Data analytics gives you valuable insights into your business and clients. The challenge is interpreting the data and knowing how to effectively apply it to your business-related operations.
In this article, you will find out about the differences and similarities between types of data analytics, how to implement an efficient data analysis, and how to use historical data for your benefit.
The four types of analytics processes
The main four types of data analytics processes in business are descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics.
Descriptive analytics
Descriptive analytics gives you insights into the events of the past. It focuses on analyzing historical data in order to identify and determine certain patterns.
Descriptive analytics is the main foundation on which all additional improvements happen, namely diagnostic analytics. Descriptive analytics fall into two further sub-categories: operational intelligence and business intelligence.
Operational intelligence refers to a real-time analysis of live data. It's an actionable approach where you can act right at the moment when the information appears.
On the other hand, business intelligence is a passive approach. You look at historical data and gather information based on past actions.
Diagnostic analytics
Diagnostic analytics is the second stage of descriptive analytics. It analyzes why those certain things happened. It takes the insights from descriptive analysis and tries to find the reason why that particular outcome took place.
Diagnostic analytics allows for more critical insights and determining certain behavioral patterns.
Predictive analytics
Predictive analytics provides you with statistical models for the future derived from historical data. It's about the anticipation of what is most likely to happen going forward. It uses data mining and current and historical data to predict future outcomes.
For instance, predictive maintenance is one valid example of predictive analysis.
Predictive maintenance takes the data of a technology that is used within your company and makes computations that estimate the life span of its critical parts. With such estimation in place, you can make an accurate prediction about its required maintenance time. As a result, the malfunction will not catch you off-guard and cost you valuable time and resources, or it won't happen at all, because you will have taken care of it beforehand.
In general, businesses use predictive analytics to determine certain patterns that could help determine risks and opportunities.
Prescriptive analytics
Prescriptive analytics recommends actions you can take to affect the potential outcomes determined by predictive analytics. If you predict that a certain possible outcome is not good for you, you can try and change its course by analyzing what course of action you can take to make it better.
Prescriptive analysis provides you with particular options and identifies the best possible solutions in terms of selected criteria. With this type of machine learning advanced analytics, you are able to build a future model of your business and scrutinize it to perfection. When you have the model in place, you can adjust your business strategies to try and achieve that polished business model.
In terms of the maintenance example, predictive analytics shows when maintenance is most likely to be required. Prescriptive analytics, on the other hand, provides you with a deep insight into those maintenance-related issues. Using machine learning, it analyzes data about the technology on a deeper level and comes up with specified suggestions to minimize the risks as much as possible.
Predictive vs prescriptive analytics
The two terms are similar and they do share some common ground, but they also have a set of key differences.
Similarities
Both predictive and prescriptive analytics are imperative to a successful data strategy. A sophisticated data analytics strategy can help small businesses get a much-needed headstart.
From a more technical side, predictive and prescriptive analytics both refer to historical data to predict what happens in the future.
Differences
Predictive analytics provides you with a foundation of raw data that can in turn be analyzed in more detail by using prescriptive analytics. In other words, predictive provides the big data, whereas prescriptive does the heavy lifting and analyzes it.
Prescriptive analytics checks the outcomes provided by predictive analytics and finds even more data-driven options that could be considered.
Comparison
Predictive analytics is on the lower level in the hierarchy of analytics.
Predictive modeling only covers specific aspects of a business, whereas prescriptive analytics has the capacity to cover the entire business.
Predictive analytics only anticipates what might happen and when, but prescriptive analytics provides you with a number of options and solutions for how to approach those changes.
Predictive analytics have a tendency to optimize a single function at the expense of others; meanwhile, prescriptive analytics accounts for all of them.
In other words, both types of analytics are required for the whole system to work and predict what will happen in the future. However, the predictive model is the less efficient of the two analytics solutions.
Both predictive and prescriptive analytics are imperative to a successful data strategy. A sophisticated data analytics strategy can help small businesses get a much-needed headstart.
The importance of analytics in business and finance
Business
Analytics helps businesses make better-informed decisions regarding customers, personalized marketing campaigns, product development operations, and much more.
Enhancing existing information
Customer data, or CRM, is extremely important if you want to continue having them as customers. However, sometimes internal data is not enough.
In that case, you need to resort to certain solutions. One of which might be opting for a public web dataset.
Public web datasets provide you with loads of data that could improve your business efficiency by a lot. However, it is important to know how to manage and use the raw data. You can choose a dataset that fits your needs the best.
Coresignal offers a variety of datasets, such as firmographics, technographics, employee, and job postings data, among others. You can analyze the data to gain a competitive edge against your competition. Enriching your database with fresh and relevant data enhances your data-driven operations and overall success.
By having more information, you can make data-driven business decisions and distribute your resources more efficiently.
Personalized marketing
For instance, your marketing and sales teams can come up with a personalized marketing campaign to approach a specific target audience and offer them a product that they definitely need. As opposed to generic sales pitches, 80% of people are more interested in doing business with a company that offers personalized marketing.
Improved products
It could also boost your product development operations. You can access online review data on a similar product and see what people think about it. Perhaps there are some negative sides that could be fixed or a good side that could be improved even further.
Online reviews are great sources of information for product development since you get direct suggestions from the people who use it.
Finance
As far as financial endeavors are concerned, predictive and prescriptive analytics help analyze existing raw material and anticipate business outcomes.
As mentioned before, the predictive analysis provides the business with actionable information that can later be examined further with prescriptive analytics for adjusting business operations appropriately.
As a result, you gain the advantage of foreseeing the potential future outcome before it happens. It allows for more timely decision-making.
Identify risks
Companies can also use predictive analytics to better identify potential financial risks and deal with them accordingly before any serious damage occurs to a company's performance.
Risks are always a threat to financial performance.
Being able to predict and identify the financial risks can be the definitive moment in the history of your business.
Fail-safe predictions
Prescriptive analytics is often referred to as a GPS for businesses; you can see where you are now, where you want to go, and find the most optimal way to get there.
You can employ prescriptive analysis to make predictions about how a certain action would affect the performance in the future. The more important thing is that you can achieve that without the risk of doing the action until you're satisfied with the odds of success.
That is also known as deterministic modeling.
Unlike statistical modeling, deterministic modeling techniques allow you to make accurate computations to determine exactly what a future event looks like. It provides finance officers with actionable insights and drives the best course of action for overall operations.
Prescriptive analytics is often referred to as a GPS for businesses; you can see where you are now, where you want to go, and find the most optimal way to get there.
To wrap up
Both predictive and prescriptive analytics support decision-making on a foundational level. You can take the raw material and implement advanced analytics to figure out what could bring you the most value in the future. Prescriptive solutions allow you to identify patterns or a lack thereof and adjust your strategies to avoid unwanted outcomes.
Business executives make use of machine learning predictive and prescriptive analytics to improve data discovery, satisfy business users, enhance financial services, lower operating costs, and improve decision-making to fuel company growth.
