Why CIOs Need to be Investing in Digital Analytics Platform?
The future of analytics is bright with the advancements in technology, and the analytical trends is a major topic to look forward to when the companies are moving towards a more competitive stage.
FREMONT, CA: The ability for business owners to see, understand, and utilize their analytics for the betterment of their brand has never been easier, this is one of the reasons why the future of the analytics industry is so bright. Data analytics will play a major role in digital marketing because the platform helps businesses create a data-driven marketing strategy. With the introduction of data analytics tools, businesses will be able to make more informed decisions concerning the digital strategy, which will help give them an edge over their competition. CIOs around the globe are more keen to study and get informed about the digital transformation of their business, partly because the company is more interested in recognizing that the digital transformation business is the key to be a success in the future. Although the CIOs are busy following and analyzing the trend in digital marketing, a good understanding of the analytics trends is critical for the CIO and their teams across all industries.
Comparing with the business intelligence efforts of the past, the analytics of the future will be a contextual experience. Analytics, at present, refers to how much information is received and consumed. A high degree of personalization based on context is becoming a critical aspect of any analytics program.
Time and Location of the User
Effective analytics is beginning to consider the information about the consumer's location and time of day to optimize the user experience. For instance, the time zone of the recipient is more important than the time zone of a report creator. Location-aware analytics is also gaining importance. The user's location is, now, another input that determines what information the users receive.
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Growth in Mobile Users
As the number of devices, such as mobile phones, watches, glasses, in-car displays, digital personal assistants, and video gaming systems increase, the end consumption point becomes more vital in the delivery of impactful analytics. Comparing with an era when the information consumers were on a system with enough real estate on their screen to consume tables and chart full of data, at present the consumer needs the information that is delivered in a format that is optimized for their current mobile device. With multiple devices per person, the content should be personalized not only by the users but also by the user's device characteristics.
The emergence of User Journey
The user journey is becoming more interesting, considering today's analytics. The recipients of the analytics include the customers, partners, and internal business decision-makers. Each person might have interacted with the business in varied ways. All historical interactions point out to create a journey, which will personalize what, when, and how information is delivered. A B2B relationship with a business partner or communication with internal decision-makers should also consider the user journey. Therefore, greater personalization is needed and will result in becoming paramount in effectively delivering analytics to drive business results.
With an increased interest in the Internet of Things (IoT) and streaming data, the window to capture, analyze, and respond shortens. Looking at the past, the analytics programs were successful when they could provide results in days or weeks, but the platform will see a reduction in these windows to hours, minutes, and later into seconds—perhaps even milliseconds. The end-users wishing to get information more rapidly will put pressure on the analytics team to determine how many analytical teams need to know analytical processing and refinement.
Augmented Data Preparation
During the data preparation, machine learning automation is beginning to augment data profiling and data quality, modeling, enrichment, and metadata development and cataloging. Techniques and skills, including supervised learning, unsupervised learning, and reinforcement learning, are taking data preparation to a new level. Except for the processes in the past, which relied on the rule-based approaches to change the data, these enhanced machine learning processes emerge based on the fresh data to become more adept at responding to changes in the data, especially the outliers.