4 steps to turn real-time data streams into business value

Assessing usable data streams

Realizing the potential of DDSes depends on not only envisioning possibilities, but also on assessing potential data streams that may already exist or could be generated or aggregated. How can the firm evaluate which streams to harvest? The first step is to identify potential DDSes based on feasibility: how streamable and complete they are.

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Streamability enables firms to assess the viability of harnessing a given class of events or creating a data stream that does not currently exist. Those events most amenable to creation meet the criteria of detectability and measurability. An event is detectable if it exceeds a minimum threshold magnitude to sense it. For example, a sensor coupled to a telescope can only detect a star if it receives enough emitted photons. An event is measurable if it can be accurately quantified. A firm's quarterly profits have high measurability, while the subjectivity of an individual's pain level limits its measurability. In addition to evaluating the streamability of potentially valuable data sources, a company should consider whether data sources contain the information needed to describe an event. Such information can be categorized as when, where, who, what, how and why.

Once you have identified potential DDSes based on streamability and completeness, the next step is to assess how much value the firm can extract from the initiative, and consider it in tandem with feasibility. Infeasible DDSes with no perceived potential will make investment unwise. Conversely, high feasibility combined with strong upside potential calls for immediate investment.

Four dimensions of the DDS readiness framework

Frameworks for assessing opportunities can help companies identify and prioritize opportunities. However, a company must have the appropriate capabilities in place to leverage available DDSes. Through extensive research, we identified those necessary capabilities and discovered that firms high in mindset, skill set, data set and tool set capabilities report higher product and service quality as well as process effectiveness -- all key ingredients of high performance. The framework allows companies to assess their capabilities and measure readiness. Companies need to master all readiness dimensions to make the most out of their DDS initiatives.

1. Mindset

Mindset refers to a company's willingness to invest in new data-driven initiatives and assume the associated risks. The requisite cultural mindset could jeopardize established cognitive and decision processes. It requires companies to trust and understand this data and make decisions accordingly, thereby prompting changes in established decision-making habits.

Companies can use the following criteria to evaluate their readiness in terms of mindset:

• Our organization has a data-oriented culture.
• We believe in experimenting and testing innovative IT initiatives.
• We believe we can beat our competitors using real-time DDSes.
• We use real-time DDSes to envision and pursue new competitive strategies.

2. Skill set

Skill set refers to a company's ability to manage DDS initiatives by acquiring and orchestrating all the resources necessary to deliver value with a DDS. Since DDS initiatives are by their very nature typically cross-functional and challenge conventional practices, they can be highly disruptive of established organizational structures and project management practices. Consequently, DDS initiatives require strong coordination mechanisms among business functions as well as new practices.

To evaluate readiness in terms of skill set, companies should be able to:

• Design new initiatives that exploit real-time DDSes
• Select the key resources needed to deploy the initiative
• Assemble the necessary organizational, financial and technological resources to support the initiative
• Form cross-functional teams with the appropriate business functions to successfully deploy the initiative
• Exploit the data to deliver the initiative's benefits

3. Data set

Data set refers to the ability to identify, intercept and access the real-time DDSes needed for value creation. Popular consumer apps often contextualize real-time information including data on weather, transit traffic cameras, deals, events, movies, gas prices and more based on a person's location, social graph, profile, intent and time of day. These apps require accurate matching between a person's perceived value of information and the characteristics of the DDS containing it.

Companies can use the following list to evaluate their readiness in terms of data set:

• We are good at identifying valuable internal real-time DDSes.
• We are good at identifying valuable external real-time DDSes.
• We have a clear data governance policy.
• We evaluate the quality of our internal DDSes (e.g., timeliness, completeness, accuracy).
• We maintain an accurate catalog of valuable real-time DDSes.

4. Tool set

Tool set refers to the ability to use appropriate software and hardware to intercept a DDS and harvest its content. The most technical of the four DDS capabilities, it encompasses both technical competencies and resources necessary to tap into streaming data. The following elements are required at the technical and architectural level for operating real-time DDSes:

• Message-orientated middleware with an enterprise service bus implementation, allowing for standardized and abstracted communication among heterogeneous systems
• An advanced analytics engine, advanced analytics or predictive analytics that are applied on the DDS as the application requires
• A business process modeling engine, enabling flexible and deep integration into the human workflow, which significantly assists in consumption
• A rules engine, capable of executing business rules in runtime and a related rules repository (separating the business and the software/application component)

Companies can use the following list to evaluate their readiness in terms of tool set:

• We have the appropriate tools to integrate real-time DDSes with current workflows.
• We have the appropriate tools to integrate real-time DDSes in current business rules.
• Our systems architecture allows us to dispatch real-time DDSes to existing systems.
• We have the tools and the technical talent to create our own real-time DDSes.

Recommendations for data stream success

Be prepared. Those companies high in DDS readiness reported greater business performance in the form of enhanced products and services. The challenge ahead is to properly balance the mix of capabilities necessary to appropriate the value generated through DDS initiatives. We believe that the patterns identified are significant and provide guidance to IT departments on where to look. Timing is important because more companies, including your competitors, will develop the necessary capabilities to recognize and exploit DDS opportunities.

Watch your mindset. Not all readiness dimensions are created equal. Our results indicate that companies are better prepared on the technical and data management fronts than the cultural and managerial ones. Some studies suggest that we are on the verge of a radical change in the way we make decisions and develop our products and services. The chief technology officer of IBM France described this change as moving from "gut management" to "risk management." DDSes will continue to infuse more fact-based decision making into companies. Embrace this change in mindset and help your company develop a more balanced guts and facts approach.

Assess your readiness. Use the DDS readiness framework to assess your company's readiness and then to check progress. Since DDS value creation has a strong learning component, check your progress and experiment with more advanced ways to create value.

Exploit opportunities. Opportunities are increasingly becoming evident for DDS exploitation. Identify potential DDSes based on feasibility -- how streamable and complete they are -- and assess their potential to lead to business value. Experiment widely and capture learning.

About the authors
Gabriele Piccoli holds the Edward G. Schlieder Endowed Chair of Information Sciences at Louisiana State University. He was formerly with the University of Pavia, Italy, where he directed the DDS Lab. In addition, Piccoli was formerly a full professor at the Grenoble Ecole de Management in Grenoble, France, and held positions as associate professor at Cornell University, adjunct professor at Tulane University, and instructor at Louisiana State University. His research, teaching and consulting expertise is in strategic information systems and the use of information systems to enable customer service. He has recently authored the second edition of his book, Information Systems for Managers: Text and Cases.

Federico Pigni is assistant professor in information systems in the Management of Technology and Strategy department at the Grenoble Ecole de Management in France and director of the Global Tech master program. He holds a Ph.D. in management information systems and supply chain management. Prior to joining GEM, he taught at Carlo Cattaneo Unversity - LIUC, Università Commerciale Luigi Bocconi and the Catholic University in Milan. He teaches in the area of information systems and has a research interest in the strategic application of information systems in the interorganizational context and the use of innovative IT to deliver customer service.