5 Tips to Quickly Spin Up Analytics Projects
Data analysis and data science are two of the most highly sought after skills in the data world, and people with these skills are typically passionate about discovering insights through analytics. But data professionals spend a significant amount of their time on the less-than-glamorous requirements of their job: requesting hardware resources; acquiring and cleansing data; and writing complex code to answer basic questions.
What if you could get to the fun part of your job faster? Here are five tips to consider when spinning up analytics projects.
1. Make IT an Enabler
All too often, the IT department is viewed as a roadblock to new analytics projects. IT intervention and assistance is needed for vetting vendors, allocating software licenses and integrating new hardware into the data center. And with competing projects across multiple departments, IT must allocate limited resources to meet the needs of the entire organization.
Tip: Let IT make your project easier. Consider vendors that require little involvement from your IT team. Solutions that handle several areas of the data pipeline – acquisition, data storage and analysis, for example – can help you get started quickly, as can cloud-based solutions that do not require new hardware.
2. Move Fast
Many analytics projects are pilots and experiments. You do not need to overthink every facet of the potential data infrastructure or wait for a million-dollar database appliance to reach your office. Better to move quickly, try, fail and move on without major investments of time and resources.
Tip: Spin up pilots in days or hours – not months. Choose a solution that allows you to quickly deploy pilots, experiments, and proofs of concepts. If your findings prove valuable, then move the project into production.
3. Work with Multiple Data Types
To get the biggest bang for your buck, look at analytics solutions that easily ingest and process structured, unstructured, and multi-structured data in one place. Join traditional structured data from business applications with multi-structured big data from newer sources – web logs, sensors, mobile applications, and other sources – to uncover new insights that may not be visible from any single source.
Tip: Don’t be overwhelmed by multiple data types. Choose solutions that make it easy to integrate and analyze structured and unstructured data from multiple sources.
4. Access Data Effortlessly
Most tools are really good at handling one part of the data pipeline, but it doesn’t have to be that way. New solutions make it easy to acquire data directly from applications, then integrate, analyze and visualize that data all within the same platform.
Tip: You need data. Find a solution that makes it easy to acquire the data. The more steps in the data pipeline that solution can manage, the simpler your job becomes. Want to move data or results to another system? Make sure whatever you choose makes it easy to export your data whenever you want.
5. Don’t Reinvent the Wheel
Many of the analytics tools that have entered the market in the recent past require you to learn new, complex programming frameworks. And they aren’t compatible with traditional database connectors and languages, so you can’t work with the other solutions to which you’ve grown accustomed.
Tip: Your skills are incredibly valuable. Choose analytics solutions that help you leverage your existing skills and tools. When you need to quickly spin up a project and start analyzing, think twice before you invest in a tool that requires you to learn new languages and frameworks and may not even be compatible with the rest of your environment.
When you need to spin up future analytics projects, look for solutions that you can deploy quickly. You should be able to begin ingesting, integrating, and analyzing data in days – with the SQL skills and business intelligence tools you already know.
Want to spin up your next analytics project right now? Try the Treasure Data Service for free.