Data and analytics: Key trends in 2023 driving new growth, efficiency and innovation in organizations
As many businesses continue to find ways to optimize their operations and drive growth, data and analytics continue to be an area of investment. Organizations will continue putting in resources to ensure that they are equipped with the right infrastructure, tools and skill sets to anticipate market changes, respond and pivot accordingly, and transform uncertainties into opportunities for their businesses.
Understanding the top trends in data and analytics technology and practices therefore becomes imperative for organizations to stay ahead of the game. And whether you are a data-driven leader of your organization or team member looking to add value to what your organizations are doing, staying on top of these trends will serve you in what you do.
Artificial Intelligence (AI) and Machine Learning (ML)
As artificial intelligence (AI) and machine learning (ML) mature, so do its applications in data analytics. The use of AI and ML in data analytics help drive efficiencies in analyzing large sets of data, helping provide more accurate and insightful predictions and recommendations at a scale and speed. AI-powered algorithms can help organizations automate data analysis, uncover hidden patterns and insights, and make more informed decisions.
As more organizations across industries ramp up their search for highly skilled data analytics, AI and ML professionals, building your knowledge and capabilities in this areas can help you stay relevant and marketable. In May, She Loves Data is collaborating with Databricks to run a session on the topic of The Future of Open AI for Individuals and Professionals where we will chat with industry experts on AI revolution to discover the limitless possibilities & see how the evolution of open AI will change and impact our professional and personal lives.
Cloud-based analytics
Another trend that is pervasive in the data analytics space is cloud-based analytics, which refers to the manipulation and analysis of data that happens on the cloud instead of in an on-premises system. Analytics systems hosted in the cloud enable users to access, aggregate, analyze and utilize larger sets of data from across the organizations, subject to access and permissions control.
There are already many existing use cases for cloud-based analytics and this will continue to grow. Whether you are a marketer looking to analyze your customers’ purchase behaviours, a finance professional looking to analyze financial market data, or an ESG consultant wanting to gain insights into your client’s sustainability practices, cloud-based analytics enables additional sharing and collaboration, improved security, lower costs and tremendous scalability.
Data democratization
Data democratization refers to the process of making data more accessible to all employees within an organization. This trend is driven by the recognition that insights can come from any part of an organization, and that empowering all employees with data can lead to better decision-making.
According to The Data Warehousing Institute, in 2023, one of the most significant shift that the industry will see in 2023 is a push for more democratization in the data analytics space. Every data-driven company must realize that if they are to achieve the company-wide insights to drive operational efficiencies and growth, they need to make data and analytics tools accessible to more users.
According to Rohit Amarnath, CTO of Vertica and a Forbes Council Member, the “self-service” or “democratized” analytics model will become a “holy grail” standard that data practitioners will continue to strive for in 2023. This model, where all business units (even non-technical ones) will have access to data and intelligent insights, can be hard to set up and scale. But that should not mean organizations should not move ahead with democratizing the data and analytics. Cloud architectures, on-demand analytics platforms, continue to grow and deliver functionality to meet the demand.
What can this potentially mean for you? No matter your role and level of seniority in the organization, upskilling yourself to understand how to operate the analytics tools and make sense of the data will be key to stay relevant.
Data storytelling
Data storytelling is the practice of using data to tell a compelling story. It involves combining data with visualization and narrative to create a compelling and engaging story that helps decision-makers understand complex data sets.
As data and analytics gets democratized, the ability to make sense of data and tell a story from those insights will be critical for anyone operating in data-driven organizations. According to Harvard Business School, many companies have begun including data storytelling as a required skill in analyst job descriptions, while others have created specific roles and are hiring for data storyteller positions to supplement their existing analytics teams’ abilities. Having the skills to both analyze data and communicate its insights will make you a much sought-after talent in today’s data-driven environment.
As the data & analytics space continue to evolve and grow, so too will the kind of roles and skill sets that organizations will need to help them address their most pressing business challenges. Keeping yourself on top of the latest trends in the space and finding opportunities to upskill yourself in this area will help you stay ahead of the curve while making yourself a valuable asset for your organizations.
