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Finding Value in Analytics, Part V: Value Drivers

Finding Value in Analytics - Part V

Read Finding Value in Analytics, Part IV: Action Distance

This article series started with my frustration over analytic craziness, the blind adoption of analytics because it is popular, possible and affordable. The journey of this article series is my therapy to find the tangible value generated by analytics within enterprise IT systems. The question is…why should companies invest resources in analytics as part of their IT infrastructure? 

Previous articles in this series have covered:

  1. The Journey sets the stage by introducing the economic value principle. To have value, analytics must enable the organization to execute improved actions that, in aggregate, changes its behavior and improves its expected payoffs. Hence, the focus shifts to how analytics transforms data into actions, not just insights.

  2. Our BI Legacy explores our seven-decade legacy using various IT technologies to support Business Intelligence (BI). Each decade had a unique value generator, for which we noted six trends in flow patterns from one decade to the next: Cumulative, Pervasiveness, Disruption, Actionable, Generalizing, and Effectiveness.

  3. Next-Gen futurizes about the value generators for the 2020-decade, based on six trends observed over the past decades of IT systems evolution. The next decade is challenging with the emergence of neural networks analytics, AnalyticOps to manage analytic systems, continuous learning systems, and intelligent effectiveness customizing each transaction. 

  4. Action Distance examines the ‘distance’ (or coupling) of data to actions in the Analytic Value Chain. How faithfully does analytics respond to data, both in timeliness and substance? 

This fifth article investigates the implications of seven economic drivers that have enabled organizations to realize business value from their usage of analytics. It is based on a study of successful analytic projects by four organizations spanning various industries at various stages of analytics adoption. [01] 

Analytic Value Chain

The economic principle that has guided this article series is the following:


Analytics has economic value when it enables organizations to make choices whose actions yield better outcomes, as compared with choices made in the absence of those analytics.


In other words, analytic value is the difference between using analytics versus not using analytics in a specific business situation. To illustrate this, the following figure shows the Analytic Value Chain that transforms data into choices, which are governed by managers to take actions, resulting in (hopefully desired) outcomes.

For the four organizations studied, the following seven value drivers emerge, giving us insights into how analytics contributes to economic value, at least for these organizations. 

1. Data-Driven Emanates from The Top

Being data-driven implies basing decisions and business processes upon data, in balance with seasoned intuitive reasoning. Hence, the analytic value directly depends on whether the organizational culture is data-driven, starting from the top and cascading down to all levels of the organization. 

Take-Away: The implication is to invest in analytics only if top executive support is present. Otherwise, the organization is unlikely to realize sufficient value. 

2. Pervasive Data Literacy

Being a data-driven organization also implies pervasive data literacy. Everyone should use the same terminology and have an understanding of what is possible with their current analytics infrastructure. Further, many key constituents should also know how to use the analytics and operationalize it into routine processes. 

Take-Away: The implication is to invest in a data literacy program among all levels of the organization. The ongoing program will ensure that all employees understand the pros/cons of analytics and its relation to human intuition. 

3. Searching for the Signal

A Signal is the specific data that enables to infer relevant causal relationships. Hence, analytics is searching for the WHY, which is more than correlation and pattern detection. It is like the thinking of an investigative detective at a crime scene. The mystery of the business situation is the heart of generating passion for being data-driven. Further, a detective does NOT assume that this signal is part of the data that is already known, such as that in the data warehouse. It might require the analysis of new data, along with an extensive search among complex data relationships & time sequences. 

Take-Away: Constantly search for the proper signal. It is probably NOT where you expect it, especially given current IoT sensor technology. And, it may NOT currently be in the data warehouse. Allow yourself to think outside of this box. 

4. Effortless Customer Interactions

Customer experience should be as effortless (seamless and frictionless) as possible. For example, offering a product/service to customers should require only the press of a button on their smartphone. Chatbots - an interesting technology – has become practical on apps and websites to solicit richer data from customers, also in a relatively effortless manner.  

Take-Away: When conceptualizing a new analytic use case, become your customer (or supplier or distributor or whoever will be the target user). Wear their shoes! Spend time wandering the store or the like. Think deeply about it…how can analytics make my experience more seamless and frictionless?

5. Inclusive Data Ingestion

The analytic data pipeline is being simplified with reliable and cheap massive object stores from cloud providers. ALL raw data is captured on single common land which is curated into the data warehouse and other systems as priorities and resources dictate. Besides solving data retention and data recovery issues, it also enhances the curation of new data to aid V3 Searching for the Signal above. 

Take-Away: As new technologies emerge, rethinking the entire data infrastructure becomes imperative. Can your current data pipelines handle the volume and variety of your data streams? Are you spending sufficient resources on acquiring new data streams? Consider a single common land to capture ALL raw data, way beyond that of a messy Hadoop data lake.

6. Social Issues

A variety of social issues are part of the current business reality and, hence, must be part of the analytics that attempts to make sense of that reality. It is a wake-up call for techies! With plenty of technology, the problem is not what-can-we-do but what-should-we do. Next-gen analytics will enable solutions for real problems that we have yet to imagine. One example is the unethical bias in analytic results based on gender, age, and race hidden within model training procedures. How should we govern this problem? 

Take-Away: It is also a wake-up call for the executives! The social issues of IT and especially analytics are now at a maturity level. Ignoring these issues should be deemed as professional negligence! Be proactive and get ahead of the power curve at managing these issues. Have you noticed that the privacy of customer data has become a competitive advantage for some corporations? 

7. Complete Data Lineage

Knowing the usage and source of all data items used within analytics is essential when these analytics guide actions that have a significant impact on people. This is the first step toward the difficult task of explaining the results generated by analytic models. 

Take-Away: Just like every person should know from where their food comes, every corporation should know from where the data for their analytics comes. Think about testifying in a jury trial about why your corporation made a decision that resulted in harm and suffering. Will your data save you from jail time? 

Conclusion

Most of this series has emphasized the upside of analytics – it's potential to increase revenues or reduce costs. However, this article emphasizes the downside of analytics as to the seriousness of the executive policies that should be in place prior to any sufficient investment in analytic systems. 

It seems that most organizations are not culturally ready for state-of-the-art analytics, even though the technology is less expensive and more powerful than that of a few years ago. So, tread carefully and slowly into analytic investments. 

Notes 

[01] Soon-to-be published. When available, a link to the full study will be here.

Richard Hackathorn

Richard Hackathorn, Ph.D., of Bolder Technology, Inc. is a well-known industry analyst, technology innovator, and international lecturer in business intelligence and data analytics. He is currently focusing on the managerial...

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