A massive amount of data will remain raw figures unless you understand what they mean for your business.
It is easy to say that you need to store your data for future use. But how are you going to use it? What does this data tell you about your customers? How does this help you generate more income? How does this help you automate your process? These and more questions will keep on piling unless you start analysing your data.
Add to that, not all data is valuable. Are you storing the fluff as well as the useful ones? If you know how to segregate them, you can maximise your storage and focus on more valuable data.
You can only make proper business decisions if you use the correct type of data analysis that fits your needs. To simplify, you have to define your goals to know which analysis will be the best right now.
Goal: Summarize Past Data
The simplest type of data analysis is descriptive analysis. If you want to know what is happening to your business and if you are going in the right direction, you need to summarise your past data.
What does your data tell you about your business performance? Was it able to generate more income this year? Was it profitable for the past year?
With a massive amount of data, it is hard to tell if you are really earning, barely getting by, or losing your capital bit by bit. With descriptive analysis, you can evaluate if your business is right on track.
The most common business application for this type of analysis is the KPI dashboard. It lets you see how far you’ve gone based on your set benchmark. You can also utilise this to analyse your monthly revenue reports.
Goal: Find the Root Cause
Finding out what is happening to your business process is one thing. Learning why such things happen is another thing which diagnostic analysis will uncover.
While many start with descriptive, oftentimes their next step is diagnostic. It gives you a sense that certains problems have root causes that you have to identify. Some may be easy to see while other challenges may be hard to see.
Many tech companies design business information dashboards like PowerBi. Microsoft Power BI can turn data into understandable visuals your managers can interpret. The data is collected and organized for you and your admin team to make well-informed decisions.
Should you change your third party vendor? Are you making unnecessary expenses? Is your marketing strategy not fit for your target market and thus you are spending more without any ROI?
SAP analytics BI is also a great example of business intelligence. This powerful analytics lets you make better decisions to grow your business faster. SAP database will boost your business operations and customer services. It lets you focus on the insights that impact your operations.
Goal: Know How to Eliminate Future Problems
Predictive analysis is perfect when you are finally ready to consider various conditions. It is more on predicting what is most likely to happen in the future.
You can use this quantitative analysis to build your models. Your managers can then make highly strategic decisions since they already know the possible outcome based on datasets.
One example of a predictive analysis is google analytics. It can give you a list of search terms your target customers are likely to search in Google. Then you can focus on creating content connected to the set of keywords provided by Google analytics. If you have a massive database, you can get a data scientist to predict the behaviour of a specific group of people in your dataset.
You can also use this analysis to choose the best vendor that fits your needs. In addition, you can see where your operations are heading should you keep your model.
Goal: Automate Your Process
The most complex is the prescriptive analysis. It is more than just a one way street. It can help you choose the best action considering the various possibilities in the future.
For a small scale example: A financing company is looking for a third party to help them predict if a client would be responsible with his payments. To predict the customer’s behaviour, the company has to provide their database of previous clients for data scientists to create an algorithm.
Of course, there could be agents who can validate every personal detail, but a powerful prescriptive analysis can present a decision within seconds instead of an hour. Imagine how many applications an automated process can do compared to a manual process. Building an artificial intelligence also roots from here.
There is a high risk that the client will not pay if their address is the slum, they have no regular income and if they have a handful of existing loans as well. What if you get a third vendor to automate your business operations. What if you widen your target market? What if you add search engine optimization in your marketing strategy?
Prescriptive analysis is born from the first three types of analysis we have discussed. Once you have summarized your data and found the root cause of your challenges, you can then start with predictive analysis. Then, you can build rules for an AI to follow and predict the future. However, this data analysis will require manpower and technology. If you are ready to step up your data analysis and you need someone to help you out, NTT Data can provide its assistance.
Investing in a good data storage and data analyst will help you grow faster. You will gain impactful insights to avoid mistakes and eliminate existing challenges. Growing a business is overwhelming, but data analysis will point you to the direction you would want to take.
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