Big Data Discovery makes your job faster and easier than traditional analytics tools. This topic discusses your goals and needs in data analysis and shows how Big Data Discovery can address them.
As a data scientist or analyst, you:
Solve complex questions using a set of disjointed tools. You start with many dynamic imprecise questions. You often have unpredictable needs for data, visualization, and discovery capabilities. To address them, you rely on open source and custom discovery tools. You use tools in combination with other tools, and often need to reopen the same tools several times.
In Big Data Discovery, this fragmented workflow between tools is replaced with a single workflow that is natively part of your ecosystem.
Need to collaborate. Together with your team, you work with big data from many external and internal sources. Other team members may consume the results of your findings. You also improve and publish insights and prototypes of new offerings.
In Big Data Discovery, you can create personal projects, or create and share a project with your team.
Want to make sense of data. You often need to create and use insights. You do this by collecting, cleaning and analyzing your data.
Big Data Discovery lets you make sense of data. You use it to collect, join, shape, and analyze your data.
Generate ideas and insights. You want to create insights that lead to changes in business, or you want to enhance existing products, services, and operations. You also need to define and create prototypes (or models) for new data-driven products and services.
In Big Data Discovery, you arrive at insights by using many data visualization techniques. They include charts, statistical plots, maps, pivot tables, summarization bars, tag clouds, timelines, and others. You can save and share results of your discovery using snapshots and bookmarks.
Validate, trace back, tweak, and share your hypotheses. You often need to provide new perspectives to address old problems. The path to the solution is usually exploratory and involves:
Hypotheses revision. You must explore several hypotheses in parallel. They are often based on many data sets and interim data products.
Hypotheses validation. You need to frame and test hypotheses. This requires validation and learning of experimental methods and results done by others.
Data recollection and transparency. You would like all stages of the analytical effort to be transparent so that your team can repeat them. You want to be able to recreate analytical workflows created before. You also would like to share your work. This requires linear history of all steps and activities.
Big Data Discovery helps you by saving your BDD projects, data sets, and transformation scripts. This lets you improve your projects, share them, and apply saved transformation scripts to other data sets.