Monday, October 18, 2021

g-f(2)579 THE BIG PICTURE OF THE DIGITAL AGE (10/18/2021), HBR, Automating Data Analysis Is a Must for Midsize Businesses

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"g-f" fishing of golden knowledge (GK) of the fabulous treasure of the digital ageDigital Transformation, Automating Data Analysis (10/18/2021)  g-f(2)426 


Automating data analysis as the business grows is a very, very good idea, HBR 

  • Automation is often where programmers write algorithms that perform previously manual tasks as instructed. Doing so pays dividends quickly, drives innovation and more growth, and paves the way to implementing artificial intelligence, which makes just about everything easier and more efficient and cost-effective. AI is coded to learn to perform a task, in some sense inventing and writing its own algorithms.
  • But the data in midsize companies tends to be messy.
  • Only when your data is thoroughly prepared can you start thinking about AI. 
  • Midsize company leaders are right to be excited about the opportunities to harness the value in large datasets. Now is the time to get started on this multiyear journey and commit to hiring the right talent while taking incremental steps to produce value from data automation and other types of advanced analytics. 

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                      Improving operational efficiency is almost always a top priority for midsize companies, HBR

                      • Improving operational efficiency is almost always a top priority for midsize companies. In a Channel Company survey of middle-market IT leaders, 75% of whose firms have $50M to $1B in revenue, 58% of respondents said their top priority was improving operational efficiency. That far exceeded their second priority, increasing new revenue (36%). Both goals can be supported by automating data analytics, as they were at HdL.

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                      Getting Started, HBR 

                      • Midsize companies should begin automating their data analysis processes by focusing on areas where critical operations are either inefficient or too dependent on one person or a handful of people. Before automating, HdL had 15 people spending a significant chunk of their time doing what algorithms are doing today.
                      • Here are three things for leaders to consider when starting to automate data analysis.
                        1. Prioritize cleanup. Data in a midsize business is typically messy and needs a lot of tidying before it can become useful. 
                        2. Hire the right people. Executives are not analysts. They lack the time, patience, and skills to do data analysis as an add-on to their everyday duties. 
                        3. Prepare the data. Only when your data is thoroughly prepared can you start thinking about AI. AI creates its own logic from an analysis of the patterns it discovers in the data. Although AI and machine learning are useful and exciting, both technologies need large datasets upon which to train, with confirmed positive and negative outcomes. After enough data cleansing and a few algorithm-based sweeps, most midsize companies will have a sufficiently large and useful dataset on which to train an AI model.

                      Some relevant characteristics of this "genioux fact"

                      • Category 2: The Big Picture of the Digital Age
                      • [genioux fact deduced or extracted from HBR]
                      • This is a “genioux fact fast solution.”
                      • Tag Opportunities those travelling at high speed on GKPath
                      • Type of essential knowledge of this “genioux fact”: Essential Analyzed Knowledge (EAK).
                      • Type of validity of the "genioux fact". 

                        • Inherited from sources + Supported by the knowledge of one or more experts.


                      “genioux facts”: The online programme on MASTERING “THE BIG PICTURE OF THE DIGITAL AGE”, g-f(2)579, Fernando Machuca, October 18, 2021, Corporation.

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