Data Is The New Oil
Data Is The New Oil
What does this mean?
For most companies, it’s like a gusher of crude collecting on the ground, polluting the environment.
If that didn’t make sense, think like a trash collector. Anything a person doesn’t want anymore and finds no value in it is trash, and if the collector of it had no foresight, they would just keep picking it up and throwing it into a big pile. This was half of the 20th century on Staten Island. Trash is coming out of every borough, piling up. Then someone who realized this had no future decided to separate the collection process and organize the collection site into organic, nonorganic, and usable recycling.
Once they did this, they saw a fortune sitting in their hands of items that could be reshipped to companies to reuse base materials, compost biodegradable materials into soil, and biological and organic waste into energy.
Data is going through the same cycle. Data is coming out of everything and everyone as they walk around with their phones, in and out of cars, planes, and buildings or homes. It is a non-stop gusher of raw data that isn’t being refined into usable information.
So how do we refine data?
First we have to create a pipeline from where it is to where it is going to be held. The same for crude oil that is pumped from the ground into holding tanks. With data we are extracting, transforming, and loading into holding tanks known as tables.
If the data doesn’t quite fit the holding tank we have to transform that raw data into the wells of that holding tank we call fields.
Once that pipeline is installed and operating, we will have continuous flow of data that now requires it be made into usable products.
Now that we hold this raw data it still has limited value. It needs to be refined.
The first thing we do is design the repository into facts, lookup tables, and dimensions. This is like refining or distilling your crude into basic forms like motor oil, gasoline, and jet fuel with residues like cosmoline, vaseline, and sadly food.
If you have a restaurant that has an app and you offer great deals for people to use it, perhaps you created the store to read everything when they come in, how long it took in line, the ingredients they chose, the meal, the cashier, method of payment, the stop at the soda fountain, soda, and any topping. Right now it is disorganized and it needs to be separated into its proper bin.
All the components that go into a visit are loaded into tables that refer to codes in the table that records the facts of the visit. Components like the ingredients, customer, employee, food items. We call these dimensions.
The entire encounter is a fact where the time and date is loaded into a bin (table) and has codes to refer to everything that is in a dimension or lookup table.
A lookup is the data we need to identify things in dimensions, like zip codes to find cities and states for example.
Once you have all these elements in the right place, you can now create things like dashboards, reports, analytics, and predictive analytics.
The dashboard can tell you everything that is going on at this moment. Not really useful unless you have to make plans to call in more employees or buy more meat, but if you were a delivery company with a hundred trucks on the road, you would know immediately if there was a ‘road block’ event that would require you to send out another filled or empty truck to a location.
A report is as simple as how many customers and served meals I had yesterday or any other date or span of them.
Analytics can be things like production of waste but valuable products like used oil, composts, or food waste. My company uses calculations against the data to understand why one day was more profitable than another or why there was so little purchase on a day where everyone should have been buying away and many other situations the business needs.
Predictive analytics allow you to know, within a reasonable tolerance, what should be loaded, delivered, sold, and returned, every day. Those calculations are worth their weight in gold because you will profit the most while spending the least in inventory and expenses. The other side is you will know when to buy as much of something because you ran out of it and profited no more than what was in inventory.
Hopefully I have described
Key Considerations:
Data Quality: Ensuring data accuracy, completeness, and consistency is crucial for reliable insights.
Data Security and Privacy: Protecting sensitive data from unauthorized access and misuse is paramount.
Data Governance: Establishing clear policies and procedures for data collection, use, and sharing.
Ethical Considerations: Using data responsibly and ethically, respecting individual privacy and avoiding bias.
In essence, “refining” data involves a multi-step process that transforms raw information into actionable knowledge and valuable insights. It requires a combination of technical expertise, analytical skills, and a deep understanding of the business domain.
Essentially, “refining” data involves a multi-step process that transforms raw information into actionable knowledge and valuable insights. It requires combining technical expertise, analytical skills, and a deep understanding of your business. There is no room here for general business questions. You must know what you are looking for. The costs and the time to set this up are significant.
This correlates perfectly with the term, Data Is The New Oil.
Does this type of refining interest you?
Contact William J Ritchotte II at 603–860–3331.
Main profile Linked In — https://www.linkedin.com/in/williamritchotte/
NH Advanced Database Technology — https://www.linkedin.com/company/nh-advanced-database-technology
