Moving from data to analytics and insights

The best path to data-driven insights is to start with a small project, learn from your mistakes and continue to refine the process until it reaches near perfection

Today, many organisations are trying to move towards data-driven insights, but the biggest obstacle is not access to the technology, but compiling the data and then analysing it in a way that will provide employees with insights to help them make smarter day-to-day decisions.

In the recent The State of Salesforce Report, 76% of companies said poor data integration and data quality processes continue to be the biggest barriers to deriving business insights from data. Despite these hurdles, most organisations understand the importance of data-driven insights and continue to invest in this area.

For the last two years, more than two-thirds of companies reported that they are planning to increase their investment in analytics. Consistent budget growth signals that companies are continuing to experiment with ways to get more out of their expanding collections of data.

The best path to data-driven insights is to start with a small project, learn from your mistakes and continue to refine the process until it reaches near perfection. From there, you can start to expand the project or move to other ones.

Three steps to a productive analytics project

1.Be predictive. Be very clear and decide on the project’s desired outcome. Without a specific problem or goal to focus the project, knowing when to stop researching becomes very challenging.

2.Be precise. Determine how analytics will solve the problem. This helps decide what data is required and which questions need to be asked of the data. It is important to ensure you only collect quality data by constraining and standardising data input, integrating the right data (as opposed to the most data) and find ways to keep it up-to-date.

3.Be prescriptive. After analysing, identify specific employee behaviours that will help achieve business outcomes.

However, even those companies that feel confident in their analytical skills and visualization tools wrestle with the task of assembling and cleaning data used for reporting.

For most employees, reporting is not only a daily task, but often the most time-consuming activity. Clean, easily accessible data reduces the time it takes to produce reports, not only improving productivity and efficiency, but also increasing the impact of analytical data on a company’s business outcomes. That is why 58% of companies have prioritised defining and sharing best practices for analysing customer data.

Best practice tips for keeping data clean

Identify a data steward. Choose a specialist who defines and oversees policies, processes, and responsibilities for administering an organization’s data.

Perform a data assessment. Identify issues with the data in order to plan cleansing and enrichment strategies.

Standardize data fields. Ensure data fields have consistent definitions and formats across applications.

Enforce data hierarchies and relationships. Not all data has a flat, one-to-one relationship, so maintain affiliations and relationships between records.

Trap data entry errors. Manual data entry is highly error-prone, so capture errors at point of entry to preserve data quality.

More best practice tips can be found in The State of Salesforce Report.

Remember, analytics can come in all shapes and sizes. It’s not just a chart or a graph—it’s a story, a conversation, a collaboration. The best companies aren’t simply analysing data—they’re integrating different data sources to speed up their ability to mine huge datasets and uncover new insights. This allows them to arm their employees with recommended actions based on insights that help them make smarter day-to-day decisions, which has a positive impact on the bottom line.

Adam Bataran is senior director of analytics at Bluewolf .

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