Know the business value of your current data engineering services

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Data is turning out to be one of the most critical business assets. Digitalization across the globe is multiplying the volume of the data generated every day. Organizations are moving to a new digital world where every business action is data-driven and not gut-based actions like before. Financial teams are making a considerable budget for data analytics, and this budget will increase over the years. The recent IDC report shows that the Global Datasphere will grow to 175 zettabytes (175 trillion gigabytes) by 2025.

Organizations resort to automation powered by AI and ML to organize, store, analyze, and retrieve data due to the pooled information scale.

IDC report also highlights that 60% of this volume of data will be generated and managed by businesses driven by IoT, AI, and ML. ML and AI are gaining popularity among several businesses and are expected to grow to $57.6B.

With all this in the backdrop, have you ever wondered about the business value of the organizational data poured in huge volumes?

How can one calculate the business value of data?

The question gets ignored because most people imagine that information is an intangible asset that we cannot evaluate to extract its value when businesses use it. However, this notion is wrong because most accounting professionals consider companies’ information and data while calculating the valuation. Let’s discuss how we can calculate the business value of the data. Business Value of Information (BVI) focuses on extracting the relevance of the data to the business operations, including the timeliness and quality of the data. It is a quick way to get the potential value of the information in the real world.

  • Relevance(p) — The potential significance of the data to the business operations. You need to rate it between 0-1(
  • Validity —   Percentage of collected data with the correct values
  • Coverage — The total number of records in the dataset in the form of a percentage of the total potential data records
  • Timeliness — The probability that the data is current at any point in time.

When to use BVI Formula?

To implement the BVI formula, you need to create a list of business activities that can benefit from the data and put the relevance score of the data against each activity. By comparing the business activities that are already using the information with those that are yet to use the data, you can determine the opportunities for a data value. Further, you can analyze to invest or dispose of information with a low BVI score.

How does Data Engineering Accelerate Business Transformation?

Organizations have a raw or unstructured data pool referred to as a gold mine supporting business activities. Organizations put data science at the core of their data analytics process to get insights, at an accelerated pace. Data engineering also holds the significance of steering a sustainable and productive data landscape than can power data-driven business decisions. An organized data reservoir ready for rich visualizations is a possible outcome of effective data governance and engineering process. These data reservoirs help companies find answers to pressing business challenges and prepare them for the future. The beauty of these solutions is their scalability and flexibility in aligning with business objectives with data assurance to all stakeholders.

Companies are losing market share due to operational issues because of ineffective data engineering practices. Most companies do not pay attention to data engineering but instead focus only on data science. Data engineering helps build the foundations of a robust data infrastructure, enabling organizations to achieve accurate insights from the data.

Three advantages of effective Data Engineering:

1) Boosts the Data Science Performance

2) Solves the obstacles from Data Infrastructure

3) Democratizes data from diverse data sources for data analytics.

In the current scenario, AI and ML have become an integral part of every company’s data strategy, enabling them to achieve higher efficiency, tap new opportunities in the market, launch new products faster, and provide higher customer satisfaction. However, the recent MIT Tech Review suggests that 48% of companies said that the biggest obstacle in implementing AI is accessing quality data from diverse data sources. Data engineering builds a sustainable foundation for ML and AI to solve this issue.

Data Engineering – The Value Across Industries

Missing Data Engineering is like missing the most important ingredient while cooking your favorite dish. However, most prominent industries have acknowledged the significant role of data engineering in their data analytics infrastructure. Let’s find the actual value of data engineering in different businesses.

Manufacturing – Industry 4.0  

Manufacturing Industry is undergoing a fast-paced digital transformation phase and will soon be equipped with the advanced equipment to match the current market conditions. Industry 4.0 has brought the shift from process-driven culture to data-driven culture in the manufacturing industry. Companies are adding new digital elements or updating their existing digital components with innovative features. But they still have challenges with disparate data sources as different manufacturing functions operate in silos. A robust data engineering solution helps companies bridge the gap between various departments and help them retrieve the data from various data sources. It results in quality improvement, optimum resource utilization, and product optimization.

Retail – Customer Insights

Data sources are helping the retail industry to access the insights and tap into the new market opportunities based on the insights. However, retail stores with multiple locations and customer touchpoints need to collect data and consolidate them for effective insights.

A robust data engineering solution manages structured and unstructured data from a diverse and extensive data set.

Entertainment – Personalization

Data can make or break your business. If you utilize the data effectively, it helps you do your business. One of the most famous examples is Netflix. Netflix used its data engineering process to get insights across the business functions to determine which content they should invest in and determine the content predictions for all their global audience.

BFSI – Process Transformation

The BFSI sector is increasingly transforming its processes to make them more convenient and secure for its customers. They process a huge pile of data every day. Apart from the regular data collection work and data cleansing, data engineering helps financial institutions in Risk Management, Predictive Analytics, Fraud Detection, and Algorithmic Trading.

Healthcare – Patient-centric Care

Prominent healthcare service providers are building a robust data infrastructure with the help of data engineering. Data engineering integrates data from hospital data, gene sequencing, wearables, and EPR, which gives them a dashboard where professionals can get their insights in one place.

Benefits of Data Engineering

For an effective data strategy that manages data at a large scale, data engineering plays a critical role.

Data Engineering creates scalable data pipelines

Distributed data processing enables the organization to develop data pipelines with a low level of network management to manage the large data volumes.

Ensures consistency and reliability of the data  

One of the essential aspects of data processing is to ensure that data is compatible with the formats. Ensuring the data’s consistency and reliability helps in getting meaningful insights.

Keep Data Processing Latency Low  

Most businesses look for real-time insights; data engineering keeps low data process latency. It helps in getting the insights faster.

Data engineering optimizes the computing resources. 

The correct data engineering algorithm reduces a considerable amount of money spent on computing resources. Optimizing the computing resources helps in utilizing the entire technology landscape.

Data engineering value is proportionate to the data quality and validity. It is essential to ensure that you utilize the data as soon as possible; otherwise, it will be obsolete, or the total business value percentage will be decreased. The data engineering process helps keep the data latency low, ensures quality, and optimizes the computing resources. The business value of data engineering differs from business to business depending on the data infrastructure and tool.

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