Unknown Answers — Known Questions
In the current era of Data revolution, we are definitely eating more data than we can chew to such an extent that we need to call it enormous data rather than big. The role data is playing or assumed to play in dictating the mandate at corporate analytics level is far more than expected. However, it seems that this big data has not yet offered any effective solutions for solving either business or social problems.
The data revolution made truth a more precious commodity than it was decades ago. Truth was far more accessible, as information used to be far more condensed. In stark contrast, most information today is trivial, and sometimes is designed to influence our thoughts to think a certain way. It has become harder than ever before to find truth. In the blare of information the voice of truth is almost completely drowned out and we have to listen very, very hard to hear it.
The main reason for this might be the data in this information age which is not only dense but complex and more often it takes cajoling to extract sense from it. It may often seem that data is not helping us to get the answers we need or not imparting any knowledge that can be of use. There are two ways in which this argument can be countered. Firstly what are the answers/knowledge we expect from the data and in our endeavor to get those answers are we really having a literate approach.
The second volume of April -2017 “The Economist” edition had a small mentioning of Mind meld where the recent work of Steven Solman and Philip Fernbach regarding Knowledge illusion was discussed . This book titled “Knowledge illusion- why we never think alone” characterizes an apparent paradox of human thinking on how it is powerful although being shallow. The main takeaway from this interesting read is that we are social species and have evolved in the context of collaboration. Wherever and whenever possible, we have outsourced abilities and garnered knowledge through the hive rather than individual absorption. This thought becomes more evident on the current day scenario where we are not skeptical about our own knowledge and wisdom of people around us (Data/social media). This marks the apparent questioning of whether we are actually asking the right question to the data/information around us.
There are answers in that data, if we only ask the right question or it deems to show itself to us somehow, but getting there remains a monumental challenge. We are however, discussing at large on how the data is not solving purpose and imparting knowledge but never are quite getting to the point of solving it. When we are dealing with massive amounts of information and are trying to figure out answers to complex questions, there are no easy paths to get there expect studying the data.
Shifting the focus on how are we approaching the data there is a concrete need of data literacy which involves the two paradoxes –
- The things we want to measure, but don’t know what data to collect.
- The data we want to collect, but don’t know how to capture.
Without Data Literacy, we might end up scenarios where we don’t collect or ignore it, look at it, but don’t apply it or apply it incorrectly and more over we extract the wrong meaning from it or twist it to support our (wrong) ideas.
Data Literacy can help us solve problems, but it’s only one part of the puzzle. Anyone can throw numbers together to make a quick statistic, or compile tons of them into massive spreadsheets, but without any real meaning to be extracted we’re left with numerical gibberish. This brings us to the important element of contextualization, where we need to look at any information/data in a particular context. After figuring out the context we need to look up on the pre-hoc of the data. Considering the elements like who created the data, for what reason, under what conditions, for which purpose, What are the barriers, entry points, and backgrounds that impact their ‘data exhaust’ can help us in drawing meaningful understanding of the conditions in which the data is obtained. This will clear the cloud of doubt or possible misinterpretation of data analysis.
In a recent article about unconventional ways of interpreting data, emphasis was given on narration and its necessity in data analysis for effective knowledge extraction. If we are to move from Big Data / Even Bigger Data to More Meaningful Data from Data Science to Data Literacy we must employ the art, craft, and science of narration. The danger in not doing this leaves the understanding, application, and adoption of data and Data to those who are skilled in the art of collecting, storing, and parsing it.
Effective design/visualization can support the narration in extracting meaning out of data. Design — a wonderful tool for communication and doesn’t require a minimum level of functional literacy to understand it. Data isn’t particularly fun or interesting right at a glance, but it can be, especially with the help of design and visualization. When we combine Data with meaning (story) and intention, we get better, smarter, faster, and more reliably predictive decision making. When we do so with clarity, conscientiousness, and empathy for our audience, we get more attention, more engagement, and less frustration.
If we collect the right data; filter, analyze, and contextualize them intelligently; and narrate and visualize them based on the right set of logic, then data and Data can be transformative in so many aspects of society. What’s the right logic to employ? It’s our very best attempt to establish an elegant concordance between data as logical evidence, data as supporting characters in a story, visualized data as stimuli to engage the audience, and contextualized data to control for bias when possible and to always give perspective.
Hence, in this era of data revolution there is no doubt in saying that reality gap exists and we are failing to find a truer meaning from the data available. If pursued in an appropriate way, this can not only help us in boiling down to the innate truth but can also help us in finding the answers we are looking for.
It’s easy to be overwhelmed by the deluge of data, rapidly evolving technologies, changing rules and roles that are hallmarks of the Information Age. But the trouble is with the avalanche of news directed at us, our wisdom is degenerating into knowledge and our knowledge is degenerating into information. We need to reverse this trend and evaluate our personal communications in terms that favor wisdom over knowledge, knowledge over information, and information over data. If we listen for wisdom, we will hear it; if we are a source of wisdom, we will be heard. Additionally, an important source of wisdom is the “media” in each of us: dreams, reflections, imagination, experience, revelation, reason, insight, emotion, intuitions, and inspiration. While we may draw our data from the outside world, it is our internal processes that give it personal value. The Information Age is a great cacophony; but that should not make us overlook the richest source in any age — ourselves. Finally to end the description of pursuing truth in this information age, I remember a great Sanskrit verse which goes on like
Vinadagu nevvaru ceppina
vininantane vegapadaka vivarimpadagun
gani kalla nijamu telisina
manujude po nitiparudu mahilo Sumati!
This verse says, in this world, we should listen to whatever anyone says but should not jump to conclusions at once. We have to judge what is false and what is true. A man who can distinguish between truth and falsehood is a virtuous person. We should get inputs from whatever sources we can and make our judgments.