In 2020 global data creation reached 64.2 zettabytes, but that’s predicted to soar to more than 180 zettabytes over the next two years. Today, organizations recognize the value of their data, but the reality is most struggle to harness accurate data in a meaningful way to solve high-level business challenges. They are drowning in data chaos, and the impact is felt across the organization. For example, lack of clarity around IT assets, who is using them, where they are located, what’s running on them, and how the assets are being used translates into massive security risks, an inability to accurately forecast needs and budgets, and operational inefficiencies that drive up costs and inhibit growth and innovation. There’s a lot of data coming in from multiple siloed systems but making sense of the data and being able to action on it to positively impact the business is a real challenge.
The cost of bad data
According to Gartner, poor data quality costs organizations an average of $12.9 million every year. It increases the complexity of data ecosystems, negatively impacts revenues, and leads to poor decision-making. In contrast, good quality data is a competitive advantage that can be applied across the organization to grow revenue and margins, enhance the customer and employee experience, and protect the company’s reputation and financial exposure by identifying security and compliance vulnerabilities.
Unfortunately, as new digital tools are implemented, new analytics capabilities are enabled but complexity increases, along with the number of data silos, data inaccuracies, and data gaps. Data is questioned and the inability to address the unknown becomes a universal data intelligence problem shared across the business units.
Going back to our earlier IT asset example, the problem plays out in several ways:
- Chief Financial Officers (CFOs) need a view of the IT estate that supports cost optimization strategies. Having data intelligence that helps identify ways to consolidate hardware and applications, reduce tech debt, and optimize cloud costs is meaningful to them.
- Chief Information and Security Officers (CISOs) require a view of the IT estate that identifies security vulnerabilities such as unpatched or unsupported systems, missing assets, and policy violations.
- Chief Compliance Officers (CCOs) want to know if the organization is in compliance with legal, security, and industry regulations (environmental policies, for example).
- The Chief Experience Officer wants a view that enables decisions to improve the customer experience, increase customer retention, and ultimately drive revenue growth.
The key here is to use data analytics intelligently in a highly structured way. And that begins with understanding business leaders’ needs and turning data chaos into data intelligence.
Using data analytics to inform decisions across the busines
CFOs are among those being stretched further this year and managing traditional roles such as forecasting are getting harder to do in this volatile environment. They can leverage data analytics that combine current asset inventories, resource usage figures, and projected headcount data, along with vendor pricing to optimize future asset costs. By factoring in potential market risks, they can model the potential impact of changes, for example rising prices, and put in place plans to protect the business. Resource usage figures too can be used to identify where changes can be made, to solve the issue of tech debt and optimize cloud spending. As they expand their roles, CFOs can use data analytics in other ways.
One recent study has found that 40% of CFOs are acting on ESG initiatives and concentrating their efforts around gathering the right data and creating consistent reporting metrics and frameworks to prepare for new SEC disclosure rules. By integrating financial data, they can see potential impacts of ESG initiatives as well as use this data for reporting.
ESG, security, data, financial and other regulations are complex, constantly changing and differ at region, country, and industry levels. By leveraging analytics that incorporate these nuances and evolving requirements with information on how and where data is stored and used, along with asset inventories and system status, CISO’s can better identify vulnerabilities and document plans to follow for resolution in every region they operate. They can also use this information to define comprehensive future best practices, policies and reporting guidelines that must be followed to remain in compliance.
The Chief Experience Officer can leverage analytics that combines data from observability tools together with system, location, and other information to understand how customers are experiencing services and put plans in place to remove friction and increase customer stickiness.
To use data analytics at scale, teams must also find ways to automate. Gartner suggests putting a framework in place that enables cross business automation. That requires breaking down barriers to cross-team collaboration and working together to identify the data analytics programs that have the biggest impact on the business as well as the overlaps in their requirements.
They should work with IT and vendors that can help them clearly identify the data sources they must mine to access data intelligence, not chaos. For the greatest return on investment, they should select tools that allow them to constantly leverage the latest and greatest data, those that present outcomes in a clear and actionable way from any angle to meet the need of a variety of stakeholders.
One such tool is a digital platform conductor DPC. Discover how a DPC can help you turn data chaos into data intelligence. Book a demo today.