According to the MIT Technology Review Insights Survey, an enterprise data strategy supports critical business goals such as increasing sales, improving operational efficiency, and speeding time to market. It also helps organizations enter markets for new products and services, as well as innovate, preserve physical assets, and improve ESG.
The problem is that today, only 13% of organizations are good at delivering on their data strategy. Common barriers include well-known data silos and duplication, as well as systemic issues of limited, low-quality, and inaccessible data.
As you plan your next data strategy, here are six strategic imperatives to help you connect your strategy to your business.
Align and enhance your technology business strategy
As with most technology-specific strategies, it’s important to connect it to your overarching IT and business strategy. Your data strategy should not only recognize what your IT and business strategies are, but also be embedded within those strategies to help unlock more business value for your organization.
According to Sam Ansari, CEO of Accure, a data engineering and machine learning (ML) platform, in today’s digital age, data has gone from being a mere by-product to becoming a vital fuel that drives innovation and business success. Evolved. Strategic use of data gives organizations a competitive edge and opens up opportunities for growth. CIOs eliminate data silos, ensure security and governance, and adopt a unified horizontal platform for streamlined data management to reduce integration complexity, skilled labor requirements, and costs , should prioritize adaptable technology infrastructure.
Consider ways to monetize your data
Many industries rely on the raw data itself and various forms of post-processing applied to gain additional insights, depending on how customers consume and derive value from their products and services. You can monetize your data across multiple layers of your tech stack, including the data you’ve applied. From data consumed via visualization and analysis tools, to data consumed via industry applications such as digital twins.
For example, in the architecture, engineering, and construction (AEC) industry, these scenarios include geospatial data such as aerial imagery served directly via ecommerce-enabled websites, AI-enabled defect analysis like Manam, May include drone-based photogrammetry of roads and bridges. , traffic congestion data visualized via a GIS platform such as the Urban SDK, or EV charging data provided by a live digital twin.
The focus here is to consider all the ways your customers are consuming data today and new ways they want to achieve better results. For example, the recent Interstate 95 bridge collapse in Philadelphia was an unexpected event, but traditional bridge management systems can be augmented with AI fault analysis and past, present, and live digital twin platforms. You may benefit from upgrading or replacing . and future models.
Leverage automation, data analytics, and AI/ML
Many of today’s modern data platforms go beyond traditional relational databases, data warehouses, and data marts to offer built-in support for automation and AI/ML. There is also a no-code data engineering and AI/ML platform that allows data engineers, scientists, DevOps staff, as well as ordinary business users to rapidly develop, deploy and unlock business value.
By putting these platforms at the core of their data architecture, organizations can get a jumping-off point for automation, analytics and AI/ML projects while completing with additional partners and investments to complete the ecosystem. .
Integrate various data sources
Integrating data sources is another strategy to provide additional insights to clients, and can be done in 2D environments such as analytics dashboards and GIS, or with XR capabilities for visualization such as Magic Leap or Apple Vision Pro. It can be achieved through a 3D environment such as a digital twin.
Proprietary data and third-party data (as Look for opportunities to combine (including open data if you want to).
Embed data inherently into your product and service portfolio
Data and analytics can become an essential part of your product and service portfolio, as explored in our five scales for assessing digital portfolio maturity. In fact, there is value in measuring what percentage of your portfolio uses data and analytics as part of your services and tracking this over time. A good place to start is to work with departments and departments to self-assess their current percentages and determine where they want to be in 2025 and beyond based on their organization’s digital strategy.
How to use data to turn projects into products for pension income
If your organization offers consulting services, revenue is often driven by one-off projects, and the average revenue per FTE is limited by the size of engagements and billing rates prevailing in the market. Incorporating data into deliverables can turn these projects into software products and a source of pension income that can be monetized through a SaaS business model.
Physical assets can also be monetized this way. While the purchase of an asset is a one-time transaction, the data associated with that asset during day-to-day operations can be monetized throughout its lifetime. As consulting firm Cognizant famously emphasized, even the usage data swirling around his $50+ household items like electric toothbrushes can be monetized this way.
Of course, no data strategy must-have set is complete without considering people, process, and technology. We’ve talked about business imperatives here, but to make this possible there are broader technical imperatives and considerations (such as data quality and hygiene), and resource imperatives and considerations ( data skills, competencies, training, etc.) are required. Spending time on your next data strategy and how to monetize and extract value within your company will benefit your automation and AI strategy, as well as your overall IT and business strategy.
