You need a strategy to succeed

Let’s start with a short anecdote; when Thomas Edison invented the first viable commercial electrical light-bulb in 1879, everyone thought that overnight the world of manufacturing would take a giant leap forward due to the new efficiencies that the electrical bulb would bring; however initial this was not the case, it was not until they realised that it was not just the introduction of the bulb, but also the fact that they needed to redesign the layout of the factories, how the lighting should be positioned, the employees working hours, the working tables, along with other aspects etc.; only once all this was in place did the major leap occur. 

 

It is “data” that we now see as the modern days equivalent to the electrical light bulb. Let me explain why!

 

Understanding your data assets

Over the last few years as organisations have accelerated their digital agenda, through the growth of big data, the advancement of technology, and the rise of the data science discipline; we have encountered a recurring question with our clients across the Nordics:

with so much data, how do I identify the relevant data within my organisation and how do I link it to my decision making?

We all hear the term that data is everywhere… from logging customers purchases and transactions to their interactions with your brand and company, to their behavioural choices, their location statistics, and so on; organisation’s today have never had so much information at hand to utilise in their day to day operations.   But of course, with this comes the challenge on how to navigate this “data asset” and ensure the data is relevant for the purpose at hand. This becomes more and more applicable now that predictive analytics with machine learning & AI is advancing across all levels of an organisation and is no longer only a tool for large organisations.

 

What’s your organisation’s analytical maturity?

As organisations continue to mature digitally, with a goal to be data-driven in their decision making and the utilisation of more advanced predictive algorithms, we are seeing a strong correlation between the need for deeper understanding of your data assets, coupled with the quality, integrity and overall accessibility of this information. It is here that the need for a clear data analytics strategy is paramount.

Source: Davenport, Harris and Morrison, Analytics at Work: Smarter Decisions, Better Results, 2010

 

To support this agenda – we at Geomatic are big advocates of working with the data analytical maturity model D-E-L-T-A (Data Enterprise Leadership Targets Analysts). This model helps you clearly benchmark your existing situation vs. your appetite or ambition. By focusing on these 5 distinct pillars, it enables you to create a clear strategy on where you are now and what is needed to be implemented to reach this goal. With this model at the core of your plans, a tactical approach can be implemented that allows you to understand your data needs mixed with your analytical appetite which in turn helps fast track your journey towards a true data-driven business.

 

The rise of the data lake

Traditionally organisations have used their data warehouses for sourcing relevant internal data, however this is now causing limitations for data scientists due to the design and structure of the data warehouse – i.e. now-a-days not all the key data is available directly from this source; but in turn this has now meant the rise of the data lake – which is a fantastic thing for the data scientists, enabling them to work with both structured and unstructured data, meaning their internal data suite is considerably enhanced, helping them create better and more predictive models. However, regardless of the internal data source, the next driver for using the data lies in the data quality aspects – this can always be a challenge for an organisation, but our experience shows a strong and reliable data quality structure, with continuously updated customer details, is paramount to success. 

 

Identifying relevance in the data

When identifying the internal data components you want to use, a key message we would always say is – “weigh-up the necessity for “need to have” vs “nice to have” along with the time and effort required to extract it” – for example a data point that from a business stand-point appears paramount, but when weighed against the effort to extract the data, its real importance can be re-evaluated quickly, meaning that it is in fact not required. This is what we identify as the “relevant data components”.

 

Even though your internal data suite is extremely rich in its content and alone can give strong results in your predictive models and decision processes; to get the full insights on your customers there will always be aspects that you are missing.  It is here where the ability to blend the internal data view with that of external 3^rd party data is a key to ensure you get the full 360^o view; understanding fully what your customer does outside of your environment/world. This external data can include either geo-demographical data; socio-economic data, behavioural data, or opinions data; which blended with your own data will optimise the predictive models and their business decision capabilities (if you want to know more about this, take a look at our Data Analytics Platform).

 

The strategy is paramount to succeed

Summing up; as we embrace this new dynamic world with data at the forefront, this data relevance and selection challenge, coupled with the utilisation of predictive analytics will remain the key pillars in unlocking value for your organisation and more importantly for the optimal deployment of data within your design processes for optimal customer journey roll-out.

 

If you're curious to learn more about how we can help you develop your data strategy or help you realise it, you're more than welcome to get in touch.