1How to Choose a Data Warehouse That Is the Right Fit for Your Company
Successful businesses depend on a steady stream of high-quality decisions. An enterprise data warehouse can provide the core infrastructure that fosters smart decisions across a company and its extended value chain. Specifically, a data warehouse is critical to the growth of machine learning, big data and artificial reality. However, if you buy and implement a system that doesn’t meet your core business needs, you won’t get the most out of your data warehouse and could end up spending more money to make it work in your environment. To avoid the excruciating pain of being stuck with a poorly fitted solution, here are some best practices to consider before choosing a data warehouse. Industry information for this eWEEK slide show comes from Gary Orienstein, senior vice president of products at MemSQL.
2Define DW Requirements, Build a Robust Architecture
Before picking a data warehouse provider, consider what performance characteristics you want to optimize for (e.g., cost, ingest speed, concurrency, latency). Depending on your requirements, there are different options in the market that will provide the value you’re looking to get. In fact, many issues from security to scalability and flexibility are critical in selecting the best approach for your organization.
3Design for Future Scalability and Performance Requirements
Before you begin, evaluate your current ingest and CPU requirements. Then determine how your needs are going to grow or change in the future. This information will help set you down the right path. Companies embracing big data need to prepare for the increase in data with an environment that can handle future workloads.
4Incorporate Popular Ecosystem Technologies
It is also critical to be aware of trends that new technologies enable, such as machine learning and big data. You don’t want to over commit to flashy new methodologies, but you want to be able to take advantage of the latest trends. Creating a flexible and agile environment will allow you to grow and change as necessary.
5Try Out Agile-Practice Pilot Projects
Several traditional companies are experimenting with agile practices in discrete pilot projects and realizing modest benefits from them. But fewer than 20 percent consider themselves “mature adopters” with widespread acceptance and use of agile practices across business units. Meanwhile, according McKinsey & Company’s observations, the companies that are deploying agile practices at scale have accelerated their innovation by up to 80 percent.
6Build End-to-End Data Pipelines to Drive Real-Time BI Dashboards
To drive value across your organization, it’s important to connect your data to BI dashboards. Since the data being streamed is valuable, you want to make sure everyone in your organization has in-depth visibility to the data. To create this value, ensure that your database products are compatible with the BI tools your team currently uses.
7Use a Lean Approach for Rapid Go-Live Deployments
When evaluating database products, important attributes to consider are usability and accessibility. Actions and processes should be automated when possible, and have external management layers that allow you to easily navigate to create clusters. This will lead to increased customer satisfaction through the rapid deployment, reduced costs and increased time to value-add projects.