A data driven organization is an organization that has the capability to collect and refine data and find ways to use the minded information to drive growth and profitability. Companies like Netflix, Google, Coco-Cola, and Uber are using business intelligence to assist in making logical decisions with high probability of success. If you are not one of the CEOs of these giants with stockpile of cash and resources, how do you to build these capabilities. Good news you can with the right approach. Following are the key steps to start a successful data analytics program in an organization irrelevant of the maturity level of the organization.  

1. Scope
   1. Prioritize the key business areas to analyze and improve
   2. Determine the initial business questions to investigate
   3. Educate stakeholders about the benefits of business analytics
   4. Plan the effort: define success, cerate timelines and outcomes
   5. Build internal rapport and congruence
2. Collect
   1. Inventory data sources and decide how much to include
   2. Establish a Master Data Management policy
   3. Create a dictionary the defines your common business terms
   4. Combine and integrate key data sources in central data mart
3. Clean
   1. Define acceptable standards for data cleanliness
   2. Correct duplicate, missing and inconsistent data
   3. Standardize procedures to reduce future data discrepancies
4. Analyze
   1. Teach “The Analytical Mindset” to shift the culture
   2. Visualize your data with interactive dashboards
   3. Forecast outcomes with Predictive analytics
   4. Discover patterns and correlations through data mining
   5. Ask new questions with data discovery
   6. Iterate through the process to refine
5. Communicate
   1. Demonstrate results to communicate value
   2. Ensure the level of detail is appropriate for the audience
   3. Describe the visualizations with stories
   4. Encourage questions and new hypotheses
   5. Tech “A Conversation with Data” to speed adoption
6. Prescriptive Analytics
   1. Understand the difference between AI and ML
   2. Prioritize the main driver(s) of value
   3. Select the areas to build cognitive capabilities  
   4. Evaluate your internal capabilities
   5. Consider consulting a domain specialist to build models for your business
   6. Iterate to improve the models and ways to measure improvements

No matter where the organization is on their data analytics journey. The above approach will add value at any stage of this journey. We have seen improvements in all areas of business by taking a systematic approach to decision making using clean data with the right tools. Some of the most valued areas of improvements includes employee productivity, marketing activities, product innovations, and personalized customer experiences.