in many situations.
This use case illustrates another important factor: the silo trap. Interesting use cases often remain unused because data sets are buried in two or more organizational silos, and nobody thinks about joining the dots. We will look at this effect again later.
Summing up the first fallacy: not everything needs to be big data. In fact, far more use cases are about small data, and the focus should be on managing portfolios of profitable analytics use cases regardless of what type of data they are based on.
FALLACY #2
MORE DATA MEANS MORE INSIGHT
Companies complain that they have far more data than insight. In 2014, the International Data Corporation (IDC) predicted that the amount of data available to companies will increase tenfold by 2020, doubling every two years.4 In one conversation, a client compared this situation to a desert with the occasional oasis of insight.
“Marc, we are inundated with reports and tables, but who'll give me the ‘so what'? I don't have enough time in the day to study all the data, and my junior people don't have the experience to come up with interesting insights.”
The ratio seems to worsen daily, as the amount of available data rises rapidly, while the level of insight remains constant or increases only slightly. The advent of the Internet of Things puts us at risk of making this ratio even worse, with more devices producing more data.
As the Devex consulting practice writes:
Stanley Wood, senior program officer for data, evidence and learning at the Bill & Melinda Gates Foundation, has said that while large sums have been invested to collect various types of data, much of the results these efforts yielded were nowhere to be seen. In a previous interview, Wood even told Devex that one of the biggest points open data can help with is the waste of billions of dollars that have been spent in data collection.5
FT.com writes:
According to Juerg Zeltner, CEO of UBS Wealth Management, a mass of information does not equal a wealth of knowledge. With global financial markets continuing to be volatile, the need for interpretation has never been greater.6
Before we proceed, let me introduce a simple but necessary concept for talking about data. It is quite surprising how confused the discussion of the term data still is, even among data scientists and vendors. In reality, there are four fundamentally different levels, depicted in Figure I.3:
Figure I.3 Pyramid of Use Cases (Levels 1–4)
● Level 1: Data – raw data and “cleansed” or “preprocessed” data
This could be a sequence of measurements sent from a temperature or vibration sensor in a packaging machine, a set of credit card transactions, or a few pictures from a surveillance camera. No meaning can be gleaned without further processing or analysis. You may know the term cleansing, but this just refers to readying data for further analysis (e.g., by changing some formats).
Returning to our restaurant analogy from the start of this section, raw data are like raw vegetables just delivered from the grocery store, but not really scrutinized by the chef. Data quality remains a very big issue for companies. In the 2014 Experian Data Quality survey, 75 percent of responding UK companies said that they had wasted 14 percent of their revenue due to bad data quality.7
● Level 2: Information – data already analyzed to some extent
Simple findings have already been derived. For example, the sensor data has been found to contain five unexpected outliers where vibrations are stronger than allowed in the technical specifications, or an analysis of market shares has shown the ranking of a product's market share in various countries in the form of a table or a pie chart. The key point is that we have some initial findings, but certainly no “so what.”
In the restaurant analogy, the chef might have cut and cooked the vegetables, but they haven't been arranged on the plate yet.
● Level 3: Insight – the “so what” that helps in making value-adding decisions
This is what the decision maker is looking for. In our restaurant analogy, the vegetables have now been served on the plate as part of the full meal, and the patron's brain has signaled this wonderful moment of visual and gustatory enjoyment.
There is definitely some room to improve, as shown in a BusinessIntelligence.com survey sponsored by Domo: only 10 percent of 302 CEOs and CXOs believed that their reports provided a solid foundation for decision making,8 and 85 percent of 600 executives interviewed by the Economist Intelligence Unit (EIU) mentioned that the biggest hurdle of analytics was to derive actionable insights from the data.9
● Level 4: Knowledge – a group of Level 3 insights available to others across time and space
This is the essence of what analytics, and indeed research, aims for: insights have been made reusable over time by multiple people in multiple locations. The decision maker might still decide to ignore the knowledge (not everyone learns from history!), but the insights are available in a format that can be used by others. In the restaurant analogy, our guest was actually a reviewer for a major and popular food blog, magazine, or even the Michelin Guide. The reviewer's description informs others, sharing the experience and helping in decisions about the next evening out.
A core question that I am posing here is how these four levels of data relate to the concept of mind+machine: where does Mind have a unique role, and where can Machine assist? The short answer is that machines are essential at Level 1 and are becoming better at their role there. At Level 2, some success has been achieved with machines creating information out of data automatically. However, Levels 3 and 4 will continue to require the human mind for 99 percent of analytics use cases in the real world for quite some time.
It is interesting to see that companies are experiencing challenges across Levels 1 to 3, with a higher focus on Level 3. A 2013 survey sponsored by Infogroup and YesMail with more than 700 marketers showed that 38 percent were planning to improve data analysis, 31 percent data cleansing, and 28 percent data collection capabilities.
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.
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.
Aaron Kahlow, “Data Driven Marketing, Is 2014 the Year?,” Online Marketing Institute, January 2014, https://www.onlinemarketinginstitute.org/blog/tag/analytics.
