Data Synthesis: 5 Ways To Process Data For Strategy Reporting

In a previous blog post, we talked about the first step of the strategy reporting process: data collection. Here we’re focusing on the second step, which is data synthesis.

Even if you’ve collected all the right data—information that is key to understanding your organization’s performance—you’ll have a hard time making heads or tails of it without first putting it in some kind of meaningful order. In preparation for strategy reporting, ClearPoint automatically synthesizes data (similar to what some data processing tools do for other business applications), so it’s ready to be analyzed when you need it.

Below we’ll explain in more detail what data synthesis means, and the types of synthesis that are most useful for strategy reporting.

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Data Synthesis: What is it?

Most organizations pull data from numerous sources in various formats for strategy reporting. But that data can’t be properly analyzed unless it’s in the right format, which varies depending on the type of analysis you want to do. For example, you’d want to view sales data in a line chart to understand sales growth over time, or look at a Gantt chart to analyze the timeliness of project deliverables. You might even want to see status indicators (red, green, or amber) instead of numbers to quickly understand your performance in certain areas. Different data processing techniques work better for different purposes. Once the data you’ve collected has been organized appropriately, only then is it ready for analysis.

Unless you’re using a tool like ClearPoint that does data synthesis automatically upon collection, this can be one of the more burdensome steps of strategy reporting. Sometimes an IT team needs to get involved; other times it requires someone from the strategy team to copy and paste data from one place to another (which introduces errors). In Excel, for instance, you need to create your own charts and change data formats manually for each new reporting instance. And while Excel has conditional formatting for individual cells or pieces of data, it doesn’t have automatic status indicators for Objectives or Measures as a whole.

5 Types Of Data Synthesis

Below are the different ways data can be synthesized in preparation for analysis, and the scenarios for which each application is most appropriate to use.

1. Calculations

Calculations allow you to create new data points from your raw data. (Note that everyone should be using the same set of raw data for calculations so there’s no question where data came from or who is using the right number.)

When To Use Calculations: Calculations are used to determine averages, growth rates, conversion rates, and more.

2. Aggregations

Aggregations combine data from different areas of the organization. For example, you can combine the sales numbers from all your regions to get a total sales number. This allows you to see the big picture, but also drill down into the details when needed.

When To Use Aggregations: Aggregations are great for organizations with multiple service/business lines, locations, regions, etc. You can also aggregate status indicators. For example, you could count the percentage of projects that are “on track.”

3. Visualizations/Charts

There are so many ways to visualize data in different types of charts. Some people love looking at raw data, but it’s often hard to understand the big picture if you’re just looking at a data table.

When To Use Charts: There are all sorts of charts to choose from. Line, gauge, bar, and combination charts are some of the most frequently used in management reports and each has its own purpose. In general, charts can show the progress of data over time, help you compare a variety of data points, and get an overview of data quickly.

4. Regression Lines

Regression lines allow you to see the averages of data and how a particular series is trending.

When To Use Regression Lines: They are especially useful for forecasting, so you can predict future business developments based on trends and data.

5. Status Indicators (RAG Status)

Status indicators (red/amber/green icons) are a quick way to tell if your performance data is on or off track based on your goals.

When To Use Status Indicators: Use them when you want to be able to check your performance across numerous KPIs at a glance. Based on the RAG status, you can then dive into the supporting metrics to figure out why the evaluation is what it is.

Simplify Data Synthesis With ClearPoint

With ClearPoint as the center of your strategy reporting process—the place where all your strategy data is housed—you can significantly reduce the work involved at every stage of the reporting process, including data synthesis. Once you’ve decided how you want your data prepared, you simply create those settings once in ClearPoint and the software handles it automatically upon data collection.

Once new data is uploaded (which can also happen automatically!):

• Any desired calculations are executed
• Charts update with new data
• Evaluations update to red, amber, or green depending on how your actual values compare to your target values
• Aggregation tables are created

The next time your users log in, they’ll have up-to-date data, charts, evaluations, and summary reports ready for review and analysis.

One final—and important—point to consider: Saving time leads to a timely analysis! The more you automate the manual stages of the strategy reporting process, the sooner you can have each review meeting, and the sooner people can react and make decisions. (Wouldn’t it be nice to meet a week after the end of the month instead of the month after the end of the month?!)

FAQ:

How can data processing improve the accuracy and efficiency of a marketing strategy report?

Data processing improves the accuracy and efficiency of a marketing strategy report by automating data collection, cleaning, and analysis. It ensures that data is accurate, up-to-date, and relevant, allowing for precise insights and informed decision-making. This leads to more effective marketing strategies and better resource allocation.

What are some examples of data processing applications used in sales strategy reports to identify trends and optimize sales performance?

Examples of data processing applications in sales strategy reports include CRM systems, data analytics platforms, and business intelligence tools. These applications help identify sales trends, customer behaviors, and market opportunities, enabling sales teams to optimize performance and tailor strategies to meet target goals.

How can investment firms leverage data processing to generate comprehensive investment strategy reports for clients?

Investment firms can leverage data processing to analyze large datasets, perform risk assessments, and identify investment opportunities. By utilizing advanced analytics and machine learning algorithms, firms can generate comprehensive investment strategy reports that provide clients with detailed insights and recommendations based on accurate and timely data.

What role does data processing play in compiling and analyzing data for brand strategy reports?

Data processing plays a crucial role in compiling and analyzing data for brand strategy reports by ensuring that data from various sources is integrated, cleaned, and analyzed efficiently. It helps in identifying brand performance metrics, consumer sentiments, and market trends, allowing for the development of data-driven brand strategies that align with business objectives.

Can you provide a step-by-step guide on how to use data processing tools to create a data-driven strategy report?

Step-by-step guide to using data processing tools for a data-driven strategy report:

- Define Objectives: Clearly outline the goals and objectives of your strategy report.
- Collect Data: Gather relevant data from various sources such as CRM systems, marketing platforms, and financial databases.
- Clean Data: Use data processing tools to clean and organize the collected data, removing any duplicates or errors.
- Analyze Data: Utilize analytics tools to process the data, identifying key insights and trends.
- Visualize Data: Create visual representations of the data using charts and graphs to make the information easily understandable.
- Generate Report: Compile the analyzed data and visualizations into a comprehensive report, ensuring it aligns with the defined objectives.
- Review and Adjust: Review the report for accuracy and relevance, making any necessary adjustments before finalizing.