Reinventing Business Intelligence: 10 Ways Big Data Is Changing Business

How big data is changing business

1. Better business intelligence

Business intelligence is a set of data tools used to provide better business insights. It goes hand in hand with big data. Before the rise of big data, business intelligence was rather limited. Big data has given rise to business intelligence as a legitimate career. Many companies are gearing up by hiring business intelligence experts because they help take a company to the next level.

Any business that generates data can utilize business intelligence. Nowadays, it’s rare to find a business that doesn’t generate any data at all, so any business can benefit from better business intelligence. New uses for business intelligence are being devised regularly.

2. More targeted marketing

Big data’s first big mark on businesses has been its insights into customer shopping behavior. Before big data, companies only had the data from actual sales. Big data, by contrast, captures minute customer actions, allowing businesses to create more targeted marketing campaigns. Big data analysis may not always be perfect, but it is highly accurate. This high accuracy allows companies to target marketing to perceived customer needs.

Big data can provide very specific information based on purchase and browsing history, enabling companies to create highly personalized offers to existing customers. These offers can be presented via email, company websites, streaming services and online advertising. Big data can also be used to analyze text, videos, images and audio data on review sites, social media and other websites to determine customer attitudes, spot patterns and deliver appropriate content.

Imagine how your business would benefit from being able to market the products that you knew your customers needed and from knowing enough information about them to tailor your message to their specific needs.

3. Proactive customer service

Big data will turn customer service upside down, as it allows businesses to know exactly what their customers need before they even voice their concerns. This kind of proactive customer service will revolutionize businesses that want to differentiate themselves with superior customer service.

Imagine a customer experiences a problem after a purchase and they call the business. Real-time big data analysis of the customer’s account and their company website visits can predict one or two issues that may require assistance. A voice prompt could even ask the customer if they were experiencing a particular issue and provide automated help.

Either way, customer support representatives would have a good idea what the call was about and deliver knowledgeable customer service. Further big data analysis could allow representatives to proactively contact customers on accounts where predictive analysis determines that the customer might have an issue in the future.

4. Customer-responsive products

Big data not only promises to improve customer service by making it more proactive but will also allow companies to make customer-responsive products. Product design can be focused on fulfilling the needs of customers in ways that have never been possible. Instead of relying on customers to tell your business what they are looking for in a product, you can use data analysis to predict that information. Data could be captured from customers who share their preferences via surveys and buying habits. You can even use-case scenarios to create a better picture of what a future product should look like.

5. Rise of the CDO and data departments

Big data is not only changing how businesses deal with customers but also how they operate internally. During the ’80s and ’90s, the IT department came to the forefront as the driving force of productivity increases and general business growth. Along with the IT department came the rise of the chief information officer. Now, businesses are developing data departments that are separate from IT departments, as well as appointing chief data officers (CDOs) who report directly to the CEO.

6. Improvements in operational efficiency

Industrial engineers are focused on efficiency, and they know that you need data to make a process more efficient. Big data is supplying a wealth of information about every product and process. 

Engineers are analyzing big data to look for ways to make processes run more efficiently. Big data analysis works well with the theory of constraints: Data makes constraints easier to recognize and, once recognized, easier to identify. When the most binding constraint is discovered and then removed, the business can see huge increases in performance and throughput. Big data helps supply these answers.

7. Reduced costs

Big data has the power to reduce business costs. Specifically, companies are now using this information to find trends and accurately predict future events within their respective industries. Knowing when something might happen improves forecasts and planning. Planners can determine when to produce, how much to produce and how much inventory to keep on hand.

A good example is inventory expenses. It’s expensive to carry inventory; there is not only an inventory carrying cost but also an opportunity cost of tying up capital in unneeded inventory. Big data analysis can help predict when sales will happen and thus when production needs to occur. Further analysis can reveal the optimal time to purchase inventory and even how much inventory to keep on hand.

Businesses need to embrace big data if they want to achieve more. It won’t be long before businesses that haven’t embraced big data find themselves left behind.

8. Fraud detection

Companies in the financial services and insurance industries use big data to detect fraudulent transactions and insurance fraud by finding anomalies. Banks and credit card processors can also use big data to spot fraudulent payments, sometimes even before the cardholder knows that their card has been compromised. Big data analysis can also reduce the incidence of false positives in fraud detection, whereas previously, the financial institution would have frozen the merchant’s account and it might have turned out to be a false alarm. 

9. Cybersecurity

IT and cybersecurity professionals can use big data to predict threats and vulnerabilities in advance to prevent data breaches. In addition to the information garnered from computers and mobile devices, big data includes data from networks, sensors, cloud systems and smart devices to spot potential problems. Capabilities include unified data representation, zero-day-attack detection, data sharing across threat detection systems, real-time analysis, sampling and dimensionality reduction, resource-constrained data processing and time-series analysis for anomaly detection.

10. Supply-chain risk mitigation

What if you could spot potential problems in your company’s supply chain so you could proactively switch suppliers, reroute goods or use different shippers? Big data enables you to do so. 

Amazon has changed the delivery game with its one-, two- and same-day delivery options. To keep up, other businesses can use big data for delivery fleet management by optimizing routes, coordinating delivery schedules and providing the precise locations of items. This added efficiency results in savings on fuel, since delivery vehicles can take the most efficient routes. When UPS implemented big data in this way, it ended up increasing its on-time delivery stats and saved 1.6 million gallons of gasoline a year, according to Crayon Data.

Do’s and don’ts of using big data in your business

If you decide to implement big data initiatives at your business, make sure you’re aware of these best practices and potential pitfalls.

Do’s

• Be clear on your purpose and starting point. Think of your potential uses for big data, and then consider the cost of implementation, the anticipated impact on the business, and the length of time to start getting results.
• Protect your data. If you are going to be using third-party companies for data analysis and collection, it is important to draw boundaries regarding who will use the data and how they will use it.
• Build a collaborative culture. Because data often has implications for different parts of your business, you will get the most out of it if you enable collaboration among departments in regard to accessing, analyzing and creating new initiatives based on the data.
• Carefully choose your big data infrastructure. The sheer volume of data means you will most likely need to use a data center for storage. Data is an asset, so evaluate potential data centers based on cost, management practices, backup, reliability, security and scalability.

Don’ts

• Don’t use too much data. While it can be tempting to try using all of the data your company has ever collected, you will get better results if you choose only the type of data that fits with your current business needs. 
• Don’t do everything at once. Choose one business objective that you want to address with big data, and plan around that before you tackle other big data projects. 
• Don’t forget about security. Once you have actionable insights from your data, it is more important than ever to plan for the confidentiality, integrity and availability of that data. Your big data results are intellectual property of the business and need to be protected.
• Keep your focus too narrow. Look at the big picture, and address companywide critical areas with your big data strategy to have the largest return on investment.

Cameron Johnson contributed to the writing and reporting in this article.