ChristianSteven BI Blog

For many organizations, the question that keeps their executives up at night is deciding on their IT spending strategy for the next two years. Recent reports indicate that Microsoft is reducing staff in areas such as consulting and partner solutions while adding staff in other -- more technical -- areas. At the same time, Gartner suggests that a tight labor market, rising inflation, and a possible recession have more companies looking to outsource IT to stay within budget. Is there a right strategy?

The best strategy is a data-driven one. Management must look at technology trends to decide what technologies to implement. They have to evaluate the cost and determine what resources are needed to achieve a successful deployment. Without data, executives are only guessing at what the impact of their IT spending strategy will be.

Did you know that our brains are biased towards the negative? That negative orientation is probably why we are more familiar with a vicious cycle than a virtuous one. Yet, a virtuous circle also known as the snowball effect is a recurring cycle of events that reinforces itself through feedback resulting in continuous improvement. It is the process that underpins Amazon's Flywheel. 

In 2001, Jeff Bezos sketched what he called the virtuous cycle on a napkin. Data is collected during each customer experience. That data is used to refine the customer profile. The more a consumer uses Amazon, the more data is assembled ultimately creating a personalized experience for millions of users. Each iteration moves a potential buyer through the sales funnel until Amazon is recommending products customers didn't know they needed. 

Amazon's feedback loop allowed the company to inform its product design. As the company interjected data earlier in its customer experience design, the better the end results and at a lower cost to the company. Without data, Amazon would not realize its virtuous cycle.

More organizations are moving to distributed architectures for their computer networks. The traditional monolithic systems do not provide the flexibility, resiliency, and scalability that distributed computing offers. With the growing number of users, devices, and data that reside outside the security perimeter, organizations have to re-evaluate how to secure the edges of their networks without restricting access to critical data for Power BI Reporting.

And why you need to know

Literacy usually refers to the ability to read and write; however, literacy is really competence or knowledge in a specific area, such as data literacy. To be data literate, individuals need a skill set that includes reading, manipulating, communicating, and producing data for critical use. These are skills that 82% of all employers expect their employees to have. Yet, few educational systems spend time teaching data skills across the curriculum. As a result, less than 30% of the US workforce feels data literate.

While employers expect data-literate employees, 68% of business executives struggle to create measurable value from data. Almost 75% of executives said their data analytic projects failed to produce actionable insights. The lack of data literacy has a significant impact on productivity. According to a recent study, companies lose an average of 43 hours of work per employee per year because of data-induced procrastination and absenteeism.

What is even more concerning is how few employees rely on data when making decisions. Almost half of employees trust their "gut" over data when making a decision. More than 65% of C-suite executives and senior management go with their gut over data-driven insights. Such weak data literacy skills are impeding business growth. For example, 36% of employees avoid using data and find an alternative method to complete a task. Almost 15% of workers would avoid doing the data-driven task completely.

Gavin Wood coined the phrase Web3 in 2014 to refer to the next iteration of the internet. In his view, decentralized data secured with blockchain technology would lessen big tech's control over the data generated on the internet. Why should Facebook and Google make millions off consumer data? Shouldn't people have more say in how their data is being used?

Although Wood's vision of democratized data is open to debate, the technology that underlies Web3 is not. Organizations are already looking to decentralize their data as part of edge computing initiatives. Many are implementing artificial intelligence tools to help assemble and analyze data faster and more accurately. The following technologies are what will fuel the move to Web3.