K
Kathleen Martin
Guest
When we think of operating costs for medium and large businesses, we immediately think of heavy-expense line items like payroll, commercial real estate, vendors and suppliers. Over the past decade, however, another form of (costly) expense is on the rise — data management. A recent study by McKinsey found that "a midsize institution with $5 billion of operating costs, spends more than $250 million on data across third-party data sourcing, architecture, governance and consumption." In fact, the annual cost of data consumption alone (report generation, business and marketing intelligence, data analysis, distribution) for a midsize organization can be as high as $90 million.
Why the massive investment? Because inaccurate data loses companies money.
Back in 2016, IBM had already estimated (via Harvard Business Review) that "the yearly cost of poor-quality data, in the U.S. alone" reached an astonishing $3.1 trillion, and in 2018, Gartner, Inc. found that "organizations believe poor data quality to be responsible for an average of $15 million per year in losses." Now, with increased reliance on data, we can only imagine the macro impact that bad data is having. With all this in mind, organizations are looking to invest more in good data management to drive good business decisions. This trend becomes apparent when looking at the rapid growth of the data management market, which is projected to increase by nearly 60% from 2020 to 2025, from $78 billion to $123 billion.
The rise of bots, fake users and invalid traffic is jeopardizing that investment.
It's no secret that a great portion of today's web traffic is driven by crawlers, scrapers, automation tools, fake accounts, proxy users, malicious botnets, hackers, fraudsters and click farms. In 2017, The Atlantic cited an Imperva report claiming that bots accounted for 52% of all internet traffic, while some estimates go even higher than that.
For a truly data-driven organization, this poses a strategic threat. If a large percentage of all site visits, ad clicks, form fills, sign-ups, chat requests and page engagements are coming from bots and fake users, then the data that BI and marketing intelligence teams are looking at is completely skewed. Just this past Black Friday, we found that over one-third of all online shoppers were fake. Think what that means if you're running analytics for a large e-commerce site and you're not aware that one in every three site visitors isn't real.
Cotninue reading: https://www.forbes.com/sites/forbestechcouncil/2022/01/24/the-great-contamination-how-bots-and-fake-users-can-skew-an-organizations-data-and-analytics/?sh=a09fdbb2572d
Why the massive investment? Because inaccurate data loses companies money.
Back in 2016, IBM had already estimated (via Harvard Business Review) that "the yearly cost of poor-quality data, in the U.S. alone" reached an astonishing $3.1 trillion, and in 2018, Gartner, Inc. found that "organizations believe poor data quality to be responsible for an average of $15 million per year in losses." Now, with increased reliance on data, we can only imagine the macro impact that bad data is having. With all this in mind, organizations are looking to invest more in good data management to drive good business decisions. This trend becomes apparent when looking at the rapid growth of the data management market, which is projected to increase by nearly 60% from 2020 to 2025, from $78 billion to $123 billion.
The rise of bots, fake users and invalid traffic is jeopardizing that investment.
It's no secret that a great portion of today's web traffic is driven by crawlers, scrapers, automation tools, fake accounts, proxy users, malicious botnets, hackers, fraudsters and click farms. In 2017, The Atlantic cited an Imperva report claiming that bots accounted for 52% of all internet traffic, while some estimates go even higher than that.
For a truly data-driven organization, this poses a strategic threat. If a large percentage of all site visits, ad clicks, form fills, sign-ups, chat requests and page engagements are coming from bots and fake users, then the data that BI and marketing intelligence teams are looking at is completely skewed. Just this past Black Friday, we found that over one-third of all online shoppers were fake. Think what that means if you're running analytics for a large e-commerce site and you're not aware that one in every three site visitors isn't real.
Cotninue reading: https://www.forbes.com/sites/forbestechcouncil/2022/01/24/the-great-contamination-how-bots-and-fake-users-can-skew-an-organizations-data-and-analytics/?sh=a09fdbb2572d
