Data Delirium: Why and How You Should Embrace Big Data

What is big data?

Big data describes data sets that are so complex, voluminous and hard to manage that typical off-the-shelf data processing apps, like Excel and Access, can’t handle them.

There are three types of big data:

• Structured data: Structured data is uniformly recorded data. For example, structured data may be an Excel spreadsheet with fields such as “item purchased,” “price,” “date and time” and “payment method used.” Data is sorted into the correct columns.
• Unstructured data: Unstructured data lacks structured data’s rigid format. This data may come in any form, including text files, videos, audio and imagery. Businesses have only recently learned how to draw meaning from unstructured data, thanks to machine-learning-powered and other artificial intelligence (AI) tools.
• Semi-structured data: Semi-structured data contains some defined field data (like structured data). However, so much of it is unstructured that this data requires significant computer processing power to make sense of the information.

Why should businesses embrace big data? 

Many business owners don’t realize how much data their companies generate. This data contains crucial information to help them increase sales, motivate their team, improve cash flow and boost customer retention.

Businesses should embrace big data to gain these benefits: 

• Increased efficiency: Big data helps identify opportunities for automation, giving employees more time to focus on strategic work. With big data, you can eliminate redundant tasks, track your remote staff’s productivity and streamline operations.
• More effective marketing: Big data empowers you to truly understand your customers and competitors. Analyzing customer behavior data can help you identify consumer preferences and predict future needs. You can also gain valuable insight into competitors’ strategies and tailor your marketing campaigns to reach the right audience with a targeted message.
• Improved customer service: Big data is the key to unlocking exceptional customer service and creating a great customer experience. By analyzing customer interactions, you can get a better understanding of their needs and pain points and create proactive solutions and personalized experiences that foster customer loyalty.
• Boosted revenue: Big data’s impact on your bottom line is twofold. Cost savings come from streamlining operations and eliminating inefficiencies. Additionally, improved marketing and customer service lead to increased sales and customer acquisition and retention. For example, you can use big data insights to design email marketing campaigns that reduce abandoned shopping carts. Retaining satisfied customers through personalized experiences further boosts your bottom line.
• Better decision-making: Big data allows you to track the performance of marketing campaigns, product launches and more. Then, you can analyze that data to identify what’s working and what’s not. Your insights will also help with cash flow management because big data can help you predict optimal product order volumes, optimize strategies and fuel future growth.

How can businesses use big data? 

Big data insights can help redirect your efforts and improve your company’s bottom line. Here are a few ways businesses can use big data: 

• Personalize the customer journey: Big data allows you to analyze past purchases and website behavior to understand individual customers’ preferences. Then, you can use this knowledge to tailor product recommendations, marketing messages and even website layouts for a more engaging and personalized experience that keeps customers returning.
• Optimize pricing strategies: Don’t leave money on the table — analyze customer data alongside sales figures to identify ideal prices for different products and customer segments. Big data can also reveal patterns in competitors’ pricing, allowing you to stay competitive and maximize profit margins.
• Predict customers’ needs: Big data goes beyond understanding past behavior. Advanced analytics can help you predict what your customers will need in the future. You’ll be able to anticipate seasonal trends, identify potential upselling and cross-selling opportunities, and proactively suggest products or services that complement past purchases.
• Improve inventory management: Is excess inventory an issue for your business? Analyze sales data and customer behavior to forecast demand for specific products. Big data can also help optimize your supply chain by identifying potential delays or disruptions, thus ensuring you have the correct inventory levels at the right time.
• Boost marketing return on investment (ROI): Big data can help you accurately measure the effectiveness of your marketing campaigns. Use big data to track key metrics across different marketing channels, identify which strategies are resonating with your audience and optimize your ad spend to maximize your marketing ROI.

What data should you collect, and how do you collect it?

Your business should collect any relevant data that you must measure to improve operations. Here are a few examples of typical types of data that many businesses collect:

• Sales data: Collect sales figures to measure and improve how much money customers spend.
• Location data: Collect ZIP codes to analyze the distances your customers travel. 
• Time data: Collect sales time and date information to gauge optimal sales periods. 
• Purchase data: Collect purchase information to target future recommendations. With each subsequent purchase, you’ll gain additional insights. 

Your business can gather customer data manually or with software. For example, retail stores might collect an email address at the point of purchase, while e-commerce retailers gather email addresses during online transactions.

How do you analyze the data you collect? 

Businesses analyze collected data in three primary ways: 

1. Analyze data with basic data analysis.

For small companies, a working knowledge of spreadsheet programs like Excel should be enough to analyze data to improve and measure digital marketing campaign ROI and sales efforts.

Use drop-down selection options to segment customers, analyze average spends and more. Remember to keep your spreadsheet up to date so your insights remain current.

2. Analyze data with DIY data tools. 

When your business grows, you’ll need to move beyond a basic analysis to crunch your data properly. You might need to have some coding knowledge or hire a developer to tie your systems together. Still, this option is much more reasonable than paying for a bespoke big data analytics package for your company.

One option is to connect your customer relationship management platform and other business software to one of the growing number of low-code and no-code AI tools available via sites such as Akkio. These tools will help you do the following functions and more: 

• Score leads for quality
• Estimate closing times
• Predict deal sizes
• Optimize your sales funnels
• Detect fraud
• Direct customer service queries by language
• Improve employee retention 

DIY big data analysis tools emphasize visualization to help novices collect, analyze and understand their data. This is far more user-friendly and understandable than spreadsheet analysis.

3. Work with a data analytics consultancy.

If the idea of coding and programming doesn’t appeal to you, consider working with a data analytics consultancy. 

A data analytics consultancy will charge you for consulting, system setup and maintenance. You can reap the rewards of your data insights without handling the technical aspects. Additionally, an experienced data analytics consultant may spot opportunities and data insights that aren’t apparent to you.

Example of big data in the real world

We’ll use restaurant marketing as an example of applying big data. Some chain restaurants use loyalty program cards to collect the following data about their patrons:

• Personal information
• Amount of money they spend
• Times and dates they visit the location
• Favorite orders

From this data, the restaurant’s marketing team can identify loyalty-card holders who haven’t visited in a few months. They could segment this group into a cohort of customers who spend an average of $100 per visit. With these insights, the restaurant could entice high-value customers to return by offering a discount or a free meal on a specific date.

What are the privacy concerns with big data?

As more companies rely on data collection and analysis to grow and differentiate themselves from the competition, security and privacy issues are becoming more prevalent. Here are big data’s four primary privacy and security pitfalls:

• Big data cannot always predict outcomes. For example, you may bombard a consumer with specific marketing because big data says they’re ready to buy, but in reality, they might not be interested at all.
• Big data makes anonymity unlikely. Big data is often analyzed in giant data pools. With massive amounts of information, many consumers in your database could become identifiable, removing any semblance of privacy.
• Big data complicates government regulations. Penalties for mishandling data are substantial. Companies in the U.S. risk fines and class-action lawsuits if they mishandle data. In the United Kingdom and the European Union, businesses also face fines if they violate the General Data Protection Regulation (GDPR). [Find out what you need to know about GDPR and digital privacy.]
• Big data raises the risk of data breaches. If your company is the victim of a data breach, you risk stiff fines and a potential loss of customers who no longer trust you. Protecting consumer data is paramount.

Big data and your business

Big data offers major benefits for businesses in the age of software subscriptions, AI, machine learning and data analysis tools. Your competition is likely leveraging the power of big data insights, so now is the time to get started if you haven’t already.

Sean Peek contributed to this article.