While talking about how your tech company made its fortune by throwing things against the wall to see what sticks makes a fascinating story, following up on hunches and wild experimentation is not exactly how most businesses win big in a fiercely competitive industry.
In the real world, you have a better chance of success if you make a few good decisions; and the quality of a decision, whether of a person or an organization, primarily depends on the quality of the information.
How do you find high quality information to base your decision? Usually, the best way is through a combination of research techniques and business software analytics.
What is Business Analytics?
There is a lot of confusion about the term “business analytics.” It is used in a wide number of ways and often confused with business intelligence. The best way to think about it is to recognize that it’s an umbrella term and can be used in a wide number of contexts.
So, for example, the term can include a diversity of analytical tools like business intelligence, enterprise performance management, governance, enterprise information management, data warehousing, analytic applications, and so on.
Sometimes, too, the term can be used in a very specific way by a certain company to suggest a level of domain knowledge, statistical analytics, or predictive analytics.
How to Use Business Analytics
Imagine that you’re thinking of setting up shop as a smartwatch maker because you have several brilliant ideas on how to make a better type of wearable device than Fitbit or Apple. Moreover, your current business has the technical expertise and the financial wherewithal to enter this market.
Naturally, you will have a lot of questions before you jump in:
- Is your idea a viable product category for a small player new to the scene?
- What type of ROI might you hope to expect if you go for it?
- What would it take to be a success in this industry?
Burning money by adding random features in a smartwatch is not a winning solution. Just because a market looks promising does not mean that it’s wide open to any type of entrepreneurial initiative.
How will you be able to evaluate the feasibility of your idea? One way would be to build a prototype and use business analytics to gather all the information you need to decide whether or not to enter this market.
4 Best Practices
Here are 4 suggestions on best practices to use:
- Organize your data. Uniform data is efficient. The more uniform your data, the more efficiently you will be able to analyze it. Errors arise when the data belongs to different categories of things (e.g. apples and oranges) or if it’s formatted in different ways (e.g. area codes separated in some places with dashes and in others with periods).
- Get everyone on the same page. When working with a business team to analyze data, it’s essential that everyone understand the context for the analysis to streamline the best approach. A diverse team where everyone has a different technical background, understanding of the issues, and interpretative skill can quickly results in chaos.
- Understand the problem. The problem has to be clearly defined to know what you’re looking for and the purpose of the project. Ambiguity about the problem being addressed or the end goal of the project will simply waste everyone’s time.
- Ask for expert help when overwhelmed. If an analyst or a team can’t make sense of the data when trying to find answers to some important questions asked by upper management, it’s better to ask for outside help than to consider the data nonsensical or irrelevant. An outside perspective from a seasoned analyst can prove invaluable in getting insights. They might see new possibilities that are obscure to someone with less interpretive or predictive data-reading skills.
Better Decisions with Business Analytics
Business analytics can be used to make better decisions in all types of situations. It’s routinely used for big, complex ideas. A manufacturer of self-driving cars could use it to make better key decisions on this emerging technology, and it’s possible that a smartphone company like Blackberry used it to smartly exit the hardware business, giving them a path forward.
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