The Economic Value of Data

There’s gold in that data. That’s right. Your enterprise’s data is worth a mint – so long as you perform Value Engineering through Data Analytics right.

Who says? McKinsey says that’s who. “Results from the newest McKinsey Global Survey on data and analytics indicate that an increasing share of companies is using data and analytics to generate growth. Data monetization, as a means of such growth, is still in its early days—though the results suggest that the fastest-growing companies (our high performers) are already ahead of their peers, the McKinsey Global Survey on Data Monetization report found. “Respondents at these companies say they are thinking more critically than others about monetizing their data, as well as using data in a greater number of ways to create value for customers and the business. They are adding new services to existing offerings, developing new business models, and even directly selling data-based products or utilities.”

That value includes: becoming more competitive, building new revenue streams, or becoming more operationally effective by streamlining processes.

The journey is well worth taking. “Data and analytics are changing the nature of industry competition. Seventy percent of all executives report that data and analytics have caused at least moderate changes in their industries’ competitive landscapes in recent years,” the McKinsey survey found. “The most common change, cited by half of respondents, is entrants launching new data-focused businesses that undermine traditional business models.”

Benefits go well beyond being more competitive, the McKinsey survey found. “Respondents at high performers are at least one-third more likely to report significant or fundamental changes to business practices in areas such as supply chain, research and development, capital-asset management, and workforce management,” McKinsey learned. “Additionally, they are more likely to report changes in competitive pressure, whether from new entrants launching new data-related businesses, traditional rivals gaining an edge through data and analytics, or companies forming data-related partnerships along the value chain.”

Data Monetization Underpinnings

To get value from data, you must understand your data economics and monetization strategy and where the value is created, not just the pure ability to store data. For instance, descriptive analytics provides deep insights into a market, area of your business, or even a process. Diagnostic data shows why something happened. Up a level is predictive, explaining not just what will happen, but perhaps even when. Even better, it can prescriptive where the data actually advises the organization on the best course of action. These insights increase and deepen as your organization adopts Machine Learning and Artificial Intelligence (ML/AI).

Monetizing data happens in many ways. The most obvious is selling your data, such as to marketers. This is external data monetization.

Internal data monetization, on the other hand, delivers measurable economic benefits without packaging and selling data. Examples include new product or service invention, streamlining to cut costs and increase efficiency, and boosting market share by out-smarting your competition with data-driven decisions. “Across industries, most respondents agree that the primary objective of their data-and-analytics activities is to generate new revenue,” McKinsey argued.

Other data monetization examples include:

  • Increasing efficiency and productivity of operations
  • Boosting customer experience
  • Cutting operational costs
  • Radically improving planning and decision-making
  • Improving customer experience and loyalty
  • Increasing profits
  • Risks managed and mitigated
  • Discovering new opportunities
  • Data-driven marketing creates more leads and sales
  • Boosting competitive advantage

Data-Driven Inventions

Data is now the lifeblood of medical research and invention. “Being able to intelligently search vast data sets of patents, scientific publications, and clinical trials data should, in theory, help accelerate the discovery of new drugs by enabling researchers to examine previous results of tests. Applying predictive analytics to the search parameters should help them hone in on the relevant information and also get insight into which avenues are likely to yield the best results,” argued events company IQPC. “Pfizer is combing data from electronic medical records, clinical trial and genomic data to spot opportunities for “drugs for specific patient populations. Using this approach the company was able to identify that a small subset of lung cancer patients had a specific genomic defect – a mutation in their ALK gene. Using this insight, Pfizer developed Xalkori specifically for lung cancer patients with the ALK gene mutation.”

Monetizing and Value Engineering: Monetizing data leverages the concept of value engineering, which is mining data to get the value, and setting the foundations of governance and data operations to drive re-use and refinement. Value engineering involves bringing data together in a consistent re-usable manner which shrinks time to deliver and increases the economic value.

Monetizing and Use Cases: Before you can truly monetize data, you need to decide what you want it to do, which is where use cases come in. Here you pick the ones with the most financial value. Examples of these include: acquiring more customers, reducing customer churn, increasing quality of care, and boosting customer satisfaction.

Once you see data as an asset, you may soon find that that asset has a transactional limitation. You are constrained with transactional assets, because they’re siloed, they’re all in their own location. You get past those limitations by bringing the data together into a more powerful asset you can now invest in, which supports a use case going out.

This siloed data can include non-traditional data sources, such as social media, college rankings, or even items you can get from external data sources such as weather.

Leadership and Culture: “Successful data-and-analytics programs also require real commitment from business leaders, along with a consistent message from senior leaders on the importance and priority of these efforts. Overall, respondents report that senior-management involvement in data-and-analytics activities is the number-one contributor to reaching their objectives,” the McKinsey surveyed detailed. “At the analytics leaders, senior-management practices prove the point further. Respondents at these organizations are five times more likely than those at analytics laggards to say their executive teams spend more than 20 percent of their time at high-level meetings discussing their data-and-analytics activities.”Bring it Together with a Data Platform: A Data Platform is a way to bring multiple data sources together. Once brought into a Data Platform, you want to use the data, so you need good exploratory capabilities within the Data Platform to understand what’s there, and just as important, what’s not there. The missing gaps in the story can be filled in by leveraging external data sources to increase the value you gain through this platform.

Metadata is vital to understanding what is within each one of those data sources, at least at a high level, and have it tagged as well. Every single time you add more metadata, you increase the value of that data that sits within the Data Platform.With this approach, you are maturing your Data Analytics practice by operationalizing data into a more structured and tagged version you can use effectively for analytics. At the same time, you achieve more collaborative value creation at this level.

Data as a Service/Data Analytics as a Service: With mature Data Operations through Data Governance and Management, your data is now always and easily available as a service, whether you want to call this Data as a Service or Data Analytics as a Service.

These services bring a proactive approach to value engineering that puts the data into an environment with ease of access and ease of exploration capability. This proactive approach will accelerate the adoption of a data-first culture and accelerate the value realization.

Data Analytics the Secret to Industry-Leading Performance

Data monetization is already making itself felt in the value of companies. As companies are looking at digital transformation techniques, utilizing a data economics approach will drive real value and identify the use cases that provide the most value generation. “Data monetization seems to correlate with industry-leading performance. Respondents at the high-performing companies in our survey are more likely than others to say they are already monetizing data and to report that they are doing so in more ways, including adding new services to existing offerings, developing entirely new business models, and partnering with other companies in related industries to create pools of shared data,” the McKinsey Global Survey on Data Monetization survey discovered. “Perhaps unsurprisingly, respondents at high performers also see a top-line benefit: they are three times more likely than others to say their monetization efforts contribute more than 20 percent to company revenues.”

Next Steps

If you’re looking to monetize, drive innovation and radically boost ROI with your data, ACTS empowers you to unleash that value. We deliver solutions for the entire data journey map from data collection to data management to downstream analytics, to cognitive AI and Machine learning.  Get started with our Modern Data Maturity Assessment  or contact us directly at [email protected] for a personalized consultation.

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David Baldwin

Practice Manager – Data & Insights, ACTS, Inc.

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