Studies show that investors favor companies that demonstrate data-driven behaviors, and even more so, companies that productize their data. IT and business executives may talk about data as one of their most important assets. But few wield it like one. Becoming data-driven is more than self-service or advanced analytics. It’s about measurable outcomes and it starts with identifying innovative and easy to generate new value streams from your (and others) data assets. Fortunately, data has unique economic qualities compared to other assets that enable it to be refined, integrated, packaged, and deployed in infinite ways. Unfortunately however, most organizations have no defined process or function for monetizing their data. In this workshop, Mr. Laney guides attendees through his formal approach to ideation, feasibility assessment, economic analysis, and the design, development and support of data products and services. Participants will work together to generate and assess real ideas for their own business. Participants will learn: How to take advantage of data’s unique economic characteristics Techniques for generating innovative ideas for data and analytics Whom to involve in these ideation sessions How to identify the wants and needs of a full range of potential users, buyers and stakeholders How to adapt data monetization ideas from other industries How to consider data products and services augmenting your own data with a variety of external data sources How to test ideas on a spectrum of feasibility characteristics How to package and price data products and services in different ways

Almost every industry sports a set of guiding principles and standards for how certain assets should be managed. For physical assets, financial assets, technology assets, and even human capital, accepted standards regulate the handling, flow, and utilization of resources. Yet no comparable overarching set of principles and standards exists for data. Rather, perhaps for lack of an authoritative data standards body, they are scattered across and buried with industry regulations. Moreover, we data professionals, rather than paying homage to the way other assets are managed, have been making it up as we go, creating an obtuse vernacular that further distances us from the businesspeople we serve. One set of approachable frameworks, however, comes from the world of accounting: The set of Generally Accepted Accounting Principles (GAAP)—a simple set of assumptions, constraints and tenets that form the basis of all accounting. Borrowing this framework and sponging from other asset management standards, Mr. Laney will share and discuss the set of Generally Accepted Information Principles (GAIP) he developed that can and should underpin any data strategy, data management organization, or data governance initiative. These principles include guidance on: Why the concept of “data owners” is one of the worst ideas ever How to be economically circumspect whenever moving or copying data How data responsibilities should be split among business, IT, and data functions What assumptions and constraints should guide our data-related initiatives What we can learn and apply from the management of other types of assets

Treating data as an actual business asset should go beyond just talking about it as one. Typically, other traditional assets are measured, managed, and monetized with great discipline, using standards, frameworks, specialized roles, and technologies to do so. Becoming data-driven requires a similar degree of maturity regarding the organization’s data assets, as well as an appreciation of data’s unique properties. In this workshop, participants will explore how to thrive in the data economy and help their organizations become more data-driven by understanding and gauging their organization’s data & analytics maturity. They will also collectively discuss ways to remedy identified challenges and inhibitors to improving maturity along several dimensions, including: Culture and Leadership Strategy and Approach Metrics and Key Performance Indicators Organization and Skills Architecture and Integration Governance and Quality Deployment and Usage Technology and Operations

Shifts in global and local economies due to the Covid-19 pandemic and the discrepant, ever-changing national and local responses have rendered the rudimentary analytic models of many organizations useless, or worse—detrimental to the business. Although forecasts crunching historical data may work fine in normal times when markets are relatively stable, in anomalous times like today these trend-based models falter. In times of turmoil, leading indicators prevail. Economists, business analysts and data scientists are finding that historical data from the last several recessions is of little use today. Trend-based models rely on the company’s own historical data, such as sales or production data, and sometimes macro-level industry data. These formulae expect trends to continue on a similar path and at a similar pace. Driver-based models, on the other hand, rely more on leading indicators of performance or business activity. They incorporate external data about situations or observations that highly correlate to and presage one’s own business outcomes. In this session, Mr. Laney will share findings from his groundbreaking global study of external data to explain: What kind of organizations are using external data sources and how they use them Which external data sources are used for which kinds of purposes Which kinds of external data sources are being leveraged the most How the use of external data leads to improved competitiveness Differences in the reliance on external data by high-performing versus low-performing organizations

Abstract available upon request.

Data literacy (or data fluency) has risen in the ranks to become the top challenge and opportunity for data & analytics leaders in helping their organizations become more data-driven. Cultural and political issues abound in many organizations regarding data, mainly due to a lack of appreciation for its role as an actual business asset, a lack of understanding of its unique economic properties and how to capitalize on them, a lack of concern for data governance and how it contributes to business value, and an overall lack of being able to “speak data.” In this session, Mr. Laney will share de-identified insights from West Monroe’s ongoing study of hundreds of client personnel. These assessments have revealed typical data literacy levels, gaps, and incongruities among different roles and parts of the organization, leading to targeted recommendations for data literacy training and embedded enterprise programs. Key session topics will include: Data literacy study findings Data literacy challenges, opportunities, and recommendations Building a business case for a data literacy program Crafting a data literacy training and certification curriculum Connecting your data literacy program to other business and technology initiatives

New, critical yet overlooked roles are the keys to a data-driven organization. Aside from increasingly commonplace roles such as the chief data officer (CDO), data scientist, and data governance lead, we’re starting to see new essential roles beginning to emerge. Filling these roles are a strong indicator of how seriously some organizations consider data and analytics as paramount capabilities in the Information Age. Moreover, these roles tend to parallel, or are borrowed from, similar roles in other parts of the organization--an indicator that information too has become an asset on-par with other kinds of assets. In this talk, Mr. Laney will share a number of emerging and anticipated roles and organizational constructs that set apart “old world” organizations from those striving to capitalize on the broad economic power of information. Key topics include: What are new roles for engineering and managing information as an actual asset. What are new jobs and skills for understanding and generating value from information assets. How analytics centers of excellence are evolving. How data literacy and change management efforts can be enhanced via new and upgraded skill sets.

Abstract available upon request.

Organizations expecting to thrive not just participate in the Information Age and the Digital Economy must consider any and every way that data can drive economic value for them and their stakeholders. That is every business is, can be, or should be a data business, monetizing information in a spectrum of ways. But where to turn for inspiration? Sure you can look to what others in your industry are doing, but do you really want to be in 2nd or 3rd place? Instead, why not consider what organizations in other industries are doing with data, then be adopt and adapt them for your business? This session will explore a variety of real-world use-cases including those: in retail, manufacturing, telecommunications, insurance, life sciences, financial services, high-tech, and the public sector leveraging data assets from inside and outside the organization focusing on targeted business problems and opportunities applying a variety of analytics techniques and technologies

Classic macro- and microeconomic principles were developed to better understand and improve the consumption and value of traditional goods and services, not data assets. CDOs must master how the economics of information can be capitalized upon to design high-performance architectures, innovative data monetization schemes, and disruptive digital business models. In this session, Mr. Laney will discuss how basic economic concepts like supply-and-demand, pricing, scarcity, non-rivalry, marginal utility, etc. apply to data (or not), and what we can learn about and capitalize upon the economic nuances of data assets. Bring only your fuzzy recollection of high-school econ and a willingness to go well beyond the trite “data is the new oil” metaphor! Topics will include: Why data is not the new oil How the supply and demand of data is artificially maintained How to think about pricing data given its unique economic properties The implications of data consumers increasingly being machines, not people How data can be used for collateral and leverage

Abstract available upon request.

The need for an executive responsible for an organization’s information assets today may seem obvious. But some organizations still struggle with making a business case for the role. And even existing chief data officers can be confounded about how to formally justify their existence. This session will share eye-popping findings and analyses from Mr. Laney’s study of hundreds of organizations with and without a CDO. As any good scientist knows, and any good data scientist should know, most discoveries begin with a hypothesis. We see a lot of surveys about the CDO role but don’t really have much of a point to make or look at the impact a CDO makes. This study examined over 500 organizations to determine how businesses with a CDO operate differently, including: Do organizations with a CDO benefit in any way? How does having a CDO affect the level of data quality and data governance? How does having a CDO affect data democratization and monetization? How does having a CDO affect the ability of an organization to value its data? Is there a difference between actual C-level CDOs and those who are not? How do investors feel about companies with a CDO? Where do great CDOs come from and what is their desired career path?

At their own peril, PE firms, boards and CIOs discount the financial value and market potential of corporate data assets, especially during times like these. Currently, on the cusp of a massive global economic reconfiguration, corporate entities themselves will transmogrify at a rate we have not ever experienced. Executives and boards are compelled to evaluate and divest entire flagging business units, or worse, put the assets of their insolvent businesses up for sale. However, most PE firms, VCs, and the corporate transaction units of consulting firms have done little to keep up with the Information Age other than merely favoring digital businesses. And CIOs and other executives have done little to measure and promote the value of their off balance sheet assets. In this session, Mr. Laney introduces the concept of data diligence, and how this process addresses key considerations such as: Data Valuation — How does data contribute to business efficiencies and revenue generation? What is the data’s cost basis and potential market value? Data Synergies — How well will the acquiree’s data management function, architecture, and culture integrate with those of the acquiror? Data Opportunities — What are innovative ways to generate value, or monetize, the company’s data assets, either on their own or when combined with those of portfolio companies or business partners? Data Risks — Are their quality or consistency issues in the data? How well does the data environment mitigate regulatory or other compliance requirements? Data Challenges — What challenges will there be in integrating or leveraging the acquired data assets? Or in integrating the data management or analytics functions of the two businesses being merged?

A survey of leading businesses finds that beyond the traditional data management roles we have seen and our organizations have, are a growing set of targeted roles that are focused on managing data as an actual enterprise asset, helping the enterprise become more data-driven, and close the gap between the data organization and business functions. This session will share a dozen or more roles your organization should consider including those for: Applying asset management practices to data management Understanding and applying economic and valuation models to data Advocating for increased data utilization and benefits Driving data monetization initiatives Curating external data sources