The importance of being a data driven organisation

The importance of being a data driven organisation

Don’t just ask “what happened?”  Ask “Why?”

A data driven organisation does this routinely, daily, constantly.  More importantly, they leverage their organisational data to support strategic decision making.  Imagine knowing not only the details of which products outperformed and underperformed this month (or last month) but also the reason why.

Armed with the what gives you the ability to respond to the past.

Armed with the why allows you to influence the future.

Data analytics

For many businesses, data analytics can seem like an intimidating prospect, and a “nice to have” rather than a “must have”.  Despite becoming increasingly essential in maintaining a competitive advantage, the importance of being a data driven organisation is sometimes misunderstood.

Common barriers experienced by organisations that have attempted or considered data analytics include:

  • It’s expensive
  • You need a lot of data
  • Our systems don’t talk to each other
  • It’s all too technical
  • We don’t have the skills

And these are all valid concerns.

Technology is evolving at an increasing pace and what we are now able to achieve, is far superior to the capabilities we had even five years ago.

The problem is, many of us struggle to keep pace with these changes and rarely have time to assess how we think about our problems. Worse, we still think in terms of the technology we know and learnt in the past and are unsure what this new era means to us and our organisations.

For data analytics be effective, you need to challenge the current thinking.

Ask “What can I learn from my data?”.

And follow these key steps in establishing an effective data analytics platform:

  1. Understand the context
  2. Organise your data and people
  3. Analyse
  4. Tell a story
  5. Socialise

1. Understand the context

You may know every facet of your business but defining the context is a crucial exercise.  Get back to basics and ask:

  • What do I need to know?
  • Why do I need to know it?
  • When do I need to know it?
  • Where do I need to know it?
  • How do I need to know it?

The value of group think

The team approach is so essential, mainly from the group think perspective.  Collectively think about all the issues you’d like to tackle. Don’t just think about data, focus on what you’d like to explore. Think:

  • Business pain points
  • Operational bottlenecks
  • Data credibility

2. Organise your data and people

Data analytics is as much an endeavour about people as it is about data.

People

You will need a champion; someone who is going to drive this like a business unit.  This someone must take ownership, be accountable but also have a budget (time and money) plus the support from the top.

Organising the team is also essential.  It pays to assess what skills you have and get the right balance.

  • Identify what skills are available in house
  • Work out what skills can be attained through training
  • What skills can be brought in from outside
  • What skills are not essential

Going outside may be less than desirable but our industry has a significant shortage of quality data scientists, which means inhouse may not be an option in the early stages.

Data

Inventorise all data sources, no matter how relevant.  When assessing each, ask yourself:

  • What data does it include and how much of it is used?
  • Does the data sources support the context you defined earlier?
  • What data is needed?
  • Are they all connected or can they connect?
  • How well is each data source managed?
  • Where are the gaps are how will you fill them?

ETL tools automate the process of extracting data from all sorts of data sources and consolidate them in to a format and structure that suits your needs.

For a deeper dive: How to cut your month-end reporting time in half

3. Analyse

There are two types analytics: Exploratory and Explanatory.  The distinction is clear in their titles but often the lines are blurred when put into practice.

Exploratory

One of the key attributes of exploratory data analytics is that by its very nature, the outcome is unknown.  It’s crucial to explore deeply and learn from your exploration.

You will fail, probably quite often in your quest for knowledge, but that’s the point of exploration.  The key is to learn from these failures and build on them.

Explanatory

You will discover highly valuable insights that can be shaped in to actionable dashboards that support the organisation.

Once you’ve found a diamond in the rough, thoroughly test it before publishing.  Make sure it can stand up to scrutiny before you hang your hat on it.

4. Tell a story

It’s one thing to have the data but it’s another to effectively communicate it. And there’s no better way that with an interactive dashboard!

When creating a dashboard keep these points in mind:

  • Be concise
  • Eliminate any clutter
  • Make it cohesive
  • Highlight the message

Oh, and sprinkle on some glitter!

Take inspiration from examples of graphs and visualisations by experts like Nathan Yau (https://flowingdata.com/about-nathan/) & Cole Nussbaumer Knaflic (http://www.storytellingwithdata.com/)

Gift wrap your message with story telling

5. Socialise

For your organisation to benefit from data analytics, valuable findings must be available to the right decision makers at the right time.  BI tools are the ideal platform or building compelling dashboards.  When developing dashboards make sure they are:

  • Live
  • Accessible
  • Transparent
  • Challengeable

Why you need to ditch excel and start using BI tools

To Recap

So, if you’ve not already made the leap to becoming a data driven organisation, now is the time.  Just make sure you take a considered approach:

  1. Understand the context
  2. Organise your data and people
  3. Analyse
  4. Tell a story
  5. Socialise

For a broader perspective take a look at the recording of our Webcast:

Reaping the benefits of data driven analytics for the SME

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