Getting Data Preparation Right for Advanced Analytics

The volume of data that enterprises generate has been growing by leaps and bounds, and one estimate expects it to increase from 12 zettabytes in 2015 to 163 zettabytes by 2025. Apart from organizational data, businesses also have access to a variety of external data such as social networks, global trends in markets, politics, climate, etc. which can have an impact on demand and supply. This along with the availability of tools has led to an increase in the demand for advanced analytics, which is projected to grow at a CAGR of more than 20% from 2021-2026.

However, while data and advanced analytics are inseparable, the quality of data determines the quality of analytics as well. Raw data can be incomplete, inaccurate, or filled with errors. Data could be in multiple formats, and structured and unstructured. To ensure that the insights gained from the data are meaningful and help deliver the desired outcomes, the data needs to be processed to make it usable for further analysis. This process is called data preparation and it is an essential step before running advanced analytics.

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What is Data Preparation?

Data preparation or data wrangling, as it is also called, is a complex process that encompasses several steps including:

  • Finding relevant data
  • Collecting it from different systems, both internal and external
  • Combining data from different sources
  • Structuring
  • Imputation of missing data
  • Removal of Outliers

Further, this data needs to be processed, profiled, cleansed, validated, and transformed to ensure the accuracy and consistency of BI and analytics results.

Enriching and optimizing data can by integrating internal and external data or from across systems enhance its usefulness and provide greater value and insights. Data preparation also enables curating data sets for further analysis.

Making Data Preparation Efficient

While data preparation is an integral part of advanced analytics, data science is a complex field and often businesses find that this takes up a large portion of their time. It requires the involvement of specialists along with specific tools and technologies to achieve the desired results. The ideal is for businesses to be able to catch errors and correct them quickly, create top-quality data fast for timely decision making.

This requires automation and machine learning to accelerate the preparation process and ensure scalability, future-proofing, and accelerated use of data and collaboration. Automation plays a crucial part in ensuring speed and efficiency, but even to train algorithms, data preparation remains a crucial step.

Some of the best practices in data preparation include:

  • Data Governance: Data governance, not a part of the preparation, provides the framework based on which businesses can lay down their advanced analytics goals, define processes and establish the standards for data preparation.
  • Ensure Reliability of Source: Establish the reliability of the source and the relevance of data by defining the data needed for the task, identifying the sources, and ensuring the span of time you will need it.
  • Start Small: Begging by creating a random sample for validating your data preparation rules before taking on larger volumes.
  • Try Different Cleansing Strategies: Find out which strategy works for your stated need by trying them on the small data set before making it operational.
  • Cleansing is Iterative: When you run the data preparation processes on your entire data set after clearing the proof-of-concept, there could still be some exceptions. This requires you to constantly finetune your data preparation process to improve the quality of your analytics
  • Automate and Augment Data Processes: Creating AI and machine learning-based tools to help with collecting relevant data, scanning it, and transforming it based on organizational goals for repeatable tasks can speed up your data preparation process.
  • Enable Self-Service: Normally, data science teams are required for data preparation and this can be a bottleneck as different functions need different data sets. Relying solely on one set of data scientists can delay getting the data for analytics. By enabling self-service using AI-based data preparation tools can allow business users to get the data sets they need for their analytics faster and in real-time.
  • Create Collaborative Workflows: Enabling sharing can improve the reusability of authenticated data pipelines and accelerate data preparation.

Indium Approach

Indium Software has a large team of data scientists and data engineers who can help businesses with their data preparation. For instance, one of our customers in the US, a real estate and infrastructure

consulting services provider, helps their customers make informed decisions that can improve the cost-efficiency of their infrastructure projects. They wanted to create a solution that could detect the different types of wires that were present in the thousands of images that they and needed them to be accurately annotated before using them for wire detection models.

The challenges included acquiring good quality annotated data for training the model, ensuring the quality of data with consistency and accuracy, and controlling the cost of labeling data.

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Data Annotation was a key part of the engagement and was a precursor to the supervised ML training.

  • Labelme software was used to annotate the wires in the photos.
  • Two different types of wires – transmission (red) and communication (green) were tagged.
  • A total of 3000+ documents were annotated and converted to VOC and COCO formats, which can then be directly consumed by the AI models.

Indium’s streamlined process approach significantly reduced the effort taken to identify the different types

of wires by 40% and reduced the time taken for the entire data pre-processing activity by 45%. A high level of accuracy was achieved by employing effective quality control mechanisms, thereby minimizing human errors.

Indium can also help with automation and enabling self-service to free the IT teams of our clients. Our cross-domain experts understand the different needs of different industries as well as cross-pollinate ideas for developing innovative approaches to solve complex problems.

To know more about how Indium can help you with your data preparation needs to facilitate advanced analytics in your business, contact us now. To know more, click here:

Author: Indium
Indium Software is a leading digital engineering company that provides Application Engineering, Cloud Engineering, Data and Analytics, DevOps, Digital Assurance, and Gaming services. We assist companies in their digital transformation journey at every stage of digital adoption, allowing them to become market leaders.