How to Support Data-Driven Decision Making in Your Organization

Today’s IoT-driven landscape requires you to make data-driven decisions quicker than ever before. While companies have been collecting customer data for some decades, they’ve only recently realized that insights from big data can help them make predictions and identify trends that lead to long-term success.

Data-driven decision making

However, cultivating such a data-driven mindset is an obstacle for several organizations, particularly the ones that are just beginning to unlock and analyze the data accessible to them. In such enterprises, top managers shrug their shoulders when they’re asked to connect the dots from multiple data sources to make decisions that will optimize their company’s growth. Seeing the data and making sense of it are two different things.

To help you deal with the ramifications and realities of big data influx, we’ve compiled a list of measures you can take to support data-driven decision making.

1. Consider Business Intelligence

Business Intelligence refers to practices and software for the integration, gathering, presentation, analysis, and decision-making of business data. The main purpose of BI is to inspire data-driven decision making. Systems with BI features generate current, historical and predictive views of company operations. Most companies that use BI intelligence are Tableau embedded analytics customers, and they’re empowering managers to make better decisions from clear, in-depth analytics.

However, companies are also opening up to Tableau alternatives. The new options are allowing businesses to better utilize business data assets via self-service BI delivered through the cloud. Some of the solutions are also linking cloud data, big data, and relationship database. Also, by hooking up diverse sources of incoming data, Tableau substitutes are refining data-driven decision making to help companies develop a unified business model.

2. Execute Pilot Projects

Pilot projects are necessary to check the existing analytical knowledge of staffers. After the buy-in from important figures within your enterprise, you should create pilot projects rooted in business intelligence. Take small steps by proposing new processes and encourage participation of all departments in your organization. The goal is to judge data analysis literacy of different departments.

Because deeper reliance on data brings great responsibility, employees should play an active part in data governance. To this end, you need to ensure they’re consistent with their analysis and follow strict guidelines when it comes to making decisions. The pilot projects should test their skills for data accuracy, quality, and ownership. Good governance is only born from credible data analysis by staffers.

3. Support Data Sharing

To be able to transform large amounts of data into digestible portions, you should remember that the different departments in your organization are an invaluable resource able to share challenges and parse collectively through roadblocks. Make sure no corporate egos are in place because companies need to have data sharing practices in place to uncover additional opportunities for business growth.

A vital step to ensuring that there are no obstacles to data sharing is to collect feedback from those who’re using business intelligence and analytics software firsthand. Feedback will show their current mentality towards data ownership and if they’ll be open to giving data to another department if it’s necessary to support data-driven initiatives.

It’s evident that an integrated approach to data-driven decision making is a time-consuming undertaking that requires significant levels of learning. However, you can take the steps mentioned above to become data-enabled and create a meaningful change in your company.

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