How to Harness the Power of Data and Tech for Success in 2025

Brought to you by Francis Taloen, Kline’s Vice President of Innovation

Our eight core pillars for a data-driven planning strategy are the jet fuel that will propel your business to a successful 2025.

1. Define Objectives

Failing to prepare is preparing to fail. Engage with your key stakeholders to understand their responsibilities, the insights they require, and the market factors needed to make fully informed decisions. For example, executives may primarily require predictions for investment decisions, while CMI teams need more detailed insights into the forces influencing market shifts to develop relevant marketing campaigns.

2. Decode Data

Assess the data your company currently holds and identify any gaps that need to be filled to create a comprehensive solution. This data may include market estimates, point-of-sale data, social listening metrics, economic information, or trend quantification. To forecast a product’s growth potential in 2025, you will need not only its sales history across different channels or formats but also an understanding of the consumer trends driving product uptake and the economic shifts affecting consumer spending power and patterns.

3. Data Readiness

Now it’s time to plan thoroughly, ensuring your team has the right expertise to execute this technical build. By employing a Scrum management style, you empower your data engineers, architects, scientists, and designers with a culture of continual improvement and transparent communication. Your tech stack must be capable of handling big data tasks, from storage to analytics and ultimately to dashboards. Using an end-to-end solution such as Azure will optimize integration.

4. Automate Processes

Your data needs to move from one platform to another, and unless you want to input every number manually, you will need a data pipeline (such as Data Factory). These tools refresh analytics when new data becomes available, minimizing human effort while providing real-time information. Not all steps can be fully automated (such as results review), but developing systems that allow for manual overrides will reduce busy work and human error. At each stage, map out your data schemas, how they are transformed from one platform to the next, and any data cleaning tasks required to ensure robust data sources.

"With a fully integrated insights and forecasting solution, you can predict future demands, spot the next hot consumer trend ahead of time, and anticipate market movements in response to changing conditions. Without it, your budget allocation, innovation pipelines, and marketing activities risk falling behind." - Francis Taloen

5. Develop Models

Selecting and testing the right data science techniques is a pivotal aspect of any forecasting exercise. Multiple linear regression, Bayesian recursion, and ARIMA are some of the methods used here at Kline, but each project will have different needs and modeling requirements. Results should be measured not only against key accuracy statistics (such as RMSE, AIC, BIC, F-statistics) but also against expert expectations and historical data. This ensures that quantitative outputs reflect the real-world market landscape and the factors selected at the start of the project.

6. Visualize Information

Data, like oil, is redundant if not used correctly. Ensuring you build a relevant dashboard solution that presents stakeholder-defined insights in a compelling way is crucial to the success of the project. Showcasing market growth and areas of opportunity is likely critical for all stakeholders, while determining the impact of the drivers of this growth is relevant for marketing or innovation teams. Super users may even wish to use scenario planning capabilities, inputting future events to see their effect on market development. The tool selected here is key, with Power BI able to quickly process more static data demands, while a flexible programming framework, such as Angular, may be needed for more advanced requests like scenario planning.

7. Stakeholder Engagement

Now it’s time to see what your stakeholders think. Testing should never be overlooked and should first be conducted with a small group of super users for initial feedback before opening it up to the entire group. Ensure you cover data accuracy, dashboard usability, and onward insight usage in these tests before looping development points into your Scrum process. Upon full launch, you will need to develop a schedule of in-depth training, user guides, and ongoing query support to fully embed your solution.

8. Continual Enhancement

Post-launch, your work is not done; a continual culture of improvement should be fostered. Onboard user suggestions, reinvigorate data richness through driver workshops, and incorporate new best practices. Reviewing the accuracy of your forecasts with each iteration by comparing forecasted versus actual data is the key success factor for your project. Your pipelines and data systems will also need to be maintained and managed through Scrum lifecycle management.

Are you ready to harness the power of data and tech? Kline’s expert industry and data science teams can help you deliver the predictive solutions you need to build for success in 2025. Click here to get started

Or visit our website to learn more about Kline’s forecasting and simulation tool.

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