10 Key Analytics And Data Trends Every Business Need To Know
From small data and graphics technology to artificial intelligence, analytics and data
leaders should consider taking advantage of these trends.
When COVID-19 hit, organizations that used traditional analytics techniques that relied heavily upon large amounts of historical data realized one crucial thing: Many of these models are no longer relevant. The pandemic changed everything and rendered a lot of data useless. In contrast, analysis and forward-looking data teams shift from traditional AI techniques that rely on “big” data to more diverse secondary or “small” analysis categories.
Moving from big to small, end-to-end data is one of Sonora’s key
analytics and data trends for 2021 to 2022. These analytics and data
trends represent the market, technology, and business dynamics that data and analytics heads cannot neglect.
These analyses and data trends can help organizations and society deal with disruptive changes, fundamental uncertainties, and the opportunities they will bring in the next three years. In addition, data and analytics leaders must actively research how these trends can be helpful for mission-critical investments to improve their ability to predict, transform, and respond.
Each Analysis And Data Trends Falls Under One Of The Following Three Main Themes
- Accelerate the change of analysis and data: use artificial intelligence to innovate, improve integration, and integrate more diversified data sources more flexibly and efficiently.
- Drive business value by more powerful XOps: make better decisions and transform data and analysis into an integral part of the business.
- Distribute everything: Require flexibility in data and insight to support a broader range of people and things.
Top 10 Analytics And Data Trends
Accelerate The Change
- Smarter, scalable,
- more responsible
- Configurable data
- and analytics
- Data fabric as a
- From small to big
- and wide data
Drive Business Value
- Engineering decision
- Data and analytics as
a basic business
- Graph relates
- The increase in the
- Data and analytics
at the edge
1.Smarter, Scalable, More Responsible AI –
More intelligent, responsible, and scalable artificial intelligence will empower better learning interpretable systems and algorithms and more time to value. Organizations will start making more demands on their AI systems, and they need to figure out how to scale these technologies; so far, it has been a challenge. While traditional AI technology may depend closely on historical data due to how COVID-19 has changed the business environment, historical data may not be relevant. It means that AI technology must work with fewer data through adaptive machine learning and “small data”
technology. Also, these AI systems must comply with federal regulations, protect privacy, and reduce bias to support ethical AI.
2. Configurable Data And Analytics
Configurable data and analytics aim to use components from multiple analytics, data, and AI solutions for a user-friendly, flexible, and usable experience that enables leaders to connect data insights to business actions. Sonora customer inquiries show that most large organizations have
more than one business intelligence tool and “standard enterprise” analytics. Supporting the creation of new applications from bundled business capabilities promotes agility and productivity. Collectible data and analytics improve an organization’s analytics capabilities and enhance collaboration and expand access to analytics.
3. Data Fabrics As A Basis
As digital business accelerates and data becoming more complex, data fabrication is the architecture that will support configurable data, analysis, and its various components.
The data fabric reduces implementation by 30%, integration design time by 30%, and maintenance by 70%, as technology projects are based on the ability to combine and use/reuse different data integration models. In addition, data fabrics can take advantage of existing technologies and skills from data warehouses, data lakes, and data centers while providing new tools and approaches for the future.
4. From Small To Big And Wide Data
Small and wide data, unlike large data, solve many problems for businesses dealing with increasingly complicated questions about Artificial intelligence and challenges with rare data use
cases. Using X-Analytics technologies, wide or broad data allows the analysis and synergy of various small, diverse (broad), structured, and unstructured data sources to enhance decisionmaking and context awareness. As the name suggests, small data can use data models that require less data but still provide helpful information.
Drive Business Value
The goal of XOps (Platform, Machine Learning, Data, and Model) is to achieve efficiencies and economies of scale using best practices of DevOps and ensure redundancy, reuse, and reliability while reducing process and technology duplication and enabling automation. These technologies will empower the scaling of prototyping and provide agile coordination and a flexible design for controlled decision-making systems. Overall, XOps will let businesses leverage analytics and data to drive business value.
6. Engineering Decision Intelligence
Decision-making intelligence is a discipline that includes a wide range of decision-making processes, including traditional artificial intelligence, analytics, and complex adaptive system
applications. Engineering decision intelligence applies to individual decisions and decision sequencing, their aggregation in business processes, and even emerging decision-making networks. Moreover, it allows organizations to obtain faster information needed to conduct business actions. When combined with standard data fabric and composability, engineering decision intelligence initiates possibilities to rethink or redesign how organizations improve decisions and make them more traceable, repeatable, and accurate.
7. Analytics And Data As A Basic Business Function
Business heads are now realizing the importance of using analytics and data trends to accelerate digital business initiatives. Instead of a secondary focus – supplemented by a separate team – analysis and data are moved to a primary function. However, business leaders often underestimate the complexity of data and lack opportunities. If Chief Data Officers (CDOs) set their strategies and goals, they can increase business value
output by 2.6 times.
8. Graph Relates Entirety
The graph is based on modern data and analysis to improve and enhance interpretable AI, machine learning models, and user collaboration. Although graphing techniques are not new to analysis and data, there has been a shift in thinking around them as organizations identify an increasing number of use cases. In fact, up to 50% of Sonora’s AI customer questions include a discussion about the use of graphics technology.
9. The Increase In The Augmented Consumers
Business users, traditionally, have been limited to manually exploring predefined data and dashboards. Often this meant that data and analysis dashboards were limited to data analysts or citizen data scientists exploring predefined questions.
However, Sonora believes that these dashboards will be replaced with conversational, automated, mobile, and dynamically generated information, customized to user needs and delivered to their point of consumption. Further, it transfers insight from a handful of data experts to anyone in the
10. Data And Analytics At The Edge
As more and more data analysis technologies appear outside of cloud environments and traditional data centers, they are getting closer to physical assets. It provides more real-time value and eliminates or reduces the latency of data-centric solutions.
Bringing data and analytics to the edge will allow data teams to expand their capabilities and expand the impact on different parts of the business. It can also provide solutions for situations
where data cannot be extracted from certain regions due to regulatory or legal reasons.