The no-hype guide to:
Data analytics

What it is, what it isn’t,
and what it can do for
your business

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Data analytics gets a lot of hype.

A lot of that hype is good hype.
The kind that powerful data-analytics initiatives deserve.

But a lot of it is bad hype.
The kind that confuses data-analytics with things it isn’t…

data science
artificial intelligence
data mining
neuro computing
big data
deep learning
machine learning

The difference between analytics and AI, ML and DL

A system for discovering, interpreting, and communicating
meaningful patterns in data.
Artificial intelligence (AI):
This is a machine’s ability to learn through
problem solving
, and improve on that ability over time, by itself, until it
demonstrates ‘intelligent’ behaviours like visual perception and decision-making.
Machine learning (ML):
This is a system’s ability to automatically learn from
– and respond to – data without additional programming.
Deep learning (DL):
This is about using multiple layers of artificial neural
to power complex data initiatives, unearthing insights humans
couldn’t possibly spot on their own.

So let’s talk about what
data analytics really is

- without the frills.


Data analytics is the practice of applying an
algorithmic or mechanical process to derive insights
from data that inform queries set by users.

Here’s what it really is:

Data analytics is how you give the smartest people in
your business the ability to find verifiable answers to
the most important questions.

The kinds of questions that help you run your business well. Things like:

The kinds of questions that help you
run your business well.

Things like:

What do my highest
lifetime value customers
have in common?

If I could predict X (the
weather, the traffic, etc)
…how would I change
my prices?

Do some product managers
have an outsized impact on
our speed to market?

Do we lose customers
gradually or suddenly?

What are the anomalous
behaviors of a fraudster
and how quickly can we
spot them?

Could certain elements
of our business data
indicate the presence
of fraud?

How would we build on our
prevention policies if we
could detect banking fraud
in real time?

See, analytics for the sake of analytics is meaningless.

You need to be aiming for a
specific business outcome:
a clear goal.

Speaking of specific outcomes:

– here are the three big
things you’ll be able to do
when you embark on a
data-analytics initiative:

your business with new product
ideas and market insights

Customer and supplier 360 | Market segmentation |
Recommendation engines | Predictive product development

your products and services

Predictive maintenance | Predictive health | Customer churn
prediction | Financial risk assessment and prediction

your business

Cyber security | Fraud prevention | Anti money laundering |
Insider trading | SPAM detection

(And yes, build the foundations for ML and
AI initiatives further down the line.)

“If your company isn’t good at analytics, you’re not ready for AI”
Harvard Business Review, 2017

So let’s talk about how to achieve
those three big outcomes.

If you haven’t already, you’ll want to do
something called data “governance”.

This is where you get your data ready for analysis
– and make sure it stays tidy.

Data governance:

Data can be messy. Here’s an example. Some customers get called ‘customers’ only
once they’ve paid. Others get called ‘customers’ when they’re only using a free trial.

If you’re a company trying to analyze your customer list, that small discrepancy could
wreak havoc.

Data governance is about finding discrepancies like that, then setting up organization-
wide policies, standards, and metrics for dealing with them.

The next step is where
the fun starts

This is where you actually ask the data questions.

The good news – it’s not that hard to write a basic data query.

The good news

– it’s not that hard to write a basic data query.

What you
want to know:

How many of our products that
cost between 50 and 100 dollars
are out of stock?

How to ask
your data:

SELECT * FROM Products
AND Inventory IS 0


And with some practice, you’ll be writing more complex data queries, like:

And with some practice,

you’ll be writing more complex data queries,

What you
want to know:

From my selected sample of
different hotels and room
availabilities, which particular
hotels have at least two rooms
available for less than $186?

How to ask
your data:

SELECT a.hotel_name, a.city,
COUNT(*) AS room_count FROM
hotels a INNER JOIN rooms b
ON a.hotel_code = b.hotel_code
AND b.price < 186 GROUP BY a.hotel_name,
a.city HAVING COUNT(*) > 2
ORDER BY room_count DESC


Now for the tech.

The first thing you need to know
– the cloud is way better for data
analytics initiatives.

How older data warehouse architectures
hurt you:

Your data warehouse architecture has a huge impact on your ability to do data
analytics well.

Older architectures can make analytics projects cost as much as 60-80%1 more
– they slow things down, make it hard to pivot, and generally get in the way of
everything you want from an analytics initiative.

They just weren’t designed for what we’re doing today.

1 https://nortal.com/blog/behind-bi-infrastructure-basics-for-data-driven-organizations/

In a nutshell:

Cloud   faster data insights cheaper data insights smarter data insights better data insights

It’s why we only ever recommend
cloud-native services for data analysis,
like Google Cloud Platform

By the way, that’s the same platform that Google use to analyse their own data.
– you know, all the information on the planet.

Using Google Cloud Platform, some pretty
big companies have brought about some
pretty neat outcomes

Drop us a note here and let’s answer
that question together.

Read “Three ways enterprises win with
data analytics”
to make a solid business
case for data analytics to the rest of your

Read “The Cloudreach data analytics
to see our methodology and
how we’ll work together.

It wouldn’ be fair to keep this
knowledge to yourself.

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