In the last article in our Anatomy of a Decision series, we examined the benefits of collaborative analytics and how they enable companies to expand beyond individual analyses and leverage the strengths of sharing and discussing insights and analyses between colleagues to reap the benefits of collective wisdom and harness diverse perspectives for multidimensional decision-making.

Still, for individual users such as analysts and researchers, analytics tools require certain attributes in order for users to achieve success. Because of the nature of their work, analysts need powerful self-service tools that can provide them with instantaneous access to data. This includes the ability to retrieve data in the moment so that they can make immediate decisions based on current information when it is needed.

In data-driven organizations, analytics are always available when needed. Self-service analytics puts analysts in control, giving every knowledge worker immediate access to data anytime, anywhere. They can quickly explore data and discover answers to their most pressing questions, get answers to follow-on questions that BI reports didn’t anticipate, add new data, change their reports, build their own dashboards, drill down to the level of detail they need–all without involving IT. This increases the agility of business analysts and the speed at which insights are discovered and decisions made.

Visual data analytics can help analysts gain a quick understanding analytics of what data they are looking at and make it possible to determine whether the data is good enough to base decisions on. Not all data will be ready to start interacting with it in the most productive way: some calculations or transformations may be required. A visual data analytics tool can help to transform and augment data, performing anything from simple tasks to complex operations. These data connections, mashups, and transformation can often times be automatically reapplied the next time the analyst loads the data, effectively creating a repeatable process.

Analysts also require a broad and deep array of analytics capabilities at their fingertips to explore & analyze all types use cases, e.g., to measure business performance, diagnose root cause, forecast trends & optimize business decisions. An analytics tool that can support descriptive & diagnostic analytics and at the same time enable predictive & prescriptive analytics can meet the needs of even the smartest analyst and data scientist. The strongest tools can let analysts transform and augment datasets right from within the software or enhance insights with map-based location and text-based content analytics. Having all these capabilities on a single platform makes the analyst’s life even easier.

For instance, when an analyst for a petrochemical company needs to determine future yields from an oil well across millions of data points, she requires a scalable solution that encompasses the data mining, pattern recognition, forecasting and predictive modeling (predictive analytics) along with best course of action to take based on her objectives, requirements, and constraints based on a variety of choices and alternatives available to her (prescriptive analytics).

By drawing from an all-encompassing analytics suite that provides the full range of analytical resources that are available, the petrochemical analyst can make faster, more informed decisions. This includes the use of data visualization tools to clearly identify what the expected yields from the well should be based on the variables includes in the data set. The use of intuitive and interactive data visualizations enables the petrochemical engineer to analyze data gracefully without interrupting the flow of analysis.

Intuitive, interactive data visualizations don’t interrupt flow of analysis—they actually make it easy to explore data quickly. A visual and interactive approach to data analysis leverages our natural human ability to see patterns. The result is an intuitive data exploration experience which lets the analyst instantly spot trends, patterns, and outliers. In interactive interface can provide an analyst with the flexibility to manipulate data however they need–slice, dice, drag and drop, highlight, filter, or add entirely new sets of data–in order to quickly find the information and insights need.

Next Steps:

  • To learn more about the factors that are driving self-service data discovery requirements, check out the Blue Hill Research study, “Anatomy of a Decision”.
  • Try Spotfire and start discovering meaningful insights in your own data.
  • Subscribe to our blog to stay up to date on the latest insights and trends in Big Data and Big Data analytics.

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