Across industries, enterprise leaders are ramping up analytics initiatives at an unprecedented pace. And it’s all for a good reason. Equipped with insights from data, they can develop new and better manage current products and services, elevate, and monitor sustainability goals, and, ultimately, get closer to customers, employees, and business partners.
Over the past few years, the massive demand for data-driven insights has sparked a need to democratize access to data, especially as organizations make considerable investments in artificial intelligence (AI) and advanced analytics. By mining their treasure trove of data, enterprise leaders understand the potential to reinvent how they do business, developing more sustainable and scalable solutions.
For many organizations, the evolution from manual processes to automation and greater efficiencies has already occurred. As a result, there are three key trends that we will likely emerge in the upcoming months:
1. Analytics leaders move deeper into the spotlight.
In 2022, the rate of digital innovation will only accelerate. Therefore, analytics leaders will need to take on new roles and responsibilities. From my conversations with clients, I already see analytics leaders bringing their expertise to all departments, empowering teams to rely on data to make decisions at speed and accuracy.
2. Data Analysis becomes a crucial skill for everyone.
One of the biggest challenges for most organizations includes getting their workforce up to speed with data tools and techniques. With an increased focus on analytics, it will be essential for senior leaders to upskill all employees in data science techniques. Business leaders realize that all data carries value. And as such, it can’t be the responsibility of only a few colleagues.
3. Responsible AI becomes a priority.
As enterprise leaders scale their AI and machine learning initiatives, they will incorporate fair and responsible principles to tackle governance challenges with real intent and purpose. With an ethical AI framework in place, teams will root out biases like gender or race discrimination. These actions will help organizations sustain long-term growth, improve competitive advantage, and create value for all customers, clients and employees.
How to achieve your analytics vision
For those ready to embark on a data-driven journey, begin by exploring the end goal you are trying to achieve. One way to do this is to organize objectives by persona/department (CFO, controller, marketing, and so forth.) By understanding the goals –and pain points– for each stakeholder, you can then look at how data can help you make progress.
Once you understand what you are trying to achieve, you can rely on solutions like a data fabric to streamline processes and increase efficiencies. Here are some examples:
- Data fabric: The key to the enterprise data strategy. A data fabric is an interconnected network of data products connected to business objectives and metrics that can distribute valuable insights across the business. In essence, a robust data fabric becomes the foundation that powers AI and other emerging technologies.
- Data-driven customer experience (CX): Real-time analytics powers the customer experience, helping organizations connect with customers to build memorable experiences. Consider a travel website that automates most of the search filtering in the background, curating the most relevant options for the end consumer.
- Scalable machine learning operations (MLOps): At its core, machine learning operations provides a roadmap for achieving AI at scale. Bringing together people, processes, and technology, MLOps helps teams develop resilient AI solutions across departments and functions.
Bringing it all together
The way an organization handles its data is critical, mainly as it typically resides in silos. Therefore, distributed, democratized management is crucial for successful AI and analytics implementations and sustainable governance. Similarly, it’s essential to establish a framework to measure, learn, and act on data-driven insights — effectively; it is a blueprint for using analytics in the future.
But the key ingredient in all of this is people. Employees are our greatest asset, and we should see value in offering them the skills they need to succeed in today’s dynamic marketplace. Sustainable innovation can’t happen with technology alone. Instead, it will be up to enterprise leaders to maximize the benefits of data and analytics by inspiring employees to become stakeholders who reinvent how organizations operate today and into the future with data at the core.
