Why It’s Vital for Companies to Focus on Data Engineering

By : Aditya January 9, 2019 No Comments

With the rapid growth of digitization, it can easily be argued that data has become the most valuable asset in the world. Organizations are steadily moving to insight-driven models, with business decisions, process enhancements, and technology investments all being driven by insights gained from data. Enormous budgets are being spent on trying to make sense of the abundant data available and this is only set to increase.

According to a recent IDC report, it is estimated that by 2025 the Global Datasphere will grow to 175 zettabytes (175 trillion gigabytes). It also states that 60% of this data will be created and managed by businesses, driven by the growing reach of Artificial Intelligence (AI), the Internet of Things (IoT), and Machine Learning (ML), among others. AI and ML are gaining mainstream focus among many industries and global spending is expected to grow to $57.6B by 2021.

To manage data of such enormous scale and to segregate business-critical data from the rest, organizations need a long-term data strategy plan in order to be future-ready, with Data Engineering as the key foundation of the strategy.

How Data Engineering Is Helping Businesses Succeed

Organizations often consider Data Science as the sole method needed to gain meaningful insights necessary to drive their business goals. But the real potential lies within Data Engineering, which allows companies to build large maintainable data reservoirs, design data processes that are scalable, and ensure relevant data is available for Data Science and Data Analytics to process complex statistical programs and algorithms to provide useful outcomes. Only with reliable and accurate insights created from diverse sources can analytics harness the full power of data.

Today, AI and ML have become integral to organizations, helping them achieve higher operational efficiency, become agile, tap new market opportunities, launch new products with faster go-to-market, and provide higher customer satisfaction. But according to a survey done by MIT Tech Review, 48% of companies said that getting access to high quality and accurate data was the biggest obstacle in successfully implementing an AI program. To overcome this hurdle, it is essential that businesses focus on effective Data Engineering, which forms the basic building blocks for AI and ML.

Three key advantages of effective Data Engineering:

  1. Data Engineering accelerates Data Science
  2. Data Engineering removes bottlenecks from Data Infrastructure
  3. Data Engineering democratizes data for Data Scientists and Data Analytics

 

Once organizations understand and internalize this, it is easy to see how the potential of Data Engineering is almost limitless.

How Data Engineering is helping businesses across industries:

Manufacturing

Industry 4.0 is here and the sooner organizations start their digital transformation, the better equipped they are to handle the evolving market conditions. What Industry 4.0 has brought is a major shift in how manufacturing businesses are changing from being purely process driven, to becoming data driven. This essentially means that companies are adding new digital components or are updating existing components with digital features. However, this creates a complex technology landscape where legacy systems have to interact with newer systems.

An effective Data Engineering solution that can communicate and retrieve data from all these different systems, segregate critical data from the vast pool of data, and process them to be analyzed further is of vital importance. Data Engineering bridges the gap between Production, Research Development, Maintenance, and Data Science. Data Engineering can help in enhancing the key aspects of the manufacturing industry—production optimization, quality assurance, preventive maintenance, effective utilization of resources, and ultimately, cost reduction.

Entertainment

Data has the power to make or break a business and nobody understands this better than Netflix. The incredibly successful data-driven company uses insights across its business functions to decide what new content to invest in and launch, how to enhance operational efficiency, and most notably, by providing predictive recommendations for a global audience.

Netflix has also used its strong Data Engineering system to convert over 700 billion raw events into business insights, which is one of the main reasons why the company continues to be a market leader.

Retail

The retail industry is constantly trying to tap new business opportunities by gaining insights from data sources across a physical and virtual ecosystem. To gain these business insights, data must be gathered from a large network comprising point-of-sale systems, e-commerce platforms, social media, mobile apps, supply chain systems, vendor management systems, inventory management systems, in-store sensors, cameras, and a growing list of new sources.

An effective Data Engineering solution can bring together massive sets of structured and unstructured data from the entire value chain to provide trends, patterns, customer insights, and more. A retailer with stores across the globe and an omnichannel presence can harness data sources in innovative ways with Data Engineering to gain a detailed understanding of the market, the competition, and every step of the customer journey.

Healthcare

Leading healthcare giants are increasingly investing in integrating ML into their core functions. However, they are also focusing on setting up their data infrastructure to support this by building Data Engineering platforms. The healthcare industry is looking to unlock value from data to gain insights into the patient, the healthcare worker, and the healthcare system at large.

Data Engineering securely brings together insights from not just electronic patient records and hospital data, but also new advanced data sources like gene sequencing, sensors, and wearables, and offers them for Data Analytics to provide better medical treatment.

How Data Engineering is fueling the businesses of the future:

    1. Data Engineering creates a data pipeline that is scalable:
      Distributed data processing tools can help build reliable data pipelines that can scale up to massive event volumes with minimal infrastructure management and tap into a growing number of data sources across a growing ecosystem of touchpoints.

 

    1. Data Engineering ensures that data is consistent, reliable, and reproducible:
      For data processing to be successful through the stages of ingestion to analytics to insights, it is essential that data is consistent by making sure it conforms to expected formats and requirements. By providing data that is reliable and reproducible, data science can derive improved insights from data.

 

    1. Data Engineering helps ensure that processing latency is low:
      Most important business insights need to be in real-time for them to have an effective impact, be it in customer experience in the retail industry or predictive analysis in the financial industry. If the data being analyzed has a significant time delay, the insights can be less effective or completely ineffective.

 

  1. Data Engineering optimizes infrastructure usage and computing resources:
    Using the right algorithm for data engineering can save considerable cost spent on resources. This can provide significant savings to organizations and help them optimally utilize their technology landscape.

 

It is critical for businesses to design Data Engineering solutions that are unique to their needs and to create customized frameworks rather than follow trends. While many new startups begin their data journey with clearly defined data sets, traditional organizations may have broader ones from legacy systems, as well as datasets from new sources through recent digitization projects, that may need to be accessed. It is important to understand that while zeroing in on the Data Engineering tools for a particular organization, no blanket rule can be used. Only a comprehensive study of a company’s unique technology ecosystem and business needs can determine the type of Data Engineering systems that should be used.

Data Engineering solutions must also be flexible. The ways in which data is produced and consumed is constantly evolving, so it is crucial that Data Engineering solutions or frameworks are flexible to accommodate future requirements. Guiding the movement in this direction is the shift from traditional extract, transform, and load (ETL) methods of data pipeline to more pliable patterns like ingest, model, enhance, transform, and deliver. The latter provides more adjustability by decoupling Data Pipeline services.

Many experts are taking the focus on Data Engineering one step further by encouraging companies to adopt a “Data Engineering Culture.” This essentially recognizes the need for Data Engineering at all levels of an organization across functions and warns that business predictions will fail without effective Data Engineering and an appropriate ratio of Data Engineers to Data Scientists.

The sooner organizations push for Data Engineering Culture and create organizational alignment, the more equipped they will be for the future, to which data holds the key.

How TVS Next Created a Data Engineering Solution for One of India’s Top Utility Companies

In the energy sector, large enterprises are turning to real-time data to drive effective energy management. Energy corporations rely on data for efficient resource management, operational optimization, reduced costs, and increased customer satisfaction with better insights into supply and demand in real-time.

TVS Next helped one of India’s leading utility companies build a distributed compute engine for processing and querying data at scale. The solution provided the company with the tools to visualize key performance indicators using real-time data. With effective data engineering, the client was able to improve the customer experience rather than relying on complex algorithms to predict outcomes.

What are some of the achievements and challenges you have faced while planning a Data Engineering system for your organization? Share your story and get in touch with us here.

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