Driving Product Success: The Role of Data Services in Technology Management

Driving Product Success: The Role of Data Services in Technology Management

Infusing data services into product management technology gives the industry a new look through improved decision-making and product performance. Technology trends leading this evolution include generative artificial intelligence (GenAI), machine learning (ML), and automation. These technologies vastly improve the efficiency and effectiveness of any activity in any product’s lifecycle. The growth of data democratization empowers non-technical users by providing them with easier access to data and analytics tools previously limited to technical experts. At the same time, this increased accessibility raises concerns for data privacy and security. Comprehensive data sources applied through companies deliver invaluable insights into operational efficiency, market trends, and customer behavior, enabling informed decision-making and optimizing product offerings. This data-driven approach leads to better products, business growth, increased market share, and more satisfied customers.

Innovations driving data services evolution

Integrating AI and ML into product management is transforming the industry. These technologies facilitate predictive analytics, helping companies forecast market trends, customer preferences, and potential issues. Massive amounts of data are analyzed to facilitate actionable insights that guide decision-making in product development.

Implementing automation throughout the product lifecycle enhances efficiency by managing repetitive tasks and processes, enabling humans to focus on strategic aspects such as informed decision-making. Automation tools generate faster product development cycles, reducing time to market and improving overall productivity. Automated systems also ensure consistency and accuracy in data handling, reducing human error.

Data services: A powerful strategy for business growth

Integrating data services helps businesses understand customer needs and optimize product features. Big data is a significant reason Amazon recently reached a 50 percent share of the e-commerce market. The company analyzes millions of instances of real-time data daily to enhance the customer experience, making ordering, delivery, price optimization, and shopper scrolling faster and easier.

A recent article by McKinsey projected that big data combined with AI would contribute to a global economic boom. It described seven steps organizations can take to become truly data-driven enterprises and capitalize on this trend. One of these steps is to treat data as important as a product the company sells to the public.

Data-driven strategies are essential for increasing market share and user engagement. By leveraging data insights, companies can identify new market opportunities, tailor marketing strategies, and improve customer retention. This approach drives growth and enhances overall business performance.

Benefits of real-time decision-making in product management

Real-time data enables product managers to make timely decisions about features and adjustments, ensuring products meet market demands and customer needs. Quick access to relevant data about customer patterns and real-time feedback allows agile responses that ultimately improve sales and customer service.

Data insights are valuable at all product lifecycle stages—from development to post-launch—to determine areas that require further improvement. This continuous feedback mechanism leads to better-quality products and increased likelihood of success.

Another use of data is to analyze the effectiveness of past programs and investments. Historical data is used to evaluate the best strategies and investments previously used. This is invaluable information for planning and allocating future resources.

Ensuring data quality and security in integration

High-quality data supports good decision-making for operations and management. Data accuracy, completeness, and consistency are critical. Bad data can be costly. In 2018, Samsung’s securities branch accidentally sent out $105 billion in dividends to employees instead of the correct amount—$2.1 million. It took 47 minutes for the error to be discovered; by then, 16 employees had sold $187 million in shares. As such, it’s essential for companies to establish a system for verifying data to achieve high-quality data levels.

Prioritizing data privacy and security is critical. Cybersecurity threats have grown alongside the rise of big data and AI. It’s imperative for companies to stay a step ahead by implementing effective security measures to protect sensitive information. This includes encryption, access controls, and regular security audits. Compliance with regulations like the California Consumer Protection Act (CCPA) in the United States and the General Data Protection Regulation (GDPR) in the EU is mandatory to avoid legal issues and protect customer data. Emphasizing data security builds trust with customers and safeguards brand reputation, while data leaks can have disastrous effects on a company’s reputation.

Data protection compliance includes vast information protection, anti-ransomware measures, and disaster recovery strategies. Informing company officers of any regulation changes lets them amend practices accordingly.

Balance data-driven decisions with strategic intuition

Product managers have extensive data on customer interactions, but relying solely on data for decisions is impractical. A fundamental flaw in the data-driven approach is over-reliance on key performance indicators (KPIs), which, although derived from rigorous analysis, can slow decision-making. Product management is a science and an art. As such, it’s imperative for product managers to understand leading and lagging indicators. For example, while lagging indicators may suggest improving website speed, leading indicators like customer satisfaction highlight deeper, long-term issues.

Today’s successful product manager

Integrating data management services into the product-development lifecycle starts with the customer’s perspective. This means designing data integration to bring value to the customer experience. It’s crucial for product managers to have a comprehensive understanding of all the data available to the customer and not just the data generated by the product/solution deployed by the product manager’s organization. This holistic view allows for better-informed decisions and more effective product management.

Inspecting the data at every stage of the product lifecycle ensures that no phase is overlooked or treated as an isolated entity. By doing so, product managers can fully leverage the power of data to drive innovation, optimize performance, and maximize utility in technology product management. Succeeding means becoming part of the data-driven global economic boom.

About the author

Parul Sharma is the director of product management for NetApp, a hybrid cloud intelligent data services and data management organization. She has more than 17 years of experience leading multinational teams, enterprise-wide business process transformation initiatives, and products and programs across high-profile oil, gas, manufacturing, and technology companies.