Why is understanding data important, and how can breaking data down with custom data analysis benefit an organisation and help it solve its challenges?
What is Custom Data Analysis?
Custom Data Analysis is also known as bespoke analysis, exploratory analysis or data deep dives. It is the process of reviewing and analysing a specific data set to better understand what these numbers mean and uncover insights that were previously unknown that can then be leveraged to help solve unique business challenges.
Some specific examples of the goals for custom analysis are:
- Understanding your Customer Lifetime Value (CLV)
- Identifying the causes of customer churn
- Measuring the effectiveness of a loyalty program
- Ensuring marketing campaign spend is effective
These are just a handful of examples where bespoke data analysis can be applied – in the end the applications are almost limitless.
Why is Understanding My Data Important?
Performing bespoke data analysis benefits organisations by allowing them to leverage large data sets more effectively to identify whether specific programs, campaigns, processes etc are working optimally and if there is any potential room for improvement. The volume of data that organisations capture these days is staggering, but without the power to analyse the data and derive insights that lead to beneficial outcomes these hugely valuable assets just sit in data stores collecting proverbial dust.
Getting Started with Custom Data Analysis
Initially, getting all of your data cleansed and wrangled into usable buckets can involve a lot of heavy lifting in terms of time and energy, but it is the fundamental first step in getting your data set ready for a deep dive. Once completed, the power of technologies like Machine Learning and AI are able to be applied effectively and efficiently ensuring the outcomes they deliver are valid and relevant.
Begin your data journey by auditing your data sources to ensure governance, recording and measurement is in place. You will have to make sure the data that will be analysed is clean and all outliers are removed. As part of your strategy, you will need to ensure that data is centralised and that the democratisation of data is in play so multiple stake holders across the business can benefit from the analysis.
The Data Maturity Growth Curve
The second step is to understand where your business is positioned within the data maturity growth curve which is comprised of:
1. Descriptive Analytics:Review past data to answer the question “what has happened”. This is formalised with on-demand standard reporting and regularly scheduled ad-hoc reporting based on specific business needs.
Determine why certain events have occurred from the reporting. This includes the reviewing of query drilldowns and exploring insights surfaced in the data.
2. Diagnostic Analytics:
Review the probability of what is to occur by applying statistical analysis, forecasting and predictive modelling. This is where it really starts to get exciting.
3. Predictive Analytics:
The end goal that all businesses strive for. This is combining all phases in near real time to help decide the best next steps to tackle a business problem.
4. Prescriptive Analytics:
There is no time like the present. There will always be business and customer challenges that you will need to answer and with the ever-growing amount of data available the longer the wait the more opportunities you will be missing. Utilising customised data analysis can be a powerful way to give you an edge over your competition.
When Should You Start?
Like all strategies, a realistic and practical plan is required based on where your business is currently positioned in relation to its digital analysis and reporting, along with clear goals detailing what you want to achieve with your data and analytics strategy.