Data analytics trends are constantly evolving. With the new technological advancements, data analytics is expected to continue its upward trajectory in 2023. In the era where business is growing rapidly, data collection and analysis have become more critical for companies to stay ahead in the competitive market.
Some key trends in today’s data-driven and accelerating market include data science, artificial intelligence, and big data analytics. In this data analytics interview series, we will dive deep into the latest data analytics trends and scope in 2023. Experts will reveal the key component of data analytics in the future, i.e., real-time data visualization and skills required to be successful data analysts.
Expert Insights: Future Trends, Skills & Tools for Data Analysts
The global big data analytics market is expected to grow significantly over the coming years. The data analytics market’s forecasted market value will reach 650 billion dollars by 2029. The rise in data analytics solutions is the rapid digitalization observed across healthcare, retail, manufacturing, and other industries. With that in mind, businesses must focus on data preparation, data exploration, and data storage using the best self-service data analytics software.
In order to help business owners and data analysts understand the trends, the Inferenz team has conducted an interview with Ms. Aparna Varma. Being a Microsoft Certified Technology Specialist, she has hands-on experience in technologies, including Business Intelligence, Business Analysis, and Artificial Intelligence.
Internal Team: Hello, Ms. Aparna. We hope you are doing well, and thank you for being a part of our interview series.
Ms. Aparna: It is my pleasure.
Internal Team: So, Ms. Aparna, before we understand the skills, tools, and trends of data analytics, could you please let our readers understand the future scope of data in 2023? Will data continue to be a revolutionary and world-changing tool for businesses?
Ms. Aparna: In 2023, the scope of data is expected to continue growing rapidly in both volume and complexity. The following are some of the key trends that will shape the future of data in 2023:
- Increased adoption of artificial intelligence (AI) and machine learning (ML) techniques: Organizations will continue to invest in AI and ML technologies to better analyze and extract insights from large and complex datasets.
- Greater use of real-time data: Businesses will rely more heavily on real-time data to make faster and more accurate decisions, particularly in areas such as customer service, logistics, and supply chain management.
- Greater emphasis on data security and privacy: With the high volume of data being generated and shared, there will be a growing need for robust data security and privacy measures to protect sensitive information.
- More extensive use of Internet of Things (IoT) devices: The number of IoT devices in use will continue to grow rapidly, generating huge amounts of data that will need to be analyzed and acted upon in real-time.
- Greater use of data visualization and storytelling: As data becomes more complex, businesses will need to find new ways to make sense of it and communicate insights effectively to non-technical stakeholders.
Overall, the future scope of data in 2023 will be characterized by a growing need for advanced analytics and visualization tools, greater emphasis on data security and privacy, and increased adoption of real-time and IoT-generated data.
Internal Team: Thank you for helping us understand the future of data in 2023. But, as you said, data visualization will be used more. Could you please tell us how real-time data visualization will impact the future of data analytics?
Ms. Aparna: Real-time data visualization will have a significant impact on the future of data analytics by enabling businesses to make faster, more informed decisions based on real-time insights. With real-time data visualization, businesses will be able to monitor as well as analyze key performance indicators (KPIs) in near real-time, allowing them to quickly identify trends, patterns, and anomalies. This will enable businesses to take proactive measures to improve performance, reduce costs, and increase revenue.
Additionally, real-time data visualization will make it easier for businesses to share data and insights across different departments and teams. This will enable businesses to make better use of the information they collect, as well as improve collaboration and communication between different teams.
Furthermore, real-time data visualization will also enable businesses to make better use of big data. With the ability to process and visualize massive data in real time, businesses will be able to gain in-depth insights and make more accurate predictions. This will help them to identify new opportunities and make better decisions.
Overall, real-time data visualization will play a crucial role in the future of data analytics by making it easier for businesses to make better use of the data they collect, make faster and more informed decisions, and gain deeper insights into their operations.
Customer experience is the bottom line when it comes to decision-making. When customers are waiting in backed-up queues and support agents are struggling to stay afloat, having access to the most recent and relevant data can help you make decisions that make this better for agents and customers alike.
Real-time analytics are crucial to a modern support strategy. This looks like a live feed of all the necessary and important information on your dashboard or can be broadcast to support agents so they can see where to allocate their time. In real-time, you can visualize spikes in calls, changing queues, and which channels need support.
As increasing amounts of data are generated, collected, and stored in data centers around the world, It’s more important than ever that you can quickly analyze and understand what the data is telling you so you can make informed decisions. Real-time data visualization takes visuals to the next level by letting you update charts and graphs in real-time. Having real-time data available helps stakeholders to make better decisions that are based on actual data rather than on intuition.
Advanced analytics tools able to visualize real-time data and historical data can immediately highlight critical conditions so that users can immediately spot and alert teams to intervene at an optimal time.
Internal Team: As the need for data analysis and visualization is increasing for businesses, chances are there will be a rise in data analyst jobs too. According to you, what skills should one have to be an expert data analyst?
Ms. Aparna: Sure, here are a few important skills:
- Strong mathematical and statistical skills: A data analyst should have a solid understanding of mathematical concepts such as probability, statistics, and linear algebra.
- Programming skills: Data analysts should be proficient in programming languages such as R, Python, and SQL.
- Data visualization skills: The ability to present data clearly and concisely using tools such as Tableau, Power BI, and Excel is crucial for a data analyst.
- Machine learning and artificial intelligence skills: With the increasing use of machine learning in data analysis, a data analyst should have knowledge of these technologies and how to apply them to solve real-world problems.
- Strong problem-solving skills: A data analyst should be able to analyze complex data sets to identify patterns and trends.
- Communicating effectively: A data analyst should be able to communicate their findings and recommendations to non-technical stakeholders in a clear and concise manner.
- Attention to detail: A data analyst should have a keen eye for detail to spot errors and inconsistencies in data.
- Creativity and curiosity: Data analysts should be curious and creative in their approach to problem-solving and be open to new ideas and approaches.
Internal Team: Besides skills, they need tools to work with small data sets and complex datasets. So, what are the top tools that aspiring data analysts should learn?
- Excel: Excel is spreadsheet software that is commonly used for data analysis. It allows users to organize, manipulate, and visualize data easily.
- R or Python: R and Python are popular programming languages used for data analysis. They offer a wide range of tools and libraries for data manipulation, statistical analysis, and machine learning.
- SQL: Structured Query Language or SQL is used for managing and querying relational databases. Aspiring data analysts should learn how to use SQL to extract and manipulate data from databases.
- Tableau or Power BI: Tableau and Power BI are data visualization tools that allow users to create interactive and visually appealing charts and graphs.
- Git: It is a version control system that allows users to track changes in their code and collaborate with others. It is an essential tool for data analysts who work on projects with multiple contributors.
- Jupyter Notebook: It is an open-source web application for users to create and share documents that contain live code, equations, visualizations, and narrative text.
- SAS: SAS (Statistical Analysis System) is a software suite that provides a wide range of tools for data management, statistical analysis, and data visualization.
- Machine Learning libraries: Aspiring data analysts should learn how to use machine learning libraries such as Scikit-Learn, TensorFlow, and Keras to build predictive models and analyze data.
Internal Team: What can experts predict about data analysts’ trends for 2023?
- Increased use of artificial intelligence and machine learning: As these technologies are advancing, data analysts will increasingly use them to automate data cleaning, analysis, and prediction.
- Greater focus on data privacy and security: With increasing concerns about data breaches and cyber-attacks, data analysts will need to pay more attention to ensuring that sensitive data is protected and secure.
- More emphasis on data visualization: With the growing volume of data, data analysts will need to develop more sophisticated ways of visualizing and communicating data insights to stakeholders.
- Greater use of cloud computing: Data analysts will increasingly rely on cloud-based tools and platforms for storing, processing, and analyzing massive data sets.
- Increased use of natural language processing: Data analysts will use natural language processing to extract insights from unstructured data sources like social media and customer feedback.
- More focus on real-time data analysis: With the growing need for real-time decision-making, data analysts will need to develop the skills and tools to analyze and interpret data in near real time.
- Greater collaboration between data analysts and business teams: As data becomes more central to business operations, data analysts will need to work more closely with business teams to understand their needs and goals and to provide actionable insights.
Internal Team: Thank you, Ms. Aparna, for helping our readers understand the key data analytics trends, skills, and tools.
Manage Real-Time Data With Inferenz Experts
As the data analytics industry transforms with new inventions, businesses must maintain data quality and utilize data to improve their operations. Predictive analytics enables organizations and business users to use the common data stored to predict future trends. Additionally, understanding the latest trends in data analytics can help organizations with better security and data privacy capabilities.
Now that you know the future trends in data analytics, it’s time to use analytics for mission-critical data to leverage the power of data storage. If you are confused about how to future-proof your business using real-world data, it is best to contact the data analytics leaders of Inferenz. Our experts can help you analyze data and prepare businesses by understanding data analytics trends.
Quick Recap: Data Analytics Trends For 2023
In today’s market trend, data is one of the driving forces for organizations. Many enterprises have already adopted data-driven analytics models to streamline in-house business operations. The inclination towards data analytics will help businesses embrace the fact-based decision-making process.
- In 2023, there will be increased adoption of technologies like machine learning and artificial intelligence for better data analysis.
- Real-time data visualization will enable businesses to leverage effective data and make more informed decisions.
- As data analytics helps businesses in multiple ways, we can expect a rise in jobs in this sector.
As more data trends emerge in 2023, businesses need to identify, experiment, and invest in future technologies. Feel free to contact Inferenz experts if you want to dive deep into the key data analytics trends and how they will impact the future.