1.
Quick guide to
data analytics
How to turn your data assets into customer
insight to add value to your buiness

2.
Quick guide to data
analytics
1
“Generate insight from
your data with 6 top
tips plus a case study:
start thinking like a
data scientist.
1. You already know more than you think
You probably already have a good idea of what
you think is right and wrong with key areas of your
business. You might even have something
specific that you want to investigate. Just be
prepared to learn as you go.
2. Get to know your data sources
Start with a single data source that your business
already knows really well. Check for obvious
errors in your data. If you have a realtively small
amount of data you can do this simply by
exporting to a spreadsheet. The most efficient
way is to profile you data. You can use a free data
profiler tool to create a report on how well your
data source rates on different data quality scales.
You can then make a value based decision on
how much to invest in correcting the poor data.
See our free data quality fact sheet for more
information on data profiling.
You want to get your data to work harder
for you and to be able to use the ‘data
lake’ of cusotmer information that you
have stored; but you don’t know where to
start or what questions to ask. These tips
will help you to consider where to start
gathering that valuable insight.
”
“Profile the data from
each new source
before you introduce
it into your analytics
reporting structure. As
your understanding
improves across each
data source you can
start to consider
blending the data
between the data
sources.
”

3.
Quick guide to data
analytics
2
This might sound
strange but it is
important to be
prepared to get
things wrong. A
scientist creates a
hypothesis that is then
tested through
experimentation.
“
3. Keep it simple when you can
If you already have reports from separate
systems and you can compare the report outputs
easily then you don’t need to integrate the data at
source you could perhaps just produce an Excel
spreadsheet.
Gaining an understanding of just what you can
glean from the data available with the tools at
hand is important and controls the scope of
demands for reports from the wider business.
Using systems that are already in place is the
best way to start. The business may well learn so
much from these first inroads into data analytics
that it decides to invest further to gain more
insight.
4. Think like a scientist
IWhen you fail you learn more than when you
succeed. Fact based evidence leads to a
working theory that can then be used to create a
conceptual framework.
As a data scientist your aims are to understand
the relationships between the data in your
organisation. You may start off with a hunch
about a particular business issue; so consider
what data sets surround the business issue
process and then test your theories. Just
remember to document the entire journey. ”
It isn’t always
necessary to merge
data sources and
sometimes it just isn’t
possible.
“
”

4.
Quick guide to data
analytics
3
5. Fail fast, fail cheap
Analytics is a fast moving process and it is all
about experimenting, documenting, learning and
then moving on. Once the learning has taken
place, the analyst can share the findings with the
wider business, then move on to the next
analytics project.
6. Data specialists need to get out more
Get the analytics team out to the different
business departments and out to the customer so
that they can be aware of data related issues and
witness impact. Always remember that your data
is your competitive advantage – it is a key asset of
your business.
“By using your own
customer data you
will be able to
create more
accurate models
that provide
meaningful insight
to your own
business processes.
”
Next step data analytics
Here at KETL we are a data integration partner
with TIBCO Spotfire - a powerful data analytics
tool. Each week the Spotfire team provide a new
demo for visitors to explore. The advantage of a
tool like Spotfire is that you can have a central
analyst that creates the analytics environment that
can then be used by multiple business teams who
are not trained analysts.
http://spotfire.tibco.com/solutions/technology/big-
data

5.
Quick guide to data
analytics
4
Case study
An online retail call centre based in South
Wales. The call centre can easily track call
volumes to establish the busy periods for their
Customer Service Agents (CSAs) so then they
decide to develop their reporting by measuring
call volume by length of call and start to track if
there are patterns developing on length of calls at
particular times of day.
They use date and time, as these data elements
will be constant in each of their data source
systems. The telephone software they use also
has a good reporting system that the business is
comfortable using.
Now the business decides it can match the stock
inventory against the call centre volumes to get
an impression of the number of calls per sale, the
number of items per sale and the value of each
sale.
So even though the two systems are not
integrated they are able compare the data from
each source to plot productivity over different
departments over one day. With this information
the business is then able to establish measures
of activities against each department.

6.
Quick guide to data
analytics
5
Immediate gain
1. The business insight that has been gained
allows the business to plot trends across its
departments.
2. Once the business identifies these daily
measures it can then make progress on how to
make improvements by assigning Key
Performance Indicators (KPIs).
3. Putting in an analytical process that is making
use of systems already in place is almost always
less expensive than creating new data
warehouses.
Learning
The business realises through the data profiling
exercise that there are frequent input errors made
by the call centre CSAs. Although the codified
data input errors can be resolved quite easily, it is
not so straightforward with free text.
If the customer service CSAs have a data entry
screen to input free text but then they forget to
code the complaint it will be difficult to analyse
and learn from this vital customer interaction.
Data input errors can be rectified through better
entry code design in consultation with the CSAs.
What you can do next
is download your own
copy of The Essential
Guide to Better Data
from ketl.co.uk
We also can offer
small workshops or
information evenings
to help you and your
team to learn more
about data analytics.
please email
helen@ketl.co.uk for
more information.
“
”

7.
Quick guide to data
analytics
6
Get in touch
For further information or help with your
data analytics project speak to Helen to see
how we can help >
Helen Woodcock
helen@ketl.co.uk
Illustration www.thirteen.co.uk