Democratizing Analytics for Organizational Success
Data and analytics used to be the province of anyone with a task to complete, so how did it become a field for the specialists only? How can we make it for everyone once again?
Those who think data and analytics is a relatively new field would be surprised to find out just how long ago the first analytics projects took place. For example, sometime between 4000 B.C. and 3800 B.C., the first known census was taken in the Kingdom of Sumer (aka, Babylonia) on clay tablets. More “modern” examples of data and analytics in action included Florence Nightingale (1820–1910), a nurse and statistician, pioneered the use of infographics to improve hygiene and sanitation practices as critical ingredients to healthcare outcomes for the army. Similarly, John Snow (1813–1858), a British doctor, used data and geospatial analytics to understand that cholera was being transmitted by contaminated water from the Thames River.
If we think about the history of data and analytics from this perspective, we see that analytics has been used by accountants, tax collectors, public health officials, doctors, and nurses, among others. It wasn’t only used by the elite or the specialists.
Fast forward to today, the world still seems to run on paper records and spreadsheets, and organizations are still unable to make progress on data and analytics initiatives. Although modern analytics software is more available and research from NewVantage Partners stated that 92 percent of organizations invest heavily in artificial intelligence (AI) and analytics, only 19 percent of business leaders feel they are truly data driven.
This dichotomy between analytics investment and value realization creates an analytics gap that conspires to impede organizational progress. The causes of this gap are many: lack of investment in improving the workforce’s skills, legacy architectures, manual processes, data silos, and a stubborn, organizational inertia that prevents sharing of data across the enterprise and ecosystem. This gap continues to widen and, if left untreated, will swallow up your organization.
What if organizations increased the investment in their people, modernized their infrastructure, reduced reliance on manual processes, and removed their data silos? There are 78 million advanced spreadsheet workers in the world who waste about seven hours per week repeating the same or similar activity steps every time a data source has been updated or refreshed. What if they could automate some of these mundane tasks? Would things improve? Could we have self-service analytics for all? Could we achieve modern AI-driven architectures and ubiquitous access to data, resulting in more real-time, analytics-fueled decision-making?
The benefits could be enormous. Organizations could achieve innovation at a scale they previously could only imagine. We’ve seen companies unlock their potential by using analytics to gain highly accurate predictive models that helped them adapt to changes caused by the pandemic. Others have been able to increase revenue retention by automating processes and leveraging predictive models to offer targeted incentives.
For organizations to make the most of data and analytics, there are four foundational principles they need to consider.
Make analytics easy. Organizations need to move away from relying on experts and specialists to write code and move to code-free/code friendly analytics automation platforms that allow everyone, no matter their skill level, to use data and analytics. Platforms should have built-in smarts to increase efficiency by finding stories, anomalies, and insights hidden deep in data.
Cover everything. Do away with the bespoke systems of the past. Modern analytics platforms should cover the entire analytics life cycle, including data access, ELT, prep and blend, data enrichment, analytics, geospatial, automated machine learning (auto ML), and automated insight generation leading to business outcomes.
Be everywhere. The platform needs to be everywhere, needs to accept all the necessary data types and sources, from legacy on-premises databases to modern cloud data warehouses, applications, and robotic process automation (RPA) bots.
Enable everyone. No matter the job role, skill level, department, or industry, the analytics platform needs to allow everyone to participate in the analytics process. The platform needs to “meet people where they are” in terms of skill level: from the tech-savvy data engineer wrangling huge data sets to accountants who need to extract insights from PDF files. In the end, organizations that can unleash the creativity and innovation of their knowledge workers will have a competitive advantage over those who rely on specialized teams alone.
Once organizations have applied these four principles, there are two further keys to helping them unlock their organizational potential and achieve innovation at scale with analytics.
Improve the skills of your workforce. To create a culture of analytics and become truly data driven, your organization needs to become data literate. This creates a community through learning, empowers employees with new and diverse projects, and increases total ROI. According to the World Economic Forum, more than half of all employees around the world will need to improve their skills by 2025 or they risk becoming unemployed. The Harvard Business Review recommends the following six strategies to increase workforce skills:
- Treat skill improvement as an investment, not a business expense
- Use projects rather than simple modules to improve skills
- Make it fun — gamify the experience
- Be data driven; use analytics to understand skill levels and opportunities across the organization
- Form learning partnerships rather than doing it alone
- Give employees the time and flexibility to design their own curriculum
Democratize analytics across the organization. This is one of the core elements of the future-of-work road map for organizational success. Creating actionable data and analytics programs to educate employees is one of the most effective ways to democratize analytics. Once employees are educated and have access to analytics insights, roles within an organization should reflect data-driven decision-making.
In the end, dark data — data that is hidden or not being utilized — will always exist within an organization. Therefore, the business needs to take a principles-based approach to improving data sharing. Although policies, procedures, and technological safeguards should be in place, organizations will also need to empower their employees to shine light on the insights from that data.