It is said that over 90% of data projects never make it to production. Amongst all the reasons, we consider these two core reasons, why any analytics project might fail.

• Data quality which I would say is the “salt” of any data project.
• Lack of clear business objectives.

Addressing these two issues can increase the chances of having a successful data project.

Program management and data analytics are critical to any organization looking to achieve its strategic objectives. Program Management involves the overall planning, execution, and delivery of multiple interrelated projects which contribute to the overall business goal. While data analytics involves analyzing and interpreting data to help identify patterns, trends and gain insights for better decision making.

Notice: We might interchange product and program management. However, the goal is to understand how data analytics play a key role in product development.

PMs can leverage data analytics to better make data-informed decisions as it provides them valuable insights such as helping determine proper allocation of resources, areas of inefficiency, or predicting any failures. On the positive side, it helps ensure that we align the project with the business goals.

Some key data strategy that can be followed are:

1. Define your goals: the first step is to define the business goals as it pertains to the program and the product. This will help set criteria for the needed data.
2. Collect the right data: There is a lot of data out there. Collecting the right data is essential. Anything short will set the product for failure. Here, you also look out for the quality of the data collected. Like salt is for a meal, so is data quality for product development. The flavor of the insights from analysis is determined by data quality.
3. Analyze the data: Once we have quality data. Proper analysis to gain insights into user behavior, similar product performance, and market trends. The goal here is to help make better decisions about product development, marketing, or just to better understand product performance.
4. Use data to drive product decisions: Use the insights gained from data analysis to make informed product decisions. This can include identifying new features, improving existing ones, or even discontinuing a product that is not performing well.
5. Continuously monitor and adjust: Data strategy is not a onetime event. Continuously monitor and adjust your data strategy as you gain new insights and as the market grows.

In all to have an excellent product, one must have set a proper data strategy and analysis is vital to the success of the product based on what the business goals are.

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