Home Articles How to properly capture, clean and manage data for better business outcomes

How to properly capture, clean and manage data for better business outcomes


Today, running a ‘data-driven business’ is the new standard for success — with organisations both big and small understanding that comprehensive and accurate data is essential in guiding effective sales and marketing outcomes.

However, Forrester reports that while 74% of businesses say they want to be ‘data-driven’, only 29% are actually successful at putting analytics into action. And interestingly, Australia lags behind the rest of the world when it comes to extracting useable value from their analytics.

With many SME’s running on tight budgets (especially in the start-up stages) and having fewer resources at their disposable, it’s unsurprising that many report data analytics as a nice-to-have, not a must-have. But it’s a risky view to take because good data practice is not something any business can afford to let fall by the wayside.

A lack of data or poor-quality data can have a detrimental impact on campaign ROI, sales forecasts, inventory, or worse, a company’s bottom line. In fact, companies lagging behind with data analytics stand to boost profits by as much as 60% just by improving their data practices.

Valuable data is something most businesses already have access to in some form or another. But it’s knowing how to ‘capture’, ‘clean’ and ‘manage’ this data that sets businesses apart when it comes to improving performance and sales.

Capturing data

Whether you’re aware or not, you’re likely already in possession of a ton of information you could be leveraging, so let’s examine the art of ‘collecting’ this data.

Best practice data collection is usually done through a CRM system, which allows people to capture, store and manage client details, establish trust, and provide a greater level of personalised care.

But data lives in a wide variety of sources including your website, analysis tools like Google Analytics, email subscription details, and your e-commerce platform. I’d encourage you to think outside of the box too. Are you a retailer that’s had a customer complaint filed in store? Or maybe you’ve had people enter competitions? Are you a professional service that has sent a quote via email to a potential customer? Try to think about all your daily interactions with customers in order to glean data from each possible avenue.

Once you’ve identified your available data, it’s important to bring all of it together in one central location to see what information you already have. This activity alone will give you an in-depth look into your clients’ demographics, location, and engagement habits.

Ongoing, SMEs should build data collection into their daily business operations to collect as much data as possible. For a fitness studio, this could be ensuring casual users check into the system, or for small retailers, taking the name and email of customers who make enquiries or purchases in store.

Cleaning your data

It’s one thing to have lots of data, but it’s equally important that this data is ‘clean’ or good quality.  Bad quality data can result in poor decision making or misinformed action across multiple areas of the business including marketing, sales, customer service and product innovation. Not to mention an inefficient use of resources, decreased customer retention, and ultimately revenue loss.

Common data quality issues include duplicate data, incorrect data, invalid data, incomplete or inconsistent data, or poorly defined data (which means that data is sectioned in the wrong category). Poor quality data often goes undetected until it becomes obvious that it’s negatively affecting client care, impacting on revenue, or resulting in overspending.

You can prevent bad data from becoming a recurring issue by looking at how data enters the system initially. This means educating all employees that interact with the data management system on how to correctly input and manage data, as well as establishing protocols for keeping data complete and accurate, or clean.

Rules for adding data should include using standardised naming conventions and formats (including abbreviations), inputting all required fields for a record, and updating customer details as they change.  

Prevention should also include evaluating third-party tools to help ensure data quality. For example, Validity’s Dupe Blocker is a useful tool that finds and prevents duplicate data from being entered into the system.

Managing data

Over time, data that started out as ‘clean’ can become compromised. A person’s details might change, or their needs from your service might need to be adjusted.

Implementing frequent data cleansing processes to regularly remove or merge duplicates, standardise content, and verify information are key. Double checking each client’s information every time you interact with them, for example when they are in store making a purchase or calling to make an appointment, is vital. Third-party software can make staying on top of your data simple by automating these processes for you. Some accessible examples include Validity’s Demand Tools which identifies and removes duplicate data, or your CRM system may even have some native tools you can use depending on the capabilities you need.

While the process of capturing, cleaning and managing data can be quite simple with the right systems in place, not doing it well can create a flow on effect of problems that can be difficult to identify and fix. To become a successful data-driven business, data needs to be treated with care and given frequent attention.

Tunc Bolluk is the Regional Director APAC at Validity Inc.

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