Importance of Data Engineering
Estimates show that the world will create and store approximately 200 Zettabytes of data by 2025. Storing this data is not the biggest challenge; it is significantly more complicated to derive value from this vast amount of data. From 2020 to 2022, the total enterprise data volume will increase from about one petabyte (PB) to 2.02 PB, which is approximately an annual growth of 42%.
The Big Data analytics market is estimated to reach $103 billion by 2023 and poor data quality is costing global companies trillions of dollars each year. The Fortune 1000 companies can increase net income by $65 million by increasing data accessibility only by 10%. This means that companies that can derive value from their data can improve their decision-making process, protect their organisations and consumers, and grow their business.
Why Data Engineering is more crucial now than ever?
Knowledge is power and large companies are creating, ingesting, and processing more data than ever before. It has never been more crucial than now for organisations to invest in various resources to gain beneficial insights via data engineering. Here are some ways in which these can benefit organisations:
Dynamic pricing – Large retailers use dynamic pricing to sell their goods and services. For example, airlines regularly update their prices based on real-time data models built by data scientists, implemented by ML engineers, and fed by data engineers. Another example is Marriott Hotels which employ a large number of analysts to study various parameters, such as local and global economic status, availability and reservation behaviour, weather, cancellations, and others to build dynamic and competitive pricing models.
Product development and digital marketing – Companies build marketing campaigns specific to their target audience. One example is Airbnb which customised search experiences based on demography and geography to increase reservations. Similarly, Coca-Cola used machines to track the flavours that consumers were mixing in different parts of the world to convert this into purchasable drinks.
Reduce costs – Modern hardware comprises various mechanisms to track its functioning. A company wanted to track its machines comprising sensors to predict when maintenance would be needed and enable customers to operate their products more efficiently. The company migrated to cloud-drive infrastructure to offload maintenance and re-architected the current system for higher scalability and availability thereby saving millions of dollars in administrative and management costs while enhancing customer experience.
How data lying in silos or scattered manner can affect an organisation’s decision-making system?
Departmental silos within an organisation are a large detractor of innovation. The biggest challenge with these is they often create isolated teams instead of an organisation where people are working towards achieving a common goal. Business silos result in data silos, which make it extremely difficult to connect multiple sources into a single view thereby hindering decision-making based on accurate analytics. Data silos affect companies in four common ways:
Limited view and analysis – This may result in decisions being made within departments rather than the company level
Lack of integrity – It may cause data inconsistency hindering integration and reducing the accuracy of the information
Duplication – Since data is stored separately, hosting, analytics, and personnel cost may increase as every department manages its own data
Lack of collaboration – Data separation may result in inter-departmental competition to access the funding to pay for their resources
Our experts work with organisations to collect data from multiple sources and collate it in a single location. Using advanced data analytics, we present easy-to-understand information to enable informed decision-making. With our rich experience and expertise, companies can transform chaotic data into manageable information for their advantage.