Did you know the average salary for a data scientist is over $100,000 USD, and for a data analyst, it’s $63,800 to over $82,000?
Data scientists are in short supply and high demand. The US Bureau of Labor Statistics predicts data analyst jobs will increase 25 percent between 2020 and 2030, much faster than the average for all occupations.
Data scientists make sense of your organization’s data. They deal with your technological infrastructure, do testing, oversee machine learning for decision-making, and manage data products. They also collect and clean data, build dashboards and reports, and manage data visualizations.
The demand for data scientists reflects the high value of leveraging data to gain insights into business operations. The need for data analysis is constant, whether the economy is buoyant or in recession.
Data is the cornerstone of any good business today, big or small. A data-driven approach achieves efficiencies and saves business costs. Most critically, it enables more informed decision-making on almost any business matter.
While businesses have long used statistics to help make decisions, data analytics is a game-changer because it’s now using Big Data and machine learning technologies to discern hitherto invisible patterns in much more massive volumes of data.
Such enhanced detection fuels better models for analysis, forecasting, and decision-making. It also helps enable more intelligent optimization of processes and technologies to enhance efficiency, build a better employee experience, and foster business growth.
Here are 12 influential trends in the world of business data.
Data constantly multiplies. With increased volume, there's a greater need for data systems that are efficient, scalable, and governed by good policies to ensure data quality and security.
Gartner cites three technology trends related to scale:
Gartner predicts by 2027, over 50% of enterprises will use industry cloud platforms to drive business. That’s interesting when you note that in 2021, less than 10% of businesses used them.
Industry clouds offer cloud services purpose-built for a specific industry. Manufacturing, retail, government, healthcare, agriculture, insurance, and banking all have industry clouds.
An industry cloud connects buyers, suppliers, and intermediaries within one industry to deliver web-based services. It offers agility through a composable approach—a modular approach to building software from small, independent components that you can combine to form a highly flexible, scalable system.
This means developers can reuse code and components to enable businesses to speedily adapt the industry processes and applications relevant to them.
Platform engineering is the discipline of designing and building toolchains and workflows that enable self-service capabilities for software engineering organizations. It optimizes the developer experience and accelerates digital delivery.
Gartner gives examples of the US sportswear firm Nike and the Norwegian police body Polities as enterprises using platform engineering to good effect.
Nike, for example, used modular, composable technologies exposed through APIs to build platforms that were more responsive to change, shortened the time to market, increased scalability, and lowered operating costs.
Meanwhile, Politiet, Norway’s police, replaced legacy middleware with a self-service developer platform.
Gartner predicts by 2025, half of all enterprise wireless endpoints will use networking services that deliver more than simple communication. For example, Bosch-Siemens uses wireless to connect with ultrasonic sensors to slow down forklifts in real time.
More than 80 percent of companies surveyed in the World Economic Forum’s Future of Jobs 2020 report said they’d be using big data by 2025.
As TechTarget defines it, big data combines structured, semi-structured, and unstructured data collected by organizations. You can mine these vast data collections for information and use them in your own machine-learning projects, predictive modeling, and other advanced analytics applications.
Big data matters because companies use it to improve operations, provide better customer service, create personalized marketing campaigns, and pursue other data-driven tactics and strategies to gain a competitive advantage and increase revenue.
Meanwhile, more and more organizations are using data analytics to inform decisions.
Data analytics is the collection, transformation, and organization of data in order to draw conclusions, make predictions, and drive informed decision-making (Coursera).
McKinsey projects that by 2025, data-driven organizations will be more widespread, with smart, automated workflows enabling people to focus more of their time on innovation, collaboration and communication.
The use of graph and NoSQL databases, self-service analytics tools, and the rise of data-sharing platforms and marketplaces are just some data trends McKinsey predicts.
Augmented analytics, a form of data analytics, is the next new trend, promising to democratize data analysis. Augmented analytics involves three things:
Thanks to machine learning, augmented analytics looks for patterns in the data without the involvement of data scientists—essentially automating some analysis.
Artificial intelligence is “the science and engineering of making intelligent machines, especially intelligent computer programs” said Professor John McCarthy, one of the founding fathers of AI.
AI is spreading fast, enabling self-learning factories and many other uses.
The top three use cases for AI in 2021 (McKinsey, The State of AI in 2021) were:
Meanwhile, machine learning is a branch or subset of AI focusing on the use of data and algorithms to imitate how humans learn. It gives computers the ability to learn from huge amounts of data with complex patterns, without being explicitly programmed.
If you’ve spoken to a chatbot, used language translation apps, or enjoyed the movie suggestions Netflix gives to you, you’ve benefited from machine learning.
“Machine learning is changing, or will change, every industry, and leaders need to understand the basic principles, the potential, and the limitations,” says MIT computer science professor Aleksander Madry.
Traditional AI solutions are built on static models that learn how to handle data in a certain way.
Adaptive AI is different. It updates itself as it handles evolving data over time. It can change its own code to adjust its operations according to the current need. It makes better predictions and provides you with better, more tailored solutions over time, as it learns your patterns and needs.
This makes adaptive AI precise, efficient, and agile.
Healthcare and education are blazing the trail in uptake of adaptive AI. For example, in education, the US Army uses the AI-based training software Cerego to enable adaptive learning that adapts lessons to each individual’s learning progress.
Edge or distributed computing processes and stores data close to its source, rather than sending data to a central location for processing.
Growing demand for apps that rely on real-time data processing, as well as privacy concerns, is helping to drive this trend.
Gartner estimates that 75% of enterprise-generated data will be created and processed outside a traditional centralized data center or cloud by 2025 compared to 10% in 2018.
Data as a service (DaaS) uses cloud services to provide data storage, integration, processing, or analysis services over a network connection.
While DaaS is decades old, its use will spread in 2023 due to increasingly low-cost cloud storage and bandwidth, combined with increased use of cloud platforms. This combination lets you manage and process data quickly and at scale.
As many firms adopt cloud solutions to modernize their infrastructure and processes, Data as a service (DaaS) will become a more widespread solution for data integration, management, storage and analytics.
To cope with increased data processing needs, some software firms are making it easier for users to do parallel processing and data partitioning with the use of a “no-code” interface. This is helpful for enabling on-demand data for decision-makers.
A trend that began in 2021, this will continue to mushroom as more organizations seek flexibility in using their own preferred mix of on-premise, cloud and edge data sources and solutions.
The 2022 Global Hybrid Cloud Trends Report, sponsored by Cisco, found that 82% of the organizations it surveyed have adopted hybrid cloud. The report concludes hybrid cloud is the de facto operating model for IT organizations.
IBM defines observability as the extent to which you can understand the internal state of a complex system based only on its external outputs.
The more “observable” a system, the more quickly and accurately you can diagnose the root cause of a performance problem without additional testing or coding.
In cloud computing, observability also refers to software tools and practices for aggregating, correlating, and analyzing performance data from a distributed application and the hardware it runs on, to better monitor, troubleshoot and debug the app.
Observability is not the same as APM (application performance monitoring), but it helps to do APM much better in light of the speedy, distributed and dynamic nature of cloud-native app deployments.
Systems of the future will need to have good observability.
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