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.
