The past year showed us how critical real-time information is in solving major societal challenges.
Data & Analytics are essential to weathering the storm during periods of disruption and uncertainty and equally important for recovering and leading in the "Next Normal" that will follow.
To be a leader, it's necessary to be a few steps ahead and be prepared for what's coming next. We named the 2021 Data & Analytics Trends to help you succeed this coming year and beyond. The trends are segmented into three categories that are defined by:
These trends are a must, and they require action now.
The Cloud is prevalent now. This past year, a wave of Cloud migration came from a remote workforce that needed access to centralized data from anywhere. Migration to the Cloud will continue because of its benefits in speed of deployments, security, performance, and analytics.
When it comes to analytics, the Cloud is resolving scalability issues related to the volume of data and the number of users. It enhances collaboration by bringing data closer to the business and enables data marketplaces. The Cloud also makes Artificial Intelligence more accessible because it enables Machine Learning, Natural Language Processing, and Facial Recognition capabilities.
Companies will focus on Financial Cloud Management (FinOps) because although Cloud providers' pricing models can be a financial advantage, they also present challenges. How do you create a budget when you don't know how much storage you'll use? How do you ensure you're not overpaying or restricting usage and limiting adoption? Experts with technical depth play an essential role in ensuring a company is financing its Cloud environment optimally.
Last year we brought you the Three Knights of Digital Transformation, and this year, we have the Three Knights of Data Management. In today's data economy, we know that data is the "new oil," and the demand for more self-service access to unstructured data sources continues to grow. But, this comes with risks that will be addressed through AI to optimize the key forces in Data Management: DataOps, Metadata, and Data Governance.
AI will be applied to the DataOps framework to automate ingestion, processing, and storing of data from new sources and the discovery of Metadata to add context. This will be key for detecting anomalies and auto-monitoring Data Governance, so there is data quality, data lineage, and security at every stage of data without an engineer's analysis.
Companies will embed Advanced Analytics capabilities, such as AI, Machine Learning, and Natural Language Processing, into all analytics layers to generate game-changing Business Insights. It’s a must to apply these capabilities in Diagnostic Analytics to define the data models, Descriptive Analytics to react to business problems, Prescriptive Analytics to recommend actions, and Predictive Analytics to forecast outcomes.
Advanced Analytics capabilities must be on an ongoing basis to make the business more efficient and uncover the next competitive advantage.
The pandemic increased the demand for more centralized data and processes that can improve Machine Learning and provide critical insights and predictions. Pharmaceutical companies that are competitors collaborated on data sets to develop solutions, major Cloud providers collaborated to facilitate contact tracing, and governments requested access to their citizens' sensitive health data.
There are already regulations such as GDPR, HIPAA, and FERPA, etc. Still, now that there is a more significant interest in shared data platforms and AI algorithms that can help solve major societal challenges, data ethics is increasingly critical. Companies need to navigate using AI without falling into ethical pitfalls along the way.
The use case for Blockchain technologies to address data security continues to surface because of the capabilities they have to mismatch data in infrastructures and share data without compromising confidentiality.
These trends will have a significant impact.
Dashboards are useful but no longer a key differentiator. There are more compelling and intuitive ways people can interact with data thanks to Advanced Analytics capabilities such as AI, ML, NLP, Augmented Reality (AR), Virtual Reality (VR), etc. We can create immersive experiences and completely new Data & Analytics environments for users.
In the current remote workforce, autonomy is a must. Training isn’t readily available; applications must be easy to use for non-technical users.
There are innovative and niche technologies in the market that offer analytics experiences that go beyond a dashboard. Data & Analytics leaders should look to solutions that embed insights into interfaces and alert them as they occur, instead of building point and click dashboards that report on the past.
Now that many software vendors offer subscription or consumption-based pricing models, customers are no longer locked-in to technologies for long periods. Today they have the flexibility to get rid of technologies that no longer serve the business's needs and move quickly to new technologies in the market. These new pricing models are enabling a Service Architecture that uses best-of-breed technology for each specific task.
The Service Architecture will make it possible for small, niche software companies to compete with global enterprises and allow customers to mix and match the best technologies, push beyond the boundaries of legacy data environments, and keep pace with innovation.
For a Service Architecture to be successful, it's a must that technologies can be removed and substituted without affecting the rest of the architecture.
There is more data at our disposal than what’s being collected and used. Unstructured data in text, video, audio, images, emotions, vibrations, etc., is often missed. What can we solve if we use all of it?
Gartner coined the term to describe this data, “X Analytics,” saying it is an umbrella term where X is the data variable for a range of different structured and unstructured content. These unique data sets are being used to solve some of society’s toughest challenges, such as climate change, disease prevention, and wildlife protection.
Better Artificial Intelligence and Machine Learning will drive X Analytics and make it possible to find critical irregularities that would typically go undetected. For businesses, this trend could be how the next competitive edge is uncovered, and they should expect that innovation with X Analytics will likely come from startups and Cloud providers.
Graph Analytics are resourceful in interpreting the unstructured, fluctuating, variable datasets. It allows for exploration and provides context about relationships between different companies, people, or financial transactions that can enhance predictions and decision-making accuracy.
Graph technologies show potential to enhance Machine Learning and Artificial Intelligence because they can incorporate new data sources and mine relations among those data sets with ease.
Gartner predicts by 2023, graph technologies will facilitate rapid contextualization for decision making in 30% of organizations worldwide.
The need for Business Operations to react quickly has become more apparent in the past year. We experienced shortages of many products, such as medical masks, cleaning products, and medicine. How could this have been avoided?
Continuous Intelligence (CI) powered by the speed of a 5G network. This is the solution to optimizing business processes and correcting irregularities and failures right when they occur.
The Continuous Intelligence work approach on a 5G network can process new and historical data from sensors in real-time and prescribe actions. Supply chains, warehouses, and procurements are key areas that can benefit.
This trend is starting to surface.
AI techniques have been developing rapidly lately, but their true potential is yet to be unleashed because it's limited by modern computers' capacities, which fail to process the currently available amounts of data within a reasonable timeframe. Quantum Computing can do more operations and process more data than the supercomputers of today.
Quantum AI could impact Machine Learning, Predictive Analytics, and Natural Language Processing. It shows the potential to spark the development of breakthroughs that can help society and business. Such as Machine Learning that can diagnose illnesses sooner, produce materials to make efficient devices and structures, and optimize algorithms that can come up with financial strategies, and much more.
Quantum Computing could take digital transformation to a place most haven't conceived of yet.
Quantum Computing could take digital transformation to a place most haven't conceived of yet.