How to Achieve Data Agility

In recent years, the wave of digital transformation has unleashed innovation and productivity across all industries around the globe. Every organization is grappling with the challenge of creating business value from the massive explosion of new data arriving in real time from multiple sources.

From the COVID-19 pandemic to the current economic headwinds, we’ve all seen just how quickly the business landscape can change. If this keeps you up at night, rest assured you are not alone. The solution is a modern approach to connecting, creating, interpreting, and consuming data that allows you to integrate facts with everything that is known about them to put the information in context for different business users and use cases.

We refer to this as data agility: the ability to make simple, powerful changes to how information is interpreted and acted upon.

To Achieve Data Agility, You Need a Semantic Data Platform

Data agility is what happens when you can connect active data, active metadata, and active meaning. To achieve data agility, you need a semantic data platform. It works like this: Data, together with its metadata, is collected and introduced into a semantic data platform that works with whatever data you collect. Once the semantic data platform ingests the data, it can be classified, enriched, stored tightly coupled with its metadata, and put into context. The metadata is effectively the connective tissue between the data and its meaning.

By deploying a semantic data platform, you can reduce time to value and deliver business outcomes that can transform your organization.

Look to Visionary Organizations to See How It’s Done

Our visionary customers invest in capturing and codifying organizational knowledge today. Here are some examples of how they solved a wide range of business problems by deploying a semantic data platform to achieve data agility:

• Boeing drove efficient model-based engineering. Boeing, the world’s largest aerospace company, used the MarkLogic data platform’s multi-model flexibility and semantic data capabilities to transition to a model-based systems engineering (MBSE) methodology. The result? The company improved efficiency, streamlined processes, connected disparate data sources, and provided better data analysis.
• AbbVie reduced time-to-market when creating and delivering lifesaving drugs. AbbVie, a biopharmaceutical company, held its information in silos, making it difficult for its scientists, analysts, and other employees to access the data to create organizational knowledge. They needed to modernize their infrastructure to achieve data agility and turned to MarkLogic and AWS to make it happen. The result? AbbVie was able to achieve a $5M cost reduction, a tenfold increase in productivity, quicker time-to-market, and boosted security.
• WoodmenLife delivered an enhanced experience to its members. WoodmenLife, a not-for-profit life insurance company, needed to make data from across its systems more accessible to the business and easier for its users to search. To achieve this, the organization turned to MarkLogic to help them transition away from legacy mainframes to a modern and agile data infrastructure. The result? By combining current and historical policy data with other unstructured data from across the enterprise, WoodmenLife enabled its users to more easily find all member policy information for servicing, reporting, and compliance. Providing a 360-degree view of the customer has greatly enhanced the customer experience.

Start Your Journey Toward Data Agility

Data agility is transformational. As with all transformational initiatives, it requires a bold vision, well-defined business outcomes, and tight alignment with key stakeholders. With the wisdom we’ve gained helping our customers and partners go down this path, we recommend that you:

1. Start by finding the business problem you can solve with data agility. It bears repeating: Data challenges are everywhere. Look around to find the places where your organization is currently struggling with data. Define the problem, the dependencies (people, process, technology), and the business outcomes you can achieve by solving it.
2. Choose the right technology. Look carefully at your business case to decide what technology is the best fit. Industry analyst reports, such as the Gartner Cloud DBMS Magic Quadrant or the Gartner Market Guide for Active Metadata, provide an in-depth analysis of solutions that can help you achieve your vision.
3. Get buy-in for your initiative. Develop a business case that clearly articulates your desired state, how you plan to achieve it, and why it’s important to your organization. Socialize it with internal stakeholders who are critical for success. Once you have aligned with your key stakeholders, build a tiger team to help develop a proof of concept that will demonstrate the capabilities of the technology you are vetting.

Ready to achieve data agility? Check out our eBook, A Business Leader’s Guide to Delivering Change with Data Agility, to learn more.