Data Quality Challenges and How to Trespass Them

Data is essential for several reasons in the pharmaceutical and life sciences industry. With the dominance of digital channels globally, it is becoming paramount for pharma businesses to take a data-first approach to reach their customers. In this case, an effective sales or marketing campaign relies heavily on accurate data to make informed decisions, reach target audiences, and ensure compliance with regulations. However, the overall pharma commercial landscape is fraught with data quality challenges. In this blog, we will explore the unique data quality issues that the pharmaceutical industry faces in its commercial endeavors and how Xcellen is looking to empower businesses to leverage sophisticated technologies to achieve commercial excellence and optimize their SFE.

Why is data quality so important?

Firstly, commercial success relies on identifying and reaching the right audience. Accurate data is essential for targeting specific customer segments effectively. Hence, there is a need to perform targeted marketing. Secondly, effectively utilizing the available data drives market research and analysis, providing insights into market trends, competitor strategies, and customer behavior. Lastly, the necessity of high-quality data is not only limited to marketing. Reliable data is critical for predicting sales trends, optimizing inventory management, and ensuring product availability for HCPs.

For understanding market trends, customer behavior, and the competitive landscape, ensuring the quality of the data businesses must work on is crucial. Accurate data enables pharmaceutical companies to conduct effective market research, identify opportunities, and target specific customer segments with the right products and messages. Data plays an invaluable part in understanding the behavior of HCPs and other stakeholders. Resources at any organization are finite, and hence, it is not always possible to reach all potential customers.

Such a finite nature of available resources is why there needs to be a coherent approach to first segment the high-potential leads and then use the available resources to target those potential customers. Also, since much of the data is collected through 3rd party sources, there is an inherent need to closely monitor and check the quality of the data before making decisions based on it.

As the number of touchpoints with the customer increases, pharma businesses need to identify the correct medium to focus on that provides that tangible impact. For all those activities, the stakeholders need to decide based on data. In this case, Xpower Customer Targeting, our end-to-end customer profiling, segmentation, and targeting platform is one tool that covers all the relevant aspects to reach Commercial Excellence objectives for pharma businesses.

The need for proper data integration

The pharmaceutical industry’s commercial activities often involve data from various sources, including sales databases, customer relationship management (CRM) systems, and third-party data providers. Integrating these disparate and, in some cases, numerous data sources can be challenging. Such data silos can severely hinder performing a collective data analysis that can help identify unique and helpful patterns in company data.

In this case, implementing data integration platforms that streamline aggregating and harmonizing data from various sources can be extremely helpful. There can also be several problems with data inaccuracy. Inaccurate customer information, such as contact details or previous history, can lead to failed marketing efforts and missed sales opportunities.

Hence, there is a need to ensure that the data collected and analyzed is correct. Otherwise, there can be costly implications in the form of effort, opportunity, time, and more. In such cases, regularly validating and cleaning customer data to ensure accuracy is necessary for effective sales and marketing strategy planning. Also, duplicate customer records can lead to wasteful marketing efforts, incorrect sales forecasts, and frustration.

Important factors to consider

Completeness of the data

For data to be considered high-quality, it needs to be complete. However, two salient aspects must be kept in mind in this regard.

  • Nirvana is not possible; No one can obtain a dataset that is 100% complete and accurate. There will be biases, and one must learn to live with them.

  • Do not worry because if you are not achieving Nirvana, no one else will. All your competitors are probably sailing in the same boat.

Now, what separates a company from its competitors is how they manage data collection and quality and navigate the most common pitfalls and obstacles. Innovation and out-of-the-box thinking are critical in this regard.

The actuality of the data

Outdated data is simply inaccurate. In an industry where real-time decisions need to be made daily, and changes occur instantly, timeliness is an important factor. Not updating information may lead the company to make incorrect decisions. Again, it can be costly in terms of effort, money, or time. It can also happen that some customers may get lost along the way by acting on outdated data. Hence, the mantra is simple: when you make data-driven decisions, ensure that they are made timely.

Relevance of the data

The data collected by a business should be relevant to the overall business strategy. Even if your data is detailed, accurate, and up-to-date, it will only be useful if it relates to your business plans and goals. Hence, its quality will be adversely impacted if there is no alignment between the data and strategy.

So, it is critical to ensure that the data you want to collect is relevant to your business goals and larger strategy. For this, considerable evaluation and analysis are necessary by expert professionals within the team. Ensure to involve all relevant stakeholders and departments from the beginning.

For example, if you are collecting data with your sales team, it can be a good practice to make your sales managers accountable for profiling. They should be given access to the real-time profiling data for the following reasons:

  • Regularly analyzing the progress made to ensure that the profiling effort is consistent and on time.

  • Checking the data quality through an insightful dashboard so that any inconsistencies can be identified in real-time.

Continuous monitoring of data quality is essential, but it can be resource-intensive. Hence, businesses urgently need to leverage technological tools and resources to implement real-time data monitoring and perform regular data quality checks.


Data quality challenges in pharmaceutical commercial activities are unique and complex and require a strategic approach to overcome. The success of marketing, sales, and distribution efforts depends on the accuracy and reliability of data. Establishing robust data governance, implementing automated data validation and cleansing processes, and harnessing advanced technologies such as machine learning (ML) algorithms can ensure commercial activities are data-driven and effective.

In a competitive and highly regulated industry, overcoming data quality challenges is essential for commercial excellence and optimizing sales force activities. In this context, our Xpower range of products is geared towards making data analysis and engineering simpler for business users. Its intuitive dashboard, scripts, and advanced technologies make it convenient for sales managers in pharma companies to analyze voluminous data in a matter of seconds and make informed decisions.

Our Xpower Customer Targeting platform also comes with an easy-to-use mobile app that allows you to easily collect additional data about your target audience or update (keyword completeness and actuality). The whole platform supports collaboration and provides access to a wide range of knowledge articles to ensure to keep everything in mind when you plan your next PST initiative. To sum it up, it supports all relevant aspects to keep your data quality high.