As our world becomes increasingly digital, there are massive amounts of data being generated, stored and maintained. Such data has huge untapped potential—harnessing it can help organizations improve business outcomes, differentiate products and services, develop a competitive edge and provide better customer experiences. That said, extracting insights from big data is not easy. It requires a scalable, efficient and effective cloud analytics platform that can plug into disparate data sources and transform structured as well as unstructured data into meaningful information.
Rather than jumping right into a platform trial, buyers should first look inward and have a clear understanding of the business goals and strategy behind organizational as well as technological considerations. Organizational considerations can include things like budget availability, the workforce skillset, the goals and ambitions of business leaders and other organizational dependencies. Technological considerations can include things like assessing the state of existing technology, the overall data storage strategy (cloud, on-premises or both), data lake and data science requirements, advanced analytical needs, business intelligence (BI) or analytics ambitions and the costs associated with ongoing systems management.
Once there is complete clarity on where the organization is coming from, where it intends to go and the level of its commitment to technology, skills building and analytical innovation, buyers can then proceed toward narrowing down potential candidates based on product attributes.
Can the product be deployed on-premises or in the clouds of your choice? Does it support data science on its DBMS? Can its capabilities be exploited by SQL-savvy analysts, Python enthusiasts and power users? What kind of support is available from third-party data science platforms, marketplaces and ecosystems? How onerous is the deployment experience? Is it a serverless offering that will automatically scale up as your data requirements grow? Can it sustain a given level of query performance? Does it support your expectations of users, and can it drive reports, views and dashboards at scale? Can it address diverse analytical and data science requirements? Does it provide built-in machine learning algorithms for predictive analytics? What is the total cost of ownership?
In addition to the above considerations, from a best practices perspective, here are five key factors to keep in mind before zeroing in on your new analytics solution provider.
1. Think big and think long term.
It’s typical for organizations to outgrow their estimates in just a matter of a few years, either through organic growth or inorganic activities (such as mergers and acquisitions). That’s why it’s always a good idea to look back at history, accommodate emerging requirements and plan deployments that will stand the test of time. Imagine what would happen if data estimates doubled over the next three years. Could the platform handle it?
2. Look for consistency and flexibility.
