Digital technologies have reshaped the business landscape of countless industries, dramatically boosting productivity and efficiency. Yet far too many companies are unsuccessful at their own digital transformation initiatives. According to research from McKinsey & Company, just 16 percent of organizations say that their digital transformations have improved their performance in the long run.
To beat the odds and see lasting value from digital transformation, companies need to stay on top of the latest digital analytics trends. By collecting and processing data more effectively, digital analytics technologies assist in transforming the business from top to bottom for the better—from sales and marketing to R&D and human resources.
In this article, we’ll discuss 10 of the most important digital analytics trends of 2019 and beyond, so that you can build more resilient business ecosystems and be better prepared for the future.
Leveraging customer data to your advantage is a key element of digital transformation initiatives. When compared with their competitors, businesses that intensively use customer analytics are 23 times more likely to excel at new customer acquisition, and 9 times more likely to have high customer loyalty. Yet with more and more touchpoints and methods for collecting customer data, centralizing and making sense of this information is harder than ever.
It’s in this context that customer data platforms (CDPs) have emerged. CDPs are software applications that aggregate and organize customer data for use by other systems and marketing activities. The information stored in a CDP may include a customer’s name, location, contact details, demographics, career, hobbies, beliefs, opinions, transactions, customer service interactions, and more.
By accumulating and assembling customer data, CDPs help you understand your customers better and unify your marketing efforts. CDPs are especially important because they help to break up business silos, an all-too-common problem that occurs when different teams and departments fail to share valuable insights and information with each other.
Paradoxically, the rise of CDPs is occurring at the same time as interest in data privacy accelerates. Recent news stories such as the Facebook-Cambridge Analytica scandal and the massive Equifax data breach have made many consumers increasingly skeptical about how organizations use their personal data. According to a recent Pew Research Center survey, 49 percent of Americans believe that their information is less secure than five years ago.
In reaction to these controversies, legislation such as the European Union’s General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) has been enacted. Businesses that wish to avoid fines and other penalties (not to mention customer backlash and the risk of cybersecurity incidents) have no choice but to comply with these new regulations.
GDPR, CCPA, and other regulations place strict controls on how organizations can store and process personal data. For example, GDPR considers individuals to be the ultimate owners of their own personal data. As such, individuals have the right to know what data an organization holds on them, and to request that this data be deleted.
Concerns about data privacy are valid, and they aren’t going away any time soon. This means that organizations of all sizes and industries need to rethink their relationship with data privacy and their methods of building customer relationships.
Machine learning (ML) is far from a new trend, but it’s more important for digital analytics than ever before. According to a 2017 survey, 75 percent of enterprises said that machine learning played a significant role in their digital transformation.
Data analytics capabilities have traditionally been limited to only a subset of an organization’s data at a time, providing only a limited picture. Machine learning has shaken up the data analytics landscape, dramatically increasing the bandwidth of analytics workflows and even enabling real-time analytics.
By analyzing more and more data, machine learning algorithms make better guesses and learn from their mistakes, becoming more “intelligent” over time. Marketing professionals can use machine learning to slice and dice through massive volumes of data and find the insights hidden at their core.
As with data privacy, many consumers have concerns about the ethics of machine learning. For example, Amazon recently scrapped a recruiting tool that used machine learning to find the most promising resumes from job applicants. Since most resumes were submitted by men, the tool initially learned to penalize resumes indicating that the applicant was female. Tech giants such as Google and Facebook are exploring ways to promote inclusion, fairness, and ethical behavior within the machine learning field.
Machine learning is just one component of the broader field of artificial intelligence (AI), which seeks to create intelligent machines that can perform human-like activities and even exceed human capabilities.
The question is not if AI will transform the business landscape, but when and how. Companies that take steps to incorporate AI into their business will be better prepared for this digital future. A 2018 McKinsey survey has found that 47 percent of companies are already using at least one AI capability in their business processes, while another 30 percent are running an AI pilot program.
AI means different things to different organizations, from manufacturing robots to computer vision techniques such as facial recognition and self-driving cars. What’s clear, however, is that the impacts of AI on business and society at large will be no less than transformative. IT research firm Gartner estimates that by 2021, 80 percent of emerging technologies will have AI foundations.
Embedded analytics is a business practice in which employees have easy access to data analytics within their standard processes and workflows, without the need to switch to a separate application. Tools such as Tableau can be used to integrate powerful business intelligence and analytics features directly into your software and web applications.
Software for customer relationship management (CRM) and enterprise resource planning (ERP) is one of the best use cases of embedded analytics. CRM and ERP software is often used by business decision-makers who need to chart the best path forward by receiving valuable insights quickly and conveniently—which makes embedded analytics the perfect match.
The numbers on embedded analytics speak for themselves. Over 90 percent of application teams say that embedded analytics has improved their user experience and helped increase end-user adoption.
A tag management system (TMS) is a software application that makes it easier to manage the digital marketing tags (snippets of code) that you use on your website to track user behavior. Traditionally, these systems have used a client-side approach, in which the user’s browser sends data to a server by executing a JavaScript file.
Server-side TMS solutions take an altogether different approach. The TMS is responsible for handling network requests, rather than the client’s machine. Data is sent directly from the client website to third-party analytics tools, without placing unnecessary strain on the user’s web browser.
A growing number of large, digitally oriented enterprises are migrating to server-side tagging, which has several benefits. First, server-side tagging is more secure due to requiring fewer cookies and lines of code. Second, server-side tagging is typically faster, more lightweight, and more reliable than client-side tagging.
There’s no doubt that the cloud is the future of data analytics. In a 2019 survey, more than half of companies reported having a data warehouse in the cloud, as opposed to running on their on-premise servers. Cloud data analytics provides advantages such as lower costs, better data security, on-demand accessibility, and speed and performance.
Organizations are growing more comfortable with the cloud for data analytics, using it more and more often to perform critical business procedures. According to Reuters, the cloud analytics industry will see a 7.5 percent annual growth rate by 2023.
Perhaps the greatest strength of the cloud is its scalability and capacity to hold very large amounts of data. Cloud-based data analytics solutions can expand to accommodate more storage and compute on an as-needed basis, putting smaller companies on a level playing field with the big dogs. In addition, collaboration and sharing among employees are much easier when using a cloud analytics solution, making it ideal for a geographically distributed workforce.
The Internet of Things (IoT) is a vast, interconnected network of devices that can exchange information using protocols such as Bluetooth, Wi-Fi, and cellular. IoT products include everything from smart home devices to industrial sensors for detecting errors in the manufacturing process.
Unifying data analytics with IoT is one of the most promising digital analytics trends. At any given moment, IoT devices are producing and exchanging inconceivably large quantities of information. Cisco estimates that by 2020, the IoT will generate more than 500 zettabytes (500 trillion gigabytes) annually, produced by 20 billion IoT devices.
While much of this data is useless, businesses must be capable of separating the wheat from the chaff, performing real-time analytics on IoT data to capture the valuable insights it contains. Real-time analytics for IoT devices has wide-ranging implications for use cases such as supply chain management, driverless cars, and even traffic and pollution control in “smart” cities.
Augmented analytics is a recent development in data analytics that uses statistics and natural language processing (NLP) techniques to streamline the data processing pipeline. More specifically, the goal of augmented analytics is to cut out the data scientist “middleman” as much as possible, letting non-technical business users get as close to the data as possible and perform their own analyses.
The well-documented “data science shortage” has left many organizations struggling to meet their data analytics needs. Even when they can find the right people for the job, data scientists spend the majority of their time—60 percent—on trivial preparatory tasks like cleaning and organizing data.
Augmented analytics takes an entirely different approach by using machine learning and NLP to “augment” human efforts. These tools can help automate the process of analyzing information and finding business insights, without requiring the oversight of a data science professional.
The field of augmented analytics is still not very mature, and more developments are sure to come. Projections by the market research firm Market Research Future estimate that the augmented analytics industry will reach a valuation of $13 billion by 2023. In the future, augmented analytics will likely play an essential role in the processes of data mining, preparation, and management.
Organizations would pay through the nose for the ability to see into the future, unlocking future risks and opportunities. Energy companies would like to know usage patterns and outage patterns, while financial firms stake their very existence on the ability to anticipate market trends.
While a true crystal ball remains out of reach, companies can use current and past information in order to make their best guess about future outcomes. Business intelligence and analytics platforms need to not only analyze historical data for insights but also make predictions about the future—a subfield of business intelligence known as predictive analytics.
Predictive analytics uses a slew of tools and techniques, from historical data and statistics to machine learning and artificial intelligence. By uncovering hidden patterns, predictive analytics helps organizations become more proactive and responsive to changes in their industry and customer base.
The field of business intelligence and analytics is truly sink-or-swim. Companies need to adopt new ideas and technologies in order to survive and even thrive. The digital analytics trends above are some of the most important developments in the field of data analytics for 2019 and beyond. By staying in tune with these trends, you’ll better understand how to outperform your business rivals and provide your audience with truly excellent customer experience.
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