Nowadays data analytics is no longer a luxury only large enterprises can afford, but a necessary ingredient in inefficient work of very business. Data analytics tools facilitate data-driven decision making (find out how to become data-driven here) which allows companies all over the world to not only stay in the game but advance ahead of their competitors.
To make the best out of the data you are receiving, it’s crucial to properly distinguish the types of data analytics you can use. Below is the description of the 4 Types of Data Analytics to Improve Decision-Making.
Ready to meet them? 😉
Descriptive analytics for decision-making
Answers: What has happened
Descriptive types of data analytics collect information from single or multiple sources and display them in an easy-to-read format for your convenience. It outlines how many people clicked an ad for your product, how many bought it, what’s the age group of the most active buyers and how much did it all cost you.
Descriptive analytics is the most basic data analytics software type on which all other types build upon and turn to. Descriptive analytics is present in Facebook Manager, Instagram Insights, Twitter Analytics and other platforms that focus on correct displaying of the statistics, without offering any further insights.
You can’t survive without having it but it also can’t be the only type you are using – it will tell you when Michael the horse has stopped working but won’t suggest any tips to why he’s mad at you.
Although when it comes to say, budgets, descriptive analytics allow you to see which ad campaigns are successful and which could be discontinued based on their ROIs, other methods of analytics should be employed for precise decision-making.
Only knowing something doesn’t work is not always enough to put a project to sleep, most of the time you’ll need insight into why and how can you fix it, presented by the next data analytics software categories.
Diagnostic analytics for decision-making
Answers: Why did it happen
The second level of complexity among data analytics type belongs to diagnostic analytics. Diagnostic analytics involves a deep analysis of existing data by transforming all of your descriptive analytics into Business Intelligence dashboards.
After doing so data scientists utilize filters, correlations, comparison tables, data mining, time-serious data reading, and others in order to drill down and discover the cause of a problem.
This involves analyzing a large number of data and is the most time-consuming process among the different types of data analytics. You are not sure what you are looking for, but hoping that assessing dynamics and correlations will give you an idea of why a project or a product, in particular, doesn’t seem to work the way you want it to.
Business driven-decision making is then carried out after you have found out what’s the problem and prepare the necessary steps to fixing it.
Predictive analytics for decision-making
Answers: What will most likely happen
Thanks to its name you’ve already guessed predictive analytics uses a number of data resources and forecasting techniques in order to predict what will happen. In order to decide if a store will benefit from expansion in a certain region data scientists use a company’s historical data, supply and demand rates among the population, predicted market growth for the next half a decade and so on.
The tools of predictive data analytics for business decision making include predictive and statistical modelling, machine and deep learning in order to recognize trends, causations and correlations among events and variables.
Predictive data analytics is considered advanced, and only companies who take their data seriously currently utilize it. It is offered within advanced Business Intelligence systems like Tableau and Power BI and helps companies stay ahead of their competitors by calculating risks and forecasting the following market movements.
Even though predictive data analytics does not presently offer a hundred percent accurate predictions, or even close to it, having an idea of what will most likely happen allows owners to stay at the forefront of data driven-decision making.
Prescriptive analytics for decision-making
Answers: What should be done to make it happen
Coming from the word “prescription”, prescriptive analytics creates offers and recommendations on how to improve certain business elements. The area of their prescriptive analytics improvement includes items you are selling, product categories, business departments and pretty much every area that needs a fix or an upgrade.
Prescriptive analytics works by utilizing information generated within descriptive, diagnostic and predictive types of data analytics. Prescriptive analytics is currently the most advanced data analytics type, as it requires the use of all the previously mentioned kinds combined with advanced algorithms of machine learning, business intelligence and even AI.
Prescriptive analytics allows you to find out which variable will be the most successful – say expending into North-West or not – without actually implementing it and suggests new variables and their combinations that are expected to produce even more effective outcomes.
Prescriptive analytics is extremely important for advanced data driven-decision making, considering you essentially allow the more advanced technology that operates only numbers and facts, bypassing the downsides of human error to make decisions for you. Due to all of its advanced benefits, prescriptive analytics is quite complicated to implement, so only companies that see a hands-on benefit from its application should consider investing in it.
Now that you’ve learned about 4 Types of Data Analytics to Improve Decision-Making, find out more about high-end business intelligence, proper data analytics and effective data visualization in the following visual, reader-friendly articles:
- How to Choose Data Visualization Software?
- Top Ways to Become a Data-Driven Company in 2020
- What is Business Intelligence? BI Definition, Systems & Tools
- Top 20 Metrics to Visualize in Google Data Studio
- A Comparative Analysis of Data Visualization Tools: Google Data Studio, Power BI, and Tableau
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