Table of contents
What is data quality?
The way to define data quality when it comes to your business intelligence, is to consider whether your data is fit for purpose. Can you do with it, what you want to do with it?
It sounds simple. But it’s easy to make mistakes, and end up in a mess. Especially if you prioritize having data, over having the right data.
You can measure data quality by reviewing if you have:
Too much unnecessary data
It’s easy to be greedy when it comes to data governance. Thinking that the more you have, the better you’ll be. But that isn’t the case.
Think about what's relevant and useful for your business processes. One of my pet peeves is B2C brands collecting phone numbers during purchase orders. Why is that necessary? If your company doesn’t actively make sales through cold calling, there’s no benefit for retaining numbers. While creating a worse customer experience.
Not only does it add another piece of data to your pile, but it can also become a hurdle for your conversion rate. Will a customer still sign up if you ask for too much info?
Don’t collect data just for the sake of it.
Not enough necessary data
But do collect data if you know it will help.
This is where data quality can be a fine balancing act. You don’t want to measure data that isn’t useful. But if you don’t measure it, how do you know it isn’t useful?
Here, you should go back to the question, what do you want to do with it? If you can think of a business use or benefit, then go for it. If not, it’s probably wasteful.
Another thing to consider here is perspective. Not the type of data you’re collecting, but where you’re collecting it from.
Say, for example, you’ve built a customer persona based on Twitter data. You could expand it by adding more demographic details, but that won’t help without a use case. Instead, you could incorporate data from another channel, like Sina Weibo, and expand that persona to cover another market.
Or, you could incorporate internal customer service data to define the pain points that your brand needs to tackle.
Inaccurate or outdated data
Today’s big data can be tomorrow’s problem. As soon as you have gathered the data you want, it starts degrading.
Client information can quickly become outdated, as people move home, switch cell providers, get married, or die. The dead could outnumber the living on Facebook within a century. If your database has the same issue, you could be wasting money on comms to people who just aren’t there.
Having a strategy to prevent this type of data degradation is essential. Proactively target people to update their details if they go through any major life changes.
It’s not just customer data that can change. Insights, market research, and trends age as well - often faster. To ensure you stay relevant, you need a real-time solution that ensures data accuracy. There is no point in making decisions based on insights that aren’t bang up to date - regular data cleansing is key.
Your marketing team has reorganized their database, and now have a nice, tidy understanding of your consumers.
Problem is, your sales team has done the same with their data - and their understanding is different. And your customer experience team has done the same. And your…....
One major hurdle for data quality are data silos - departments across the company using unaligned data to complete their day-to-day task. Each department thinking they know best, because their data backs them up. But if that data contradicts other data streams in the company, you’re going to have issues with broken customer journeys, mixed messaging, and general misunderstandings.
Why data quality is important to an organization
That’s a lot to consider, and action, to obtain high quality data. But it can have a huge impact on your brand. I’ve discussed before the benefits of data-driven decision making for businesses. Data quality is about ensuring that when you make those decisions, you’re basing them on the right data.
With 66% of organizations with clean data reporting a boosted revenue, it’s worth getting your data in order now.
You can’t afford to dawdle when it comes to marketing. A slow response to a crisis will get you burnt. Late engagement with a trending story will make you appear dated and out of touch. Being certain of your data analytics will ensure you can react to changing environments quickly and confidently.
Whether it’s your next press release, media advertisement, product release, or marketing campaign, basing your strategy on quality data helps improve your chances of success. An accurate customer-centric model will not only help identify where to target your audience, but also shape the messaging for more relevance. And engagement.
Improved personas and targeting
Quality data helps you activate the voice of the customer, giving you a comprehensive understanding of what the customer needs and wants. This offers more in depth customer personas, which leads to better targeting, which leads to more engaging brand positioning.
Improved customer experience
Personalization is on the rise, with 80% of regular shoppers choosing to shop only with brands that offer a personalized experience. Quality data helps shape all your brand experiences to suit your audience’s expectations. Meaning, you can offer a better customer experience by being more like what your consumers expect.
This improved agility and customer-centricity, will ultimately drive more revenue. Consumers will find you more relevant, more able to meet their needs (and expectations), and more capable of offering the best customer experience. This will boost brand loyalty, and increase customer spend.
How to improve data quality
Great. Now you know why data quality is so important - and how it will boost your bottom line - here’s how to improve the data you’re using across your brand.
Single source of truth
This is the most important. As mentioned above, data silos are one of the biggest hurdles to getting absolute data quality. And the bigger the enterprise, the harder it is to break those silos into a single source of truth.
You’ve market research information, customer call data, social media analytics, CRM, website data, etc., etc., etc. All of that needs to be combined into one comprehensive overview that every department can use. It’s not easy, as that means numerous APIs and integrations, but a consumer intelligence platform such as Talkwalker will be your solution.
Better Booleans and dapper dashboards
All of that data in one place could become unmanageable pretty quickly. Unless you know how to shape your data to be more useful. Boolean operators and custom filters, mean you can adapt your data to your needs.
While dashboards can help you quickly interpret the data for each user. Ultimately, although we are talking data here, what you really need are insights. Actions you can take now for immediate results. Dashboards help you identify the relevant insights for every team member, making you more agile.
With all that information buzzing around, it would be impossible for one person to manage. Heck, it would be impossible for a team of 100s to manage. That’s why you need the support of AI. To translate millions, even billions, of data points into easy to understand insights.
Worried about sentiment analysis? The power of natural language processing (NLP) will categorize all your mentions, to quickly help you identify the most important negative ones - the fires you need to extinguish - and positive ones - the good news stories you can boost.
And I’d be remiss not to mention the importance of AI when it comes to data quality management. Helping you tidy your data lake, to remove false positives, and even help you find the data that you’re missing.
Data quality = quality brand
As you can see, data quality can be the bane of your business. But also the savior. With the right steps and the right tools, you can optimize the data across your company. For the ultimate customer experience - and even better sales results.
To see consumer intelligence in action, and the types of actionable insights you can gain, download our free simulated consumer intelligence dashboard.