and disruptive for businesses.

2. Use data management solutions to manage your cloud

Enterprise-scale applications involving the cloud can be extremely complex, with data frequently flowing between different clouds and on-premise data platforms. This creates a need for data management solutions to manage the huge array of data that sits across the different platforms and ensure that this crucial business asset is well-maintained for optimal performance, security and compliance. Today, businesses are only as strong as their data makes them, so keeping all datasets well-maintained is a must.

Businesses must also ensure their data management infrastructure allows for easy onboarding of third-party vendors, which is especially important for companies with extensive active supply chains such as those in retail and manufacturing. This then allows vendors to seamlessly access the necessary databases for optimal business operations, allowing businesses to focus on securing more customers.

3. Govern your data holistically

In the privacy-first, post-GDPR age, data governance is perhaps among one of the biggest hurdles for companies. Over 50% of consumers in Asia Pacific surveyed by The Internet Society expressed that they would not use an online service if protection of their personal data was not guaranteed. Businesses need to recognise that they do not own the customer information under their charge, and are duty bound to protect and manage the data their customers have entrusted them with. The fallout from the Facebook-Cambridge Analytica scandal should serve as a warning for businesses to only use customer data for intentions it was originally collected for.

Data can sit anywhere - on premise or in the cloud. Company policies need to cover all platforms that organizations are currently using. Data compliance and governance should not be seen as an inconvenience, but as an avenue to business success. Robust data governance can also help to elevate data standards and quality, allowing for more effective usage by businesses. For companies without a robust data governance strategy, the shift to cloud should serve as an opportunity to put one in place.

4. Prioritise data integration requirements

Many business applications and other operations now require access to data to perform optimally. Therefore, during data migration to the cloud, the IT and data management teams need to coordinate closely and ensure data can be easily but securely accessed. In addition, cloud-based applications may involve the use of differing interfaces, protocols and data formats from common on-premises enterprise systems, so businesses need to ensure their data integration toolsets meet all the different requirements across their IT infrastructure.

5. Upskill your staff to thrive in the cloud

Employees and data are some of a company’s most important assets. With the shift to cloud and hybrid data environments, the demands on IT and data management teams will be very different from before. Therefore, businesses need to ensure their support teams are upskilled to stay informed of the new developments in the data management space.

Like with any new technology, businesses need to be clear on how to best implement it and gain maximum ROI with fastest time to value. The last thing businesses want is for their investment to be wasted, but with these best practices companies stand the best chance of their investment having a transformative impact.