The data analytics market is ripe with strategies and tools that are evolving at a fast rate to keep up with the increasing volumes of data being collected and applied by enterprises.
See below to learn more about what trends data experts are seeing in the data analytics market and what they predict we’ll see in the market’s future.
5 Trends to Watch in Data Analytics
- Growing interest in self-service data analytics
- Stronger data integration through embedded analytics
- AIOps and other comprehensive analytics uses
- Finding tools that support data integrity
- The emergence of analytics on blockchain
Data analytics informs both internal operations and client-facing decisions on everything from revenue goals to marketing touchpoint performance and employee churn data.
Every type of data that companies produce can and should be fed into data analytics software in order to better understand what’s happening in the business and how that information positively or negatively impacts planning for the organization.
The problem that many companies run into is that many stakeholders who know the most about these key areas or who need to have this information don’t have the skills or access level to analyze relevant data on their own. The solution? Many companies are investing in software that supports self-service data analytics or data democratization with low-code/no-code, user-friendly dashboards, and a variety of data visualizations.
Jeremy Levy, CEO of Indicative, a product analytics platform, believes that data democratization is not only making data more accessible, but also making businesses smarter when they leverage input from several voices:
“More companies across diverse sectors are leveraging data to make strategic moves within their industries, setting off a growing shift in how data is shared within the company and how decisions are made — data democratization,” Levy said.
“Every department and nearly every employee can benefit from the information gleaned through the analysis of customer journeys, campaign performance, and other critical measures. The democratization of data is more critical than ever in that it empowers the organization as a whole in the decision-making process.
“Democratizing data within a company fosters transparency and allows a range of expert voices — product managers, marketers, and data analysts, among others — to become part of the strategic conversation, leading to a greater impact than confining decisions to the C-suite.”
Not only do many users not have the skills or necessary access to use data analytics tools, but many also do not have the time or user interfaces to make data analytics part of their work. That’s why the data analytics market is trending toward stronger data integration through embedded analytics or analytical insights that directly show up in the applications and interfaces where employees already work.
With this approach, users can get relevant notifications and other information directly, instead of needing to log in to an analytics-only platform and combing through information to get what they need. This approach lends itself to efficiencies in data analytics and also eliminates much of the potential for user error since there’s no need to search for information.
Ashley Kramer, chief product and marketing officer at Sisense, a BI analytics company, believes embedded analytics make it easier for users to understand data and turn it into actionable insights:
“Breaking down the analytics adoption barrier and moving beyond the dashboard is a bigger picture trend happening in the data analytics market right now,” Kramer said. “Making data available to all types of users with various skill levels at the right time, every time is key. It will require providing data and analytics where people spend their time (in products, applications, and communication devices), without disruption to their daily workflows.
“We extract insights from data without thinking twice about it. For example, Netflix feeds us recommended content based on past behaviors and our smartwatches tell us when it’s time to stand up to meet our personal step goals for the day.
“The data is so easy to consume because it’s right there when we need it and actionable — we don’t need to go looking for the answer within dashboards or stop what we are doing to get the insights we need. Extracting value from data is not so simple in the business world today, but it’s heading in that direction with the rise of embedded analytics.”
Using only the most readable data available is no longer an option. In the world of big data, data comes from all corners and can be used to understand and optimize the most minute details in business intelligence, the Internet of Things (IoT) and consumer analytics, and artificial intelligence (AI) technology. Data analytics is trending toward more comprehensive analytics uses, avoiding data silos in favor of holistic data practices like AIOps and DataOps.
Avoiding data silos with comprehensive tools
Many companies are moving away from custom-built or niche analytics tools that focus on individual pieces of the business. Instead, they are looking for tools that can analyze all types of data across departments and operational functions.
Sri Raghavan, director of data science and advanced analytics at Teradata, a cloud data warehouse provider, believes that piecemeal analytics only serve individual departments or theoretical experiments and that broader analytics approaches are needed to optimize business practices and avoid unnecessary data silos:
“As the data analytics market continues to evolve, likely gone are the days of piecemeal analytics and reporting solutions that are fulfilling niche business use cases,” Raghavan said. “This is unsustainable. Companies cannot have highly departmentalized analytics implementations that have the effect of localized problem solving and the larger business not seeing the full benefit.
“This current situation is changing into one where analytics will be done on all data that the company has access to, with the capability of these analytics being implemented in a collaborative manner by a variety of interest groups with different skills sets (e.g., data science, lines of business leaders) and with a full-on focus towards operationalizing analytics insights in near real time. In other words, no more piecemeal and no more just science experimentation.”
Data analytics practices or tools are fruitless if the quality of data is not assured and if data compliance requirements are not followed. One of the greatest trends in data analytics right now is ensuring data integrity or that data is accurate and ethically sourced.
Amy O’Connor, chief data and information officer at Precisely, a data integrity company, believes that data integrity and data governance enable both businesses and consumers to trust and act on the data they receive.
“Some of the hottest tools these days are the ones typically considered to be the least sexy – quality profiling tools and data governance tools,” O’Connor said. “Tools that automate insights into the quality of data and enable that quality to be significantly improved through automation can have an exponential impact on the quality of analytical insights.
“And data governance tools are essential to ensuring interpretability of analytical results through lineage tracking, to enabling data democratization and self-serve analytics, and for ensuring appropriate use of data in compliance with data privacy policies and regulations.”
More on data integrity and compliance: GDPR Compliance & Requirements 2021
Perhaps the most interesting and least recognized data analytics trend comes with performing data analytics on blockchain. Most people think of blockchain as a technology for cryptocurrency or secure transactions, but because of its high levels of documentation and security, it’s increasingly being used for accurate data analytics calculations.
Nate Tsang, founder and CEO at Wall Street Zen, a stock market analytics company, believes blockchain’s presence is growing in the data analytics world:
“Blockchain technology is often associated with cryptocurrency as it is one of its most prevalent use cases, but it is more than that,” Tsang said. “This technology can enhance predictive analytics because of its inherent ability to verify data validity, preventing false information from being included in the analyses.
“Furthermore, if someone wants to hack a system, they need to change all the blocks in the blockchain to tamper with data. This makes data analysis more secure than ever.”
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