Data is playing an increasingly crucial role for today’s enterprise – it’s the engine driving businesses, economies, and societies as they navigate a growing digital economy.
Every globally-used service, system, and device generates new data – and the volume, variety, velocity, and value of that data is expanding all the time. By extracting valuable insights and monetizing that data, 21st century organizations are able to overtake their competitors in the race to engage customers. Future success lies in becoming a data-driven business.
This is a fairly new realization. According to Gartner, in 2019, less than 50 percent of business strategies featured data and analytics as key components to delivering enterprise value. If businesses are to excel and become autonomous digital enterprises (ADEs) of the future, they need to transform, and engraining data strategy within operations will be a crucial first step.
To compete in a global marketplace, businesses will need to capture real value from the massive amounts of data being generated within their organization. What’s more, they will then need to feed the insights from that data into their daily processes. Establishing a data-driven mindset – supported by analytical capabilities – will play a key role in achieving this in the future.
What is a data-driven mindset?
A data-driven mindset lays the foundation for new technology development and greater customer understanding across the business. Enterprises that want to create a strong data culture should first focus on extracting value from their data sources, and should look to handle and manage data like any other business asset. By monetizing these data assets, enterprises can become more advanced and autonomous, and therefore shift their focus from reducing costs to growing the business.
Generating data from multiple sources is also important. Becoming a data-driven business requires more than simply gathering data from traditional sources; it also involves capturing new data from Internet of Things (IoT), social media, and customer engagement systems, and creating artificial intelligence (AI) and machine learning (ML) systems to improve and execute business-wide operations.
As the number of data-generating devices – and the volume of data they generate – continues to expand, so does the complexity of the IT infrastructure required to collect and analyze the data. Systems and tools that can sort the data, and train and implement corresponding predictive models effectively, should be integral components of a business’s data strategy. Having the right tools in place will be crucial if enterprises are to successfully handle rising data volumes.
Extracting business value with AI and ML
Collecting endless amounts of data brings no real rewards by itself; the true value lies in data extraction and analysis, and AI and ML are both fundamental to achieving that. AI- and ML-powered predictive models can use data to analyze and predict behaviors of both people and technology, which in turn can help to optimize actions and operations at a lower cost.
As these systems become increasingly important, it’s important to understand that their associated infrastructure and management models can also be complex. Enterprises will need to embrace the idea of IT and OT (operational technology) convergence across operations, so that data is shared across – and used by – the whole business for real-time decision making and insights.
While enterprises are striving to get there, there’s still room for improvement when it comes to data. Most have yet to fully transition towards becoming a truly data-driven business, despite understanding the value of data assets. Data sources and the value they generate are only continuing to grow, and therefore it will become increasingly important for enterprises to empower teams with the tools they need to make optimized, data-focused decisions.
Monetizing data as an asset
Monetizing assets is an essential part of business operations, and data is quickly becoming a crucial currency that should also be monetized to facilitate analysis and insights. A few approaches to monetizing data include insight and data bartering, business intelligence, and data brokering. Establishing a central data strategy with appropriate governance models will also be essential for any organization looking to drive value from their data.
The future will be data-driven
At the end of the day, the enterprise technology landscape is rapidly evolving, and businesses that want to succeed should be striving to become an ADE. To do this, they will need to reinvent existing tools and processes and re-evaluate how they drive value across operations – all while ensuring that existing processes across the business aren’t disrupted.
By deploying data-driven processes and generating value from data assets, businesses will be able to develop a deeper understanding of the behaviors carried out by customers, employees, competitors, and existing technology processes. This will in turn help them to identify areas which should be transformed across the business. Enterprises that want to thrive in the future can start now by harnessing the potential and value of data within their operations today.
- Herb VanHook, Vice President, Enterprise CTO Services, BMC Software.
