# Do you really need big data to start using data science?



> The key to getting started with data projects is starting small. Learn more at Nightingale HQ.
> 
> **URL:** https://www.gosmarter.ai/blog/do-you-really-need-big-data-to-start-using-data-science/

**Date:** 2019-10-21
**Author:** Mia Hatton

**Categories:** Archive

**Tags:** artificial-intelligence, data-strategy, research

## 


All businesses generate data. Even the smallest business has access to
hundreds, if not thousands, of interesting data points that they could
explore. But it is not uncommon for business owners to think their data
is small, inferior and not yet worth analysing. This is where they are
wrong every time. Starting small is the best thing you can do, so we
say, the time to start your first data science projects is now.

Big data projects are expensive, time-consuming, and - by [some
estimates - link no longer works]() - carry a
shockingly low success rate. Investing in such big data projects is
unwise unless you have a [strong
foundation - link no longer works]() of data science
competency and a culture of appreciation for AI and its benefits.
[Redman and Hoerl
suggest - link no longer works]() that
instead of waiting for big data to be available, you implement a series
of \"small data\" projects, to build this foundation and tap into the
benefits of data analysis right away.

The benefits of these small data projects are far greater than you may
expect. They cite a much higher success rate, lower costs because of
smaller teams and reduced time requirements, and - at the bottom line -
an annual financial gain of \$10,000 - \$25,000. Perhaps more key than
any of these benefits though is the impact on company culture. Involving
your staff in small analytical projects builds skills, confidence, and
appreciation for the benefits of automation and AI. Not to mention, they
can be really fun!

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height="180"
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What should your first small data project be? Ask your staff what would
benefit them - what do _they_ want to know? Identify a business process
that you want to streamline, automate or speed up, and gather a team to
work towards the goal. Even when working with small datasets, work on
building the right skills by taking a disciplined approach and not
skipping steps. You may also need to provide relevant and hands-on
training to speed up skill
development.

\"Start small\" is a resounding instruction, appearing in many guides to
preparing your business for [big
data - link no longer works]() and
[AI - link no longer works]() . As Redman and Hoerl so
nicely put it, it \"build\[s\] organisational muscle\" and fosters data
literacy. There\'s much to learn from the data you already have, and
with a small dataset you can get started right away.

If you start your data science projects while your data is small, your
big data projects will be more likely to succeed, and you will have
benefited from all the insights you gained along the way.

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