Comment pouvez-vous créer des KPI significatifs pour votre tableau de bord grâce à l’agrégation et à la synthèse des données ?

Data aggregation is the process of summarizing raw data into a more concise and manageable form. It involves collecting, processing, and presenting data in a way that highlights key trends, patterns, and insights. This can be achieved through various techniques, such as calculating averages, counts, sums, or percentages. Data aggregation is often used in data analysis, statistical modeling, and business intelligence applications to simplify data comprehension and facilitate informed decision-making. It also helps in reducing data volume, minimizing storage requirements, and improving data processing efficiency.

Creating meaningful KPIs through data aggregation and summarization is pivotal for informed decision-making. As a Google Looker consultant in gaming, we emphasize aligning KPIs with key objectives like player retention and ARPU. Leveraging Looker's features, we aggregate diverse data, including player logs, and use LookML for summarization, calculating metrics like daily active users and session duration. Custom calculations provide deeper insights, such as analyzing in-game purchases. Clear visualizations, regularly updated to reflect evolving dynamics, ensure actionable and informative KPIs. This approach guides strategic choices in game development and marketing,ultimately enhancing player engagement and profitability on a broader scale.

Aggregating data is crucial to any company - there are no silo-ed KPIs that can be tracked and improved on their. Take sales as example: Sales will deliver top-line growth, however sales depend on leads & intra-funnel optimizations (essentially lead maturity rates & conversion rate), yet this CR depends heavily on marketing investment per channel that can be summarized on CAC & how “good” leads are in terms of probability of transaction, and this may be backtracked to finance budget availability for marketing… KPI maps enable businesses to understand how metrics correlate to each other and dependencies, therefore ultimately resulting into a holistic perspective of the business, driving aggregation up.

Data summarization is the process of extracting and presenting the most relevant and meaningful information from a dataset. It involves reducing the complexity and volume of data while preserving its essential characteristics and insights. This can be achieved through various techniques, such as calculating descriptive statistics, generating charts and graphs, and creating natural language summaries. Data summarization is an essential tool for data analysis, enabling users to quickly grasp the key features of a dataset, identify patterns and trends, and make informed decisions. It helps in reducing data overload, simplifying data communication, and enhancing data comprehension.

I wouldn’t call this summarization, as visualizations are a form of “interpretation” and calculations are a form a “analysis”. This is moreover a data manipulation step. Once data is generated and extracting, manipulation comes into play, generating visualizations, calculations, and some other techniques that enable data to be better portrayed and ultimately consumed.

Data aggregation and summarization play a crucial role in effectively monitoring and managing Key Performance Indicators (KPIs). By aggregating and summarizing data at appropriate levels, organizations can gain valuable insights into their business performance and make data-driven decisions to improve outcomes. Data aggregation involves combining individual data points into more concise summaries, such as calculating averages, counts, or sums. This allows for a more comprehensive view of overall performance trends. Data summarization, on the other hand, involves extracting & presenting the most relevant and meaningful information from aggregated data. This can be achieved through techniques like generating charts, graphs, or dashboards.

Dashboards should be divided into strategic / reporting dashboards (which are a set of KPIs that can be seen as a Balanced Scorecard) and; Operational dashboards (such as funnels, front-line level data, etc) The purpose of each is different, mostly strategic ones are directed towards executives to showcase performance and business health, which if at any point (always) in time a KPI portrays not desired or forecasted levels, executives may then turn into “KPI Dependencies” to deep dive and find root causes to build action plans and generate an impact. Op-dashboards are directed towards mid-management to provide them either tools that will enable them to move the needle through daily activity improvement.

Data aggregation and summarization are essential techniques for effectively managing and analyzing Key Performance Indicators (KPIs). By aggregating and summarizing data at appropriate levels, organizations can gain valuable insights into their business performance. For instance, in sales performance monitoring, aggregated data can be calculated for total revenue by product, region, & sales channel. This can be further summarized into a line chart showing total revenue by product over time, providing a clear overview of sales trends. Similarly, in marketing campaign analysis, aggregated data can be collected for lead generation by campaign, which can then be summarized into a bar chart comparing lead generation across different campaigns.

KPI trees are key to understand performance & department dependencies. Looking at divisions as silos, may generate opposing efforts between BUs, leading to halted performance. Company alignment is critical through proper strategy design & cascading E2E. The OKR methodology may then jump in right beneath the strategy, to break it down into quarterly objectives & KRs and those broken down into daily task KRs, hence forcing dashboards to be built based on tracking specific dependencies to later on generate a performance-driven impact bottom-up.

Alignment with Business Objectives: Ensure KPIs align with the organization's strategic goals and objectives. Measurability: KPIs should be measurable using reliable and accurate data sources. Avoid ambiguous or subjective metrics that lack clear definitions and measurement methods. Actionability: KPIs should drive actionable insights that inform decision-making and improve business outcomes. Relevance to Stakeholders: Select KPIs that are relevant to the specific needs and interests of dashboard users. Consider their roles, responsibilities, and decision-making processes. Timeliness: Ensure KPIs reflect the most up-to-date data to provide real-time insights into performance. Consider data refresh rates and dashboard update schedules.