For many businesses, the COVID-19 pandemic changed everything from top to bottom. The recent global village shift has taught us that the world is evolving faster. Companies need to be agile and ready to move more quickly to respond to threats and opportunities that define their destiny. And this constant need to be responsive isn’t going to fade soon.At the core of successful and informed response time is data modernization that enables businesses to understand their data better. To do this, more businesses are making a transition from conventional mainframe databases to the cloud, either on-prem or a hybrid model. To implement machine learning methodologies, Robotic Process Automation, cloud computing, or any other state-of-the-art strategy to balance the turbulence of the past couple of years, businesses must adopt the changes that enable strategic utilization of the data.[lwptoc skipHeadingLevel="h1,h4,h5,h6"]Many organizations have capitalized on using data to attain scalability in their business models. The next step is to use data to yield more agility required to address troublesome black swan events in the future.
Data Strategy Trends for 2022 and Beyond
To achieve this strategic supremacy, businesses need to plan their data strategies to address trends effectively.
Data governance will take centre stage in a hybrid cloud world
Hybrid cloud will be the default option for most businesses when it comes to capacity and scalability factors. As data is distributed across the hybrid cloud, it is crucial for businesses to effectively secure and regulate their data irrespective of where it is stored or used. Businesses that lack robust security and regulatory mechanisms are on the verge of experiencing cyber-attacks and insider threats. Moreover, they’ll also struggle to comply the regulations like data privacy laws.Businesses having foreseen approaches have eliminated such issues by using an enterprise data cloud that can inhibit a state-of-the-art mechanism of security and governance policies across hybrid cloud facilities such as fine-grained access controls, data lineage, and audit logs. For instance, banks possess the most effective security methodologies and systems that can curb modern-day problems like money laundering, fake accounts, and fraudulent transactions due to the visibility of their entire data lifecycle. To counter the increasing number of cyber-attacks and data privacy concerns, more businesses should ensure that. Their data platform can provide adequate data security, regulation, and control across their hybrid cloud platforms.
Machine learning becomes accessible to everyone
As organizations are digitally evolving, they are experiencing exponential growth in amounts of data and the notable increase in complexities of modern technologies. More businesses are depending on machine learning to address those challenges. Organizations are gearing up to uncover the hidden potential of machine learning that can help them handle their network-related issues and effectively predict future uncertainties.Before adopting Machine Learning methodologies, organizations need to understand and trust on ML model’s ability to improve their business process. Businesses that have already integrated ML methodologies in their existing business model will be more likely to survive and thrive in the future.
The rise of 5G and the resulting data storm
The world will experience the 5G revolution sooner, but readiness varies between countries and regions. While China and South Korea are rapidly deploying 5G network coverage, many countries are adapting 5G infrastructure.However, more telecommunications providers around the globe are starting or continuing to enhance their existing networks to offer high-speed, low latency, and more reliable connectivity in the form of 5G. Emerging markets or new telecommunication vendors may surpass developed countries by establishing a monopoly in the 5G market as they can make a complete transition to the latest system instead of reforming their existing network infrastructure.The culture of 5G will cast a notable impact on the organization’s data strategy, as the technology can provide state-of-the-art connectivity for the Internet of Things (IoT) and live stream of data.As a 5G network can rapidly handle up to one million connected devices over one square kilometre, organizations depending on IoT must be prepared to handle the data storm created by those connected devices. It can be achieved by enterprise data cloud, which allows them to handle IoT data effectively and quickly analyze it independently or with data from other utilities like data warehouses or data lakes. This will enable the extracting of fruitful insights while securing data at every stage of the data lifecycle.
AI gets a dose of data ethics.
As more organizations capitalize on using Artificial Intelligence (AI), it will also increase regulatory and legal risks. As AI models learn from the datasets they are trained on, businesses must deal with the ethical aspect that arises from the vast collection, analysis, and use of heavy amounts of data. In recent times, ethical AI debates circulate data anonymization as more and more countries will create regulations and set SOPs for AI innovation in 2022 and beyond. Apart from that, organizations can fulfil their duty by setting up rich data governance and regulation policies across the board. Enterprise-level cloud models make this governance easy and ensure strict regulation across the data models and information infrastructures driving AI frameworks.
Provenance and Blockchain
Informed and rational data strategy involves data lineage to ensure regulatory compliance. Another way of adapting governance and regulation is to use blockchain. Provenance is a notable advantage of using blockchain. It is typically based on metadata chronicling data’s enterprise roadmap and modern approaches by utilizing distributed ledgers. Blockchain technologies are rich enough to provide the provenance of every file, including who originated and disseminated every segment of data you shared. Blockchain’s rigid traceability ensures data governance policy while restricting users from complying with it. This overwhelming capacity is applicable within and between organizations to provide transparent and satisfactory auditing.
Data Quality, Data Modeling
Data quality will always remain proactive in data strategy, data management, and data governance. It’s the basis for cleaning and preparing data to be used by decision-makers to make accurate decisions. Organizations can enhance data quality by implementing the rules and regulations that standardize how data is represented and stored. Data virtualization, knowledge graphs, and Master Data Management are unique centralized methodologies used to overcome the issues. Data modelling won’t allow you to enter bad or wrong data. Machine learning is a primary reason to ensure data quality, as flawed datasets and inputs can make ML models deliver dubious and irrational outputs. The primary centralization methods are effective because they standardize the data modelling involved, which serves as the foundation for integrating data to eliminate garbage entities from data.
Data is undoubtedly a strategic asset that enables organizations to attain the agility required to navigate through the opportunities in 2022 and beyond. However, the skyrocketing importance of data and the birth and evolution of modern technologies such as AI, ML, and Blockchain will likely create new challenges. It bounds businesses to develop intelligent and effective data strategies to address the anticipated challenges of the future and provide every employee with rich access to data to make informed and rational decisions.