What do these data analytics trends mean for businesses?
The landscape of data analytics is changing more rapidly than ever. This is relentlessly evolving with the rise of digital data. An IDC report predicts that there will be 175 zettabytes of data generated in the world by 2025. Today, market players have realized the significance of data and analytics to derive a business edge from both proprietary and outside data sources. The availability of technologies such as the cloud, open source platforms and the emergence of data-intensive tools and solutions like machine learning, AI and the Internet of Things (IoT) has also transformed data analytics in recent years, making it more accessible across the board.
Significantly, Data Analytics refers to the process of assessing raw data to make conclusions about that information. Most of the data analytics processes have been automated into mechanical processes and algorithms that perform with raw data for human consumption. Meanwhile, managing a massive amount of data certainly poses both significant challenges and opportunities. So, what trends will shape the data analytics landscape in the coming years, and why businesses should consider these?
Rise of Cloud-Native Enterprises
Businesses using analytics tools are increasingly shifting to the cloud for efficient business performance. Already, a large number of organizations and startups have migrated their functions to cloud infrastructure. Using cloud-native applications can enable enterprises to better contribute to business agility and innovation. Currently, 15 percent of new enterprise applications are cloud-native and this adoption is set to reach 32 percent by 2020. By embracing cloud-based business intelligence and data analytics tools offers companies numerous benefits over traditional infrastructure. This delivers improved performance, minimized upfront investment, and greater flexibility and scalability to manage growth and unanticipated demand.
Advancements of Real-Time Data Visualization
Today, businesses are running at lightning pace producing vast data volumes. Thus managing these amounts of data becomes critical to deriving actionable insights. This is where real-time visualization comes into the rescue managing daily operations and enabling businesses to access, analyze, visualize and explore live operational data and take control of overall business operations. Generally, the visualization of data could deliver much more information than the data behind it.
Automation of Data Analysis
Automation of data analysis is considerably useful when a company deals with big data. Automated data analytics can be utilized for a variety of tasks, such as data exploration, data preparation, data replication, and maintenance of data warehouse. It can also make decisions on behalf of enterprise stakeholders and develop constructive feedback mechanisms. According to Gartner, over 40 percent of data science tasks will be automated by 2020, providing insights that might be otherwise unavailable to an enterprise.
Data-as-a-Service will Become Strategic
Data-as-a-service (DaaS) will become a more widespread solution for data integration, management, storage, and analytics, as more and more businesses are increasingly turning to the cloud to modernize their infrastructure and workloads. As data sharing between departments within a company is one of the biggest issues, DaaS solves this challenge by enabling companies to access real-time data streams from anywhere in the world. Further, it removes the limitations that internal data sources have. Companies using data-as-a-service typically focus entirely on collating data and compiling it into germane data streams.
DataOps for Better Data Analytics
DataOps defines the streamlining of processes involved storing, interpreting and deriving value from big data. It intends to shatter the siloes that have traditionally alienated different teams from one another in the data storage and analytics fields. The DataOps model is a crucial part of data analytics because data does not only need to be readily available for decision making, but it needs to be put in place effectively that ensures it is moved and processed continuously, as automatically as possible. Thus, DataOps can help a company’s data analytics and storage workflows in the same way that DevOps does for application development.