There is no doubt that the ‘Data Revolution’, which has accelerated exponentially over the last decade, has caused a fundamental shift in the way we do business across all aspects of the industry.
By Jason Dunk, head of data science at Ambledown Financial Services
Data is now king, and organisations that are not taking full advantage of advanced analytics, Big Data and machine learning are being left further and further behind. There is a proverbial ‘gold rush’ to reap the benefits and market opportunities that this technology provides.
In this regard – more data is good. Being able to understand customers, business partners, and the overall market, on more granular levels, allows for improved outcomes. As data professionals, it is our job to ensure there is ample access to large quantities of data, this acts as the fuel for the analytics engines and algorithms that will be used to segment, cluster, or predict. This data should also be as recent and as up to date as possible. If these two characteristics are present – data access should be plentiful.
Those familiar with tech-jargon, may recognise the phrase ‘garbage-in- garbage-out’, and in terms of data and analytics, nothing could be truer. Simply, this means that if the information you feed a system is poor, then the quality of the results will, likewise, also be poor. What has become more important as data has become an integral part of many decision-making processes within a business, is that the data is accurate, analysed and transformed appropriately, and can be deployed into the correct channels. These are the attributes that lead to business value creation – the fundamental goal of any data project.
Finding the perfect formula can be difficult, and it often requires coordination between different areas of the business. There are, however, fundamental areas to focus on.
Firstly, data collection should be streamlined and automated as much as possible. Having few manual inputs reduces the chance of human error and creates standardisation across datasets. Collecting data whereby as few errors are present at entry as possible significantly reduces potential headaches down the road. Storing this data on a secure but accessible platform will then allow easy distribution across the business.
Once the data has been collected and stored, ensuring the analysis performed is robust should be the next goal. Depending on the requirements of the project, data analysis and modelling can range from simple charts and tables to highly complicated algorithms and statistical functions. Ensuring this analysis is performed using the correct techniques and with due care, will reduce the chance of bias, skewed results, and, ultimately, the wrong conclusions.
The final piece of the ‘quality data’ puzzle is getting the analysis and predictions to those that can use it to create value for the business. This involves deploying the data, be this visually, in reports or dashboards, or integrated directly into software pipelines. Deployment should be done carefully, with the intended recipients given special attention. Ultimately, data collection and modelling are only effective if they can be communicated correctly or used to fundamentally improve the product offering or service provided to clients.
Focusing on optimising these key fundamentals serves as a platform through which a robust and highly efficient data infrastructure can be built. Such an infrastructure will be capable of taming the vast supply of data, good and bad, that now exists across the industry. Better outcomes, and more business value, can only be achieved if data is leveraged in intelligent ways, following a structured methodology allows for optimal results from input to output.