We’re living in a new “normal” and 2020 thrust data and analytics to the forefront of every industry. Health officials went through countless reports, graphs, and charts to assess infection rates, monitor medical supply levels, and find ways to manage the pandemic. World leaders and economists relied on data analytics to craft effective strategies to combat a global economic crisis. Business enterprises and individuals also used data to survive during lockdowns and adapt to the new business landscape.
This rapid adoption of analytics in a variety of settings was a significant shift. However, more changes are on the horizon, including moving from predefined dashboards and manual data exploration to automated and dynamic insight generation. Dig into these 2021 trends predictions, originally published by Sisense here, to learn how the world of analytics and data will continue to change.
Moving Beyond the Dashboard
In a recent survey by International Data Corporation (IDC) on behalf of Sisense, two out of three business leaders said they consider the ability to embed analytics deep into processes and applications “very important.” Aside from making better data-based decisions, they want these insights available where and when users need them, without having to leave their workflows.
As data management moves beyond the dashboard, there will be relevant shifts in every industry. It would be hugely advantageous for business leaders to watch out for these emerging trends and understand their implications on enterprises.
It is becoming evident that data and analytics will continue to play a vital role in all sectors, which is why we’re so excited to have Sisense as part of our community, as they go beyond just dashboards with a leading AI-driven analytics platform for infusing analytics everywhere.
2021 Trends in Data Analytics
Widespread infusion of insights into workflows
One of the most critical data and analytics trends this year is the shift of insights from dashboards to workflows.
A few years ago, business intelligence and analytics platforms were among the most popular solutions for enterprises looking for technology to enable data-driven decision-making. These platforms highlighted dashboards where users could view insights in a single user-friendly interface.
However, these dashboards required users to deviate from their workflows just to access insights. Moreover, they merely presented insights without offering possible options a user could take based on the data given. This is perhaps why business intelligence adoption has plateaued at 30% across industries.
To become truly data-driven, business enterprises must shift from standalone BI&A platforms to infusing insights into applications, workflows, and processes, placing data and analytics right where users work to enable them to arrive at data-driven decisions faster and more efficiently. Ultimately, this will boost win rates and revenue.
“The challenge of standalone dashboards is we can’t act on them,” says Ashley Kramer, Sisense Chief Product and Marketing Officer. “Instead of requiring people to change the way they work to access insights, we see successful organizations flipping the script and infusing analytics where people work. By putting the right intelligence into workflows, processes, and applications, analytics-fueled decisions become automatic and instinctive, so organizations can surpass their objectives.”
Increased need for code-driven analytics
A recent report by Mordor Intelligence shows the data lakes market is growing at a rate of 29.9% (CAGR) in the next five years. The use of cloud data warehouses is also expected to grow as more enterprises veer toward data-driven decisions.
In the IDC report, 81% of those surveyed already use a cloud data warehouse or a data lake. With this number expected to grow significantly in the next few years, more enterprises will be relying on advanced analytics to gain the most benefit from such massive datasets.
Artificial intelligence aids non-technical personnel in understanding complex datasets such as data lakes and other big data stores. However, knowledge of advanced code is needed to pull out more valuable insights from them. Enterprises building new technologies such as autonomous vehicles or rehabilitation robots will require advanced analytics based in code. Even enterprises in other industries will need advanced coding languages to forecast future scenarios and enhance customer experience.
The main challenge this trend poses is finding the right people with the right skillsets for understanding Python, R, and SQL. You’ll need a skilled data team that includes software engineers who are highly knowledgeable in advanced coding languages. If you don’t have one yet, now is the ideal time to start building your team.
Increased use of customer-facing data in apps
Another important trend that business enterprises must strive to understand is the increased need for customer-facing analytics. Presenting data to users will impact a company’s revenue and chances of survival in this digital era.
According to the 2019 Localytics by Upland Software, users who feel listened to (based on their unique choices and data) are more likely to continue using an app. Apps that push data-informed messaging to engage users have a retention rate of up to 74%, while those that merely use broadcast campaigns have retention caps of only 49%.
To increase value for your consumers, competitive companies must meet this need for intelligent personalization by adding customer-facing analytics to products and services. Finding ways to drive revenue with data is a key to remaining relevant and competitive in an evolving business world. Major companies such as Zoom, Rosetta Stone, and Slack have already harnessed data to benefit their users and improve their ROI.
A rise in the emerging data fabric technology
Data fabric will serve as the foundation that supports composable data and analytics. It will solve complexities in managing data from disparate sources, varied formats, and numerous deployments.
Forrester predicts a 30% growth in insights-driven businesses. To gain deeper insights into customer behavior and preferences, business enterprises must extract data from various sources, including web logs and embedded devices. This results in increased demand for the storage and analysis of big data.
But instead of a single storage infrastructure, businesses are now using a hybrid approach with a mix of on-premises and cloud storage solutions. Data fabric enables access to massive data by integrating them into a unified platform.
To optimize your data assets, start working on the implementation strategy for your data fabric solution: Your strategy needs to be flexible enough to allow you to leverage big data to improve decision-making capabilities. Ultimately, this will lead to increased business operations efficiency, enhanced customer engagement, and better business growth.
Harnessing data from disparate sources to innovate products and services
According to Sisense, the next unicorn company will build its killer app by harnessing data from disparate sources. Gathering insights from customers and prospects will be vital in creating the next killer app or vertical-defining service.
In a consumer pulse survey by Accenture, 65% of Gen Y and Z consumers indicated that they are more likely to buy something from a company that asks for their input. This shows that the next generation of consumers prefers more purpose-led innovation that enables them to participate through sharing their ideas.
To drive your business forward, take data and analytics from dashboards to design boards. Innovate products and services that fulfill the needs of your consumers and align with their values. Strive to gain access to robust datasets, discover new insights, and incorporate these into your product development strategies.
More intuitive AI will be essential for business growth.
In the IDC survey, 43% of respondents stated that they draw data from 10 to 30 different sources. These sources churn out large data volumes that humans are unable to process on their own. To deal with such massive volumes of data, you need AI technology. It will enable you to survive in the current business landscape.
AI assistance is particularly useful for non-technical users who need more guidance in deriving value from their data via their analytics solutions. AI plays a vital role in turning data into actionable data intelligence that impacts business growth.
However, enterprises will require more from AI systems. Smarter, more intuitive, and scalable AI will be integral in enhancing user experience. It will also be important in protecting consumer privacy and ensuring compliance with regulations.
Today’s businesses are relying more and more on intelligence derived from big data, putting data and analytics at the forefront of every industry. As it continues to play a key role in accelerating business initiatives, data and analytics will become a core business function.
To gain a competitive edge, ensure that you can optimize your data and maximize the value it can bring to your business. Start by creating a data team with expertise in advanced analytics and involve them in setting business goals and strategies.
Interested in how Sisense can help you leverage an AI-driven embedded analytics platform that infuses intelligence into your workflows, processes, and applications to enhance the customer experience and transform your business from the inside out? Let us know today.
Vation Ventures is also incredibly excited to share that Sisense has successfully completed our Channel Certification and which is based on over 100 qualification points for building and supporting effective programs with channel partners.