Reinventing Business Intelligence: 10 Ways Big Data Is Changing Business

How big data is changing business

Here are 10 ways big data is changing business.

1. Better business intelligence (BI)

BI is a set of data tools used to provide better business insights. It goes hand in hand with big data — BI involves analyzing big data sets to inform data-driven business decisions. Before the popularity of harnessing big data, BI was rather limited, but now it’s given rise to BI as a legitimate career. Many companies today are hiring BI experts because they can help take an enterprise to the next level through their data analysis.

Any business that generates data can utilize BI. Nowadays, it’s rare to find an organization that doesn’t generate any data at all, so any company can benefit from utilizing better BI to analyze that data. New uses for BI 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 to conduct data analysis. Big data, by contrast, captures minute customer actions, allowing businesses to create more targeted marketing campaigns based on that data. 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 if it could market the products you knew your customers needed and knew exactly how to tailor your message to their specific needs. This is the kind of value big data provides.

3. Proactive customer service

Big data is turning 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 companies that want to differentiate themselves with superior customer service.

Imagine a customer who has a problem after a purchase and calls the business. Real-time big data analysis of the customer’s account and 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 of why the customer is calling and be able to deliver knowledgeable customer service. Further big data analysis could allow representatives to contact customers proactively 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 also allows companies to make customer-responsive products. Product design can be focused on fulfilling the needs of consumers in ways that have never been possible before, increasing the customer’s lifetime value. Instead of relying on customers to tell your business what they’re looking for in a product, you can use data analysis to predict that information. Data can be captured from customers who share their preferences via surveys and buying habits. You can even generate use-case scenarios to create a better picture of what a future product should look like.

5. Rise of the chief data officer (CDO) and data departments

Big data is not only changing how businesses deal with customers but also how they operate internally. During the 1980s and 1990s, the information technology (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 CDOs who report directly to the CEO. These employees and departments are solely devoted to data analysis, using all their time to collect and study data to benefit the company’s future.

6. Improvements in operational efficiency

Industrial engineers are focused on efficiency and data is needed to make processes more efficient. Big data is supplying a wealth of information about every product and process and now engineers are analyzing that information to find operational efficiencies. 

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 and demonstrates a key reason why you need BI. Specifically, companies are now using big data and BI to find trends and accurately predict future events within their respective industries. Knowing when something might happen can improve sales forecasts and budget 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 retain inventory; there’s 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 to keep on hand.

8. Better fraud detection

Companies in the financial services and insurance industries can 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 their card has been compromised. Big data analysis can also reduce the incidence of false positives in fraud detection while previously, the financial institution would have frozen the merchant’s account and it might have turned out to be a false alarm. [Learn more about credit card fraud in business.]

9. More robust 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, using big data and BI can involve analyzing 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 learning how to best optimize routes, coordinate delivery schedules and provide 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.

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

Embracing big data is smart for business. If you decide to implement big data initiatives at your company, 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 organization’s potential uses for big data and then consider the cost of implementation, the anticipated impact on the business and the length of time required to start getting results.
• Protect your data: If you’re going to be using third-party companies for data analysis and collection, it’s critical to set guidelines regarding who will have access to the data and how they will use it. Make sure the other party agrees to your data security standards.
• Build a collaborative culture: Because data often has implications for different parts of your business, you’ll get the most out of it if you enable collaboration among departments. While you initially may have one team accessing and analyzing the data, it’s likely to be other teams that create and execute new initiatives based on the findings.
• Carefully choose your big data infrastructure: The sheer volume of data involved with big data means you will most likely need to use a data center for storage. Data is a valuable asset, so carefully evaluate potential data centers based on cost, management practices, backup, reliability, security and scalability. [Find out more about data management.]

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’ll get better results if you choose to focus on only the type of data that fits with your current business needs. 
• Don’t do everything at once: Choose one business objective 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’s more important than ever to ensure the confidentiality, integrity and secure availability of that analysis. Your big data results are intellectual property and need to be protected.

Kimberlee Leonard and Cameron Johnson contributed to this article.