Just how much feedback from the data you are generating do you get?

There’s this story about a group of US Air Force generals in World War II who tried to assess ways to protect their bombers (and their crew) flying into mainland Europe on missions by examining the location of bullet holes on returning planes. Mapping the location of these holes, the generals quickly came to the conclusion that the areas with the most holes should be prioritised for additional armour.

Statisticians from Columbia University were engaged to confirm these findings. Instead these outsiders pointed to a flaw in the military groupthink; the areas where the holes were weren’t the most vulnerable, they were the least! The generals couldn’t see the holes that were taking down their bombers, which in the returning aircraft were areas where the holes weren’t. Those needed strengthening instead.

Reviewing contracts by your tender, contracts and legal department and making an assessment of your project cost estimate is a similar process. Without feed back and lessons learned from ongoing and completed projects with respect to claims and unforeseen costs which may have arisen is similarly prone to groupthink and confirmation bias, leading to systemic weaknesses in any tender appraisal. Likewise feedback to the estimating department on things like production of equipment is vital.

Those who live it, breathe it and work at the coal face of a project are often least able to see it as it truly is, warts and all. It therefore helps to have feedback as well as the occasional ‘outsider’s view’ and a checklist of those key items or contract data which make the cornerstone of any review of a contract or specification or reporting on what worked and didn't in respect of the tender estimate.

What we are seeing is the rise of data collection. Big data refers to enormous data sets gathered from numerous sources. These data sets cannot be collected, stored, or processed using any of the existing conventional tools due to their quantity and complexity.

So, there is a variety of tools used to analyze big data. With the help of big data analytics tools, we can gather different types of data from the most versatile sources – business apps, machine log data, etc.

Big Time Big Data Statistics

• The big data analytics market is set to reach?$103 billion by 2023.
• Poor data quality costs the US economy?up to $3.1 trillion yearly.
• In 2020,?every person generated 1.7 megabytes?in just a second.
• Internet users generate about?2.5 quintillion bytes of data each day.
• 95% of businesses?cite the need to manage unstructured data as a problem for their business.
• 97.2% of organizations?are investing in big data and AI.
• Using big data,?Netflix saves $1 billion per year?on customer retention.

Now, why is big data important and what is its relevance to construction? Well because once analysed, this data helps in a multitude of ways. In healthcare, it helps avoid preventable diseases by detecting them in their early stages. It is also immensely useful in the banking sector, where it aids in recognizing illegal activities such as money laundering. In meteorology, it helps study global warming. It is also increasingly being used in construction, so with things like digital twinning and monitoring and reporting on equipment.

Here are some interesting statistics about big data.

Big Data 2021 Statistics

Before Big Data analytics became a fully developed idea, companies were storing tons of info in their?databases, not knowing what to do with them. According to global statistics on?Big Data technologies,?on average,?poor data quality costs businesses?worldwide?anywhere?between $9.7 million and $14.2 million yearly.?For countries like the?US, which operate in a highly data-driven economy, that figure could rise into?trillions.?

Poor data quality?can lead to?poor decision making?or?wrong business strategy.?This will, in turn, bring about?low productivity. That’s why data tools and?data visualization software?are vital for business success in 2021.

95% of businesses cite the need to manage unstructured data as a problem for their business.

(Source: Forbes)?

In a digitally powered economy like ours, only those with the right form of data can successfully navigate the market, make future predictions, and adjust their business to fit market trends. Unfortunately,?most of the data we generate today is unstructured,?which means it comes in?different forms, sizes, and even shapes.?Hence, it is?difficult and costly to manage and analyze, which explains why it is a big problem for most companies.

45% of businesses worldwide are running at least one of their Big Data workloads in the cloud.

(Source: ZD Net)

According to statistics about?Big Data in cloud computing, the cloud is one of the most recent technological trends that is taking the world by storm. It eliminates the need for organizations to purchase and maintain costly computing hardware and develop the software needed for the day-to-day operations.?

Although?the cloud houses?67% of enterprise infrastructure, only a small percentage of businesses are currently utilizing it for data operations.

80-90% of the data we generate today is unstructured.

(Source:?CIO)

Today it would take a person approximately 181 million years to download all the data from the internet.

(Source: Unicorn Insights)

I recently got asked about a graph that I have used in my presentation. I found it on the internet in the research for my book back in 2014. That webpage has now disappeared and the information is lost, the internet is like a shifting sandbank of knowledge. With this graph even the original company cannot find it. Isn't it disappointing that the source data is not stored somewhere for later access?

13. In 2012, only 0.5% of all data was analyzed.

(Source: The Guardian)

The vast quantity of data has no value unless it is tagged or analyzed. So, the question is how much data is that? According to IDC’s Digital Universe Study from 2012, only 0.5% of data is analyzed, while the?percentage of tagged data is a bit higher at 3%. By further researching these data analytics statistics we discovered that not all data has the potential to bring value.

In 2017, the Economist claimed that data replaced oil as the world’s most valuable source. There were many sources that compared data to oil while neglecting one big difference between the two. Unlike oil, data can be easily extracted, and the supplies are endless. What’s more, unlike oil, we can use data multiple times and get new insights from it. The comparison between oil and data leads us to the conclusion that we should collect and store as much data as possible. However, if we only do that, without tagging or analyzing the information we have, its value will be far less significant than that of oil.

According to big data statistics from IDC, in 2012 only 22% of all the data had the potential for analysis. This includes data from different fields . The same source said that by 2020, the percentage of useful data, i.e., the information that has the potential for analysis, would jump to 37%.

Automated analytics are vital to big data.

(Source: Flat World Solutions)

One of the many predictions in the data field is that?automating processes will be inevitable. We can also expect machine learning to develop further in the near future. Combined with data analytics, we expect it to create predictive models to forecast the future with an even higher level of accuracy. Lastly, Flat World Solutions calculated that businesses could gain $430 billion in 2020 if they opt for a data-driven approach.

One of the key takeaways from this topic is that the data market is quickly expanding and with every passing day we have more information. The ultimate goal is not about collecting as much data as possible though, but about getting value from the data we collect and learning lessons.

“Insanity is doing the same thing over and over and expecting different results.”

Albert Einstein

He is also is quoted as saying

Everybody is a genius, but if you judge a fish by its ability to climb a tree, it will live its whole life believing it is stupid.

We need construction to learn from its mistakes and stop being a stupid fish.