Your business insights are only as good as the data they’re based on.
With high-quality data to hand, you can develop a clear and comprehensive picture of your organization and build a solid foundation for effective decision-making.
But if your data is incomplete, outdated, or undermined by errors, your insights can easily lead you astray. From poor customer targeting to inefficient resource allocation, the consequences for your business can be significant. It should come as no surprise that poor data quality costs organizations an average of $13 million each year.
To improve your business insights and optimize your decision-making, identifying potential data issues is vital. Below, we’ll outline four key data issues that may be undermining your business insights, and explain how to avoid them.
To be effective, your business insights must be based on the full range of relevant data.
For example, a clear understanding of your customer experience will need input from marketing, sales, and product teams. Meanwhile, an analysis of employee productivity rates will need data from HR, finance, and senior managers.
But if the data you need is spread across different systems and managed by different teams, accessing it may prove challenging. Without a collaborative data culture in place, data silos can emerge, making teams unwilling or unable to share information. And without a centralized store of data to consult, you can’t rule out the possibility you’ve overlooked a key source of information.
Ultimately, if your business insights are built on a partial picture, you cannot rely on their accuracy.
Even if you can access all the data you need, this doesn’t always mean you can make use of it effectively. With different teams managing and maintaining their own datasets, inconsistencies can quickly emerge. You may find that teams:
If you want to generate insights from this data, you may need extensive preparation time before you can begin your analysis. You may be forced to seek technical support or find yourself under pressure as a deadline approaches. In a time-sensitive environment, these added difficulties can impair the quality of your insights.
In a rapidly evolving business landscape, last week’s data has limited value. You can’t rule out sudden shifts in key metrics, so you can’t rely on the data to make your decisions.
But getting access to fresh data requires building data pipelines that are capable of supporting it. That means implementing effective automation.
Without access to live data feeds, you’ll have to resort to repeated access requests, with the ever-present risk of delays. By the time you get the data, it may be out-of-date. You’re left with the choice of making another request, or simply hoping that nothing significant has changed.
Knowing your data is inaccurate is a problem — but it’s one you can begin to solve. Not knowing if it’s inaccurate, on the other hand, is an even bigger challenge.
If your organization lacks consistent validation processes or relies on extensive manual data preparation, errors can easily creep in undetected. And with critical business data, even the simplest errors can have profound consequences.
Transposed figures or copy-paste errors can transform the picture of your business’ performance. The decisions you make on this basis are just as unreliable as the data that informed them.
“It is simple enough to describe how to inject data into a decision-making process. It is far harder to make this normal, even automatic, for employees.”
— David Waller, partner and the head of data science and analytics at Oliver Wyman Labs
The quality of your data counts for nothing if your teams don’t utilize it effectively. And this requires more than just tools and processes — it demands a shift in mindset.
If employees are used to drawing on experience or intuition to make decisions, it can be difficult for them to adopt new habits. And even teams used to working with data may fail to follow best practices when it comes to managing data.
To ensure insights across your organization are founded in high-quality data, you’ll need to begin with a comprehensive cultural approach. This may take the form of training and workshops, or it may involve new company-wide data processes. Either way, it will need to be implemented from the top down.
If business users struggle to access data, chances are they’ll fall back on their intuition. In fact, almost half of employees say they have recourse to a “gut feeling” when making decisions.
To avoid this, you need to make accessing data second nature. A live data catalog can offer a clear, up-to-date picture of your organization’s data. This will make it far easier to embed data into your daily decision-making processes.
And if IT retains control of publishing data to the catalog, they can ensure it is properly cleansed and validated before it gets used — and that the right people have access to it.
By empowering business users at all levels to engage with data consistently, you can support a more collaborative, bottom-up approach to generating business insights. You’ll be able to take advantage of diverse perspectives and specialist knowledge to inform deeper and more accurate analyses of your business performance.
At the same time, you’ll also improve the quality of your data. With business users working with data regularly, you will benefit from a higher level of data literacy across your organization. A shared sense of the importance of data quality can help you identify issues quicker and tackle them sooner.
Data trust issues are most common when oversight is lacking. If your data is stored and managed by different teams, establishing consistent validation processes is a major challenge.
By consolidating your data management processes and keeping IT in the driving seat, both IT and business users benefit. Your IT team can regain control over your data sources, ensuring that data quality is prioritized from the outset. Business users, meanwhile, won’t need to worry about data being unreliable or incomplete — instead, they can get down to the important work of developing powerful business insights.
As a result, your IT team and business users can build a collaborative, mutually beneficial relationship that is better for your organization in the long term.
Data-driven business insights can have an outsized impact on your bottom line. Organizations adept at extracting business value from data see significant improvements in operational efficiency, revenue, and customer retention, according to a study by Harvard Business Review.
But to ensure your business insights are based on high-quality data, you need the right tools in place.
CloverDX supports a collaborative and democratized approach to working with data. By helping you break down data silos and support self-service access to key data, it can substantially reduce your time-to-insight. It also enables your IT team to provide centralized oversight and tackle any data trust issues. Finally, CloverDX comes with the support of high-quality professional services to ensure you get the solutions you need.