The data revolution is here, and it creates an investment priority for enterprises to stay competitive and drive new opportunities. One of the brightest areas is data monetization, which describes how to create economic benefits, either additional revenue streams or savings, utilizing insights provided by data resources. With B2B and B2C data needs reaching an all-time high, the monetization strategies now and into the future should be seamless for use across multiple platforms.
To get an expert view on this matter, I recently tapped Jeremy Rader, Director of Data Centric Solutions at Intel.
The Opportunity for Data Monetization
Researchers have reported that the market size for big data is on the rise, and is fast becoming an important distinction for organizations. This age of data means that the data culture for every organization needs to be revamped. Almost any company now has the potential to be a data company.
In a research study conducted recently on big data and analytics, more than 85% of all respondents interviewed reported that their organizations had taken steps toward a data-driven culture. When asked if they had success in achieving that culture, though, only 37% replied in the affirmative.
Positioning Your Organization for Success
A key protagonist in this move toward a data culture is the Chief Data Officer (CDO), who is responsible for leading the figures behind data within an organization. However, not every organization has a CDO, and, for those organizations that do, it’s a new role with an evolving definition.
The key role of the CDO should be to take a futuristic view of an organizations’ data model that includes a data monetization strategy. This Eckerson Report includes internal recommendations for data monetization, including delivering concrete data analytics to your employees so they can prioritize, make more informed decisions, and reduce costs. There are also opportunities to enrich your existing products with the use of data analytics and customer retention models, as well as creating a whole new product line that generates revenue by selling your data products to customers.
What is needed for Data Monetization Success?
To start, companies must be able to glean timely, in-depth insights from their data. Those insights come from the ability to access, organize, and interpret the data-in effect, taking a ‘whole business’ approach to analytics.
A key focus area to help enterprises begin to align and organize around their data strategy is to get their data layer right. AI and advanced analytics workloads require massive volumes and types of datasets. To get your data ready to harness, break away from fragmented systems and older data storage models that keep your data trapped. Many organizations are achieving this by implementing a modern data lake model.
Next, tier your data based on its use. Your tiering strategy should include a storage model that matches your data tiers to reduce storage costs and optimize performance.
Here are some other tactics organizations should consider:
- Establish a clear vision: The company’s executives should share the vision of correctly monetizing data by allocating necessary resources, including time, workforce, and investment toward execution.
- Agile multi-disciplinary teams: Data monetization can be done through agile, multi-disciplinary teams of data architects, product managers, application developers, analytics specialists, and marketing and sales professionals.
- Develop a healthy, competitive, data-driven culture: Unless communicated across an organization, data remains worthless. To extract the right information and insights from structured and unstructured data, it is important to focus your efforts on cultivating a data-driven culture that empowers employees with the resources and skills they need to leverage data and obtain the right information at the right time to make more accurate decisions.
- Ensure easy and secure access: For data to be monetized, it not only needs to be voluminous in size and nature, but also clean, accessible, and consistent.
- Data management & advanced analytics: A digital data management platform is essential for integration and providing elaborate and comprehensive solutions. A proper enterprise data management platform should contain the five service layers: Engagement, integration, development, data, and modern core IT-these are the key components of every digital business. Advanced analytics provides the eventual meaning to the data through summarizations, models, calculations, and categorizations. Data is valuable once it is analyzed.
- Storage: Increased storage efficiency is critical to ensure your data is available and can be analyzed. The faster the data can be accessed while processing, the shorter the time to results-and detailed and nuanced analysis within a given response time. Intel® Optane™ DC persistent memory is a new class of memory and storage technology that better optimizes workloads by moving and maintaining larger amounts of data closer to the processor and minimizing the higher latency of fetching data from system storage.
- Processes & delivery: A continuous development process that customizes data and analytics to your target audience needs a delivery system that provides analytics up to an advanced end-user application.
The Future of Data Strategies for Organizations Dealing With Large Data Volumes
As an example of a successful data strategy in action, the business of healthcare has an abundance of data and opportunities that can help power more accurate diagnosis and improved patient care. The stakes are high in an industry where patient outcomes are impacted by quick, early detection and treatment.
For example, Intel worked with a large health system that had an older data infrastructure with fragmented systems and data silos. The legacy system was impeding their ability to rapidly access, blend, and analyze data from multiple sources to deliver precision medicine, improve patient monitoring, and drive innovative healthcare practices. By deploying a modern data hub (Cloudera* Enterprise) running on Intel® Xeon® processors, this large health system was able to see significant results. They are using machine learning algorithms and predictive analytics at scale to anticipate and account for various patient outcomes by analyzing over 100 data points per patient per day for hundreds of thousands of patients.
There will be obstacles along the journey to get your data to a place where it can be used to answer some of your biggest challenges, but those challenges can be overcome with the right focus and investment.
The AI Revolution is Backed by Data
Intel understands that the advanced analytics and AI revolution is backed and powered by the data. And that data must be constantly maintained to achieve the ultimate potential offered by advanced analytics and AI. As such, Intel is focused on leading the charge for open data exchanges and initiatives, easy-to-use tools, training to broaden the talent pool, and expanded access to intelligent technology.
The data revolution will drive demand for advanced analytics and AI workloads, requiring optimized performance across compute, storage, networking, and more. The recent advancements by Intel, as they usher in this paradigm shift, include the Intel® AVX-512, a workload accelerator, and the Intel® Xeon® Scalable processor platform. Through optimized infrastructure, modern storage and data architecture, and a pathway to run complex and massively scalable analytic workloads in any environment, as well as scale up and scale out with performance and agility, we can successfully enable the business of data from the edge to the cloud to the enterprise.
About the Authors:
Jeremy Rader, Director of Data Centric Solutions at Intel, is responsible for enabling business transformation by driving Analytics, AI and HPC solutions, while driving next generation silicon requirements. LinkedIn and Twitter.
Ronald van Loon is an Advisory Board Member and Big Data & Analytics course advisor for Simplilearn. He contributes his expertise towards the rapid growth of Simplilearn’s popular Big Data & Analytics category.
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