Making Sense and Money from your Data
Business owners are well aware of the advantages of data but may feel overwhelmed by the amount of technical terminology involved. The wild variety of offerings and costs presented by tech providers and the decision on what to go after ensuring a sensible path and a valid return on investment.
At MCD we help businesses grow and thrive through the adoption of digital technology. One of the areas we see consistent value for our customers is through data collection and analysis to help them operate more intelligently. This is an area that is far more achievable than many think but requires humans, time and knowledge to make it a success.
noun [ U, + sing/pl verb ]
Information, especially facts or numbers, is collected to be examined, considered, and used to help decision-making.
The data pyramid
The data pyramid often shows the functional relationships between data, information, knowledge, and wisdom. It starts with raw data, which means very little in its form. However, give that data some structure and contextualisation and condense it, and then it can become meaningful information. It becomes knowledge if it is compared to know-how, experience, insight and understanding.
You might know that your factory can develop 500 products a month, it sounds like a significant number, but if you don’t know you did 550 the previous month and 600 the month before that, then you don’t have the knowledge that production is slowing down for one reason or another.
Wisdom or awareness can provide a solid common sense judgement, direct a company’s strategy and grow in competitive marketplaces. Jeff Bezos used data to help him make his decision to build Amazon.
History of data collection
Scientist believe that data collection has been around for around 20,000 years. In 1960 the Ishango bones were discovered in what is now Uganda, these bones date back to 18,000bc. They show tally marks, a kind of prehistoric data storage to keep track of supplies and trading activities. The abacus, the first dedicated device constructed specifically for performing calculations, was used in Babylon in 2400bc. The first libraries also appeared around this time, representing our first attempts at mass data storage.
Only 350+ years ago in 1663, In London. John Graunt carried out the first recorded experiment in statistical data analysis. By recording further information about mortality, he had theories that he could design an early warning system for the bubonic plague ravaging Europe. He took the bill of mortality and introduced details such as a persons age. He is credited with producing the first life table, giving probabilities of survival to each age. This census method provided a framework for modern demography.
Fritz Pfleumer, a German-Austrian engineer, invents a method of storing information magnetically on tape. The principles he developed are still in use today, with the vast majority of digital data being stored magnetically on computer hard disks.
IBM mathematician Edgar F Codd presents his framework for a “relational database” in 1970. The model provides the framework that many modern data services use today to store information in a hierarchical format; before this, it needed an expert.
In 1991 Computer scientist Tim Berners-Lee announced the birth of what would become the World Wide Web as we know it today. Google arrived in 1997 – and for the next 20 years (at least) its name will become shorthand for searching the internet for data.
The internet of Things
Cloud computing has been an enabler for IoT, which transmits data for analysis online, and we’re now looking at tens of billions of new connected devices accessing the Internet in 2020. Connected devices know no bounds regarding locations or applications, so sensors can appear on Mountain tops, in Deep Sea, Space and other Inhospitable conditions.
Familiar consumer IoT devices
Iot devices are plentiful; a lot exist in our homes, pockets, and wrists. We can now heat our homes remotely, order goods at the touch of a button, listen to and watch an endless library of media, track our movement and whole bunch of other cool things. This is all possible because they are connected and use API’s to drive the connectivity of devices to data stores.
APIs allow you to connect devices over a network and collect data from those devices, pass them through a security gate to access cloud hosted software, manipulate the software and return it.
Why collect data?
It helps predict things happening
Such as weather. The UK government have just funded a £1.2billion supercomputer for the MET office to better predict the weather. This is 6 x better than they currently have. and will give a better understanding of global warming and storm prediction.
It shows trends
statistics can help us tackle and contain problems. Police use data and statistics to help police problem areas or tackle issues before they get out of hand.
Machines can learn from data
MIT recently developed an antibiotic using a machine learning algorithm. In laboratory tests, the drug killed many of the world’s most problematic disease causing bacteria. This included some strains that are resistant to all known antibiotics.
Increases productivity & Operational efficiency
Data can be used to help businesses run smarter. Using predictive analysis, automated processes, and other smarter ways of working can be beneficial to businesses.
Data about environments can mean life or death situations. With sensors collecting data on temperature, gases and movement, area can be monitored safely, ensuring danger to humans is alerted before it’s too late.
The final message that I’d like to leave you with today is to think Big and start small. It’s worth mentioning that building all-encompassing systems for smart factories is often complicated and generally involves long lead-times. However, understanding the business goals and aligning the strategic vision with technology will pave the road to success.