In the world of fast-paced and high technological advancements, any business adopting data as the primary driver of growth is enjoying seamless results and a better understanding of its functional processes. The potential of analytics drives the reshaping of existing operational areas and helps increase revenues and profits. Data-driven strategies help companies to compete, capture and innovate. Companies gain an unbeatable competitive edge when they have more data juxtaposed with relevant machine learning tools and model making.

To manage the data, companies cannot rely on traditional approaches of data handling tools like ETL (extract, transform, load) or newer tools such as Sqoop. These approaches increase the chance of human errors and are highly inefficient in maintaining data accuracy and security. To overcome the bottlenecks of data management, enterprises across industries have had to build a modern data infrastructure to access real-time data through cloud platforms.

Here are the six reasons we believe that would help determine the need to revamping data infrastructure

Data quality: The data from different sources come in a variety of formats, these data formats thus differ, and matching them can be problematic. Existing data infrastructure is not equipped to handle the volume and the variety of unstructured data from email, social media, mobile, video, voice, as well as IoT devices. Extreme inferior data quality leads to wrong decision-making, thereby hampering effective business outcomes.

Data Security: Data generated can be highly vulnerable to fake data generation and data fabrication. In parallel processing after data collection, untrusted mappers can effectively ruin the data quality. Improper data storage and security system make the company data a low hanging fruit for the fraudsters.

Scalability: Data can grow depending upon the business requirements. Companies must design data algorithms keeping their scalability in mind continuously. Infrastructure must be ready with the system's maintenance and support to attend to any changes related to data growth properly.

New Tech: Most of the companies might be using software-defined infrastructure (SDI), which has its drawbacks, mainly depending on the developer's skills and expertise. A shift in trend from SDIs to Artificial Intelligence defined infrastructures (ADI) is needed as ADIs build self-healing frameworks.

Agility: The traditional data lakes and big data platforms lack infrastructure agility and have been expensive to maintain due to the high coupling of storage and computation. Modern data infrastructure can innovate and optimize costs to reinvest in growth and transformation journeys.

Realtime: Companies need to cope with the emerging trends in edge computing, artificial intelligence (AI), and the ever-changing cloud marketplace, which will enable global reach, solve business issues and ensure the flexibility to enter new markets quickly, anywhere, anytime. This will allow businesses to meet the real-time efficiency and scalability of modern data infrastructure.

We know there is nothing more constant than change. However, improvising data infrastructure offers a way to future-proof your organization's strategy and its underlying supporting decisions. With it, the companies can ensure their business will be well placed to withstand the greatest storm of change and opportunity ahead in the industry.

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