These are the Data Analytics Trends likely to Occupy the Minds of CIOs in 2023
Digital transformation and cloud technologies have now made it easy for huge amounts of data to be collected, amalgamated, and processed. Enterprises are turning to big data, visualization, and data science for insights that can drive accurate data-driven decisions for enhancing customer experiences, business workflows, and predictive guidance for future sales and marketing trends. With data initiatives now being easily scalable using cloud technologies, data analytics takes on a greater role. Data science and analytic specialists will play a critical role as analytics accelerates in the IT arena.
Along those lines, here are some of the most prominent data analytics trends likely to occupy the minds of CIOs in 2023:
Artificial Intelligence
The advent of the cloud and advancement in storage technologies has ensured a vast amount of data can be saved, accessed, and processed. This huge amount of data becomes a fertile ground for applying technologies such as machine learning (ML) and effective artificial intelligence (AI). Data analytics now is no more just about the 'past and present.' When combined with AI-enhancing automation, accurate data-driven decision-making and future forecasting of trends come to the fore as prominent use cases.
AI, and even ML, in data analytics is also being used extensively in cyber security for automated detection and mitigation of criminal and fraudulent activities on the internet. Partnering with data science specialists with AI cyber security expertise will be a growing trend in 2023.
Data Democratization
There has been a great advancement in the sophistication of data analytics tools and dashboards for metric reporting. Data is now being accessed across all points of generation in an enterprise and is being made easily available to all authorized personnel with appropriate simple-to-use tools and technologies. CloudOps vendors will be used extensively to achieve a high-grade level of data democratization.
All employees, regardless of technical qualification, are being empowered to interact easily with data for its analysis to ensure making better data-driven decisions.
Edge Computing
With the digital expansion of cloud services running across vast geographical distances, moving core technologies and storage infrastructure to the edge of the cloud is fast becoming mainstream. Data and its analytics will be closer to IoT and endpoints that are generating this data hence reducing costs, facilitating better management, and driving faster metric-based analysis and reporting.
Data Fabric
Data fabric is a framework or architecture where all relevant data in any form is amalgamated into a common data layer which can be accessed from any location in the cloud via connectors. All applications and data analytics engines across the enterprise have easy access to this layer. Data Fabric removes the need for silos, ensuring management and quality of data are of a much higher grade. The results of metric and data study and analysis are of a higher standard using the data fabric as there is access to all possible data available in the enterprise regardless of structure, form, size, or location.
DataOps can be considered as one of the components of the data fabric where engineering, processes, technologies, platforms, and methodologies are amalgamated for the security and management of data. DataOps can be compared to DevOps, except it is applied to data pipelines.
Data-as-a-Service
Data-as-a-Service or DaaS is a service offered by cloud vendors where tools and infrastructure are offered to customers to manage and analyze data migrated to the cloud. These tools can be accessed from anywhere at any point in time, having the cost advantage of the 'pay as you go' model. With easy access to data, metrics, and dashboards, analysis becomes simpler, and better data-driven decisions can be made for the business and its customers.
Data Engineering experts are being onboarded into IT teams to take maximum advantage of DaaS.
Data Clean Rooms & Analysis
A data clean room is when a company exposes and amalgamates its confidential data with the data of a third party for purposes of analytics and data monetization. All data governance, privacy, and security guidelines are strictly followed before data is sent to these clean rooms for further processing and analysis.
Citizen Data Scientists & Low Code.
With the democratization of data, data as a service, distributed computing, and architectures like data mesh and data fabric, non-technical power users are being able to aid and assist in data analytics and metric-based decision-making.
Also, due to the shortage of data scientists and the growth of low-code platform engineering, the citizen data scientist community will be strengthened and expanded to aid and assist analytics both in the business and community sectors.
2023 will be a Big Time for Data, Storage, and Analysis
Migration to the cloud, leaps in storage technology, digital transformation, increased online activity, and advances in software solutions and services have produced huge amounts of data. Analytics of this data will become a mandatory need, continuing to evolve and adding value to business operations for years to come.
In this digital era, enterprises are increasingly using data analytics for improving customer experience (CX), reducing costs, workflow management, and projecting future trends for sales and marketing. Click here to learn more about how we can help you navigate these emerging trends of 2023.