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Pros and cons of data-driven product development

Products require regular updating to incorporate new features, and product teams are also tasked with coming up with new products.

The job is not an exact science because people do not often know what they need until they see it.

However, it is essential to go with data-based product development to mitigate specific risks and take an approach that aligns with the customers’ needs and wants. While gut feelings and emotions play a role in business decision making, emotions are sometimes blinding, so it is better to use facts and insights from data collection and analysis.

Before taking this road, you should understand its pros and cons and consider using a data visualization company to maximize the benefits of data-driven product development.

The benefits of data-driven product development

Better productivity

Product development relies on the seamless working of all the involved departments, and when all the involved parties have access to valuable data, a better understanding is realized. Useful insights open doors for innovation, growth, and learning, and that ultimately leads to better production. Through data collection and processing, companies also obtain key performance indicators, which refer to the measurement values that indicate how well a business is attaining its goals.

Key performance indicators help product teams know what needs to be done. They also help gauge whether the company is on the right path towards achieving its strategic objectives and develop an accountability culture.

Enhanced collaboration

Many companies gain valuable insight from data, and future business operations will involve broad access to useful data and critical insights. A data-driven culture enhances communication and collaboration in a company, leading to better decision-making and boosting confidence in the manufactured products. Data sharing within the company leads to employee engagement, better communication, and more productivity.

A culture of trust and transparency is fostered through proper communication, which is aided by data sharing throughout the organization. Doing so sends a clear message to the stakeholders of your commitment to information sharing and a culture of openness that helps eliminate customers’ suspicions towards your products.

Better decision making

Product development involves carrying out various processes that generate data, including competitor market intelligence, market research, gathering internal and customer feedback, and digital analytics. These processes help businesses come up with data-based decisions and successful products. For companies to fully leverage the power of data-driven decision making, they must cultivate a culture and environment that supports it.

Making decisions based on your gut feeling may lead to many errors and biases that negatively affect the product. Therefore, it is worth taking advantage of big-data and technological advancements. Insights generated from data analysis allow companies to minimize risks and make better financial and strategic decisions.

Potential downsides

Data Misinterpretation

Data is not always accurate, and without further investigation of the source and careful examination, you may make the wrong decisions based on inaccurate data or personal assumptions. Blindly trusting numbers may lead you to the wrong path and fall into a trap that may significantly affect your product and the whole company.

Data Overload

Considering how massive big data is to today’s business, it is easy for companies to get caught up in collecting all sorts of data and end up gathering more than they need or can comfortably analyze. Additionally, you may miss out on more crucial data as you focus on less beneficial insights.

Data overloads lead to wasting limited company resources. Furthermore, according to the General Data Protection Regulation (GDPR), you cannot reuse consumer data for a different cause once you collect data for a particular purpose.

Poor Quality Data

Data often falls second to the day-to-day duties of many companies. However, while handling daily operations is crucial, ensuring that you collect high-quality data should also be a priority because proper decision making depends on it. Low-quality data can lead to poor decision making, mistrust, business inefficiencies, lost revenue, and missed opportunities.

Low-quality data can result from many reasons like formatting and software errors, and human error. To prevent bad-quality data, adhere to accuracy, completeness, consistency, and timeliness when handling data. Excellent data is crucial for product management, performance assessment, efficient service delivery, and fraud prevention.

Final word

Utilizing data for decision making is a significant step, but that is only part of it. Successful companies develop a culture based on data, using it to achieve a competitive advantage.

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