Data literacy is a very popular idea these days. As business users adopt and embrace data and advanced analytics, features like predictive analytics for business users make it easier for a user with average skills to leverage data to make decisions and share information and, in so doing, to become more literate about data analytics.
There are many variables to the success of an advanced analytics solution but the primary pitfalls for this type of project can be traced back to poor planning. If the organization is not ready for self-serve advanced analytics, the initiative will fail. Organizational readiness is, in fact, one of the most overlooked issues in any type of software, system or technology introduction. If the enterprise wishes to succeed and achieve its goals for self-serve advanced analytics it must have a clear vision of what it wants to accomplish. It needs to have a comprehensive understanding of its requirements (technical, infrastructure, processes, tasks, locations, team members, etc.) The enterprise also needs to assess its need for external vs. internal assistance in implementation and training.
Many organizations have grown comfortable with their business intelligence solution, and find it difficult to justify the need for advanced analytics. The advantages of advanced analytics are numerous and those advantages are based on the ability to further improve the business, increase user adoption (and therefore user empowerment and accountability) and, best of all, improve the bottom line and the accuracy of predictions and forecasts that will dictate the success of the business in the future.
A business that does not optimize its resources is doomed to fail. In this rapidly changing business environment and market, every organization must make the best of precious human resources. No one has enough funding to hire additional resources to get the job done and, when there are extra funds, those funds are quickly earmarked for new products, marketing and other crucial activities.
The days of data silos and isolated teams of data analysts and IT staff are behind us. Today, every business user must incorporate accurate analysis into the decision-making process. Enter, the Citizen Analyst!
Many businesses are just discovering the benefits of self-serve business intelligence and establishing data democratization initiatives but, as every business manager and team member knows, business markets and competition move rapidly and yesterday’s business intelligence initiatives are morphing into advanced analytics efforts. As businesses consider the transition, it is important to understand the advantages of advanced analytics.
The ‘culture’ of an organization includes habits and processes that are familiar and comfortable. There are many aspects to the success of a culture change within a business organization. When an enterprise decides to move from the silo spreadsheet culture to Advanced Analytics it can go beyond simple spreadsheet analysis and BI reporting with a comprehensive analytical environment that identifies business opportunities, and provides metrics and measurements, predictive modeling and forecasting, and self-serve data preparation tools for business users. By understanding how and why team members employ spreadsheets, the enterprise can develop a strategy to move to a fact-based, secured environment with tools that users will want to adopt.
Don’t think that advanced analytics is too complicated for your business users. Nothing could be further from the truth! You can create Citizen Data Scientists and improve the analytical abilities and outcomes for your average business user if you give them the right tools.
Terms like Advanced Data Discovery and Augmented Analytics can seem mysterious and daunting for the average organization. Managers, executives and IT staff may believe that business users cannot and will not adopt advanced analytics tools because these tools can only be used by data scientists, programmers or business analysts.
Augmented analytics can help your business users to find and understand critical data and to do their jobs more effectively. Without the knowledge of a data scientist or an IT professional, business users can perform advanced analytics, using features that guide and make recommendations at every step so that data is prepared, viewed and analyzed in a way that ensures clarity and a comprehensive understanding of trends, patterns, and data sensitivity and success thresholds.
