More and more organizations are turning to data driven decision making to address the challenges of complex business decisions. Forty-eight percent (48%) of Corporate America claims to be using big data to develop stronger insights and better understand behaviors of customers, competitors and employees.
This is can be expected, considering the hype behind what big data can offer and with the enormous amounts of data that is all around us. The latest statistic says that 90 percent of the world’s data has been generated in just the past two years. From customer transactions, Web-browsing data trails, social network posts and even machine-embedded sensors, finding determining what data you need can be the first daunting task.
No matter how cutting-edge a BI application is, and no matter how well it is built and implemented, it is ultimately the end-user who has to make the most out of it. The business intelligence end-user can be defined as a decision-maker (of any level within the company), who does not necessarily possess IT skills and who uses business data and information from the BI solution to guide his actions. BI is notoriously underused in organizations and getting people to use, interact with it and learn from data is of the utmost importance.
The success that a BI solution will have in propelling the organization forward depends in large part on how it is received by end-users. Adoption makes or breaks a BI project.
Some ways to improve adoption:
1. Improve the visualization — Extreme simplicity and a highly engaging user interface is much more likely to drive initial adoption amongst naïve users, but more data rendered through more “efficient” visualizations is more likely to retain people’s interest over time as they become more familiar with the system. Remember that new users and experienced users want and need different things. Start with broader information and allow users to explore the different layers of what is provided.
2. Build predictive models — Don’t just summarize the data and dress up what used to be done in MS Excel. Use machine learning, a bag of advanced statistical techniques that lets companies lay out complex problems, spot patterns and come up with predictions. Machine learning has been available in one form or another for decades, but its commercial uses have traditionally been the exclusive domain of the richest, data-stuffed companies like Google, Facebook, Microsoft and Netflix. You can now hop on the predictive-cloud bandwagon.
3. Complement your process with better QC — Data quality often dictates the success of a BI project. Poor data quality leads users to abandon the system and creates considerable rework in deploying the BI solution. Ensuring complete and consistent data lays the true foundation of the successful BI adoption and continuation. By echoing at all levels that data quality is the cornerstone of the BI program’s success, adoption will improve and trust will grow.
As data analytics becomes a more critical agent for business processes, there is a need to not only capture data, but to also analyze and communicate this data to the wider organization. Be inspired, innovate your data strategy.