With data flooding in from your websites, emails, social media, third parties and more, marketers now have extraordinary volumes of information with which to learn more about their customers and create relevant, personalized online experiences for them.

However, there is a rather significant chasm between having this data and putting it to use. For big data is not only incredibly big, it’s also incredibly messy and disconnected. Getting it all into one place and making it fit for purpose is a headache that data scientists – and an increasing number of marketers – are encountering.

A recent survey conducted by BlueVenn revealed that over 40% of marketers have more than 20 sources of customer data to manage, with 9.9% claiming to be juggling more than 40. That, as you might imagine, means a huge amount of preparation is required. In the same survey, nearly 30% of marketers say they are spending over half of each day analysing and preparing data for marketing use.

These findings have been echoed by many others. In a report published by Forbes, for example, data preparation accounts for about 80% of a data scientist’s role, with 60% of that time spend with the cleaning and organising of that data for analysis. Some data scientists even put that figure as high as 90% of their time.

Clearly, organisations want to reduce the ‘time to insight’, spend less of their day wrangling data and spend more time analysing it and using it. So what solutions are available, other than asking data scientists (assuming you can afford one in the first place) to continue plugging away with ‘janitorial’ data preparation work?

Arguably the most important stage in data preparation is data cleansing and the phrase “garbage in, garbage out” remains as true as ever. Even if it was ready for analysis, dirty data (as in out-of-date, corrupted or inaccurate) has very little use to marketers hoping to create targeted campaigns.

Still, rather than use a data scientist to do this (whose cost is likely prohibitive to many customers and their time is better spent making more creative use of data insights anyway) a data quality service can lighten some of the data preparation burden. Such a service can be used to cleanse addresses, match customers, apply suppressions, screen for goneaways and ensure the data you have is high quality.

Another important stage is data enhancement. This is where your (now cleansed) customer data can be improved with additional insight beyond your first party data. Using aggregated third party data, additional consumer data can be appended to assist customer profiling and understanding.

Still, while data can be clean and enhanced, that does not necessarily mean that it is easy to access and may require the creation of a single, unified database to pull in information from your disconnected data silos.

Known as a Single Customer View (SCV), this process not only unites the data from your many sources, it matches, merges, deduplicates and standardises the data to leave you with a single ‘golden record’ for each customer or prospect. According to the aforementioned BlueVenn survey, it’s a solution 56.9% say they are already moving towards.

With these solutions, marketers don’t have to waste their days on dull data hygiene duties. Instead, they can get stuck into putting it to use, confident in its accuracy and relevance for your campaigns.