Why Professions Are Adding Analytics to Their Skillsets

Having data analytics skills is in great demand right now. A data analyst can expect a salary, at entry level, that is significantly higher than the average wage. Data analysts that are more senior can expect to rise to the very highest echelons of the company. While it’s a relatively new field, it won’t be long before CEO candidates often (if not usually) have a data analytics background.

What’s more, it’s a relatively easy field to find work, because there is a severe skill shortage, globally, for people with data analytics skills. For this reason, many professionals, regardless of their current field, are adding data analytics to their skillsets, by taking additional courses and training. It makes them much more employable in the best available jobs.

There are many different forms of data analytics, and these have different applications in business. The form that you should develop expertise in depends on your role and career aspirations, so it’s useful to know the difference between the major types.

1. Descriptive analytics

Descriptive analytics is about interpreting what has already happened. You have data come in, and based on that data you form conclusions and derive insights. So, the data analyst that tracks the attendance at events across the country for one year, and uses that data to decide which cities should host events in the next year, would be an example of a descriptive data analyst at work.

Descriptive analysts are excellent at collecting and presenting data that would allow the organization’s decision-makers to make the best moves for their business.

2. Diagnostic analytics

Where descriptive analytics shows what has happened, diagnostic analytics is more interested in explaining the causes of it. This is the “why” question in motion.

So, for example, if a company fails to meet its sales targets for the quarter, a descriptive analyst would be able to pull out a granular level of detail about where sales were down, what products were underperforming, and a breakdown of the markets that were proving difficult. A diagnostic analyst would go one step further.

They would take the data and explain why a product wasn’t selling well (for example, it was no longer fashionable), or the root causes for why one city was buying less from the company (the marketing offended people in that particular market, leading to boycotts).

The diagnostic analyst is more strategic than the descriptive analyst and provides data that is more immediately actionable by senior staff.

3. Predictive analytics

The previous two forms of analytics looked at the past. Predictive analytics is about “guessing” the future. It’s not a wild guess, however. The predictive analytics specialists will look at historic data, including the diagnostics information, and use the “what happened” and “why” information to suggest what will come next.

Predictive analysts tend to work with massive amounts of data. The more historical data you have access to, the better the predictions that you can draw from it. So, for example, you could look at last year’s sales data to make a very vague guess at what you would expect from sales in the next year. Or, you could take multi-year trends, look at the performance of competitive products, and use that to come up with more complex predictions.

In addition to the work of human analysts, AI programs are often written to perform predictive analytics functions.

4. Prescriptive analytics

With prescriptive analytics, an outcome for the future will be provided as the desired goal, and the analytics will provide the recommendations that will arrive at that solution.

Prescriptive analytics has minimal human input at the point of the analytics itself. Rather, with prescriptive analytics, a human will program the AI and machine learning algorithms that will create the recommendations, and the decision-makers will choose a course of action from the options. The success of this form of analytics depends on the capabilities of the programmer.

5. Cognitive analytics

The “holy grail” of analytics, cognitive analytics is when humans have trained AI so well that the AI can start to process information in the same way that humans do. Examples of this include natural language processing, where an AI can “read” text or inputs using the same language as humans, and can make decisions in unforeseen scenarios that are contextually responsive.

Perhaps the best example of this in action is self-driving cars, which can “read” the situation on the road and take steps to minimize the risk of accidents, regardless of the conditions on the road.

When you’re just starting out in analytics, you’ll likely focus on descriptive analytics, and then move up the proverbial ladder as your skillset becomes more advanced. However, descriptive analytics will give you enough of a grounding in analytics to properly understand, interpret, and drive action based on data, and for this reason, it is going to be an increasingly common part of the executive skillset, for anyone with ambitions to lead companies.