Data is not knowledge


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Organisations get blinded by systems that process staggering amounts of data, and expect them to generate fantastic customer insights. Big data holds real promise but to understand customers you still have to engage with them. Companies are slowly learning that knowing that the answer is “42” is meaningless without knowing the question.

Organisations often analyse data rather than seeking real understanding of customers
Easy access to volumes of internal and external data, together with complex analytics and reporting capabilities, has helped organisations identify customer patterns and behaviours. Data analysis leads to propositions that should be validated in the real world with real customers. Unfortunately, organisations often simply analyse data and don’t seek real knowledge about their customers.

Meaningful data – can you handle it?
Many large organisations struggle to get meaningful data from their operational, financial and customer support systems. Advanced analytical and processing capabilities cannot fix organisational issues such as organisational fragmentation and misaligned processes – which produce inconsistent data outputs. Even when there is accurate reporting there is the gap between information and insights.

When opening the floodgates of external data (sources) – without first cleaning up internal structures – organisations can literally drown in data.

Data is not knowledge
With plenty of raw processing power available, businesses are tempted to collect and analyse data, instead of seeking answers to important issues and problems.

What you want to know about your customers
Businesses should always first ask themselves what they want to know about their customers in areas such as sales, support, operations, promotions et cetera, before building reports. Any business can distinguish between strategic and operational reporting and analysis. Operational reports, such as sales numbers by region, give insight into what is happening. Strategic analysis provides indications as to why something is happening – for example, this type of analysis can explain why most billing complaints occur in December.

Data analysis enables organisations to understand specific behaviours of customers. However, before setting up complex analytics and tests – ask, “What do we want to know about our customers?

Customer trends and patterns
Analysing data can reveal trends and patterns about customer behaviour and preferences. No matter how high the statistical accuracy, these findings need to be validated in the real world with real customers and staff before acting on them. Engaging clients around why they behaved a certain way is perhaps more important than the fact that they did.

Customer insights through piloting
Organisations must subject their new understanding of customer patterns and behaviours to real-life testing and pilots with customers. Validating customer insights with quantitative and qualitative data guides further analysis to understand the root-cause of customer patterns and behaviours. Understanding customers and their context is an important step in exploiting opportunities and/or preventing common problems.

How to use big data to design services
Time and again businesses launch services and then gather data on its performance. Access to data about customers or specific sectors can be a big help in designing and improving services. Especially when an organisation is designing an innovative new service, related data from other sectors or businesses can help determine the key elements of a service and how customers might behave.

External data combined with data from customer trials is a good starting point for qualitative analysis. This type of analysis inspires the design of the service before launch and helps identify areas of improvements after.

Easy data can mean hard work
Simply analysing company or sector data on customer behaviour will miss the insights that come from actually engaging with customers. Organisations must subject their initial understanding of customer patterns and behaviours to real-life testing and pilots with customers. Big data, complemented with customer validation and insight generation, helps to formulate the right question to get meaningful insights.

Business data + customer context = valuable insights

Organisations gather staggering amounts of data about their processes, systems and customers and analyse it to identify patterns and behaviours. However, this data is often inaccessible and meaningless without the right context. By mapping the data onto the context of the customer lifecycle it can be used to create a heath map for problem identification and informed decision making.

Melvin Brand Flu
Melvin Brand Flu is an author, business, and strategy consultant with over 30 years of experience working for startups to global brands and governments. He advises management and leads projects on the cutting edge of business and technical innovation in industries ranging from telecommunication and financial services to the public sector and insurance.


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