5 Ways to Avoid Common Pitfalls in Large-Scale Analytics Projects

5 Ways to Avoid Common Pitfalls in Large-Scale Analytics Projects

5 Ways to Avoid Common Pitfalls in Large-Scale Analytics Projects

Data now means more and does more within the enterprise than ever before. From mitigating financial risk by detecting fraud to creating recommendation engines and optimising the customer experience, data is helping companies solve increasingly complex problems.

What, then, have we learned over the past few years as data has moved to the forefront of organisations? With options ranging from proprietary software to cloud-based software and open source tools, today’s developers, architects, and IT professionals have many choices when it comes to large-scale analytics projects. Some require an expensive up-front investment. Others require many resources. And then there are tools that hit the sweet spot: they’re easy to implement and provide extensive features to prototype at scale.

Finding tools that increase project success and help you avoid common pitfalls is key. Here are five tips for selecting the right products for your large-scale analytics projects.

The biggest mistake companies make when embarking on an analytics project is to go too big, too soon. Often, especially when projects are driven from the top down, the temptation is to start by building a complex solution with no clearly defined outcome. This results in expensive and time-intensive projects.

Read Also:
When Big Data Isn’t Enough – You Need Fast Data!

Instead, start small and focus on quick, early ‘wins’ to build confidence with end users. Leverage modern open source technologies that don’t require large, up-front financial commitments and that enable your developers to get started quickly. A desired outcome is an application or prototype built in days or weeks.

Even though you may only be building a prototype, it is critical that you test for scalability as early as possible. Many projects fail because the application wasn’t built or tested with scalability in mind, or because the technologies selected were not designed to handle large data volumes.

Make sure performance testing is not an afterthought. Model out how much data you think you’ll be capturing over time. Test it, reference it, and build the right architecture to enable horizontal scaling with zero performance degradation as data volumes grow.

 



Chief Analytics Officer Spring 2017

2
May
2017
Chief Analytics Officer Spring 2017

15% off with code MP15

Read Also:
The Much-Needed Business Facet for Modern Data Integration
Read Also:
Artificial Intelligence and Virtual Reality Are About to Transform Business. Here's How to Be Prepared.

Big Data and Analytics for Healthcare Philadelphia

17
May
2017
Big Data and Analytics for Healthcare Philadelphia

$200 off with code DATA200

Read Also:
Business Transformation Demands Modern Data Integration

SMX London

23
May
2017
SMX London

10% off with code 7WDATASMX

Read Also:
MapR delivers persistent storage for containers

Data Science Congress 2017

5
Jun
2017
Data Science Congress 2017

20% off with code 7wdata_DSC2017

Read Also:
Embedded analytics is the way forward for data-driven business

AI Paris

6
Jun
2017
AI Paris

20% off with code AIP17-7WDATA-20

Read Also:
Business Transformation Demands Modern Data Integration

Leave a Reply

Your email address will not be published. Required fields are marked *