{"id":1073,"date":"2019-01-14T05:10:45","date_gmt":"2019-01-14T05:10:45","guid":{"rendered":"http:\/\/kusuaks7\/?p=678"},"modified":"2023-07-13T07:37:39","modified_gmt":"2023-07-13T07:37:39","slug":"insight-is-dead","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/bigdata-cloud\/insight-is-dead\/","title":{"rendered":"Insight is Dead"},"content":{"rendered":"

Ready to learn Data Analytics? Browse Data Analyst Training and Certification courses<\/a> developed by industry thought leaders and Experfy in Harvard Innovation Lab.<\/em><\/strong><\/p>\n

\u201cInsight\u201d has become the ubiquitous catalyst for many business decisions.\u00a0We analyze data to gain insights and use those insights to guide decisions, large and small.\u00a0It is better than \u201cinstinct\u201d or \u201cintuition\u201d as a decision enabler, but is it the best we can do?\u00a0Or is it an actual impediment to effective Digital Transformation?<\/p>\n

Think about it.\u00a0Big Data and advanced analytics are digital, as are business applications and systems, but the decisions in the middle of it all are analog \u2013 humans interpreting information and telling machines what to do about it.\u00a0Digital Transformation has made great strides at the operational level, but it won\u2019t happen at the management level without digital decision-making.<\/p>\n

Digital decisions require artificial intelligence; and AI neither needs nor produces insight.\u00a0AI analyzes the data and makes the decision.\u00a0The days of business insight are numbered.\u00a0Let\u2019s see how that could be.<\/p>\n

In their article\u00a0Unleashing Hidden Insights<\/em>, published on the American Marketing Association website, authors Marco Vriens and Rogier Verhulst provide a serviceable definition of “Business Insight”.<\/p>\n

“A thought, fact, combination of facts, data and\/or analysis of data that induces meaning and furthers understanding of a situation or issue that has the potential of benefiting the business or re-directing the thinking about that situation or issue which then in turn has the potential of benefiting the business.”<\/p>\n

For two decades, such business insights have been gained through spreadsheets, dashboards, and other analytical tools, under the rubric of \u201cBusiness Intelligence\u201d (BI).\u00a0For the most part, the analytical computations underlying BI tools\/platforms have been based on statistical methods which have been in use for many decades.<\/p>\n

Now, here comes Artificial Intelligence (AI), a newer form of advanced analytics that uses machine learning algorithms, rather than statistical formulas, to process data.\u00a0(You can learn more about Statistics vs Machine Learning\u00a0here<\/a>.)\u00a0And the primary goal of AI is not to enable human insight, but rather it is to enable digital decisions.<\/p>\n

BI and AI have orthogonal missions, bound only by the murky shared heading of \u201cadvanced analytics\u201d, but nonetheless many BI software vendors are now grafting machine learning into their offerings and talking a lot about things like \u201cAI-driven insights\u201d.\u00a0Why is this unnatural mashup happening?<\/p>\n

Let\u2019s call it \u201cBI-agra Syndrome\u201d.\u00a0AI promises to turn BI professionals into sexy data scientists and BI products into sexy solutions to all your insight needs.<\/p><\/blockquote>\n

In this Google Trends chart, which line is the sexy one?<\/p>\n

\n

Holding aside the influence of SEO pixie dust, though, there is a deeper trend at work here, one that may herald the coming demise of human management decision-making and the business insight that enables it.<\/p>\n

Despite the empirical veneer of dashboards and spreadsheets, many business processes still depend on heuristic choices made by human experts, as they have for centuries.\u00a0The data may be digital now, but the decisions are still very analog.<\/p>\n

This is a status quo with many defenders, mostly in middle management, where most of those analog decisions are made.\u00a0Much of the \u201cAI Eats Jobs<\/a>\u201d press concentrates on task workers like service agents and mechanics, or on knowledge workers like lawyers and accountants, and ignores middle management \u2013 the \u201cdecision workers\u201d.\u00a0But a\u00a0recent executive survey<\/a>\u00a0by Marketforce said this.<\/p>\n

“According to our research, 72 percent [of senior executives] think the increasing use of AI and robotics will dramatically reduce the number of middle managers in most organisations over the next decade. While this is bad news for middle management, it\u2019s good news for those below them: almost eight out of ten (78 percent) believe support from artificial intelligence will allow workers to make informed decisions at a more junior level, leading to a flattening of traditional hierarchies.”<\/p>\n

It doesn\u2019t say how those junior workers will make those decisions, but you can bet it won\u2019t be by gaining business insight from BI tools; it will be by taking guidance from AI applications powered by predictive analytics, as suggested by this Google Trends chart.\u00a0Look familiar?<\/p>\n

\n

Digital decisions require foresight, not insight.\u00a0In the current golden age of BI (pick your \u2018I\u2019), it seems like a great innovation with an unlimited future of dazzling data visualization and discovery, the Big Data Rosetta Stone.\u00a0But maybe it is just the BI Bubble.\u00a0BI has been around in some form for as long as there have been computers – mainframe decision support systems, PC spreadsheets, web GUIs with data lakes, and cloud Big Data services \u2013 always doing the same thing: statistical data analysis for human (analog) decision making.\u00a0But a long history doesn\u2019t mean a long future.<\/p>\n

Bad human decisions are one of the greatest, most persistent causes of expense, risk, and disruption in business today.\u00a0Better BI can help people make better decisions, but human decision capacity and accuracy are ultimately limited, and the BI tools may already be providing more insight than humans can assimilate.<\/p>\n

By comparison, AI programs make far fewer, and generally much smaller decision mistakes than humans.\u00a0In fact, with enough good data, they almost never make decision mistakes.\u00a0\u00a0Plus, AI decision capacity is virtually unlimited, and all it needs to make more accurate decisions is more, better data.<\/p>\n

AI-infused BI tools may enable better decisions, but they are still analog, insight-driven decisions, not prediction-driven digital ones.\u00a0That is refinement, not transformation.<\/p>\n

Effective Digital Transformation starts at the middle, with AI-driven digital decisions replacing analog insight-driven ones across the enterprise – in Finance, Marketing, Planning, Human Resources, and other places where key processes currently depend on human perception and action.\u00a0Although it may reduce their numbers, this does not remove humans from the process, but rather it transforms them too, turning analog decision makers into digital decision managers.<\/p>\n","protected":false},"excerpt":{"rendered":"

Ready to learn Data Analytics? Browse Data Analyst Training and Certification courses developed by industry thought leaders and Experfy in Harvard Innovation Lab. \u201cInsight\u201d has become the ubiquitous catalyst for many business decisions.\u00a0We analyze data to gain insights and use those insights to guide decisions, large and small.\u00a0It is better than \u201cinstinct\u201d or \u201cintuition\u201d as<\/p>\n","protected":false},"author":438,"featured_media":3908,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[187],"tags":[95],"ppma_author":[2337],"class_list":["post-1073","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-bigdata-cloud","tag-big-data-amp-technology"],"authors":[{"term_id":2337,"user_id":438,"is_guest":0,"slug":"tim-negris","display_name":"Tim Negris","avatar_url":"https:\/\/secure.gravatar.com\/avatar\/?s=96&d=mm&r=g","user_url":"","last_name":"Negris","first_name":"Tim","job_title":"","description":"Tim Negris is SVP of Marketing and Business Development at Boston-based Rulex Inc.<\/a>, a new kind of AI platform, born from advanced government and academic machine learning research."}],"_links":{"self":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/1073","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/users\/438"}],"replies":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/comments?post=1073"}],"version-history":[{"count":3,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/1073\/revisions"}],"predecessor-version":[{"id":29166,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/1073\/revisions\/29166"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media\/3908"}],"wp:attachment":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media?parent=1073"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/categories?post=1073"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/tags?post=1073"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/ppma_author?post=1073"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}