How can data value measurement help you improve your business operations?

As a data scientist, I've learned that it's important to understand the details of what makes a business valuable. Not all data has the same value to many organizations. Some data sets may be very important for making strategic decisions, while others may be more important for making tactical or operational decisions. To effectively define data value drivers, businesses should not only focus on the direct, measurable benefits (like more sales or lower costs), but also on the less tangible ones, like a better reputation for the brand or higher morale among employees. Context is very important, and value can change based on the department or strategic goals at hand.

data is the new strategic asset upon which many companies rely not only to generate insights but also to create strategic competency that would able them to achieve competitive advantages , that is what strategic data science is all about creating that competitive edge by building strong data science teams to implement projects that transform the face of the business and bring a real paradigm shift in business processes and culture, Top challenge though is how to educate let alone persuade top management about the values AI could bring to business particularly because they have no idea about it and they are keen to seen their investment on data projects translated into profits soon rather for the long term impact on profitability

Quantifying data value, in my opinion, is sometimes similar to attempting to measure the immeasurable. While we have a variety of tools for producing numbers, the interpretation of these figures is equally important. Measurement is more than just collecting numbers; it is also about understanding what those numbers mean in the context of the organization. Being holistic in your measurement approach ensures that you are capturing the full spectrum of value that data brings.

What gets measured gets improved and managed effectively, this is true for every business the need to constantly tap into the databases to get the real value from it by analyzing the data to understand what’s behind the numbers (informative insights) diagnose the problems to pinpoint their causes ( diagnostics) prescribe solutions to avoid these problems and then apply machine learning techniques to predict future outcomes ( predicative ) ,no company in the world can attain this level of maturity with understanding it’s past performance so our job as data professionals to analyze the historical data, present them in aesthetically appealing dashboards by using tools like python libraries such as plotly and Dash , Bi tools like power Bi

I like to think of the improvement phase as an ongoing process rather than a one-time event. It is about constantly refining and adapting based on the findings of the analysis. Sometimes the improvements are simple, such as optimizing a data collection process. At times, it may imply re-thinking entire strategies or business models in light of the value data is indicating. Market dynamics, consumer preferences, and technological developments are constantly changing. Since the definition of "value" is subject to change, businesses must be flexible and forward-thinking in their efforts to improve.

Data value measurement enhances business operations: - Defining Metrics: Define key metrics aligning with business goals (e.g., user retention). - Data Collection: Gather relevant data points (e.g., user activity, feedback). - Analysis: Analyze data to identify trends, bottlenecks, or opportunities (e.g., user drop-offs in onboarding). - Product Enhancements: Use insights to improve the product (e.g., streamline onboarding), leading to higher user retention and business growth. - Iterative Approach: Continuously measure, analyze, and refine to drive ongoing improvements in operations.