The era of “big data” and data analytics was supposed to simplify our lives and make businesses more effective. However, for most companies and their employees, it has created a stressful, costly and complex environment.
As a result, Chief Data Officers (CDOs) find themselves under increasing pressure from CEOs and CFOs, who are rightly asking questions about the return on investment (ROI) and whether the business can extract value from the data. This puts CDOs in a difficult position since they championed massive data management investments just to make data usable. They must be cost-efficient and deliver results at the same time.
What broke things?
The root of the problem is that most organizations lack a clear understanding of their data resources and internal data ecosystems. Today’s data landscape is made more complicated by a complex and fragmented data solution provider environment that lacks standards, combined with the overselling of various data tools. Consequently, many enterprise customers fail to understand why they make certain purchases when it comes to data products. They are promised simplicity but end up with another siloed system that must be managed and maintained, making it unsustainable.
Data is broken, and it is a massive problem for organizations. They are spending big on data but not using it in the right way and not getting results. With today’s highly fragmented data ecosystems, enterprises must manage multiple solutions that require a singular approach, as well as their own tools and specialized personnel. Multiply each of these point solutions across any number of departments or functions within a given enterprise, and the complexity – not to mention cost – escalates.
Many companies have invested significant amounts of money in data management, yet they end up with little usable data to drive decisions. The problem is further exacerbated by the emergence of “shadow IT” operations, where data gets duplicated, adding to the sprawl. Tribal, department-by-department knowledge around data rules in such a way that information is not shared across the enterprise. This results in companies only having analytics dashboards, and their AI/ML initiatives are more likely to be heuristic-driven models that may or may not aid in decision-making. Ultimately, the data remains siloed, and companies fail to achieve their target ROI.
So, how do we fix the problem?
To address these issues, companies need to take a holistic approach to data management. Instead of managing multiple-point solutions across different departments, they need to adopt a singular approach to managing their data resources. This will help them avoid the complexities and costs associated with managing multiple data solutions.
Companies should also invest in a centralized data management platform that can help them gain a better understanding of their data resources and their internal data ecosystems. This will help them manage their data more effectively and ensure that their data is usable and actionable.
Companies need to have a clear understanding of the value that their data can provide. They should avoid making purchases for data products without fully understanding the value that these products can provide. Instead, they should invest in data products that align with their business goals and that can provide real value to their operations.
In addition, companies need to prioritize data governance to ensure that their data is accurate, reliable and secure. They need to establish clear data governance policies to help them maintain data quality, enforce data security, and ensure compliance with regulations.
Companies should prioritize collaboration and knowledge sharing around data management. They should foster a culture of data-driven decision-making, where everyone in the organization can access the data they need to make informed decisions. This will help companies leverage their data resources more effectively and ensure that they are getting the most value from their data.
In conclusion, while big data and analytics have the potential to drive well-informed decisions, many companies are failing to achieve the desired results. The fragmented data solution provider environment, the lack of data standards, and overselling of various data tools have all contributed to the complexity and costs of data management.
Companies need to adopt a holistic approach to data management, investing in a centralized data management platform, prioritizing data governance, and fostering collaboration and knowledge sharing around data management. By doing so, they can ensure that they are getting the most value from their data and that they are making well-informed decisions that can help them achieve their business goals.
So, what’s the path to ROI?
In order to achieve ROI from data, enterprises must integrate data into their daily operations. Data can only deliver real value if interacting with it becomes as easy as using tools like Facebook, Slack and Twitter.
Doing so requires a new approach. Data must be simplified for both business and technical users, starting with streamlining the complexity of data infrastructures under a new operating system (OS) model. The OS model represents a paradigm shift that relies on operationalizing data without disrupting the underlying business. It also facilitates more comprehensive governance and greater data lifecycle observability, as well as enabling a new kind of transformation that allows enterprises to treat “data as software.”
The goal should be to achieve a state wherein data can be discovered, understood and manipulated with ease – by empowering enterprises to interact with it through APIs and other tools that help deliver valuable insights. It makes things easier for data engineers, data scientists and analysts, while simultaneously delivering the tools that business users need to drive informed decisions.
By completely changing the way organizations work and collaborate with data, this new approach can create efficiency and radically simplify data infrastructures, improving data literacy in a matter of weeks. Without the overhead of big analytics teams, companies can define their own metrics, KPIs and data products at a lower cost. The Data OS has the power to improve the lives of CDOs and finance professionals while providing business users with self-service data access, along with the assurance and control needed to make decisions that they can trust.
Ultimately, a “Data OS” approach creates efficiency. Organizations can radically simplify their data infrastructures while improving data literacy in a matter of weeks. Without the overhead of big analytics teams, companies are empowered to work in a completely different manner, defining their own metrics, KPIs and data products, at a lower cost.
A Data OS has the power to improve the lives of CDOs and finance professionals while providing business users with self-service data access, along with the assurance and control that they need to make decisions that they can trust. When data is “broken” in the enterprise, a new future – in the form of a Data OS – can create significant business value and fix a number of costly issues.
