The Future Frontier of Big Data
In this part two of our series on digital transformation, we explore 5 key trends relating to big data management and analytics – an increasingly strategic and important component of digital transformation efforts. The disruption of the global pandemic accelerated changes in the way organizations process, store, analyze, and leverage data.
Big data has proven to drive tremendous value for organizations of all sizes and industries, with an increasing number of enterprises realizing tangible business benefits – from increased operational efficiency and product/service optimization to improved visibility into quickly changing environments.
In fact, recent research supports a clear movement of Big Data into mainstream business operations:
- 99% of firms are investing in data initiatives
- 65% of firms have appointed a Chief Data Officer (CDO)
- 96% of firms are reporting measurable business outcomes
- 96% of firms say that Big Data and AI efforts are yielding results, up from just 48% just five years ago.
Meanwhile, the benefits and outcomes of Big Data are wide-ranging, according to organizations in a recent survey. Consider that organizations were able to quantify their gains from analyzing big data with an average 8% increase in revenue and a 10% reduction in costs. Other benefits topping the list are:
- Better strategic decisions (69%)
- Improved control of operational processes (54%)
- A better understanding of customers (52%)
- Cost reductions (47%)
As big data and AI continue to evolve, organizations of all sizes and industries are finding uses for these large stores of data, big data technologies, practices, and approaches. Here are the 5 big trends for 2022.
Data Mesh/Data Fabric
Data fabric is an emerging concept that is gaining popularity. According to Gartner, data fabric is “a design concept that serves as an integrated layer of data and connecting processes.” It runs continuous analytics over metadata assets to support the deployment of integrated data across all environments, preparing it for machine-reading and AI.
If you aren’t implementing a data fabric into your data strategy, it’s time to consider doing so. Gartner predicts that by 2024, data fabric deployments will quadruple efficiency in data utilization while cutting human-driven data management tasks in half. And, according to MarketsandMarkets, the global data fabric market size is expected to grow to $4.2 billion by 2026.
Data fabric delivers an agile, robust integration of data sources across business users and platforms, churning out available data everywhere it’s needed, regardless of where the data resides. It provides an opportunity to apply analytics to learn and actively recommend where and how data should be used and/or modified, thereby reducing data management efforts by up to 70%.
Data Lakes and Data Warehouses.
Alongside many digital innovations that accelerated due to the COVID-19 pandemic, companies are adopting new data architecture approaches that allow for better, more cost-effective management of big data through data lakes, data warehouses, and data lakehouses. The rise in remote and hybrid working environments has created a greater need for these solutions that drive faster and more efficient data manipulation. Innovations in cloud storage and processing, such as solutions from Microsoft, Google, Amazon, and other tech leaders, are also driving the popularity of data lakes.
With more migration to the cloud – and therefore, cloud data lakes – companies are also moving to unite the data warehouse with the data lake. Rather than trying to centralize data storage in a data warehouse that requires complex and time-intensive data extraction, transformation, and loading, organizations are evolving into data lakes.
Data lakes store structured and unstructured data sets in their native format, shifting the tasks of transformation and processing to end points that have different data requirements. Data lakes also offer shared services for data analysis and processing. Perhaps that’s why the adoption of data lakes will continue in 2022 and beyond, with the market expected to grow from $3.74 billion in 2020 to $17.6 billion by 2026, at a combined annual growth rate (CAGR) of 29.9% over the forecast period 2021-2026.
Top of mind for organizations in 2022 is data monetization – finding ways to generate return-on-investment (ROI) from data above and beyond using it to enhance analytical capabilities. While capitalizing on new revenue streams and monetization can clearly drive the bottom line, the challenge is that data monetization can only be achieved when a data infrastructure is set up on rails.
The first step to figuring out data monetization will be for enterprises to ensure they have the data infrastructure in place to generate data products and distribute them securely into the hands of consumers. Also, impactful monetization strategies allow data science functions to continue using data to experiment with new models because the investment in the team is balanced by revenue-generating datasets they are producing. However, for companies that are able to build this environment, there is tremendous opportunity to own a large market share of this exploding market.
In fact, according to a report published by Allied Market Research in May 2022, the global data monetization market generated $2.1 billion in 2020, and is expected to reach $15.4 billion by 2030, witnessing a CAGR of 22.1% from 2021 to 2030.
Few organizations were early pioneers of data governance in the early 2000s, but there was little awareness of the true benefit of data governance for business operations. Today, data governance is becoming increasingly important as enterprises continue to seek more innovative and robust ways to utilize and leverage big data. Enterprises have begun to understand and leverage the true business benefits of data governance, including the identification and mitigation of risk, opportunity for revenue growth, and increased productivity and efficiency that can drive additional expansion opportunities. In fact, data governance was listed as very important or important to 92% of IT leaders in a recent survey.
As data governance evolves from strictly risk management to operational functionality, more companies are focused on metadata management, data modeling, data stewardship, machine learning and Ai, among other components. This transition from a largely static, passive set of principles and procedures to real-time applicability allows for greater use cases and business value.
A recent report projects significant growth of the data governance market over the next five years. Valued at approximately $1.3 billion in 2018, the market is anticipated to grow with a healthy growth rate of more than 22% over the forecast period 2019-2026.
Our last big data trend is data democratization – the ongoing process of making data more accessible, to both specialists and non-specialists, and driving usability, data-informed decisions, and the ability for the majority of an organization’s workforce to gather and analyze data autonomously.
In addition, data democratization exists to solve many common data challenges faced by today’s enterprises and their workers. Meanwhile, with a faster pace of change in the data landscape and people’s needs, even the best data teams struggle to meet the expectations of various teams. This reality is also driving greater adoption of self-service data analytics, unified analytics automation platforms, and other solutions to support data democratization.
Supporting a greater push toward making data more accessible are several studies that have consistently shown the data-focused organizations:
- Make better strategic decisions
- Achieve higher efficiency
- Benefit from improved customer satisfaction
- Generate larger profits
In fact, Forrester predicts that data-centric organizations will make $1.8 trillion annually by the end of 2021. And, according to a recent report by Experian, 81% of surveyed business leaders said data democratization is a key initiative.
Capturing the Value of Big Data.
Spurred by digital transformation, big data management and analytics has become more strategic in recent years and is being leveraged for competitive advantage and even to monetize data assets. Further evolution has occurred due to the pandemic and current economic disruptions with more organizations taking on a greater sense of urgency to better utilize data for tasks such as managing supply chains and retaining employees. And increased cybersecurity incidents have driven the need to step up data governance operations.
These macro trends among others are drastically changing how enterprises collect, manage, leverage, and analyze their growing volumes of data.
This is the second in a series of blogs addressing the emerging trends in digital transformation. Part three in our series will address critical trends relating to cybersecurity as an important component of digital transformation efforts.