ketl quick guide to data analytics

Quick guide to data analytics

How to turn your data assets into customer insight to add value to your buiness


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“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.

”


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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.

“

”


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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


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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.


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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

[email protected] for

more information.

“

”


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Get in touchFor further information or help with your

data analytics project speak to Helen to see

how we can help >

Helen Woodcock

[email protected]

Illustration www.thirteen.co.uk

ketl quick guide to data analytics

Data & Analytics