Monetizing Data - Avoiding Data Swamp

4 Strategies to Avoid the Data Swamp

Organizations are increasingly eager to monetize their mountains of data. There are many ways to make that happen.

In recent years, the old maxim that every company is a software company has been given an update. Today, every company is a data company – if they’re serious about innovation, that is.

That’s because as the volume and variety of data generated has exploded, with businesses and consumers becoming ever more digital, a data-savvy elite has demonstrated how that information can drive transformation and innovation. As a result, organizations of all types have been relentlessly collecting and storing information from across their operations and customer base for at least a decade (and often longer).

Yet for most organizations, as their data has grown to mountainous proportions, nagging doubts have surfaced that it’s not being leveraged as well as it could be. As advanced data analytics technologies such as AI and machine learning become more accessible, questions are beginning to be asked of IT leaders about how this data could be better monetized.

The right answer lies in understanding that while data itself always carries a degree of latent value, as long as it remains raw information, that value can never be realized. Converting it to insights through analysis goes part of the way to unlocking data’s potential, and many organizations are good at doing this. But to truly monetize your data, those insights must be acted upon.

Some will, sadly, take the wrong path. Contrary to common wisdom, monetizing your data is not about selling the data you hold — which, in addition to being a bit short-sighted, is a reputationally risky practice. Instead, companies will unlock far more value from their data by generating improved customer or operational outcomes. And this only happens by turning data into insight, and insight into action .

For organizations that achieve this, a powerful cycle of innovation can take hold, with their data’s value significantly appreciating over time to become an important strategic asset.

The path to data monetization

Data’s potential for value creation and therefore monetization is tied to the analytics continuum, as described by Gartner.

Gartner - Four Analytics Types
Gartner Analytic Continuum

At the low-difficulty, low-value end of the spectrum, descriptive analytics provide hindsight into what happened. Moving up the difficulty and value scale are more insightful outcomes for diagnostic (why did it happen?) and predictive (what will happen?) purposes. At the highest levels of both difficulty and value, analytics applications are leveraged to provide foresight to organizations — how can we make it happen?

The path to monetizing data lies in taking the answers to these questions — what happened? why did it happen? what will happen? how can we make it happen? — and acting on them to create outcomes that improve operational excellence, revenue growth or innovation.

Internal routes to data monetization might include enhancing your service offering through improved customer service efficiencies or optimizing your supply chain. External routes might include the creation of new revenue streams through the discovery of unmet customer needs, or boosting existing revenue streams by enhancing customer experience.

The scope of action available to you on these routes is heavily linked to your data and analytics maturity. At the foundational-level is a data-as-a-service type model: you’re able to collect and organize data, and analyze it for descriptive and prescriptive outcomes. At the transformational-level, an insights-as-a-service model allows you to build a platform or a business around delivering diagnostic or predictive insights in a way that they can be acted upon.

Regardless of your maturity level, however, all organizations require four things to effectively monetize their data.

Four Strategies to Avoid the Data Swamp

1. Ensure your data is trusted

The first aspect is obvious, and not a thing that many companies are short of: data. However, quantity is only half the story. That data must be trusted, to ensure its insights are reliable, and it must be readily accessible. The latter is both an organizational and a technical challenge: data must be collected, stored and connected to analytics applications. And any silos separating the sources of that data must be eliminated. You also need to have the technical capabilities, in-house or through partnerships, to handle that data correctly.

2. Have an end-to-end strategy

Second, a strategy built around a fully mapped and measurable understanding of desired outcomes — and a plan for actioning against those outcomes — is essential. Even a world-class analytics pipeline fed with highly trusted data will fail to make any meaningful impact without of a clear vison of the outcomes you are trying to achieve.

3. Secure Leadership Alignment

For that reason, leadership and organizational structure are also critical success factors. Without executive support for the strategy and a structure that empowers the organization to execute against that strategy, your insights will never be anything more than interesting facts. To ensure they instead become outcomes that can be monetized, mechanisms and channels are required to quickly get insights to the people that need them and in an actionable format.

Leadership and organizational structure also set the tone for culture, which can make or break data monetization efforts. Culturally you must be driven to iterate on your outcomes to create a cycle of innovation. If not, you risk coming out on the wrong side of predictions such as these from Gartner:

  • Through 2022, only 20% of analytic insights will deliver business outcomes
  • Through 2020, 80% of AI projects will remain alchemy, run by wizards whose talents will not scale in the organization

4. Build support structures and processes around your data

Unless it is collected, curated and corralled in the right ways, data will always have limited value and limited potential to be monetized. For it to become an important strategic asset — and one that can rapidly appreciate in value — organizations need to build the right capabilities, support structures and strategies around it.

And yet too many data warehouses or data lakes are allowed to become wastelands of unused insights. Heaps of untapped information, depreciating in value as its size grows and its utility diminishes.

The risks associated with missteps and errors around data monetization are relatively high considering the role it plays in informing your business strategy, and empowering and feeding the systems that trigger automated decisions and workflows. That means there are no shortcuts but, equally, it also means the rewards are similarly high, in the shape of transformed solutions, services, processes — even entire business models.

To reap those rewards, organizations must have cultural and organizational alignment around actioning data-driven insights. Because actions speak louder than insights when it comes to data monetization.



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About the Authors

Nihar Gupta

General Manager, Data Services

Nihar Gupta

With over 10 years of experience working with high-growth SaaS/Tech companies, Nihar helps companies achieve their growth objectives through strategic partnerships, corporate development efforts, and executing cross-functional strategic initiatives. He brings significant breadth of experience across strategy, finance, product, and sales and marketing.

Read more about Nihar Gupta
Lara Indrikovs Director of Product Management Rackspace

Director of Product Management

Lara Indrikovs

Lara Indrikovs serves as the director of product management for the Global Data organization at Rackspace. She is responsible for leading the development, management, prioritization and execution of the GDO product portfolio that encompasses end-to-end enterprise data initiatives. Lara is a certified SAFe leader and led the GDO’s agile transformation. Prior to her current role, Lara led the marketing intelligence and data science teams, which were dedicated to developing actionable reporting and insights for Rackspace’s marketing activities and creating predictive analytic tools to enable business transformation initiatives. In that role, she led the global migration and consolidation of web analytic platforms to Google Analytics 360, directed the Salesforce Cloud and Google Analytics 360 integration, and established the first Marketing datamart in GCP Big Query. Before Rackspace, Lara held various leadership positions at global advertising agencies, where she focused on multicultural media strategy and buying. She graduated from the University of Texas at Austin with a degree in Advertising and Media, later earning a Master of Science in Predictive Analytics from Northwestern University. Lara lives in Austin with her fiancé and 8-year-old Cocker Spaniel, Indiana Bones. In her free time, she likes to balance out her analytical left brain on a yoga mat, traveling, hosting dinner parties, dancing Rumba and other Afro-Latin dances and volunteering with local non-profits.

Read more about Lara Indrikovs
Juan Riojas

Chief Information Officer

Juan Riojas

As Chief Information Officer at Rackspace Technology, Juan Riojas is responsible for enterprise-wide data strategy, management, and analytics to meet the need of the business to answer critical questions through time to insight. He has more than 20 years of industry experience successfully migrating data ecosystem across all public clouds, leading to significant business transformation outcomes. Prior to Rackspace, Juan worked for Informatica building their inaugural Data Office and has held various executive leadership roles at Gogo, Dell, Accenture, and Expeditors. A native of Texas, Juan attended Texas A&M International University, where he studied business administration and holds a post graduate degree from Said Business School, Oxford University

Read more about Juan Riojas