Here I am in seat 17D with an oblivious seat recliner in front of me and a computer so jack-knifed I can barely read the screen much less type with hands inches from my chest like tiny arms of a T-Rex.

But that won’t temper my enthusiasm because I’m flying home after two days at INMA’s Data Insights Conference in Chicago, where we heard from experts about how to successfully implement analytics strategies, no matter the budget, and quickly drive value for an organisation.

For your consideration, below are just a few highlights distilled into seven data rules of the road.

1. Develop a culture within an organisation that trusts and respects data.
   
   Tom Argiriou from Gannett warned that companies must trust the data insights from the systems and people in which they invest. Don’t build a quality data science team of people, implement supporting technology, and then distrust the answers they deliver.
   
   
2. Build governance rules and processes to ensure quality of the data and customer experience.
   
   As more data sources are linked and data analysts are developed across an organisation, rules and processes to determine and regulate the use and prioritisation of data becomes a foundational requirement that is championed by the most senior members of the company.
   
   
3. Soft data has just as much value as hard data – just apply it appropriately.
   
   We learned from the inspiring design thinker and entrepreneur, Corey Ford of Matter, that quantitative data are trailing indicators of success. Prove or kill ideas based on qualitative not quantitative data.
   
   
4. Tell a simple story with data. Use visuals and graphics to tell a story.
   
   Suneel Grover from SAS reminded us not to describe our findings in ways that communicate the complexity of the task. Rather, speak to the most senior person in the room and tell a story. This is an important distinction when our charge is to communicate data insights that will inspire change, not fear.
   
   Joe Germuska, officially titled chief nerd at Northwestern University Knight Lab, reinforced the importance of storytelling by demonstrating products developed at the Knight Lab for newsrooms to provide more accessible visuals to communicate important news and information.
   
   
5. Staff data science functions resourcefully. It is possible to build a data science capability on a shoestring budget.
   
   Laura Evans from Scripps Network Interactive built her world-class team resourcefully and strategically. Rather than hire a single data scientist with all the skills needed, which is very expensive and hard to do, Laura created a data science ecosystem that links existing skills across different departments. Data silos were connected to create a cohesive team of data science with one set of priorities and a shared roadmap.
   
   Suneel Grover suggested that Direct Marketing Association (DMA) conferences are good places to find well-trained data scientists and marketing analysts. When asked specifically about alternatives to expensive modeling expertise, Suneel and others suggested that it was possible for an analyst to teach herself R, an open-source statistical computing tool, and be up-to-speed within a few weeks. Sounded ambitious – but thought I’d share.
   
   
6. Data are not just to be collected, stored, and organised. Data are to be used.
   
   Tom Argiriou reminded the group that data without driving and influencing business decisions is nothing.
   
   
7. Define the term “data” – the word itself can be a crutch.
   
   Andy Monfried from Lotame recommended that we break down what we mean by “data” and define it for our organisations, then build data management platforms to support that vision. Andy and other speakers felt that a mission statement for data use is important.

The INMA Data Insights conference hit it out of the park. Don’t miss the great ideas and insights from our next North American conference in February 2016!

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