Organizations have accelerated their move to the cloud and are tapping into data to drive better decision-making, but overly ambitious plans without clear deliverables can lead to costly missteps.
Too often, companies forget technology is simply the building block enabler to reaching a business outcome. Enterprises still need strong use cases as well as a strong methodology that prioritises use cases, to ensure their cloud and data initiatives will yield benefits for their business.
Cloud, for one, plays a critical role in helping companies digitally transform. However, it can end up being a costly affair if modernisation initiatives are not properly qualified or planned. As it is, McKinsey research shows that inefficiencies in moving workloads to the public cloud are costing companies, on average, 14% more each year in their migration budget than originally planned. Another 38% experience migration delays of more than one quarter.
Keep Your Eye on the Prize to Avoid Missing the Data Target
Such missteps can happen when businesses go for the big-bang approach and commit millions of dollars to an implementation roadmap that lasts many months, if not years. Figuring out where to start, from the point that will eventually deliver value, is a common challenge companies face in their move toward adopting modern technologies and transitioning to cloud.
Instead of beginning by building an enterprise-wide architecture, which can consume significant resources and time, they should start with smaller value-added use cases that focus on their business objectives. The use cases then can be used as a qualification and learning point, while also assisting the development of repeatable patterns that can be reused across future initiatives.
Doing so not only helps demonstrate that the deployment can succeed, but also proves that tapping cloud to facilitate data-driven decisions can produce real value for the company. Organisations can build on top of these initiatives and gain incremental value as they expand their cloud rollout.
Businesses are better able to innovate when they adopt this approach, while also enabling a more seamless transition to modern technologies, ways of working and problem solving.
More important, data analytics platforms must actually deliver on a use case, whether it is a product or service, before they can deliver value. In short, the desired business outcome should drive the data solution, not vice versa. For some organisations, simply gaining the ability to churn data reports faster is itself a strong business case for their move to the cloud.
This singular focus on real deliverable value is critical to ensuring cloud data analytics yield the benefits businesses seek. It also will help justify further investment in the development of new use cases.
This is what CMD Solutions helps its customers achieve. The Sydney-based firm is an Amazon Web Services (AWS) professional services consultancy that specialises in assisting businesses transition to cloud and adopt modern technologies.
One such customer is RateMyAgent (RMA), which offers a platform that enables property buyers and sellers to review agents and brokers they have worked with. It generates ratings based on the reviews, and the data is used by other sellers to make informed decisions on agents they want to work with. RMA has a presence in Australia, New Zealand, and the U.S. It owns rich data sets held in relational database services (RDS) systems across AWS regions in Sydney and California.
Boost Warehouse Utility as Data Builds
As its business grew, RMA realised it lacked a centralised data repository to support queries and reports on a global level. RMA ran targeted queries at the source database to churn reports, which meant scalability and speed were major challenges, as the process was highly manual. This resulted in issues with accuracy and consistency, eventually eroding user trust in the reports that were generated.
CMD Solutions was brought in to develop a solution that continuously streamed changes out of RMA’s source RDS databases. It then cleaned, anonymised and centralised data during the extract, transform, and load (ETL) process to generate a single unified analytics schema. The solution encompassed serverless tools and fully automated deployments, which ensured operational overheads and total cost of ownership were kept to a minimum. Services rolled out included AWS Database Migration Service, AWS Lambda and Amazon Athena.
With CMD Solutions’ guidance, RMA consolidated and restructured all of its global data into a single data warehouse, enabling its executives to access accurate data reports with speed. Because all ETL pipelines now are automated, RMA’s data analysts no longer need to carry out any manual conversions and can instead focus on generating high-value reports and analytics.
Furthermore, RMA can quickly rework existing reports to be generated from the new data platform and drive more sophisticated use cases, such as data application programming interfaces and machine learning.
As businesses such as RMA build up and add data, the utility of their data warehouse increases. This means they can continue to drive more value from having all their data in one centralised system.
