Big data is one of the biggest buzzwords of the 21st century and is probably worthier than precious metals like gold. As businesses scramble to compete in the changing landscape, data – and the capabilities to effectively collect, mine, secure, and analyse it – has become an important focus and a huge challenge for companies to retain their competitive edge within the industry.
Data is crucial for any decision-making and it can be structured, semi-structured, or unstructured. Unstructured data is available in its absolute raw form and is difficult to process without consolidation. On the other hand, structured data is consolidated into well-defined models making it easier to analyse and process. Between the two, there is semi-structured data, which is somewhat consistent and has certain definite characteristics.
The Five Vs of Big Data and the impact of Big Data on an organisation
Also known as the characteristics of Big Data, the five Vs are as follows:
Volume – Big Data in itself relates to enormous amount of data and to determine if information can be considered Big Data or not depends on its volume
Velocity – This refers to the high speed with which data is accumulated from different sources like social media, machines, mobile phones, and others and determines how fast the information is generated and processed to meet the requirements
Variety – It relates to the nature of the data from heterogeneous sources, which can be from within the organisation or from external sources and is classified as structured, semi-structured, and unstructured data
Veracity – Big Data is variable due to its multitude and multiple sources and veracity relates to the uncertainty and inconsistency arising from the disparate sources and types of data
Value – Data in itself is of not much importance and requires conversion to something valuable to extract useful information, which makes value the most important characteristic of Big Data
Big Data enables organisations to collect massive amounts of real-time information related to products, resources, and consumers and repackage the data for optimising user experience. The speed at which information is updated with Big Data technologies allows companies to accurately and quickly respond to consumer demands. It allows organisations to act nimbly and adapt to changes faster than competitors.
Data management and its importance
Data management is the procedure to ingest, store, organise, and maintain information created and collected by organisations. It includes multiple functions that aim to ensure the data is accurate, available, and accessible.
Data is increasingly used to make informed decisions, enhance marketing strategies, optimise business operations, and decrease costs with the objective of increasing revenue and profitability. Poor or inefficient data management can result in incompatible data silos, inconsistent data sets, and inferior quality limiting their capability to efficiently use business intelligence (BI) and analytics resulting in faulty results.
Data management tools and techniques
Multiple tools and techniques can be used for data management, which includes the following:
- Database management systems
- Big data management
- Data warehouses and data lakes
- Data integration
- Data governance, quality, and master data management (MDM)
A well-developed data management strategy can help companies to gain a competitive edge over their competitors by enhancing operational efficiency and improved decision-making. Additionally, organisations can gain more agility allowing them to identify market trends and take advantage of new opportunities faster. Efficient data management also prevents breaches and privacy issues, which may negatively impact their reputation. It also ensured companies adhere to regulatory compliances thereby eliminating unexpected costs and legal issues.
Data engineering and its components
Data engineering is the discipline to design, build, and maintain a strong infrastructure to collect, transform, store, and serve data. This data is then used in machine learning (ML), analytic reporting, and decision-making. Data engineering enables operationalized and efficient data science.
Inaccurately organised data may result in prolonged delays and failed analytics. An efficient process organises and prepares the data to deliver the highest potential for success from the various Big Data strategies and initiatives.
Phases of Data Engineering
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.
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