Mind+Machine. Vollenweider Marc

this case, the Level 3 insights actually existed but had zero impact: right level of insight, right timing, but wrong format for that decision maker. You can imagine what wasted resources it took to create these reports. This example also illustrates the need to change the delivery of insights from lengthy reports into a model where relevant events trigger insightful and short alerts to the end users, prompting them to take action.

These two examples show the need to understand the value chain of analytics in more detail. The value is created largely at the end, when the decision is made, while the effort and cost are spent mostly at the beginning of the analytics cycle, or the Ring of Knowledge (Figure I.4):

Figure I.4 The Ring of Knowledge

      Step 1: Gather new data and existing knowledge (Level 1).

      Step 2: Cleanse and structure data.

      Step 3: Create information (Level 2).

      Step 4: Create insights (Level 3).

      Step 5: Deliver to the right end user in the right format, channel, and time.

      Step 6: Decide and take action.

      Step 7: Create knowledge (Level 4).

      Step 8: Share knowledge.

      If any step fails, the efforts of the earlier steps go to waste and no insight is generated. In our first example, step 3 never happened so steps 1 and 2 were a waste of time and resources; in the second, step 5 failed: the insight desert was not successfully navigated!

      The insight desert is filled with treacherous valleys and sand traps that could block the road to the oasis at each stage:

      ● Steps 1 and 2: Functional or geographic silos lead to the creation of disparate data sets. Inconsistent definitions of data structures and elements exist with varying time stamps. Various imperfect and outdated copies of the original sources lead to tens, hundreds, or thousands of manual adjustments and more errors in the data.

      ● Step 3: Too much information means that really interesting signals get lost. There is a lack of a proper hypothesis of what to analyze.

      ● Step 4: There is a lack of thinking and business understanding. Data scientists sometimes do not fully understand the end users' needs. Contextual information is lacking, making interpretation difficult or impossible. Prior knowledge is not applied, either because it does not exist or because it is not accessible in time and at a reasonable cost.

      ● Steps 5 and 6: Communication problems occur between the central data analytics teams and the actual end users. Distribution issues prevent the insights from being delivered – the so-called Last Mile problem is in effect. There is an ineffective packaging and delivery model for the specific needs of the end user.

      ● Steps 7 and 8: There is a lack of accountability for creating and managing the knowledge. Central knowledge management systems contain a lot of obsolete and irrelevant content, and a lack of documentation leads to loss of knowledge (e.g., in cases of employee attrition).

      Any of the aforementioned means a very significant waste of resources. These issues keep business users from making the right decisions when they are needed. The problem is actually exacerbated by the increasing abundance of computing power, data storage capacity, and huge new data sources.

      What would a world of insight and knowledge look like? Our clients mention the following key ingredients:

      ● Less but more insightful analysis addressing 100 percent of the analytics use case

      ● Analytic outputs embedded in the normal workflow

      ● More targeted and trigger-based delivery highlighting the key issues rather than just regular reporting or pull analysis

      ● Short, relevant alerts to the end user rather than big reports deposited on some central system

      ● Lower infrastructure cost and overhead allocations to the end users

      ● No requirement to start a major information technology (IT) project to get even simple analyses

      ● Simple, pay-as-you-go models for analytics output rather than significant fixed costs

      ● Full knowledge management of analytics use cases, so that the world does not need to be reinvented each time there is a change

      Innovation Scouting: Finding Suitable Innovations shows a good example of very large amounts of data being condensed into a high-impact set of insights, and knowledge being extracted to ensure learning for the future.

      1

      “Gartner Predicts 2015: Big Data Challenges Move from Technology to the Organization,” Gartner Inc., 2014, https://www.gartner.com/doc/2928217/predicts-big-data-challenges.

      2

      Jeff Kelly, “Enterprises Struggling to Derive Maximum Value from Big Data,” Wikibon, 2013,

1

“Gartner Predicts 2015: Big Data Challenges Move from Technology to the Organization,” Gartner Inc., 2014, https://www.gartner.com/doc/2928217/predicts-big-data-challenges.

2

Jeff Kelly, “Enterprises Struggling to Derive Maximum Value from Big Data,” Wikibon, 2013, http://wikibon.org/wiki/v/Enterprises_Struggling_to_Derive_Maximum_Value_from_Big_Data.

3

Columbia Business School's annual BRITE conference, BRITE–NYAMA Marketing Measurement in Transition Study, Columbia Business School, 2012, www8.gsb.columbia.edu/newsroom/newsn/1988/study-finds-marketers-struggle-with-the-big-data-and-digital-tools-of-today.

4

Vernon Turner, John F. Gantz, David Reinsel, and Stephen Minton, “The Digital Universe of Opportunities: Rich Data and the Increasing Value of the Internet of Things,” IDC iView, 2014, www.emc.com/leadership/digital-universe/2014iview/executive-summary.htm.

5

Mario Villamor, “Is #globaldev Optimism over Big Data Based More on Hype Than Value?,” Devex.com, 2015, https://www.devex.com/news/is-globaldev-optimism-over-big-data-based-more-on-hype-than-value-86705.

6

Jürg