Data Delirium: Why and How You Should Embrace Big Data

What is big data?

In its strictest sense, big data is data too complex and voluminous for off-the-shelf data processing apps like Excel or Access.

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 like “item purchased,” “price,” “date and time,” “payment method used” and so on. Data is sorted into the correct columns.
• Unstructured data. Unstructured data lacks structured data’s rigid format. Few or no fields demark meaning. Data may come in any form, including text files, videos, audio and imagery. Businesses have only recently become able to draw meaning from unstructured data, thanks to artificial intelligence (AI) and machine learning-powered 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 holds crucial information to help you increase sales, motivate your team, increase cash flow and boost customer retention. 

If you capture and record valuable data – and learn to analyze it correctly – you can gain a genuine advantage over your competitors.

How can businesses use big data?

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

• Big data can boost efficiency. Big data can reveal ways to boost operational efficiency. For example, you can track remote staff’s productivity and adjust tasks as needed. You can also look for bottlenecks in business processes to reduce pressure on staff and deliver better customer outcomes.
• Big data can enhance the customer experience. Get real-time summaries of what your customers think of you and what they want with sentiment analysis and social media monitoring. Make adjustments to create a better customer experience strategy.
• Big data can improve your marketing. Behavioral analytics – the science of discovering why people behave as they do – relies on data. This data can pinpoint where in the sales funnel you’re losing potential customers so you can make improvements and generate more sales leads. Focus your efforts on discovering what’s working and what’s not, and correct any weak areas.
• Big data can help you sell more. Use email marketing to reduce abandoned shopping carts, and forecast how much extra revenue you’ll create. If you use email segmentation, discover which customer groups will spend more, and focus your attention on them.
• Big data can make it easier to forecast demand. Cash flow management is easier because big data can predict the product volumes you should order. This helps you better coordinate your supply chain and leads to less unsold inventory. Marketing teams also benefit from advance notice of the campaign types they should run, giving them more time to prepare.
• Big data can help you master social media. Improve social media campaign performance for greater engagement, and gain insights into your target audience’s passions and motivations. 

This is only the beginning of big data’s impact on business. The more we use apps to control business activity and processes, the more data we’ll compile to measure and compare.

How do you collect data?

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

What data do you collect? 

Collect the data you want to measure:

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

Example of big data in the real world

We’ll examine restaurant marketing as an example of applying big data. 

Some chain restaurants use loyalty cards to collect the following data on their patrons: 

• Personal information
• The 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 each visit. 

With these insights, the restaurant could entice high-value customers to come back with an offer of a discount or free meal on a specific date.

How do you analyze the data you collect? 

Businesses analyze collected data in three primary ways. 

1. Analyze data with low-fi 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 low-fi analysis to crunch your data properly. You might require some coding knowledge or decide to 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 CRM solution and business software packages to one of the growing number of low-code and no-code AI tools available via sites like Akkio.com. These tools will help you do the following functions and more: 

• Score leads for quality
• Estimate closing times
• Predict deal size
• 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 better collect, analyze and understand their data. This is far more user-friendly and understandable than analyzing spreadsheets.

3. Work with a data analytics consultancy to analyze your data.

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.

What are the privacy concerns with big data?

As more companies rely on data collection and analysis to grow and stand out from the competition, security and privacy issues are becoming more of a concern. 

Below are big data’s four primary privacy and security pitfalls:

• Big data may provide inaccurate information. Big data is not a precise science. For example, you may bombard a consumer with specific marketing because big data says they’re ready to buy – when in reality they might not be interested at all.
• Big data makes anonymity unlikely. Big data is analyzed in the round, often in big 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 EU, businesses also face GDPR fines.
• Big data raises the risk of data breaches. If your company is the victim of a data breach, you risk stiff fines and the potential loss of customers who no longer trust you. Protecting consumer data is paramount.

Big data and your business

Big data is becoming big news for SMBs in the age of software subscriptions, AI, machine learning, and no-code and low-code data analysis tools. Your competition will likely soon leverage the power of big data insights, so why not get a head start?

Mike Wood contributed to the reporting and writing in this article.