data for business ideas

How To Use Data To Find Some Fabulous Business Ideas

In this digital age, data is everywhere and knowing how to use data in business for generating ideas is significantly essential. The data can be found in the most unexpected places, and it can be used for different purposes. Moreover, data scientists even use data to find patterns and predict future trends nowadays.

By researching product/service demand, running predictive analytics, identifying areas of greatest demand, tracking trending topics, and using proven competitor data, companies can find useful and lucrative gaps in the market to fill.

The key to all of this is data. It’s the driving force behind modern business intelligence, and it’s where most successful companies find their best ideas. 

This article features a complete breakdown of how a business can discover new ideas by leveraging big data.

Let’s get started.

How To Leverage Data for Business Ideas

Data for business ideas

Depending on the nature and scope of your business, data could be the defining factor behind successful business decisions. 

Here are some practical tips on how to generate a business idea from data:

1. Research Demand for Products and Services

The primary reason why big data is so valuable is the product-related insights it can provide. To gain these insights, you need to run searches for specific queries and statistics.

Demand is measured in impressions, conversations, sales numbers, and other highly measurable terms. However, researching the demand that’s related to specific product qualities can help determine which aspect of a certain product is most popular.

This will help create products with all the qualities that consumers like. 

For example, imagine you’re looking for ideas on how to create the ideal social media experience. You can start by looking at what people want out of the existing social platforms.

Software tools such as Yellowfin BI, Sisense, and Zoho can help visualize, analyze, and turn data into meaningful insight related to market demand.

yellowfin

Source: Yellowfin

2. Run Predictive Analytics

Once you have some relevant insights regarding the popularity of a product, you can run predictive analytics to confirm exactly when those products will be popular or profitable.

boldbi

Source: Boldbi

Predicting business outcomes is necessary for two major reasons:

  1. Short of a groundbreaking new invention, it’s the only way to get ahead in an already saturated market.
  2. It shows you the accuracy of your own data analysis.

Modern predictive analytics uses machine learning to further streamline the entire business intelligence (BI) process.

Most analytics engines come with built-in machine learning capabilities that are all based in and around a single database. 

3. Identify Areas of High Demand

Once you have data on the products and their seasonal popularity, you can identify which areas related to the product are in high demand.

You can use trend analytic tools to figure out which areas are growing fast and becoming more important by looking at how their dominant search terms are changing over time.

If too many people are already trying to invest there, move on. Look at existing trends in the industry and figure out how they may support your initiatives.

Although it’s not necessary to implement all the high-demand features in one product or service, it helps to provide customers with as much value as possible in one go.

This has two main advantages: 

  1. It informs the customer about the quality of the solution you offer.
  2. It prevents customers from looking elsewhere for missing features. 

The best thing about the “product demand” side of market research is that you can do it via surveys. Software such as SurveyMonkey is ideal for that. 

survey money

Source: Survey Monkey

4. Track Trending Topics

You can also track various trending topics that relate to your product, such as specific new product features or new tech that people are talking about.

Luckily there are a dozen or so platforms that all contain valuable and actionable customer insights. 

Social media platforms such as Twitter, Facebook, and even Instagram can help determine what audiences think about a specific product type. Streaming sites such as YouTube can also yield important data in this regard. 

That said, some of the most high-value data will be on Q&A boards and general discussion forums such as Reddit and Quora. This is where you’ll find customers asking very specific questions that reveal significant pain points and direct demand. 

With a good data analysis tool in hand, companies can simply implement a data search with trend-specific keywords and search terms. This will yield tons of important cross-platform data.

Google Trends is an ideal example of trend-discovery tools you will need to effectively track major topics of conversation.

google trend discovery

Source: Google Trends

5. Use Competitor Data

Lastly, you can leverage data on your competitors to discover what worked for them in the past to come up with ideas in a similar vein. 

In today’s saturated market, chances are someone has already come up with an idea similar to yours. While it’s not advisable to approach the market with this attitude, it’s still a realistic mindset to be in as it helps reduce wasteful efforts.

Your direct competitors may have some important data that you can use to optimize your idea for the market. No matter how minor, any amount of valuable data can help you avoid the mistakes they made with a similar product. 

Of course, this doesn’t apply if your product is completely unique and has no alternatives, even distant ones, in the current market. However, even in such a case, knowing how products usually perform in that industry may help you set realistic sales expectations.

Example: Tesla Motors 

Tesla is a brand known for consistent and rapid innovation in the field of electric vehicles (EVs). It’s also one of the biggest BI powerhouses in the world. 

What separates Tesla, though, is how it applies data analytics for overall product improvement. 

For example, the company gauged product demand by observing the demand for hybrid vehicles such as the Toyota Prius. Then, it ran predictive analytics to project how popular such a product (in full electric form) could be in a decade or more.

After that, they identified which aspects of electric vehicle design were in highest demand. For example, hybrids and other all-electric offerings were underpowered and lacked any significant tech upgrades over the standard models.

Finally, they collected sales numbers of competitor vehicles. Instead of basing the decision on total sales, they applied the conceptual improvements that they came up with based on earlier data analysis.

All of that resulted in a product that’s the standard against which EVs are measured today.

Top 5 Tools and Software for Business Data Collection

Software for Business Data Collection

There are tons of great business intelligence platforms for enterprise data collection and management, but here are the biggest and most trusted big data tools on the market.

1. (Microsoft) Azure HDInsight

Azure HDInsight is a cloud-based big data analytics service that can process huge amounts of historical or streaming data.

2. MongoDB

MongoDB is an open-source database management program that organizes, stores, and retrieves data for large-scale applications. Designed primarily as a storage platform, it has the added capability of retrieving relevant information quickly.

3. Microsoft Power BI

MS Power BI is a data visualization software that helps arrange data into meaningful visual presentations. Although it can be used for simple visual representation, the software is primarily used for business intelligence applications. 

4. Oracle Analytics Cloud

Analytics Cloud is Oracle’s data analysis software. It’s half of the “data management for business intelligence” software duo (the other being Data Miner). The cloud-based software can gain any type of insight from a given data set.

5. Oracle Data Miner

Data Miner is essentially a set of data collection algorithms that are built into the enterprise database software suite that Oracle offers. It performs a large variety of mining-related tasks, all with intelligent machine learning functionality built in. 

Common Mistakes To Avoid When Using Data for Business Ideas

Mistakes to avoid for business

Data is a powerful resource when determining which business ideas have the most potential. However, there are some pitfalls that are easy to fall into if you’re not careful. Here are some things to stay away from.

◉ Implementing Ideas Beyond Budget Capacity

It’s a fundamental concept of business intelligence to always consider budgetary restraints before and during implementation. Don’t fall into the trap of allocating extra resources when an idea sounds too good to overlook.

◉ Fully Copying Popular Product Ideas

Your analytics may show a certain product or feature is popular. However, don’t implement it as is. What works for a competitor (and their audience) might not work for you (and yours).

◉ Not Aligning Ideas with Long-Term Business Goals

It’s easy to ignore progress in the long term if there’s an opportunity for quick profit via an innovative idea. Make sure your ideas don’t overlook long-term financial growth and stability.

◉ Rushing Ideas to Implementation

A rushed product is often a failed product. Avoid embarrassing and expensive recalls (or worse) and test each product feature extensively before launch. 

◉ Not Getting End-User Buy-In

Your best business ideas may not connect with the audience if they don’t offer a solution to your users’ current problems. Make sure to implement the most mentioned audience demands and preemptively test the ideas using focus groups.

◉ Not Training Enough in BI

Business intelligence is great when used and implemented right. However, it may overwhelm your creative teams if you haven’t used data as extensively before. Train them specifically on BI concepts and best practices and reinforce that learning with ongoing professional development in the field. 

◉ Trusting Data Over Intuition

Innovative business ideas may help build customer affinity (and support the bottom line). However, if an idea doesn’t feel right to your values as a company or what your audience expects of you, it may be better to rethink it.

Moreover, no matter how much positive business data you have regarding an idea if it’s not the right time or business space to launch it, go with your gut. 

Final Thoughts

The success or failure of a business proposal for a new idea depends on how much research is actually behind it. By leveraging data for business, companies can ensure a positive market response via products and services that are closer to market demand.

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