Are you an enterprise company wondering how you can get better engagement and outcomes from your customers? How about more buy-in and enthusiasm from your employees? Let me ask you this: what does your data look like? Are you drowning in it? Is it all over the place? If so, you’re not alone.
There’s one problem I find more common among enterprise companies than anything else. Antiquated systems with diverse data sources spread across departments, systems, and platforms are creating inefficiencies in processes, stifling innovation, and leaving one to wonder where to actually access good data.
Accurate, reliable, and consistent information is what drives business success, and yet so many continue to struggle to establish a single source of truth for their data. Because, at the risk of stating the obvious, the days of only having one data source for something are gone. But the good news is, there is a way to get a handle on it all, and it’s almost too easy to believe.
There’s an old saying that, “If you want your project to succeed, you should start with real data, real fast.” Seems logical enough, right? But data can be a complex thing, especially the bigger – and more elaborate – the company. So let’s simplify it.
Poor data quality is a recipe for subpar outcomes, as it diminishes the integrity of analytical processes and decision-making. Inaccuracies, inconsistencies, and errors within the data can distort insights and lead to flawed conclusions. This is what we call bad data. Here are some examples:
You get the idea.
As you can imagine, relying on bad, faulty data puts decision-makers at risk of formulating strategies based on misinformation, resulting in misguided actions and undesirable outcomes. So why do so many people keep using it?
The truth is, the logical progression of how data in an organization works hasn’t changed at all in decades. However, how you act on that data and how quickly you can act, is changing rapidly. Advancements like generative AI have moved a functional step forward. Earlier this year, my colleague Lauren wrote about how the first step to create more seamless, cohesive, and personalized experiences for your customers – before you can even begin to think about using something like AI – is to centralize your data and make it actionable.
First, you need to clean your data if you have any chance of maintaining a single source of truth (SSOT). To do this, data is often collected from various sources, and any inconsistencies, redundancies, and errors are identified and rectified to ensure that the information is accurate and reliable. Regular audits and quality checks are essential to ensure the accuracy and reliability of information and the easiest (and fastest) way to do this is by investing in data cleansing tools.
Remember that question I asked you at the beginning of this blog, about getting better outcomes from your customers? Well, the answer is Salesforce’s Data Cloud. This data engine is not just about cleaning and bringing data together (though it does that seamlessly), but it ultimately enables you to build a comprehensive, 360-degree view of your customers across all products, services, and interactions. This helps ensure all of your employees can quickly access and easily act on centralized, real-time information about their customers, creating the best experiences possible. And not just for your customers, but for your employees, too.
At the end of the day, most companies’ problem isn’t that they don’t have the data they need – it’s that they have too much of it. They are literally drowning in a data lake. Some companies will actually drown, some will learn to swim, and some will build a boat.
So, who wants help building that boat?
