Is your decision-making more about your gut-feeling than an analyzed and informed thought? Maybe you get the results you desire, but not as many times as you would like.
In such a case, you might want to check whether you are using the right type of data analytics. So, if you are wondering how many types of data analytics are there?
There are 4 different types of analytics: Descriptive, Diagnostic, Predictive, and Prescriptive analytics, through which you can eradicate flaws and promote informed decisions. By implementing these methods, decision-making becomes much more efficient. However, the right combination of analytics is essential.
Analytical methods, or analytics for short, change the game for the better. Each type has its reasoning and calculated consequences, so you are rarely caught off-guard. A sorted process backs it up, which deals with analyzing data at each of its stages.
So far, we have come up with four broad categories, viz. descriptive, diagnostic, predictive, and prescriptive analytical methods.
Each one is used in a particular scenario and helps you comprehend where the company is at. Accordingly, it leads you to an insightful solution. Before getting into the intricate details of every analytical method, defining them briefly would be ideal for better understanding.
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The Four Types of Analytics DEFINED
Firstly, we have descriptive analytics, under which you do the required bare minimum of sorting and categorizing. It includes summarizing your data through business intelligence tools. The purpose is to get clarity on a particular event.
Next up, we have diagnostic analytics. As the words suggest, it focuses on diagnosing the event. You consider the past performances to understand and track why the current event has taken place. By the end of this process, you will have yourself an analytical dashboard.
Meanwhile, predictive analytics focuses on making predictions of what the possible outcomes could be.
However, these are not baseless predictions and depend on machine learning techniques. You might have come across statistical models, which will also come in handy.
Lastly, prescriptive analytics emphasizes recommending or prescribing multiple courses of action. That, of course, happens based on how the data has been analyzed.
The concepts might sound very vague, confusing, or overlapping at the moment. However, by the time you are through to the end, clarity will present itself.
Exploring the four types of Analytics
As you saw before, you will have to refer to four of the above-mentioned analytical methods for promising results. Were you able to derive from the basic definitions that there are a structure and hierarchy in which they fit?
With the descriptive method of analysis, we perform the most straightforward procedure. With every step, the complexity of the technique rises. So naturally, you will find that the prescriptive method is the most sophisticated of them all.
Alternatively, you could also look at it based on the value that it brings in its results. Now that you have a basic outline in your mind let us explore each method further.
Descriptive Analysis: answering what happened
The simplest way of understanding the functioning of descriptive analytics is the answer to the question of what. For better clarity, take a look at this example.
Through the data available on the income and manufacturing, one can derive what happened in each product category. Break your data down into the following categories:
- The income per product category
- The revenue or income per month
- Total number of products produced in each category, in each month
Descriptive analysis juggles this raw data from each category and offers you insights for the post accordingly. The shortcoming in this method is that you can only recognize if an event was successful (or right) or unsuccessful (wrong).
Since the data is just a description and not an explanation, the what is answered but not the why. It is due to this shortcoming that it is not widely recommended to highly data-driven companies. The solution is always a combination of multiple analytical methods.
Diagnostic Analysis: answering why it happened
Irrespective of the analysis type, past events will always prove imperative. Taking a step higher than descriptive analysis, this method requires you to compare that with other data. The purpose is to explore the question of why.
For example, say you have to analyze why a certain profit was achieved or was not. Here, the sales and gross profit will be primary to the analysis.
The point of diagnostic analytics is to give an in-depth insight into an event. You can attain further advantage by having detailed information of the analysis at hand. Else, your data collection could change with every issue, and correcting the error will become very time-consuming.
Predictive Analysis: what might happen
Self-explanatory as it might be, predictive analytics refers to an analysis of what might happen. By undertaking the processes of descriptive and diagnostic methods, it configures the likelihood of an event. In doing so, it becomes a forecasting tool for your business.
For example, if you are thinking of flipping your brand positioning, the following would be like performing predictive analytics:
- Considering the past events and the related data
- Analyzing what happened: descriptive analytics
- Analyzing why it happened: diagnostic analytics
- Comparing different variables based on the above to check for the best-case scenario.
Since this kind of method includes the two basic techniques as well, it is considered advanced analytics.
However, forecasting possibilities is just like estimation. The accuracy and likelihood of it happening depends on two factors. Namely, the quality of the data and how well the stability of the situation persists.
Evidently, the process will require more than observing and jotting down derivations. You will have to treat the data carefully and partake in continuous optimization.
Prescriptive Analysis: deciding on a course of action
Lastly, through prescriptive analytics, you will be able to suggest a course of action. Remember that this stage will be to eliminate future problems.
While the technical processes will immerse themselves here, you must observe a trend that shows promise.
For example, if you were a multinational company that wanted to identify opportunities concerning past purchases. You would then take into consideration the trend of purchase or past sales reports of customer analytics.
On finding negative results, you would need to eliminate its problematic factors in the future. So, you would choose a particular path as opposed to another.
Just like predictive analytics, prescriptive analytics also uses advanced tools of technologies. The category might include anything from machine learning to trend algorithms to business rules. The inclusion of such factors makes it the most sophisticated method to manage and bring to life.
Moreover, unlike any of the other analytics, this one takes internal as well as external data. Meaning, the nature of algorithms will depend on past internal data and external influences.
That is precisely why it is important to weigh your undertaken efforts against expected value before adopting prescriptive analytics.
How do you know what type of analytics you need?
It can get very confusing as to which analytics method is ideal for you. However, the process can be made simpler. The first step would require you to ask a bunch of related questions yourself.
For the right combination of analytics for you and your organization, find to answers to:
- What your company’s state of data analytics is at the current time
- What is the depth required for your analysis
- How apparent or complicated the data for past events is
- How insufficient are your current insights as opposed to the kind you desire?
After you jot down your answers, you will be in a better position to decide on a data analytics strategy. The ideal way of going about it would be allowing scope for incrementation on the hierarchy.
Start with a simple method for the foundation and build the rest of the structure on the more advanced ones.
The next step would be designing an analytics solution that has the most optimal technology stack.
Further, plan out a mind-map or roadmap for the implementation part of it. Ultimately, you will be required to launch it after analyzing the success rate and possibilities.
Seeing the process through
Figuring out the best option can be an overwhelming process if you are a first-timer. Even big-shot multinational firms hire a whole new team to settle on this. So, you might find it helpful to build an in-house team for the same.
You will either need to find an experienced crew or take the matter into your own hands. The latter situation would involve training your crew into well-qualified analytics specialists.
Although the process might seem lengthy and expensive, it will maximize success in hindsight.
If you wish to maximize your returns on investment, implementing strategic data analytics is imperative. If you seek out an experienced data analytics provider, their background in the profession and field would be beneficial.
A mature professional will offer the best advice and ideal practices to take care of your business. Be it an analysis of your current data or settling for the most compatible combination, it will be technically sound.
Descriptive vs. Diagnostic vs. Predictive vs. Prescriptive Analytics
As mentioned before, taking in all the information all at once can be overwhelming. So, the following is a table briefly comparing the determining aspects of the above-mentioned analytical methods.
You can also try to compare the tabular representation with the first illustration for better understanding.
|Descriptive Analytics||Diagnostic Analytics||Predictive Analytics||Prescriptive Analytics|
|Purpose||To answer what has happened||To answer why something has happened||To consider what might happen||To suggest a course of action|
|Hierarchy status||For basic foundational purposes||For basic foundational purposes||Higher in sophistication: building a structure||Highest in sophistication: implementing a structure|
|Factors considered||Past or historical events: data that is internal to the organization or company||Past or historical events: data that is internal to the organization or company||– Past or historical events: data that is internal to the organization or companyUsed further for future possibilities||– Considering historical events as well as outside trends: data internal to the organization and external tendencies or changes.|
– Further puts the possible forecasted outcomes in comparison with predicted consequences
|Examples or instances||– To track course enrollments – Noting the number of times a product is bought|
– Collating results of a survey – Observing the time taken to achieve a certain goal
– To track the success rate of a product as opposed to the failure of another
|– Tracking why a course of action or product is achieving success or failing |
-Determining why a certain problematic gap emerged in the track record of the organization
|– For collecting data on how well or poorly employees interact in the different possible work and learning environments|
– For tracking how often a product might achieve success or failure
– To forecast how your employees grow within the work-space.
– Tracking the company’s path or learning, training process, and development of skills
|– To track the performance of a product and see how faced problems can be eradicated in the future|
– Taking the identified gaps in progress and exploring for the best solution if the situation were to arise again.
|Benefits||– Quick and easy access to returns on investment|
– Better for spotting problematic gaps or phases in a report
|– For determining causality if any|
– Concise and comprehensive understanding of these causalities
|– For smarter detection purposes|
Helps prioritise agenda
|– For streamlining the decision-making of your organization- To overcome problems quicker by preparing for the different possibilities|
Data analytics is quite an important process of breaking down the functioning of your organization. The primary reason behind that being the help it offers to businesses. Meaning, optimizing your performances accordingly becomes swift and efficient.
By implementing the required data analytics into your business model would reduce incurred costs or failure. Moreover, it helps you identify multiple efficient ways of going about with your daily business!
REFERENCES & FURTHER READING