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Data Management

Data Management 101: Gathering the Data

Solving business problems usually requires the collection of multiple points-of-view in order to provide a complete picture. Data analytics works in the same way: datasets from various sources must be gathered in order to understand the full scope of a problem and, in the case of predictive analytics, be able to present likely outcomes. There is a lot more to data management than simply “gathering” the data, however.

The first step in data management is to secure access to data. This means making the necessary technical connections to multiple sources as well as using a solution that can facilitate (and automate) those connections; after all, data could come from any source, from a SQL database to an Excel spreadsheet.

The First Step to Data Management: Clean Data

Data is seldom pre-packaged and ready for analysis. More often than not, it is raw, contains blank fields, and has multiple outliers. Datasets may also be in different formats based on the containment medium (e.g., relational vs non-relational databases). The challenge moves from gathering the data to making sense of the data : formatting, cleansing, and enriching the content. The end goal is an environment comprised of a uniform dataset that can then be acted on for further analytics (e.g., machine learning, data mining, visualizations, and so on).

Using DSS for Data Management

A critical aspect of Data Management is the sub-field of Data Preparation: gathering, cleansing, formatting, merging, and preparing data. Data Science Studio (DSS) facilitates this process by automating many tasks that typically require significant time, labor, and effort. Automated actions are performed in a highly intuitive, user-friendly environment. This approach empowers beginner user profiles to quickly familiarize themselves with the concepts of data science as well as get up-to-speed with how Data Science Studio works.

Create Your Own Data Management Perspectives

DSS promotes an agile data management philosophy that empowers end users by enabling them to create their own perspective on data and on comprehensively controlling all incoming data. As a collaborative software environment, DSS enables multiple user profiles to work together during the data management process. Business analysts, marketing specialists, and IT trainees can work as teams to understand how connections are made, how to sort & filter incoming data, and how to effectively format the data. Integrated processors enable users to clean and enrich data as needed. DSS even automatically suggests relevant transformations to efficiently guide new users during the data management process.

Capitalizing on Efficient Data Management

The ability to accurately manage all data sources is a singular, though critical, aspect of DSS’ functionality. Data Science Studio is a powerful and robust analytics solution that enables you to train and test the best-fitting models according to the size, shape, and complexity of your data. You can then use your models, or other data, to create customized data visualizations designed to graphically bring your datasets to life while conveying predictive information.

Using the data management process as a stepping stone to DSS’ other functionalities enables beginner users to quickly ramp-up while laying the groundwork for future productivity. DSS’ look & feel is not that dissimilar from spreadsheet formulas and macros, so business analysts and Excel users will find a layer of commonality designed to hasten their learning experience. Data Science Studio’s collaborative tools and intuitive aesthetics will enable your organization to transform its predictive analytics initiatives into inclusive projects. Analysts and marketing specialists can work side-by-side with data scientists, with the result being predictive apps that are both thorough and comprehensive.

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