By the year 2020, people and connected things will generate 40 trillion gigabytes of data. Generating large volumes of data within your organisation is easy, gaining valuable insights from this data is more difficult and requires careful planning.
Your data capture strategy should align with business objectives and have several identified business outcomes, such as regulatory compliance, greater client insights and perhaps to assist with revenue growth.
Performing a gap analysis on your existing data capture processes and evaluating them against business objectives is the first step. For example, if the aim is to gain greater client insights, the gap analysis needs to focus on all existing data assets and processes relating to clients. The results from this exercise will determine whether additional data capture fields are required or whether current data fields need to be re-defined.
When designing data capture forms, it is essential to consider the end user. Are they customers, prospects or internal users? Ease of navigation and speed of data capture will go a long way towards a good user experience.
One solution is to minimise the use of ‘free text’ fields. Reducing the number of free text fields on data capture forms allows users to quickly and consistently capture data. Consider date picker fields and pre-defined lists, where there is no typing required. The alternative leaves users having to type values into free text fields. Free text presents problems such as spelling mistakes, abbreviated words and values captured such as ‘N/A’ and ‘unknown’.
Where free text fields are in use, real-time validation checks should be used to reduce errors. For example, validating email addresses to ensure an @ symbol is present and using an address verification tool will deliver better quality data. Also, validating data at the point of collection is preferable to having to review and correct this data at a later stage.
One of the key drivers of a data capture strategy is business intelligence. Organisations seek to turn their data into actionable insights. Actionable insights are not possible when the data doesn’t exist or exists in an unstructured and unusable format. The adage of ‘garbage in, garbage out’ still holds true. Data quality is vital to ensure accurate and reliable business intelligence and begins with a well though out data capture strategy.