Five Lessons In June 2017 On Big Data Success

At Contexti, we’re always striving to learn from our own experiences and from the insights of other industry leaders.

Here are five lessons we noted from our industry peers this month:

 

#1 – IT TAKES MANY HANDS, SKILLS & PERSONALITIES

Launching Big Data projects & making data-driven decisions requires a team with a variety of technical, business and soft skills. When working on projects, it’s important to have different voices and skills at the table. “Marketing and data teams should move closer together and explain in simple terms the likely outcomes of the insights created,” – Sherine Yap: global head of CRM at Shell.

 

#2 – SHARE THE VISION, THE JOURNEY, THE PITFALLS & THE SUCCESSES

In Chapter 1 of ‘Learning to Love Data Science’ by Mark Barlow, he shares his insight on communication, a fundamental part of any project.

“After you’ve laid out a roadmap of the project so everyone knows where they are going, you need to provide them with regular updates. You need to communicate. If you stumble, you need to let them know why you stumbled and what you will do to overcome the barriers you are facing. Remember, there’s no clear path for Big Data projects. It’s like Star Trek – you’re going where no one has gone before.”

 

#3 – PLATFORMS, TOOLS & DATA STRUCTURES MATTER

‘Every organization seeking to make sense of big data must determine which platforms and tools, in the sea of available options, will help them to meet their business goals.’ – Nick Millman: Data & Analytics Leader for Accenture.

Nick Millman goes on to discuss the importance of the structure of data.

‘How applications consume data should also be taken into consideration. For instance, some existing tools allow users to project different structures across the data store, giving flexibility to store data in one way and access it in another. Yes, being flexible in how data is presented to consuming applications is a benefit, but the performance may not be good enough for high velocity data. To overcome this performance challenge, you may need to integrate with a more structured data store further downstream in your data architecture.’ – Computerworld (from IDG)

 

#4 – IT TAKES TIME TO FIND THE GEMS

“What’s really important about Big Data is to understand that there’s a lot of this data, most of it’s completely worthless to the business, but there are these gems, these nuggets of information, like the fact a customer just had a baby. You want to take that information, you want to integrate it to your business decisions and make more money for your company.” – Andy Mendelsohn: Senior VP of Database Server Technologies at Oracle.

 

#5 – LEARNING FROM FAILURE

Sample, test and learn – should be the nature of your Big Data project.

“You can only fail better only if you learn from failures. And then failing is something that prompts you to move ahead.” – Pearl Zhu, Digital Agility: The Rocky Road from Doing Agile to Being Agile.

 

For more resources, please see the links below:

Google Books – Learning to Love Data Science by Mark Barlow (O’Reilly Media)

Marketing Magic Meets Big Data: How To Make Technology and Creativity Work Together

8 Considerations When Selecting Big Data Technology

An Introduction to Big Data – Smart Insights Digital Marketing Advice

Decoding Big Data with Contexti on EchoJunction Podcast

On this podcast, Adam Fraser from EchoJunction interviews Contexti Founder & CEO, Sidney Minassian. They discuss:

  • What is Big Data?
  • What size of data is considered big?
  • How should organisations approach their big data objectives?
  • Who should be involved in big data projects?
  • Details on the Seven West Media Big Data Analytics solution for the Rio Olympic games supported by Contexti
  • Achieving Big Data ROI vs experimental R&D
  • Does the CMO have a seat at the table?
  • Implications of algorithms on individuals

 

 

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