Data Quality as a Competitive Advantage for Product Leaders

The ability to make timely and accurate decisions give product managers the competitive edge over their competition.

The capability that allows you to do this with consistency relies on Data Quality. As product managers trust their data, they are more likely to execute confidently and quickly, while those who do not trust their data will suffer slowdowns associated with multiple levels of approval that are required due to poor quality data.

This article will provide information on why Data Quality is the new competitive advantage for product managers, and how Data Quality can be leveraged by product managers as a forward-thinking, proactive tool to improve Product Manager’s productivity, and enhance the value provided by products to customers. It will also provide guidance on how to implement Data Quality as a capability without limitation to your product delivery process.

Data Quality Definition

Typically, Data Quality is defined as the sum of Accuracy + Completeness. For product managers, this is not sufficient. Rather, Data Quality from a Product Manager’s perspective includes the following:

• The data you receive from your Customer will provide an accurate representation of the Customer’s behavior,
• The data you collect will include a date/time stamp to allow you to correlate customer data over time,
• The data you collect will allow you to identify any changes or growth in the Customer base,
• The data you collect will provide consistency throughout your organization.

The Recent Increase in Product Leadership’s Concerns Regarding Data Quality

In earlier years, product leaders did not consider the importance of Data Quality by way of creating this type of work. However, the division between product leaders and Data Quality has changed. Nowadays, effective product organizations are defined by:

Constant experimentation

Feature flags and phased rollouts

Prioritizing based on Usage Trends

Utilizing AI to increase the effectiveness of Personalization.

All of the above-mentioned processes increase the financial impact of Poor Quality Data. When The Feedback Loop fails for Product Leaders, the ability for The Product Leaders to Learn at a faster rate than the Market will also end.

The current Business Environment with Fast Delivery Models and Utilize A Smaller Team Size means that there is limited room to compensate for mistakes created by Poor Quality Data.

In addition to this, Poor Data Quality will affect the Outcomes of the Product beyond just the Accuracy of the Reporting.

How poor data quality will affect product execution

Poor Data Quality is often indirectly affecting product teams and is therefore underestimated in terms of its impact on product teams. However, the effects of Poor Data Quality are both tangible and repeatable.

Increased Product Team Response Time To Product Features

Delayed or Unreliable Metric Data disables The Product Teams from being able to evaluate the effects of Product Features, and The Product Features stay “In Limbo” for an extended period. The Product Teams then have to review their Roadmaps and must wait until they receive enough clarity regarding the situation.

Misprioritized Roadmaps

Incoherent Metric Data can cause The Product Teams to develop Features that are perceived as being Successful, when in fact, these were not. However, there could have been Other More Significant Opportunities that may have been missed if these Metrics had been used correctly.

Disincorporated Experimentation Opportunities

Products must be evaluated in A/B Test Mode, and The Product Team relies upon the Clean Data produced to evaluate the results. If the results of A/B Testing are disputed, then The Product Teams will not consider the results produced in the A/B Testing and will revert to relying upon their Gut Instincts, or the “Politics” of the Office Environment when making decisions.

Increased Re-Work Required

When New or Corrected Metric Data becomes available, Product Features previously Released as being completed must then be adjusted and fixed due to the release of the New/Corrected Metrics, therefore increasing The Engineering Effort Required to Correct the Errors at a Later Time.

All of the above-mentioned Outcome Inhibitors of Becoming Competitive will Slow Down Learning Times and ultimately will Lead to Decreased Competitive Positioning.

Benefits of High Data Quality for Product Leaders

The benefits of treating data quality as core to product capability multiply, making benefits of technology as a whole easier and quicker to access. Product leaders can go from insight to action much quicker because they are no longer held up by the need to validate data in meetings; they are able to focus their time in meetings on setting a direction for the product.

Clear Signals

Product leaders can detect trends and anomalies much earlier than competitors; they can prepare and respond much quicker.

Confidence in Experimentation

With the reassurance that the results are trustworthy, product teams are more likely to experiment with new ideas, and thus grow and evolve more quickly.

Alignment Across Teams

With a common understanding of the metrics, the common goal of the product will be easier for product, engineering, and leadership teams to align on; that reduces friction among those teams.

Scalability of Product Operations

As a product grows and the number of teams increases that are responsible for a product, the quality of the decisions made will not decline, but rather remain high.

These advantages are inherent; as the market continues to shift, they will remain.

Key Data Quality Dimensions Product Leaders Should Focus On

Not every dimension of data quality is of equal importance to product leaders. Therefore, product leaders should focus their attention on dimensions with the most potential to impact the decisions they make.

Consistent Metrics

The meaning of a given metric should not change from report to report.

Timely Data

The speed of data acquisition is important to being able to make good decisions.

Complete Data

You should have enough user behaviour information to make informed decisions.

Consistent Metrics

Product leaders need to understand how changes in instrumentation affect overall metrics.

Explained Metrics

Product leaders should know not only what the metrics mean but how they are calculated.

By improving these key dimensions, product leaders can create a much greater impact and deliver significantly increased value than if they were to chase the improvement of all data quality.

How Product Leaders Can Improve Data Quality Without Being Responsible for Technology

Product leaders do not need to become data engineers to influence the quality of data; they can exert influence in other ways. Data quality is an inherent component of both product success and excellence. High-quality data can improve speed and efficiency for teams when creating their experiments and developing new products and features.

High-quality data can provide companies with a competitive advantage.

Use cases in which companies use high-quality data to maintain competitiveness include:

Experiment-driven growth
A team with high-quality data is much more able to create and run new experiments compared to a team that is limited by having unreliable experimental results.

Personalization and recommendations
By having high-quality data that is clean and structured, companies are able to deliver more relevant products and increase customer satisfaction.

Retention optimization
Accurate behavioral data will allow companies to intervene earlier with their users who are at risk of leaving their service or company.

Product-led growth
Businesses can build customer trust with the metrics and data they are providing to their customers so that companies use those metrics to improve their self-serve funnels and improve their onboarding experience.

In all of these cases, data quality is what allows companies access to the benefits of increased revenue and/or greater customer retention.

Many of the beliefs held by companies and organizations regarding data quality limit the ability of companies to realize these types of advantages.

Many organizations believe:

• Data quality is an issue that only IT can be concerned with
• You must have “perfect data”
• Investing in your data quality will slow development
• You will solve data quality issues with tools alone

It is important for leaders to understand how to dispel these myths in their organizations.

A Step-by-Step Approach to Improving Data Quality

• Identify product decisions that are mission-critical
• Ensure standard definitions for success metrics
• Incorporate data readiness into product planning
• Create basic quality signals
• Perform regular data reviews

How to Measure the Results of Improving Data Quality

• Faster rate of decision making post launch
• Fewer disputes over metric definitions
• Increased velocity of conducting experiments
• Reduced need for reworking roadmap items
• Increased leadership reviews confidence

Company Perspective

At Logiciel Solutions, we believe that data quality is a critical capability of your product, and that improving the quality of data is much more than just a method of maintaining quality.

Our AI-centric engineering teams allow product leaders to construct a solid data foundation upon which their organizations can quickly learn from their mistakes and develop trust in their decision-making process. High levels of data quality will allow the product teams of every company to beat out the competition based on insight and execution, not educated guesses.

Extended FAQs