Thanks to massive gains in technology and computing power over the past few decades, almost every business function in all modern industries now has access to vast amounts of data. Information about how consumers and businesses use content and software to make purchasing and other decisions is at everyone’s fingertips.
In theory, executives have everything they need to make smart choices across every function. Having actual data in hand means less guesswork, less time being wrong, and more time to optimize every aspect of the business.
In practice, however, the amount of information available is overwhelming. The raw data is too unmanageable to sift through, much less understand. The formats used for gathering all this data are not user-friendly. The message the data has isn’t always clear, and it’s not presented in a way that managers can easily take action on it.
That’s where data science (and data scientists) come in. This is a very recent development in the technology world, so don’t worry if you’re not very familiar with it. The concept didn’t even arise until 2008, when it became obvious that experts were now necessary to translate the information to actionable items.
Data science first became popular in the IT world. Seeing how AI (artificial intelligence) techniques and machine learning automated many processes and made the data easier to understand prompted other organizations to incorporate data science into their decision making.
The primary driver behind all data science tools is the need to unravel the data into a form firms can use to take real-world actions. They combine machine learning, data analysis, statistics, AI and similar technologies to help companies leverage the massive data sets to their advantage.
Companies still use surveys and interviews either on or offline to find out what users are thinking about their products and services and how they measure up. However, there are now plenty of other places to gather this information: review sites, social media comments or posts, blogs, etc. The tools analyze sentiments as well as online text.
Massive amounts of data can’t (yet) be stored on a USB, so they’re commonly on multiple servers. Data storage platforms offer an easy way for users to assemble the information that’s kept on multiple servers in order to extract and analyze the universe of intelligence.
They trawl websites and “scrape” the relevant data to be extracted and analyzed. With a few keywords and machine learning to understand strings of relevant content, data scientists can easily find the needles inside the haystacks online.
Trying to separate signal from noise in a dataset is very time-consuming. These tools automatically remove the noise so that information is easier to understand and analyze.
Even with cleaned and refined data, you’re still looking at a pretty massive amount of information. Data analysis technology uses data modelling techniques to help pull out relevant and meaningful information.
The old saying is that a picture’s worth a thousand words. But these days it’s worth a thousand data points. Instead of manually selecting the data and creating a graph as spreadsheets require, technology makes the information come alive through displaying it in the right way.
Data scientists are the professionals in large organizations who manipulate these tools for clearer decision-making. They need a variety of skills, not just the ability to program. In addition to understanding how to gather the data efficiently and be capable of mining it for insights, they must be able to communicate with other decision-makers who aren’t as tech-savvy.
Domain-specific knowledge in their particular industry is also key, because data scientists need to understand what information is necessary and what types of analysis might be helpful. The ability to visualize the data and help others do so is critical. Management must understand how to take action on the data scientist’s findings.
Industries where data science is currently in great demand include banking and finance, manufacturing, transportation, healthcare, and ecommerce.
It’s allowed the financial sector to automate risk analysis and trading. They’re also able to create predictive models for customer lifetime value and how clients will make their moves in the market.
Manufacturing companies use data science to minimize energy costs and manage hours worked, reducing costs and increasing profits. Self-driving cars? Thanks, data science!
It’s also being used to great benefit in healthcare across a variety of disciplines including medical image analysis, genetics, drugs, diagnostics, and helping consumers with virtual assistants.
Ecommerce targets users based on previous behavior (which is why you see ads on social media for whatever you were just looking at). They also use data science to predict trends and optimize their pricing.
Data scientists, with all their expert knowledge and training, can be expensive and not all companies have a need for a full-time employee. Some businesses may only find a periodic use for data science, and others may not have the budget or the resources for a full-time hire.
The Causal app provides data analysis and visualization firms need in a less expensive framework. Causal makes it easier for companies to build data models from automated data collection, and visualize it in such a way that they take action on it.
With Causal, you don’t need to know programming languages or even be particularly tech-savvy. Causal replaces spreadsheets so your employees are able to focus on their work. No more wasting time making sure that the data is reliable, or trying to turn your columns of numbers into a graphic that stakeholders can understand.
Marketing agencies, finance functions in any type of business, and management consultants have all used the Causal app to build scenarios in real time using actual numbers. Ecommerce, VCs and early-stage SAAS companies easily build their models and forecasts with hard data. Whatever industry or type of company, Causal can help you stress-test your scenarios with Monte Carlo simulations. Then visualize the results to make evidence-based decisions.