Data is, or soon will be, a business' most valuable asset, driving everything from product design to hiring. Already, 2.5 exabytes are generated daily, and by 2025, that number is expected to grow nearly 200x to 463 exabytes per day. Companies that can leverage this resource will see faster innovation, new revenue streams, and improved operations, among many other things.

But data only drives benefits when analytics are accessible, comprehensible, trustworthy, and run on centralized, useable data. Currently, most enterprises use at least two different analytics solutions, and those systems usually don't talk to one another. This problem is compounded by lack of access; 80% of managers and executives can see organizational analytics, but only half of frontline workers can say the same.

When employees need insights but lack access, collecting the information for a single data-driven decision can take hours, if not days, turning what should be fast, routine decisions into costly, complicated processes.

To foster a shift to data-driven decision-making (DDDM), organizations are putting analytics at the center of their organizational culture, making the data they rely on more accessible, embracing the flexibility of cloud and multi-cloud implementations, and investing in more powerful, more transparent tools.

Building a culture of analytics

Becoming an organization with an analytics culture requires analytics tools that empower and enhance decision making through explainable AI (XAI) or augmented analytics, as well as centralized, accessible data to power the organization's analytics tools.

Most importantly, it requires an ongoing, evolving training process to increase data literacy. While the specific data skills needed vary by role, data literacy is fundamentally about having the ability to generate reports from data, understand their insights, and apply them appropriately. Analytics solutions capable of guiding and empowering employee decision-making are essential tools for an analytics-driven organization.

Accessibility: Democratizing BI

Accessibility is about more than just permission levels. It requires information to be readily available to help employees in DDDM, so that they aren't dependent on data specialists to generate reports. It requires data to be available to employees on demand; this means having unified data stored in the cloud, or multiple clouds. And it requires employees to act as data scientists without needing PhDs; this means implementing self-service BI so that creating custom dashboards and reports is a matter of drag-and-drop operations, not an appointment with a specialist.

Accessibility is also about understandability. If data isn't understandable, it isn't very useful. Modern analytics platforms featuring easy-to-use building blocks and no-code tools, combined with an increased focus on data literacy in every seat, are democratizing BI.

Cloud: On-prem is out the door

The benefits of the cloud—such as speed, ease of use, and cost savings—are well known. As a consequence, the cloud is where almost all innovation in data and analytics is taking place.

Among the companies already using the cloud for data storage, more than 80% use multiple clouds. A multi-cloud approach, coupled with adaptive data governance, helps ensure systems remain resilient and reliable while adhering to regional legislation around data processing or storage.

While multiple clouds (and the average of 4-7 tools that workers use to manage data) can run the risk of creating more siloed and fragmented information, UDAPs offer a solution. By consolidating data into a single source of truth, they allow the deployment of new tools without having to upend employee and organizational workflows. When data has to work across geographies, departments and systems, it's vital that each system is in conversation with the others and able to inform and be informed by them.

Augmented analytics and XAI

Augmented analytics are probably the best tool for fostering DDDM. Pairing data with easy-to-understand explanations and reports helps build confidence in, and adoption of, new systems. More importantly, augmented analytics and XAI help reduce bias and risk while improving outcomes in revenue, operations, and productivity.

Because algorithms cannot escape the biases of their programmers, unconscious prejudices can result in dire consequences for those affected by the decisions, as well as to the company's reputation and bottom line. By exposing the sources, weight, and logic used in reaching recommendations, XAI makes it easy for developers and ML specialists to make algorithmic changes to ensure better, fairer, or more effective outcomes while minimizing the chance of harm to the brand.

The continuous modeling, evaluation and optimization made possible by augmented analytics and XAI also helps in identifying new revenue streams and business models. The use of these tools is becoming de rigueur in the enterprise space; 65% of companies with ARR between $100-500 million already use advanced analytics, and nearly 80% of enterprises with 10,000+ employees plan to invest in more analytics capability.

The high costs of poor data

More data does not always mean better data. So as companies acquire exponentially more of it, good data quality management (DQM) becomes a necessity rather than an option. This is all the more true when working across multiple clouds and with multiple tools.

Lackluster DQM can have expensive ripple effects and directly impact an organization's bottom line; for almost 90% of US companies, it creates an average 12% loss in revenue.

Without good data in place, it's hard to make meaningful decisions; bad data is a big reason tech deployments fail, and it leads to poor processes across an organization, from prioritizing marketing channels that don't generate high LTV to forcing customers to repeat their issues to support reps.

From a productivity perspective, bad data causes sales reps to waste time reaching out to (and correcting) wrong contact information. And because analysts can't trust their data, they must devote extra time to vetting and validating.

Bad data forecloses opportunities; good data creates them. Quality data results in quality insights for every department. And though data science has traditionally been the purview of high-paid consultants, emerging analytics platforms are changing that. This democratization of data made possible through the pairing of accessibility with understandability empowers all employees to become data users making data-driven decisions.

Cloud and multi-cloud implementations ensure that employees can quickly find needed (and reliable) information, while XAI and augmented analytics help people fluently apply that data to their decision making. Of course, none of this is possible without trusted data; this is where DQM adds value. An organization that can fully implement all of these elements is fostering a culture of analytics that will help it meet the business challenges of tomorrow.


Zoho offers a suite of intelligent enterprise business software, including an award-winning CRM suite, the industry's only comprehensive analytics and BI platform, and a powerful low-code development ecosystem.