Cloud computing has gone mainstream in recent years. Lower upfront capital investment, flexible cost and support models, scaling on demand, and the increasing number of platforms and services in the cloud are some of the key reasons for its popularity. Adopting a cloud-first approach is becoming common while planning for enterprise and specialized solutions. Depending on the need, clients can build their cloud strategy using infrastructure as a service (IAAS), platform as a service (PAAS), or software as a service (SAAS) models. As with any new technology, there are important lessons learned from early cloud data solutions that could make or break your project. Below, we will highlight the lessons and implications for data scientists, marketers, and IT looking to leverage a cloud-based data solution.
Data scientists require an analytics playground where they can bring in disparate data sources in one place and use advanced analytics to draw insights, define and enhance audiences, and measure performance. They are constantly requiring access to new and emerging data sources to test and learn for strategic differentiation in the market place. Cloud is a great environment for the analytics environment, as it is quick to setup, ingest CRM, digital, and third-party data. Plug-in analytics tools on top of the data allow for faster insights, testing and learning, and other analytics use cases. It is critical to ensure good governance and a strong data management framework here.
Good governance translates to user access, compliance (digital data has stringent permissible/non-permissible, CCPA, GDPR compliance laws, sensitive data management), personally identifiable information, data retention, and resource management (runaway queries, storage, etc.).
Often, data scientists become data processors and extraction, transformation, loading (ETL) developers to manipulate data. We have seen examples where 40% of the data science team is spending their time to cleanse the data, manage files, manage data formats, and integrating data. This is a major dilution of the team’s value.A strong data management framework should automate ingestion, validation, and integration. ID resolution should also be done within the data management framework so that data can be linked across sources.
Today’s marketers strive to provide a personalized user experience across multiple channels and media. This is simpler said than done. It involves creating a complex marketing platform that can enable a 360-view of the customer, support program development, integrate with multiple platforms for activation, turn insights into action quickly, and accurately measure and report performance. Choosing the right architecture and tech stack within a cloud data solution is key to achieving business objectives. System integration and efficient data flow between these tools is critical to operationalize the capabilities that achieve scale in program execution and comply with regulations such as GDPR and CCPA.
Cloud is both an opportunity and a challenge for IT. While the benefits of the Cloud offer several business advantages, enabling them in a safe and secure way is essential for sustained operations. It starts with selecting the right cloud. Often, business strategy dictates or eliminates some cloud platforms (for example, many retailers would not want their data in Amazon). Beyond selecting the cloud, data scientists and marketers require their solutions with core services such as authentication, access control, automation, security, disaster recovery/business continuity, and relatively predictable cost management. Cloud environments do not come pre-packaged with these features, and it is the responsibility of the solution providers to develop and maintain these core services. Developing these features in the cloud environment could require a substantial amount of effort to get it configured correctly and to continually maintain and upgrade as new platforms and tools get integrated. Choosing the right partner to help with the configuration of your cloud solution is an efficient way to accomplish this, while client IT teams get up to speed in understanding the solution.
Overall, cloud data solutions offer several benefits to stakeholders, and many clients have successfully adopted them to execute their business objectives. At the same time, there are significant challenges that should be addressed when architecting, developing, and maintaining cloud solutions. Companies who can manage these challenges will have a strategic advantage to test and learn faster, go to market faster, and be able to integrate with emerging platforms more seamlessly. Selecting a proven and trusted partner who can help with strategy to execution is a great way to reap the benefits of cloud, while mitigating the risks.
Want to learn more? Click here to learn about Merkle’s entry-level, cloud-based data management solution called Rapid Audience Layer (RAL)