The Role of Data Quality in Decision-making and Business Success

The quality of decisions made by senior managers can only be as good as the information they use to draw their conclusions. Inaccurate, poor-quality, or missing data, can all present senior managers with a flawed picture. There’s a premium in ensuring managers have good data quality to make the right decisions.

Data has long been a core part of business processes and management decision-making, especially those involving financial investments. These data sets are typically subject to quality standards to ensure that CEOs and CFOs, in this case, make the right decisions and don’t jeopardize the viability of a business.

The recent explosion in the growth of data-driven technology innovation has placed new data sources into senior managers’ hands, especially in the sales and marketing functions. When managed correctly, these capabilities can help managers capture new insights about the markets and customers they engage with, taking their business to a new level.

However, while the finance teams might be able to manage critical data quality issues, their tools and skills will likely be absent from the sales operation teams. They need to access the same expertise without hiring a team of data scientists or interrupting their day job and ultimately still make the right decision.

Let's explore data quality, why it matters, and how technology can help you better make better-informed decisions.

What is Data Quality?

Data can be viewed as being 'high quality' if it is fit for purpose for operational management, planning, and decision making. It needs to be high quality in terms of the integrity or accuracy of the core data, but equally, it needs to be a good reflection of the reality of a business's situation. This complex, hard-to-assess situation lies at the heart of initiatives that drive data quality management.

Understanding the Importance of Data Quality

At its core, poor data quality drives poor decision-making. Data that is a poor reflection of reality, perhaps due to inaccurate data or poorly defined data sets, will (almost) inevitably lead to a sub-optimal decision. The alternative scenario is that managers and their teams know that their data is flawed but are forced to expend time and energy trying to determine what is good quality data and what needs improvement. This situation can lead to poor decisions, or worse, no decision at all, as people become reluctant to be held accountable for decisions they know use flawed data.

Factors Affecting Data Quality

Multiple dimensions of data quality reflect how customer and industry data are used in typical sales and marketing operations. Let's explore them in more detail:

Data Age

Data isn't static. As well as growing in volume constantly, data ages as the world changes. Aged data can become less accurate; people join and leave companies, companies are bought and sold, and products are launched and retired. Aged data will compromise the value of any analysis that relies on it. Any objective data quality assessment should be able to pick these issues, but it is something managers, users, and analysts must be aware of.

Data Relevance

Data does not exist in a vacuum. Its position in a sales and marketing context will impact the quality of insight it can provide. Marketing data is hugely valuable when assessing a marketing problem but less valuable when resolving a business finance problem. Likewise, if a data set can be augmented and complemented by other data sets, one might consider it to be 'comprehensive' as it paints a broader, more textured picture.

Data Completeness

If a decision typically requires 100% of a data set to make that decision, then having only 90% available to make a decision can only compromise the quality of that decision. The challenge is that it is not always clear when data is missing from a data set. Data quality metrics can help users understand where data may be missing.

Data Consistency

Data changes over time; part of its value is that managers can decide based on trends. But this only works if a data set uses consistent definitions and standards. Ideally, the definitions should be unchanged: the definition of product A should be the same in Q1 and Q4 if you are to understand that product’s performance over the year. It gets more complicated where products, in this example, change, perhaps by adding more functionality and value over time. This shouldn't be a problem, provided everyone using the data to make decisions understand this - but it isn't always clear. Inconsistent data quality can create challenges that managers and users must understand when utilizing their business intelligence.

The Impact of Poor Data Quality

Business information with data quality problems can give rise to numerous operational, risk management, and reputational management issues.

The obvious impact is that the worse the quality of information, the poorer the quality of the business decisions, and, ultimately, the worse the business performance. Insufficient quality data means opportunities can be missed, and decisions can jeopardize the business.

Another impact of poor-quality data, especially when people have doubts about the quality, is that people begin to find data they feel they can find the truth, whether it be them creating their own data sets, or using other sets, perhaps from other third parties. In this situation, decision-making can halt while everyone argues over the correct data to use.

Best Practices for Improving Data Quality

The principles of maintaining and improving your data quality are widely understood. Understand where your data came from, how it was cleansed, and how accurate it is. That said, doing this as part of your day job when you are NOT a data scientist is not straightforward.

The smart move is to use a data management platform that can help you understand and manage your data, perhaps with the help of a data quality dashboard that can tell you if data is missing or out of date and whether there are data outliers that could compromise the quality.

The Role of Technology in Improving Data Quality

Technology-based capabilities go to the core of managing data quality. Even for non-data professionals in the sales and marketing function, it can provide a valuable tool set to help them achieve their objectives.

For managers in these functions, data is often used to design sales territories, assess pipeline quality and dynamics, develop commission plans, or assess the size of markets or potential opportunities.

The data typically comes from various sources - finance, sales, marketing, and third parties. To offer a valuable picture, all these different data sets must be synthesized with other formats and definitions to paint a coherent picture.

Extract, Load, Transform (ELT) capabilities are vital in helping the sales and marketing teams maintain and enhance the data quality in their systems. These tools help sales operations teams deliver a streamlined end-to-end process that allows an ordinary staff member to collect, consolidate, and manipulate large volumes of data. In turn, it allows sales and marketing managers to find new insights and opportunities that can drive the growth of the business.

Choosing the Right Data Quality Tools

For this critical ELT process, the ideal tool would simplify the process by automating much of the data capture, consolidation, and manipulation process. You also need it to come ready to integrate with a range of critical sales and marketing applications and toolsets out of the box, so you can start to utilize the tool and gain new business insights at the earliest opportunity.

A templated data integration and management approach allows you to make straightforward but significant progress using one of your most important business assets. Central to all this is having a SaaS-based platform that allows you to deploy this capability quickly without requiring a large IT project team to deploy hardware and software to deliver the project on time.

Learn more about how technology can help you master data with our blog post, The Value of Data Transformation in Modern Data Management.