The BI & Data Analytics Trends that will Define 2021 PART ONE
Unleashing the Power of Data-Driven Digital TransformationAdd bookmark
When it comes to data, 2021 is poised to be a pivotal year. Over the past year, companies who had fallen behind the data and analytics curve found themselves at an incredible disadvantage. While their more data-driven peers were able to navigate the rapid pace of change with confidence, those who did not have an established data-driven culture or capabilities found themselves stranded in a sea of epic uncertainty.
Having learned the hard way, organizations are now investing in business intelligence (BI) and advanced analytics tools with renewed vigor. Though the market for data analytics tools slowed a bit in 2020 due to the pandemic, the market is expected to recover and exceed past performance levels this upcoming year.
Data Democratization Goes Mainstream
One of the primary objectives of digital transformation is the cultivation of a data-centric culture whereby everyone, across the enterprise effectively uses data-driven insights to enhance decision making. However, before this can be achieved, the right people have to have access to the right data as well as possess the data literacy skills necessary to action it.
Data democratization strategies aim to make data as accessible and usable as possible without sacrificing data security. Using next generation tools such as data lakes and self-service BI, organizations can unleash an army of “citizen analysts” equipped to translate business intelligence (BI) and analytics into real-word transformational strategies. Data visualization and storytelling techniques also help to make these data-driven insights more digestible and shareable across an enterprise.
Following the lead of data accessibility pioneers like AirBnB and Novartis, organizations will be increasing their investments in data democratization-enabling tools such as data visualization, data lakes and self-service analytics to help ensure they’re fully equipped to navigate the complexities of the pandemic age.
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Streaming analytics refers to the processing and analyzing of data records continuously rather than in batches. Unlike traditional methods, streaming analytics allow organizations to extract business value from data in motion just like traditional analytics tools would allow them to do with data at rest.
Streaming analytics provide organizations with a significant competitive advantage as it enables real-time decision making and immediate red flagging of potential high risk issues such as equipment malfunctions or security breaches. In addition, they are a must for organizations looking to capitalize on Big Data and High Performance Computing (HPC) Analytics. As such, the streaming analytics market is expected to grow over the next few years, eventually reaching a value of $38.53 billion by 2026 at a CAGR of 32.67%.
Organizations are becoming more and more adept at transforming customer data into personalized marketing campaigns and product offerings. As machine learning (ML) and predictive analytics powered tools become more wide-spread, companies will be able to predict and cater to customer behavior like never before, a capability that is especially important in the digital realm where shoppers may not casually browse as they would in a physical store.
Another trend poised to advance hyper-personalization even further is what’s known as the Internet of Behaviors (IoB). IoB refers to the collection and usage of data from various sources (i.e. phones, facial recognition cameras, IoT sensors, website search history, etc.) to predict and even influence future behavior.
Though the use cases for IoB are widespread, their most obvious application lies in marketing. For example, digital-forward companies such as Amazon and Nike pull data from pretty much everywhere you can think of from both their digital platforms (i.e. website, mobile app) to IoT sensors embedded in their physical retail locations to, presumably, external data such as demographics. By applying behavioral science-based advanced analytics to this data, they are already developing new ways to engage with customers through highly personalized content recommendations, product offering and digital ads.
And other companies are certainly following suit and not just in marketing but HR, healthcare and cybersecurity as well. In addition, over the next year, we expect to not only see technological advancements in this area but also an increased focus on the privacy and ethical implications of these methodologies.
Check in next week for part II........
Historical data is dead. As global corporate enterprises restructure their balance sheets to accommodate for the value of data, organizations need real-time data which must be democratized throughout the enterprise. Data acquisition, governance, visualization, and virtualization along with advanced analytics and AI- putting 'math on top of data' - all make that goal possible. Join us for lessons learned from those who are accomplishing the goal.
AI & Data Democratization Live
Historical data is dead. As global corporate enterprises restructure their balance sheets to accommodate for the value of data, organizations need real-time data which must be democratized throughout the enterprise. Data acquisition, governance, visualization, and virtualization along with advanced analytics and AI- putting 'math on top of data' - all make that goal possible.
Join us for lessons learned from those who are accomplishing the goal.Register Now