What do you think is the most important element that helps a business grow and spread its wings in the market? Do you consider revenue to be the savior? Or is it all about creating a proper and thorough structure for your workflow? Well, in either case – the answer is no.
When it comes to business growth and development, nothing else can beat the importance of data accumulation and management.
Why?
Well, by following your dataflow properly, you will easily find out what your audience base is considering getting from you. Moreover, it can also help you discover the flaws in your system and prompt you to get better in that aspect. That’s why data is considered to be highly valued!
Now, here’s the thing.
When you’re playing with data, there are three techniques that can be used – BI, data analytics, and data science. Each of these serves different purposes and, therefore, can be ideal for some specific circumstances. But how do you understand which option is the right one for you?
By learning more about them and comparing their benefits!
So, let’s begin with our discussion.
What is Business Intelligence?
Business intelligence, also known as BI, generally focuses on uncovering insights for making strategic decisions. The tools related to this tend to assess the historical data of a corporation and pin them against the current data. It, in turn, might help you to understand where you are lacking and improve your organizational workflow accordingly.
How Does BI Work?
Business intelligence, in general, works through four key steps. These are generally done in a simultaneous manner. So, make sure to follow everything accordingly.
Step – 1: Accumulate Data from a Source.
The BI tools consume the extracted data from various structured and unstructured data sources. After that, the collected trusted data is enriched before getting stored within a specific location.
Once it’s done, the analytical tools can assess the accumulated data and create an all-inclusive dataset for your business. It can be reviewed later on before making any decision.
Step – 2: Uncover Inconsistencies and Trends.
Data discovery, or data mining, generally uses automation to evaluate data patterns to gather a little bit of insight into the current state of your business. BI tools often come with various data analytics and modeling – including descriptive, exploratory, statistical, and predictive.
Therefore, it becomes easier for you to explore data even further and predict the current as well as the upcoming trends whilst making recommendations. It’s quite ideal for a budding business.
Step – 3: Use Data Visualization to Understand Your Findings.
The reporting segment of business intelligence uses data visualization to picture whatever it has found through the collected data. In this case, the critical parts of the finding are shown in a drafted manner – through graphs, visual datasheets, and so on.
Some of the more common reporting methods may include KPI, maps, charts, data dashboards, etc. You can also perform everything manually to make your understanding even better.
Step – 4: Take Actions in Real Time.
Viewing historical and current data in the context of business-related activities can provide an organization with the ability to move to actions from insights quickly. BI, in this case, enables you to make real-time adjustments and perform long-term strategic decisions that can eradicate inefficiencies effectively. Furthermore, it can also help you adapt to shifts in the market, make better offerings, take care of supply problems, and solve consumer-related issues.
How Does It Benefit Your Business?
When used correctly, business intelligence can be quite beneficial for almost any corporation. These may include the following accordingly –
- You can perform accurate sales tracking and improve your financial performance.
- It will be possible to clear benchmarks based on current and historical data.
- You will get insights into shopping patterns and consumer behavior.
- Getting instant alerts regarding customer issues and data anomalies.
- Acquiring insights into shopping patterns and customer behavior.
Previously, BI tools were being used by IT specialists and data analysts. However, now, thanks to the emergence of self-service BI platforms, almost everyone, from the operation team to an executive, can use it for the betterment of their organization.
What is Data Science?
Data science, on the other hand, is the study of information or data to extract meaningful plans and insights for your business. It is more of a multidisciplinary approach that combines all the practices and principles from the fields of –
- Artificial intelligence.
- Mathematics, and
- Computer engineering.
This, in turn, can help you analyze a large amount of data within a short amount of time. Thus you can use it to answer questions – what happened, what was the reason behind it, and so on. Once you have found out the results, you can try to better your organization accordingly.
How Can Data Science be Used?
If you have a specialist at the helm, you may use data science for a plethora of things. These might include the following –
1: Diagnostic Analysis.
The diagnostic analysis is the procedure of diving deep into data examination to understand why something has happened. It’s generally classified by various techniques like data discovery or drill-down. Multiple data transformations and operations can be done by using a given dataset to find out unique patterns in all of these techniques.
For example, a flight service might use the drill-down technique on a high-performing month to understand the reason behind the booking spike. This might lead them to discover the place that some people tend to visit during a specific time of the year.
2: Descriptive Analysis.
Descriptive analysis, on the other hand, focuses primarily on gaining insights into an action that is currently happening in your business.
It’s usually presented by various data visualization techniques –
- Pie charts,
- Line graphs,
- Bar charts,
- Generated narratives, and
- Tables.
For example, the flight booking service might record some specific data like the total number of people booking tickets every day. This way, they can understand the reason behind booking spikes, high-performing months, booking slumps, and so on.
3: Predictive Analysis.
Predictive analysis, in general, uses your historical data to come up with an accurate forecast of what might happen later. It’s generally characterized by various techniques, such as pattern matching, machine learning, forecasting, and predictive modeling. In each of these cases, the computers are trained in aspect of casualty connection within the data.
For example, a flight booking service may use predictive analysis to check how many people will book flights to Nigeria in 2023. This, in turn, will prompt the computer to look at the past data (2022) and predict spikes during certain seasons. Once the company has anticipated its consumer’s traveling behavior, it can prepare its services accordingly.
Benefits of Data Science
If employed correctly, data science can collect, organize, and evaluate a huge amount of data in an instance. Here’s how it might be beneficial for your business.
- It can help you make better decisions for your organizational benefit.
- You can measure your corporational performance and build up on it.
- Data science can also increase your efficiency massively.
- As you are getting almost each and every piece of information in your hands, you can mitigate the risk of fraud and data thieveries.
- You can predict the outcome of a specific issue pretty accurately.
Finally, data science can also be used to improve the experience of your consumer base. And it can be done by offering a more customized service to them.
What is Data Analytics?
Data analytics, in essence, is the process of evaluating raw data to find hidden trends within it. However, it can be used to achieve some other goals as well.
For instance, the entire process begins with descriptive analysis. In this step, the tool attempts to understand ‘what happened’ by describing the historical trends.
It, in general, tends to involve measuring traditional indicators, such as ROI. Nonetheless, the usage of these indicators might depend on the industry you are currently working in.
The next step of the data analytics process is advanced analytics. This specific section of data science tries to take advantage of some advanced tools and make predictions.
Your job during this step will be to find new trends and work accordingly to understand how they might grow in the future. It can also answer the ‘what if’ question from your end.
How is Data Analytics Performed?
The process of data analytics is usually done through a few steps. Most of these are important to the course of the final action. So it’s better to follow them accordingly.
- The first step, in this case, is to determine the requirement of data or how you’re going to group it. It might be separated by demographic, age, income, or gender. Besides, the valuation of the data might be divided by categories or may occur in numerics.
- The second step is all about collecting the data. This is generally done in various ways, including – online sources, computers, environmental sources, or through personnel. In some cases, this process might go wrong due to some manual mistakes. So, be careful.
- After the data has been collected, it must be organized so that you can evaluate it. Due to this reason, the data will be scrubbed and checked if there’s anything wrong with it. If there’s any error or duplication available in it, the data will be stripped off.
Usually, the process of data analytics takes quite a lot of time when done manually. Therefore, it might be best if you could use an SAP-based system to automate the entire course of action.
Benefits of Data Analytics
The benefits of data analytics might seem quite similar to that of data science. Nonetheless, it is quite different as a whole. Keep reading to know more about it.
- It can help you personalize your consumer experience.
- Your decision-making skills will get much better due to getting the correct information.
- You can streamline most of your operations and improve your overall productivity.
- It’s possible to enhance security by finding out where you are lacking in that aspect.
- You will be able to handle setbacks and mitigate risks accordingly.
Businesses can also predict an outcome with data analytics and try to prepare against it. But in most cases, it has to be done alongside data science.
The Final Verdict
When it comes to developing a business, it’s essential to use each of these three techniques in a simultaneous manner. Otherwise, you might not be able to make the most out of the data you have in your storage. So, create a strategy now and start working on it accordingly!
