Data Preparation
Data preparation is the key to refining raw data into value.
Data Preparation:
It is the process of manipulating and organizing data before analysis. Data preparation is typically an iterative process of manipulating raw data, which is often unstructured and messy, into a more structured and useful form that is ready for further analysis. Today’s businesses face great challenges, as they have done throughout history. The ability to use data methodically has become a decisive competitive advantage. Many companies have recognized this new finding, and they are striving to solve many of their data usage problems by introducing or improving data preparation. The main drivers behind projects show that the hype around data preparation, which undoubtedly exists, is backed by “concrete" requirements.
Introduction
Data Preparation is a crucial component of Advanced Data Discovery. The emergence of data preparation features in business intelligence solutions has enabled organizations to transform business users into Citizen Data Scientists. In this user transformation process to empower these users, hold them accountable for results, and improve productivity and resource allocation among professional analysts and IT professionals.
By providing sophisticated analytical features and algorithms in an easy-to-use Self-Serve portal that enables business users to perform data preparation and test theories, assumptions, and prototype on their own. The users are not restricted to complex tools or forced to wait for programmers/ transformer experts/ data scientists to ahead with their data analytics journey. Self-Serve Data Preparation empowers business users and allows them to perform tasks, make decisions, and recommendations quickly with unprecedented agility.
Why is Data Preparation needed?
In most cases, despite the availability of data, it is not usable in its rawest form. Data sets will likely have quality, accuracy, and consistency issues together with irrelevant data that needs to be weeded out especially when it's pulled from multiple sources that is where Data Preparation comes in. Data Preparation is the process of combining, structuring, and organizing data so it can be used for business intelligence, analytics, and visualization applications.
BDB data preparation:
BDB Data Preparation can transform your data according to your business need.
Process of Data Preparation:
Make your Data Efficient for Business Users
BDB is a Low-Code, Hyper Automation, Data Analytics (AI/ML) platform that accelerates (often 3x-5x faster vs competition) Data Ops & AI Ops for Enterprises on course to Digitization & Data monetization. It has a built-in low-code and no-code Data Preparation module which can turn any Business data into a cost-effective and custom-made experience. The Data Analysts can instantly detect anomalous records (rows with invalid or empty values) and purge the unwanted data sets in a few clicks using Machine-Learning based smart techniques and sampling. The users can identify errors and apply changes to the data set from any source and export the analysis-ready data in minutes. Automated detection of groups and categories in your data can be viewed through a frequency table. The user can filter the group in a single click and transform data matching the filter conditions and get intelligent Data Transformation suggestions based on data type and quality.
BDB Data Preparation displays data in a grid-like format. The grid view shows the first 10k rows in the dataset. The user can access the Data Grid view of the selected dataset or data preparation by clicking on it. The displayed data in the grid changes based on the number of transforms performed on it.
There is a column containing Data Profiling options on the right side of the data preparation grid page. It contains all the necessary options available for cleansing the data and it also has information for all the transformations that are performed on the dataset.
The user can access the Data Grid view of the selected dataset or data preparation by clicking on it. The displayed data in the grid changes based on the number of transforms performed on it.
You can unify the data according to the customer needs with the help of the features listed below:
Option to view sample data (10 K) in a paginated grid.
Option to get a statistical profile of the data
A quality bar that indicates the percent of valid, invalid and blank rows.
65+ transforms that can be performed on the data.
Ability to view the changes in data, after each transform.
Ability to undo/redo the transforms if changes are not acceptable.
Option to write nested transforms using SQL transform.
Option to view the list of transforms performed on the current data.
Option to filter data by clicking on the profiling charts.
Option to perform transforms on the filtered data.
Option to export the steps to ETL /Pipeline so that it can be performed on full data.
The user can discover, merge, cleanse the data effortlessly in no time with the help of 65+ built-in data preparation transformations. No code is required for this data-cleansing process. The user just needs to move to the transformation layer to covert the raw data into the insightful data values.
The Transformations can be broadly divided into the following sections:
Advanced transformation:
These are the transforms where all the required editing bits are done for data cleansing.
Anonymization:
Anonymization is a type of information sanitization whose intent is privacy protection. It is a data processing technique that removes or modifies personally identifiable information.
Like in anonymization, Data Hashing is a technique of using an algorithm to map data of any size to a fixed length. Every hash value is unique. The supported Hash options are Hash, Sha-1, Sha-2 and MD-5.
Column based transformation, date transformation, string transformation and so on.
After performing various transformations based on the requirement, the BDB Data Preparation has an option to export this unified data to the Data Pipeline with just a single click.
The Export Pipeline contains an option to specify the name in which the steps/transforms created as part of cleansing get exposed to the Data Pipeline module of the Platform.
Data preparation performs some tasks which will help to get the meaningful insights and take faster decisions than other data analytics sources.
Vigorous Data Grounding
Empower users to discover, merge, and cleanse data effortlessly. Transform your business data into trusted insights with BDB self-service Data Preparation. Accelerate data usage across the organization by improving data quality. Integrate agile data to make data-driven decisions.
Invest More Time in Data Analysis
Data analysts can instantly detect anomalous records (rows with invalid or empty values) and purge the unwanted data sets in a few clicks using Machine-Learning based smart techniques and sampling. Identify errors and apply changes to any size of data set from any source. Export the analysis-ready data in minutes.
Speed Up Insight Discovery
Automated detection of groups and categories can be viewed in your data through a frequency table. It can filter groups in a single click and transform data matching filter conditions. Get Intelligent Data Transformation suggestions based on data type and quality.
Reliable Self-Service Access
Improved data quality implemented by Business Users loses control and visibility by the IT, but BDB Data Preparation provides an easy-to-use and efficient data preparation tool for regular IT usage. Self-service Data Preparation functionality increases the accuracy of decisions governed by a definite process, concealing rules, and workflow-based data curation while reducing the risk of deflation compliance.
Use Tested Preparations to Accelerate Analytics
Embed prepared data into batch or bulk for streaming data integration scenarios. Save transformation rules as preparation and apply tested preparations on big data in the cloud or on-premises to accelerate your analytics process of discovering valuable data insights. Share your purified datasets with the desired group for rapid business decision-making.
BDB used its self-service data preparation in many use cases in those showing how the data preparation is helping to get the insights, one of those use case is here:
Use Case: Insurance Fraud Claim Analytics
Scenario
Insurance frauds cover the range of improper activities which an individual may commit in order to achieve a favorable outcome from the insurance company. This could range from staging the incident, misrepresenting the situation including the relevant actors and the cause of incident and finally the extent of damage caused.
Hence the insurance industry has an urgent need to develop capability that can help identify potential frauds with a high degree of accuracy, so that other claims can be cleared rapidly while identified cases can be scrutinized in detail.
BDB Solution:
BDB Solution Process using Data Pipeline, Data Preparation, Data Science workbench, Data Store and Dashboards (Visualization)
Whenever a Claim is applied in the Customer Website or in database by the end user
Directly Database API is called with the data of the claim by the Pipeline event
It hits the message channel of the Pipeline
A data cleaning process/ data preparation is called (another service in the BDB platform, perform some transformation for preparing structured data.
Then the feature Engineering Process begins (this is a Python code process)Then we store the data to a DB for Training.
Called the ML algorithms for Fraud Analytics (Decision Tree, Regression Algo’s in BDB’s data science workbench)
The output is formatted and Pushed to DB
Email is generated for the alert and web socket is created.
This is the complete output of BDB Platform Solution in the form of visualization.
With these BDB data preparations features able to give the complete solution.
Benefits of Data Preparation
Some organizations remain reticent about self-serve data preparation, believing that there is inherent risk in an environment where business users can access and analyse business intelligence. This reticence is based on the idea that business users cannot appropriately prepare and interpret data and that in such an environment, the organization risks incorrect analysis and poor decisions. However, if an enterprise prepares its environment, its business users and its analysts and IT teams, and plans appropriately with guidelines and processes to support a self-serve data preparation initiative, the benefits of this type of environment far outweigh any perceived risks.
Benefits and Impact of Data Preparation
| Impact on Business User | Impact on the Enterprise |
|---|---|
Improved productivity, Decision Timing, and collaboration | User empowerment, resulting in the evolution of power users |
Data and report sharing, improve insights | Improve data integration, management, Governance and security |
Ability to leverage data exploration and find crucial nuggets of information | |
Ability to access integrated data sources | BI tools that integrate with infrastructure and allow for future growth in user volume, data sources, etc. |
Encouraged data 'popularity' and user adoption of BI tools | Optimized analyst and IT resources resulting in better focus on crucial projects and timely response to requests |
Conclusion
Well-prepared data is crucial for the success of machine learning models. However, data preparation is a time-intensive and sensitive process that is full of challenges. Therefore, self-service data preparation tools have been designed to enhance the productivity of data scientists and accelerate the performance of ML models.
Such tools empower practitioners to work within an easy-to-use visual application for cleaning, preparing, and deploying data using clicks, not code, without compromising on governance and security.
In the end, irrespective of the terabytes of data collected and the extent of machine learning expertise, the success of an ML algorithm is only as good as the quality of the data used.
In the end this self-service data preparation you may reach out from our BDB Platform demo. Providing a YouTube link which is available below:
For BDB Data Preparation Module Demo:
For BDB Platform demo :
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