Now that you (and your team) have access to analytics tools, you want to make the best use of these tools. You want to share analysis with others, use that analysis to explain concepts and conclusions in a presentation, and clearly see the root cause of a problem, the opportunity you can leverage and the trends and patterns you need to understand in order to do your job.
If you are trying to convince your executive management or IT team of the benefits of augmented analytics, you really don’t have to look far for support. Augmented analytics benefits are numerous and, while some benefits are self-evident, many are more subtle and will be uniquely effective within you own enterprise.
The term ‘data analytics can seem daunting to the average business user. Isn’t analytics something that is the sole domain of data scientists? No, actually it is not!
If they are to help your users achieve their goals and understand what is happening within your organization, data visualization tools must be easy to use, with sophisticated functionality and an intuitive user interface. If you want business users to leverage data visualization software, you have to give them something they can easily use and adopt with smart visualization that will recommend the best way to process and visualize data, depending on what the user wants to interpret and the type of data they are working with and sharing.
Why is smart data visualization important? Consider how difficult it would be to put together a jigsaw puzzle if you were blindfolded. You might have access to all the pieces, but you could not truly see the picture unless you were able to visualize the outcome.
Data discovery is not data management. If one is to make the right decisions in business, one must engage in data exploration and data profiling. In other words, one must gather, prepare and analyze data to truly understand how the business is working, what must change, what activities and tasks support the goals of the business, and what the business should anticipate in the future in terms of revenue, risk, resources, financial investment, growth, products, location and all other aspects of business.
Business markets and competition are moving much more quickly these days and predicting, planning and forecasting is more important than ever. It is also important to ensure that every team member is a real asset to the organization and can contribute their knowledge and skill with full Insight into the effects and outcome of activities and processes and the ability to correct the course and make recommendations using clear, concise information. Advanced analytics is the logical tool to help a business optimize its investments and achieve its goals.
Data visualization may not seem important, but the way you see data can provide additional insight or it can muddle the picture to the point where you will miss critical issues or opportunities. The importance of data visualization is even more evident when that data is being analyzed by business users who are not likely to see data in an analytical way and probably do not have the knowledge or skill required to change visualization techniques to accommodate a particular type of data.
As business organizations fight for competitive advantage, funding for projects and large expenditures can fall by the wayside. In today’s competitive business market, every senior executive looks at risk, value and calculations like return on investment (ROI) and total cost of ownership (TCO) before approving a budget.
Smart data visualization is a crucial part of advanced analytics. Smart visualization allows business users to view and analyze data to identify a problem, clarify a root cause, identify a business opportunity, and to make confident decisions. Business users can interact easily with visual analytics tools to build a view that will tell a compelling story using guided visualization tools so there is no need for assistance from data scientists or delay in finding, analyzing and understanding the data. Guided data visualization recommendations are provided to the user, based on the data type, volume, dimensions, patterns and nature of data.
