• State business metrics: Determine which metrics are important for evaluating performance against business goals. Some goal examples would be to increase revenue, reduce costs, improve operational efficiency, and measure productivity. Each goal must have associated KPIs to help monitor achievement.

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• Identify data required: Business data is located in many different sources within the enterprise, including systems of record, databases, desktops, and shadow IT repositories. To measure accurately against KPIs, companies must catalog and prepare the correct data sources to extract the needed data and calculate metrics based on current state of the business.

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• Extract and prepare data: Data must be prepared for analysis. Deduplication, transformation, and cleansing are a few examples of the data preparation steps that need to take place prior to analysis. Often, this is the most time-consuming and labor-intensive step, requiring up to 80% of an analyst’s time,but it is critical for ensuring accuracy.

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• Analyze data: Data analysts can create models and run analyses such as summary statistics, clustering, and regression analysis on the data to determine patterns and measure performance. Key metrics are calculated and compared with stated

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• Present data: Results of the analytics are usually presented to stakeholders in the form of charts and graphs. This is where the data visualization mentioned earlier comes into play.