10 Common Misconceptions and Myths About Data and Analytics

Data and analytics has been subject to a great deal of hype, so how do you know what are myths and what is the “real deal”? Generally speaking, the bigger the hype, the greater any misconceptions are, and it is no exception in data and analytics.

As analytics strategies continue to be prioritized by organizations, here are 10 things in data and analytics that are common misconceptions and myths:

  1. Data and analytics requires large investments

    “How much does it cost” is one of the first questions business and IT managers get asked when they propose launching a new data or analytics project or deploying a new software tool. It is assumed that data and analytic are costly by nature and as such limited to organizations with large budgets or an abundance of internal support. However, not efforts related to data and analytics require such big efforts or a large investment, especially as the availability of open source and many other cost-effective tools can show you the true value of data and analytics.

  1. Big Data is a Must to Perform Analytics

    The concept of analytics and big data often goes hand in hand. This is because organizations usually need to gather large volumes of data before performing analytics to aid decision making and generate key business insights. However, the idea that big data is a must for analytics is a myth. Instead of more data, specific data is often better and required more by analysts.

  2. Analytics Removes the Human Element

    Automated systems and the way they perform are not meant to be biased, but technology is created by humans, therefore taking away all bias is nigh on impossible. There is a common held belief that analytics and machine learning removes any human bias, but this is a myth. Analytics and algorithms are tuned using “training data” ad will reproduce whatever characteristics that training data has.

  1. The Best Algorithm Usually Wins

    Sometimes, with enough data, sometimes the algorithm doesn’t matter. Google engineers think that simple statistical models, along with very large amounts of data, achieves better results than a superior model that contains a lot of summarizations and features. In many cases, crunching a bigger amount of data achieves the best results.

  2. Algorithms are Foolproof

    Statistical models and algorithms are trusted to a high degree, and as organizations build their program of analytics they often rely on highly sophisticated models to support decision making. However, users don’t feel they have the knowledge to challenge these models, so they trust those who have built them.  However, there is still a lot of ground to cover before machine learning and the results can be trusted.

  1. Data and Analytics is a Mysterious “Black Art”

    Data and analytics is a discipline that has received much attention over the last few years and sometimes generates confusion as to what it is exactly. In a nutshell, it involves the use of algorithms to find patterns in data. However, there is no mystery in data science, once the math’s element is understood.

  2. To Undertake Data and Analytics, More Data Scientists are Needed

    Data scientists are very much in demand and there is a common misconception that organizations can get by with fewer of these professionals if what they are working on is redirected in some way. However, a lot of data scientist time is spent on non-value-add activities and these tasks are often the least productive.

  3. Analytics Often Takes A Long Time to Analyze

    Getting things done quickly, whether it is getting a product or service to market quickly or responding to a customer enquiry in real time, is a large competitive consideration for organizations. Analytics sounds like something that will take a long time to perform, which goes against the goal of achieving speed and agility. The myth still exists that these type of projects take too long to complete and are very complex, but they don’t. With the right skills, talent and application of agile methodologies, most questions can be answered in weeks or days, not months.

  1. Technology is the Hardest Part

    With the large number of technological advances available today, selecting the right tools to deploy and integrate to get the right results from an organizations data and analytics team is not easy, and it is important to not assume that analytics tools will do all the work. Technology alone never solves any business problem.

  2. Analytics and Data Should be a Separate Department

    In some organizations the data and analytics teams often operate as a department on their own, and in others they are deeply embedded into a cross-functional team. However, with the access of data across all areas of a business and the speed at which change happens, the department model often does not work. As organizations become more customer-focused, data-driven analytics specialists should be at the center of a business unit, not operate as a separate department.

 

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