The term “big data” has been around since the 1990s, but the real explosion of data in the last decade has genuinely emphasized its importance. The world is generating several exabytes of data each day, with the amount of daily data produced expected to exceed 460 exabytes by 2025.
IT and business leaders understand that in an increasingly digital world, they must extract as much value as possible to move with speed and gain a competitive edge. It is not surprising that most organizations have already deployed big data initiatives at an enterprise level.
Gartner Research Director Jorgen Heizenberg said in a 2018 interview, “Today, we are witnessing a paradigm shift from the way we manage data and analytics. On the one hand, we have an abundance of data and information available to us, and on the other hand, we lack the culture and human capabilities to collect, analyze, and manage data properly. This impacts one’s ability to judge and make the right decisions for the business.”
2021 marks a critical point in data management. The decisions that data teams make this year about data management will define the trajectory of businesses throughout the decade and beyond. Trends that have been developing for several years and new advancements will empower data management teams to capitalize on more market opportunities across several industries.
Business leaders across different industries will need to understand the changing data management landscape to seize the new opportunities for success in 2021 and beyond. This post will discuss some of the most significant data management trends changing the world that you should know.
Table of Contents
1. Artificial Intelligence And Machine Learning Will Become More Critical
The world is already producing an unprecedented volume of data on a daily basis. As big data continues to get bigger, businesses will continue to focus on minimizing human input for data operations and rely more on Artificial Intelligence and Machine Learning to drive data management operations.
As AI and ML continue to advance, so does the reliance on these innovations. AI/ML will effectively automate critical data management tasks. Data management will harness ML to power a wide range of data management tasks, including data mappings, metadata management, anomaly detection, data cataloging, and other crucial processes.
Increasing automation through AI/ML will eliminate a substantial amount of labor-intensive tasks. As AI/ML innovations continue, industries will see a shift in the role the technology plays, from data management automation to more intelligent and learning-based processes.
By 2024, 75% of the biggest organizations plan to shift from piloting AI to operationalizing it.
2. Augmented Data Management
Augmented data management capitalizes on AI and ML techniques to optimize and improve operations. With data scientists spending 80% of their time presently cleaning data instead of creating insights, it creates a massive opportunity cost for businesses to contend with. As big data gets bigger, data scientists are some of the most sought-after professionals.
However, businesses do not have a growing demand for data scientists who spend most of their time cleaning data. These professionals have unique and invaluable skill sets that provide data-driven businesses competitive advantages. To get the best out of data scientists, enterprises understand that they need to adopt technologies that allow them to utilize their skills better.
This is where ADM comes in. ADM uses AI/ML to perform core data management functions, like maintaining, ingesting, storing, and organizing data. It also automatically refine data, performing low-level tasks like data cleansing and preparation. Augmented data management effectively eliminates the grunt work for data scientists, allowing them to focus on more important tasks elsewhere.
Gartner predicts that ADM has the potential to reduce manual data management tasks for data scientists by almost half by 2022 by increasing automation in redundant data management tasks.
3. Data Fabric
“Data fabric” is another buzzword that has entered the lexicon of data management in recent months. Data fabric is another critical data management trend changing the world. Defined in the simplest terms, data fabric is a single environment that consists of a unified architecture and services or technologies that the architecture facilitates, helping organizations better manage their data.
Data fabrics can offer seamless access to and sharing of data within a distributed network environment. Companies are building data fabrics to weave together all data and data ops into a unified framework. Consider data fabric as a weave stretched over a large space connecting multiple locations, types, and data sources, along with methods for accessing that data.
Data fabrics effectively come with several advantages that may include simplifying data management for companies, enabling hybridized cloud and on-premise infrastructures by eliminating data silos, and boosting scalability. Companies are using capabilities like semantic standards and graph technologies in combination with solutions like ADM, ETL, and ELT to construct data fabrics.
We live in a time of unprecedented pace of change. In the changing global landscape, data drives competitive advantage for every business to thrive, and organizations need to deliver data rapidly to serve business needs. With passing time, data fabrics will become even more crucial for companies going through a digital transformation. As data volumes and data types become supercharged and companies continue migrating to the cloud, the need to weave together data systems will also grow.
Implementing a data fabric to manage data collection, governance, integration, and sharing can help businesses become leaders in their respective industries. A data fabric is not a mere trend that will work as a one-off solution for a specific data management problem. Rather, it is a permanent and scalable solution that will help businesses manage all their data in a unified environment.
4. Decision Intelligence
By 2023, more than a third of all large global organizations will have analysts practicing decision intelligence. Decision intelligence is a practical discipline that frames a wide array of decision-making techniques. These techniques include, but are not limited to, conventional analytics to AI and complex adaptive system applications to design, model, align, execute, monitor, and optimize decision models.
Decision intelligence brings together several disciplines, including decision management and support. It involves applications in the field of sophisticated adaptive systems that consolidate several advanced and traditional disciplines to provide a framework to help data and analytics improve decision models within the context of business outcomes and behavior.
Decision-making has become more complex as businesses and society have become more uncertain. Decisions have a human emotional aspect or impact with trade-offs. Changing how decisions are made can have a substantial impact on the role that decision-makers play. As the decision-making process becomes increasingly automated and augmented, engineering decisions for better transparency, flexibility, explainability, reusability, and precision will improve trust and adoption.
5. Cloud-Native Solutions for Data Analytics
Cloud computing is firmly established as the new normal for enterprise IT, and it continues to be one of the fastest-growing segments of IT spend across industries. Gartner predicts that public cloud services will be essential for 90% of data and analytics innovation by 2022.
Cloud-native is an approach for optimizing systems for the cloud. It harnesses the most powerful advantages that cloud computing has to offer – flexibility, on-demand infrastructure, and powerful managed services – and it pairs them with technologies optimized for cloud computing.
Utilizing cloud analytics instead of on-premises analytics boasts several advantages. It can free up the in-house data team at enterprises to take on other tasks that make direct contributions to the company’s bottom line instead of focusing on maintaining hardware. It also allows companies to scale up or scale down machines based on workload, reducing unnecessary overhead costs. Cloud computing has become advanced enough today that the right solutions provider can meet the performance requirements of enterprise-level organizations with ease.
Cloud analytics services also provide data models and sophisticated analytics tools that businesses would have to otherwise build themselves. With the proliferation of cloud analytics services, organizations only have to pay for what they use.
6. Market Data Management
“Market Data” is another major term used to define the financial information necessary to conduct research, analysis, trades, and accounting for financial instruments in all asset classes on world markets. Market data management effectively refers to administering products and services related to market data. It also refers to managing contractual relationships with data providers.
Types of market data and real-time data necessary for many businesses include Index, Price, Price Volatility, Trading Volume, consumption, transportation, capacity, supply/demand, exchange rate, interest rates, economic indicator, emissions, energy generation, and much more.
Market data may cover essential information from various sectors, including Oil & Gas, Energy, Coal, Electricity, Finance, Freight & Shipping, Metals, Natural Gas, Petroleum and other Liquids, Chemicals, Weather, and Hydrology.
Market data providers that produce market intelligence include several well-known names, including:
- S&P Global Platts – S&P Global Platts is a provider of energy and commodities information and a source of benchmark price assessments in the physical commodity markets.
- Argus Media – is an independent provider of price information, consultancy services, conferences, market data, and business intelligence for the global petroleum, natural gas, electricity, emissions, biofuels, biomass, LPG, metals, petrochemicals, fertilizers, and coal industries.
- ICIS: ICIS provides market intelligence that helps businesses in the energy, petrochemical, and fertilizer industries.
- IHS Markit – provides market intelligence and data for the finance, energy, and transportation industry sectors
- Baltic Exchange The Baltic Exchange is for the maritime industry, and freight market information provider for the trading and settlement of physical and derivative contracts.
- Reuters – Global Market data, charts, and information. Major American, European, and Asian Stock Market Indices plus Sectors and Industries, Commodities and Currencies.
- Bloomberg – Bloomberg delivers business and markets news, data, analysis
- CME Group – The Chicago Mercantile Exchange is a global derivatives marketplace including agriculture, energy, and metals futures and options
- Barchart – real-time or delayed intraday stock and commodities charts and quotes.
- Vortexa – Shipping or Energy Transportation – Analytics. A state-of-the-art oil and gas analytics
- All Exchanges such as NYMEX, NYSE – An exchange is a marketplace where securities, commodities, derivatives, and other financial instruments are traded.
- Kpler – Shipping Data
Minimize Risk and Maximize Automate Data Insight
Accurate data is vital and an effective data management solution is a critical part of deploying the necessary IT systems that run business applications and provide analytical information to help businesses make smarter risk management, trading, and operational decisions.
ZEMA is a platform that has proven itself to be effective for this purpose in multiple data-intensive industries, including power, commodity trading, reinsurance, financial firms, utilities, and oil and gas. Now, businesses in the renewable energy and mining industries can also capitalize on such a platform to automate their data-centric processes and validate them to ensure greater effectiveness and efficiency. The end-to-end data management solution and cloud analytics solutions provided by ZEMA can help businesses minimize risk and maximize self-service data insight.
Consider booking a demo with ZEMA to learn more about the platform that has decreased the risk of data-driven decision-making through automated data collection, secure centralization, cloud analytics, dynamic analytics, automation of workflows and processes, and integration with third-party applications.