It simply means to analyze in such a way that it aligns with the business goals. If performed Data Analysis does not fit with the organization’s goals, the reason for conducting the analysis fails.
Hence before starting with Data Analysis, have the right strategy about why we are doing this.
Key Ingredients For a Winning Data Analytics Strategy
- Key Business Goals
You can’t have it all! Define your key business goals for the best clarity and crisp insights. If the intentions are clear, the data can be interpreted more accurately. When you know that you want to expand, you will read the data in a specific way & when you desire operational efficiency, you will read the data differently.
- Data & Analytics Vision
Understanding vision & goals drive your data in the required direction. It helps in choosing foundational decisions like the right stakeholders & data initiatives. Data enables you to predict & validate the desired outcome.
- Target Stakeholders
While selecting business objectives, define, “whom do you want to enable through your data initiatives?” It’s pleasing to serve everyone, but this isn’t realistic. You can choose a set of departments to begin with by targeting & generating real-time results. You may focus on Research & Department to enhance your services or on market & competitors to expand & overtake customers.
- Strategic Initiatives
Don’t merely follow a roadmap you feel is apt for the present time. Visualize the long-term goals & missions to form strategies in the backward direction. Defining a destination will get you closer to your actual business goals.
- Measures of Success
There is no point in having data for data’s sake. Data initiatives need evaluation in time to time basis to keep them relevant for a prolonged period. Documenting the outcomes helps validate whether the selected initiatives are essential. This helps to uncover the blind spots along the journey.
- Data Analysis is only a pile of figures & information unless implemented strategically. It is no big deal to use data in today’s era, but the value of Data Interpretation increases with data analytic strategy.