Information is being exchanged in ever-increasing volumes, so it is somewhat of an understatement to say that organisations today are facing a complex data landscape. Data exists in on-premises systems and in the cloud and it is used across applications and accessed across departments.
Websites and social media platforms are constantly adding data to the mix within the enterprise, as well as new sources such as the Internet of Things (IoT) via sensors and smart, connected devices.
These disparate data sources are forming a chaotic, “accidental architecture”, where organisations can't get the right data to the right people at the right time. That means users such as business analysts and data scientists can't adequately analyse relevant data and get the most value out of it to enhance the business.
As well as having to deal with growing sources and volumes of data that are increasingly difficult to manage, enterprises are also faced with emerging business demands:
Increasing expectations, both internally and externally: Enterprises are expected to be more agile, with faster time to market for products and services and more rapid response times for customer inquiries, as any delays can mean the loss of business to more agile competitors.
The constant need to be compliant: It is essential that organisations are compliant with a growing number of government and industry regulations related to data security, privacy, and management, which is part of the broader issue of data governance. The latest example is the new GDPR regulation governing data privacy for citizens of the European Union – or the new California Consumer Privacy Act. In Australia, there is the Notifiable Data Breach (NDB) scheme.
Growing demand for accessible data: Enterprises must meet growing demands for self-service, as more and more business users demand immediate access to data and to the tools to analyse the data. This new generation of workers expects to have continuous access to the resources they need.
Organisations need new ways to work with data more efficiently to address these challenges or else they won't be able to compete in today's business environment.
Data warehouses are moving to the cloud
Legacy data warehouses force organisations to purchase and maintain lots of proprietary hardware in order to meet performance and capacity demands. When mixed with the rapid increase in the amount of data and data sources, this makes controlling costs a challenge for organisations.
Because of these shortcomings, many organisations are looking to make changes regarding their data warehouse strategy. Research conducted in 2017 by the Data Warehouse Institute shows that nearly half of the organisations surveyed (48%) are planning a replacement project for their data warehouse platform by 2019.
Many of these organisations are migrating to cloud-based data warehousing, which gives them virtually unlimited capacity, greater scalability, and a more economical way to leverage warehousing. But many organisations forget that moving the databases also means moving the analytics and visualisation. They're transitioning to business intelligence as a service which presents a new challenge for data integration.
Data integration a necessity in the cloud era
Incompatibility with new cloud platforms and data warehouses exacerbates the problem of sticking with legacy data integration tools. Many tools also don't support hybrid deployment models, meaning that some workloads need to be run on-premises for the lift and shift portion.
In order to overcome these problems, companies evaluating new data warehouses should consider what their cloud strategy is and find a solution that will support them, whatever providers they use now, and grant them the flexibility to change in the future. Future proofing also means making sure they can add data quality or data governance capabilities as projects become more complex, and organisations can introduce data quality or data governance capabilities. Together, organisations will be able to offload processing for machine learning, advanced analytics, or large volumes of data, into the cloud.
Another important factor to consider when looking at cloud integration products is the total cost of ownership. If the pricing model is complex and hard to understand, the organisation may be in for a nasty surprise or two down the road when it comes to billing. Many integrators will add on charges for connectors that businesses need to operate across different cloud providers, which means that the organisation will either be locked into their current strategy or forced to pay extra.
For many companies, it's not a question of if they will be moving their data warehouse function to the cloud, but rather when.
Organisations may also be exploring entirely new projects where the company doesn't really care about the legacy data warehouse. Either way, they want to have a solution in place that ensures smooth data integration and supports their data needs well into the future.