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8 Analytics Trends That Will Shape Your Future

Analytics is a fundamental part of the future digital economy. To be successful, you must recognize growing trends that will impact your analytics program.

CIOs around the world are increasingly focused on the digital transformation of their businesses, partly because their CEOs are increasingly recognizing that digital business is the key to success in their future. As they evaluate how to drive their digital transformations, a top investment priority is business intelligence and analytics. Data is the fuel that powers the digital economy and analytics is the mechanism that transforms that raw material into usable assets that will make them successful. A good understanding of analytics trends is critical for CIOs and their teams across all industries.

For Further Reading:

User Experience: The Secret Ingredient to Analytics Success

Data Democracy Now: Advantages, Issues, and Implementation

Where to Focus to Deliver Analytics Value

Analytics Is Becoming an Experience

Unlike business intelligence efforts of the past (which focused on delivering beautiful charts and graphs that captured the state of the business), analytics of the future will be a contextual experience. Analytics is now as much about how information is received and consumed as it is about the message to be delivered. A high degree of personalization based on context is becoming a critical aspect of any analytics program.

Location and Time of the User

Effective analytics is starting to consider the information consumer’s location and time of day to optimize the user experience. For example, the time zone of the recipient is now more important than the time zone of a report creator. Location-aware analytics is also gaining importance; your user’s location is now another input that determines what information your user receives.

Mobile Device Use Grows

As the number of devices expands to include mobile phones, watches, glasses, in-car displays, digital personal assistants, and even video gaming systems, the end consumption point becomes more vital in the delivery of impactful analytics. Unlike the era when most information consumers were on a computer with enough real estate on their screen to consume tables and charts full of data, today’s consumer needs the information delivered in a format optimized for their current mobile device. With multiple devices per person, the content must be personalized not only by user but also by the user’s device characteristics.

The Rise of the User Journey

The user journey is becoming more prevalent in analytics. Recipients of analytics results include customers, partners, and internal business decision makers. Each of these individuals has interacted with your business in different ways. All those historical interaction points create the journey that individuals have taken. As analytics evolves, data points from this journey will personalize what, when, and how information is delivered.

If you are communicating with a customer, knowing their interaction history, past purchases, and what information has been sent to them creates a unique point-in-time need for information. A B2B relationship with a business partner or communication with an internal decision maker must also consider the user journey. Increased personalization will become paramount in effectively delivering analytics to drive business results.

Continuous Analytics

With an increased reliance on the Internet of Things (IoT) and corresponding streaming data, the window to capture, analyze, and respond shortens. Analytics programs of the past were successful when they could deliver results in days or weeks, but the future will reduce these windows to hours, minutes, and seconds -- perhaps even milliseconds.

The end user’s desire to have information more quickly puts pressure on analytics teams to determine how much analytical processing and refinement is enough. You must decide whether simple processing that can be delivered in seconds is sufficiently effective or whether complete processing that takes hours or days is required. You must also decide if a user can take some information now and some later -- can you deliver the simplest, most basic analytics in real time and provide more comprehensive analysis later?

Augmented Data Preparation

During data preparation, machine learning automation is starting to augment data profiling and data quality, modeling, enrichment, and metadata development and cataloging. Techniques including supervised learning, unsupervised learning, and reinforcement learning are taking data preparation to a new level. Unlike the processes of the past, which relied on rule-based approaches to transform the data, these enhanced machine learning processes evolve based on fresh data to become more adept at responding to changes in the data, especially outliers.

Augmented Data Discovery

In addition to helping with data preparation, many of these algorithms now enable information consumers to visualize and narrate relevant findings within the data more easily. These include enhancements such as automatically exposing correlations, exceptions, clusters, links, and predictions within the data without having end users build models or write algorithms themselves.

This augmented data discovery will lead to an increase in so-called citizen data scientists. These users include information consumers who, with augmented assistance, will start to identify and respond to patterns in the data much more quickly and in a much more distributed model than in the past (when only dedicated teams of data scientists could do this work).

Augmented Data Science

The advent of the citizen data scientist will not eliminate the need for a data scientist who delves into the data to find lucrative opportunities for business growth. As these dedicated data scientists allow citizen data scientists to take over the simpler work, their analysis becomes both more challenging and potentially more valuable to the business.

To optimize their effectiveness, we will see machine learning applied more widely in areas such as feature and model selection. The application of machine learning to more repetitive aspects of their job will free these data scientists to focus on the most valuable aspect of their job: to identify unintuitive patterns in the data that have the potential to transform your business and then move these into an operational state to start generating revenue.

A Final Word

The future for analytics is bright with some of the enhancements being integrated today and others on the horizon. These enhancements fall into three categories: context-aware analytics, continuous analytics, and augmented analytics. As analytics is a significant driving force behind the digital economy, CEOs and CIOs will continue focusing on investment in these programs to maintain relevance. As an analytics leader, you will need to keep a close eye on these trends and evolve your practices and projects accordingly to support your business leadership.

About the Author

Troy Hiltbrand is the chief digital officer at Kyäni where he is responsible for digital strategy and transformation. You can reach the author at thiltbrand@kyanicorp.com.


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