My Big Data

If you think ‘big data’ is already exploding, prepare to ‘hold on to your hat’ in 2016!

The team at gathered an interesting collection of predictions for 2016 from a bunch of data experts – we’ve all got a big year ahead of us.

The Microsoft Excel of Big Data

What stood out to me the most from the predictions was an expected explosion in ‘self-service’ data analysis.

Currently ‘big data’ is #toohard for most people – it looks like something you need to invest a lot of time and money in. But the predictions for 2016 are that companies will move past the experimental phase (suitable mainly for early adopters) and focus on easy-to-use analysis tools that help people with specific issues.

The introduction of user-friendly spreadsheet software in the 1970/80s was ground-breaking as it meant anyone could do evidence gathering and analysis more intelligently and a lot faster. An introduction of user-friendly self-service big data tools in 2016 will have the same ground-breaking effect, but at a whole new level.

Better decisions, more scrutiny

With self-service big-data analysis available we should see better-informed business decisions being made. More staff will be able to analyse external data easier, and then improve it further by applying contextual knowledge from internal data. New hires will be expected to be data savvy – not quite data scientists, but able to handle and analyse their own data.

The downside (or upside, depending on your point of view) might be that decisions will undergo much more scrutiny. If everyone can easily do their own evidence analysis for decision-making, you will have no excuse if you didn’t. What’s more, you can’t just hide your evidence/workings behind some fancy infographic, you will be expected to package it so others can run their own analysis over it.

Quality data

Their other common prediction is more at the technical end – a stronger focus on the quality of big data outputs. Firstly by collecting richer data (quality rather than quantity) and secondly by improving the algorithms (so their outputs align with expected actions).

The old IT saying goes “Garbage in, garbage out”, so focusing on ‘smart’ data (e.g. including more context, extracting semantic knowledge from web pages) should improve the resulting output, right?

Shakespeare said “All that glisters is not gold”, so designing & tuning algorithms around the actions/decisions that the output will support means they should output more useful results (i.e. actual ‘gold’).

Don’t be left behind

There is some debate whether big data is in the ‘early adopter’ or ‘early majority’ phase, but either way, if these predictions are correct then big data looks set to move into the next phase of the technology adoption life-cycle in 2016 – are you busy getting ready for that?


About InsightNG

InsightNG is a smart thinking-tool for collecting and analysing knowledge using our contextual intelligence engine. It helps you to gain greater clarity and deeper understanding when making important decisions, completing assignments, or getting through personal challenges.



About Douglas Campbell

Douglas spends a galling amount of time thinking about metadata and helping people find the information they need. Previously he has designed and product-managed search systems for national institutions in New Zealand. He is now working deep down in the nitty-gritty, developing our machine intelligence engine.