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Over the past few years, big data has changed the way many companies operate. Big data promises to revolutionize business as it permeates midsize and small organizations. Here are 10 ways big data is transforming your business.
How big data will change your business
1. Improved business intelligence
Business intelligence is a set of data tools used to provide better business insights. It is closely related to big data. Before the rise of big data, business intelligence was pretty limited. Big data has spawned business intelligence as a legitimate career. Many companies are gearing up to hire business intelligence experts to help take their business to the next level.
Business intelligence is available to any business that produces data. Today, it is rare to find a business that does not generate any data at all, so any business can benefit from good business intelligence. New uses for business intelligence are invented regularly.
2. More targeted marketing
The first major feature of big data in business is insight into customer shopping behavior. Before big data, companies only had data from actual sales. In contrast, big data captures the granular behavior of customers and allows companies to create more targeted marketing campaigns. Big data analytics are not always perfect, but they are very accurate. This high degree of accuracy allows businesses to tailor their marketing to perceived customer needs.
Big data can provide highly specific information based on purchase and browsing history, allowing businesses to create highly personalized offers to existing customers. These offers can be presented via email, the company’s website, streaming services, and online advertising. Using big data to analyze text, video, image and audio data from review sites, social media and other his websites to determine customer attitudes, identify patterns and serve relevant content You can also.
Imagine how your business would benefit if you could sell a product that you knew your customers needed and know enough about them to tailor your message to their specific needs. please give me.
3. Proactive customer service
Big data turns customer service upside down by allowing businesses to know exactly what a customer needs before they even raise a concern. This kind of proactive customer service can revolutionize businesses looking to differentiate themselves with excellent customer service.
A customer encounters a problem after making a purchase and calls your business. Real-time big data analytics on the customer’s account and the company’s visits to her website can predict a problem or two for her that may require assistance. Voice prompts can also ask customers if they are experiencing a specific issue and provide automated help.
Either way, customer support representatives will understand what you’re calling and will provide knowledgeable customer service. Further big data analytics will enable agents to proactively reach out to customers on accounts whose predictive analytics have determined that they may experience future problems.
4. Customer-oriented products
Big data not only promises to improve customer service more proactively, it also enables companies to create customer-facing products. Product design can focus on meeting customer needs like never before. Instead of relying on telling your business what your customers want from your product, you can use data analytics to predict that information. You can get data from customers who share their preferences through surveys and buying habits. Use case scenarios can also be used to better understand what future products should look like.
5. Rise of the CDO and data sector
Big data is changing not only how companies deal with their customers, but also how they operate internally. During the 80’s and 90’s, the IT department was at the forefront as a driver of increased productivity and general business growth. Along with the IT department came the Chief Information Officer (CIO). Now the company has developed a data department separate from her IT department and appointed a chief data officer (CDO) who reports directly to the CEO.
did you know? NewVantage reports that 73.7% of Fortune 1000 companies have appointed a chief data officer or analytics officer.
6. Operational efficiency
Industrial engineers are focused on efficiency and know that they need data to make their processes more efficient. Big data provides a wealth of information about every product and process.
Engineers analyze big data to find ways to run processes more efficiently. Big data analytics works well with Theory of Constraints. Data makes constraints easier to recognize and, when recognized, easier to identify. When the most binding constraints are discovered and removed, business performance and throughput are significantly improved. Big data can help provide these answers.
7. Cost savings
Big data has the power to reduce business costs. Specifically, companies are now using this information to spot trends and accurately predict future events within their respective industries. Knowing when something will happen improves forecasting and planning. Planners can decide when to produce, how much to produce, and how much inventory to keep on hand.
A good example is inventory costs. Carrying inventory costs money. In addition to inventory holding costs, there is also the opportunity cost of tying up capital on unnecessary inventory. Big data analytics can help predict when a sale will occur, which means when production will be required. Further analysis can reveal the best time to buy inventory and how much inventory to keep on hand.
If businesses want to achieve more, they must embrace big data. It won’t be long before companies that don’t embrace big data will be left behind.
Tips: Before implementing big data initiatives in your organization, work on making your organizational culture more collaborative and adaptable. According to a NewVantage report, nearly 92% of Fortune 1000 executives say culture is the biggest obstacle to implementing big data results.
8. Fraud Detection
Companies in the financial services and insurance industries use big data to detect fraudulent transactions and insurance fraud by spotting anomalies. Banks and credit card processors can also use big data to identify fraudulent payments before cardholders realize their cards have been compromised. Big data analytics can also reduce the incidence of false positives in fraud detection, but in the past, financial institutions could freeze merchant accounts and turn out to be false alarms.
9. Cyber Security
IT and cybersecurity professionals can use big data to proactively predict threats and vulnerabilities to prevent data breaches. In addition to information collected from computers and mobile devices, big data includes data from networks, sensors, cloud systems, and smart devices to identify potential problems. Features include unified data representation, zero-day attack detection, data sharing across threat detection systems, real-time analytics, sampling and dimensionality reduction, resource constrained data processing, and time series analysis for anomaly detection. included.
10. Supply chain risk mitigation
What if you could identify potential problems in your company’s supply chain and proactively switch suppliers, reroute goods, or use a different shipper? Make it possible.
Amazon has changed the shipping game with 1-day, 2-day, and same-day shipping options. To catch up, other companies can use big data for delivery vehicle management by optimizing routes, adjusting delivery schedules, and providing the exact location of items. This added efficiency translates into fuel savings, as delivery vehicles can follow the most efficient route. According to Crayon Data, when UPS implemented big data in this way, on-time delivery stats increased and 1.6 million gallons of gas were saved annually.
Do’s and don’ts when using big data in business
If your business decides to implement a big data initiative, make sure you’re aware of these best practices and potential pitfalls.
what I have to do
- Clarify your purpose and starting point. Consider the potential uses of big data, then the cost of implementation, expected business impact, and time to results.
- Protect your data. When using third-party companies to analyze and collect data, it is important to draw boundaries about who uses the data and how.
- Build a collaborative culture. Data often impacts many parts of a business, and enabling cross-departmental collaboration on accessing, analyzing, and creating new data-driven initiatives can help you get the most out of your data.
- Choose your big data infrastructure carefully. A huge amount of data means you will likely need to use a data center for storage. Data is an asset, so evaluate potential data centers based on cost, management, backup, reliability, security, and scalability.
Something you can not do
- Don’t use too much data. While it may be tempting to use all the data your company has ever collected, you’ll get better results if you choose only the types of data that meet your current business needs.
- don’t do it all at once. Pick one business goal you want to address with big data, and plan around it before tackling other big data projects.
- Don’t forget security. Once you gain actionable insights from your data, planning for its confidentiality, integrity, and availability becomes more important than ever. The results of big data are the intellectual property of the business, Protected.
- Don’t focus too narrowly. See the big picture and tackle key areas across your company with a big data strategy to achieve the greatest return on investment.
Cameron Johnson contributed to the writing and reporting of this article.
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