In today’s big data era, a considerable amount of data, in large variety, is being continuously generated across myriad channels within an organization and in the cloud. To drive exploratory analysis and make accurate predictions, we must garner, collate, and consume all this data to make clean, consistent data easily and quickly available to data scientists, citizen integrators, and analysts.
Ergo, data preparation plays an important role, especially in relation to self-service analytics and AI/predictive modelling.
Data preparation is an important pre-processing step wherein data from myriad sources is cleaned and transformed for analysis. In other words, it involves connecting to one or many different data sources, cleaning dirty data, restructuring or reformatting data, and ultimately combining this data prior to using it in business analytics. Data preparation plays a significant role in data integration, and also when siloed data systems within a single enterprise are brought together for the first time in a data warehouse or data lake.
Self-Service Analytics: As the demand for faster and more flexible access to data has accentuated, self-service analytics enables business users prepare data for future analysis. It speeds up time-to-insights by enabling organizations bypass the IT bottleneck and driving decision-making.
Predictive Modeling: As per a Forbes survey normally data scientists spend almost 80% of their time on data preparation.
A lot of time is spent on collecting various types of data and then preparing that data to make decisions. Feature engineering is a process where features are changed or new ones are derived to enhance performance in regards to accuracy.
This is the place where domain expertise becomes a necessity and involves adding new data sources, implementing rules, and restructuring data for smoother interpretation. Say, for example, if users want to predict retail sales for a particular time period – in the holiday season for instance – it is pertinent to comprehend the seasonal nature of the business.
Data preparation process offers a multitude of benefits that include:
There are a variety of data preparation tools available in the current business landscape. Self-service powered tools are one of them.
With unique features and functionalities to empower “all business people” prepare data for analysis, self-service data preparation tools are way more efficient and productive than the conventional ones. As the users do not need IT support, the IT teams are free to focus on governance and control.
Self-service data preparation solutions streamline data integrations. Users can use these tools to quickly make data suitable for integration. Meaning that once the data is prepared, it can be integrated into a unified database - in a matter of a few minutes. Additionally, these solutions are secure as authorized access is needed to use them for preparing data. Self-service based solutions are easier to scale to match customers’ expectations. Plus, they are compatible with major cloud service providers.
Simply put, data preparation solutions leverage self-service capabilities to extract maximum value from data for informed decision-making. By enabling users carry out data preparation sans IT support, organizations can quickly transform data and use it for driving growth and innovation.