Use big data to answer business questions

All business today is collecting data, regardless of industry, product, or services. This blog post will explain the term: Big Data and how companies worldwide can gain valuable insight by analyzing their various collected datasets.

Data is created and collected in all departments in a company. Every time a customer buys a product from your webshop, in your physical store, or through a salesperson, new data is created. New data is also produced every time a person leaves a comment on social media, reading your blog post, or pushing the unfollow button. New data is created every second of the day.

Data contains unique information about a companies customers buying behavior, areas for product optimization, improved stock management, only to mention a few. Analyzing your data can help you make the best business decisions and help you identify areas to put the future focus on.

The purpose is simple: Increase revenue or lower cost. In the end, the purpose of understanding your data is about earning money.

The definition of big data

To fully understand what kind of data is necessary for a data science analysis, let us first understand what big data means. Big data is the buzzword created in the digitalization years in the 2000s and covers collecting a large volume of collected data.

In the last few years alone, 90% of all the world’s data are created, and our current daily data output has reached 2.5 quintillion bytes []. We will not dig deeper into the amount of data created every day, but trust me, 2.5 quintillion bytes is a lot, and the daily amount of collected data makes big data big.

New data is produced daily; everything from a recorded phone call, sales transactions to entering specific rooms with a keycard to videos from security cameras is considered data. ‘

Doug Laney’s three V’

Data goes from being ‘regular’ data to big data when the collected data is so large or complex that it is difficult or impossible to process using traditional methods.

The analyst Doug Laney has formulated three dimensions of big data known as the Three V’ []:

VOLUME:
Is covering the amount of the data. Companies collected data from different sources as transactions, Smart IoT, industrial equipment, videos, social media, etc.

VELOCITY:
Refers to the speed of the data processing. With IoT, data streamers, and more people becoming online, the unprecedented pace of handling the received data is a crucial element. Therefore, the better and faster internet has a significant impact on the opportunities when working with data.

VARIETY:
Refers to the number of types of data.
From structured data as numeric data in a traditional database to unstructured data as text documents, emails, videos, audios, stock ticker, and financial transactions. The variety of the dataset creates the opportunity to find patterns across different areas that are impossible for a human being to identify.

I mean, it can be argued that there should be added two more V’s in the description of big data:

VARIABILITY:
With the increase in velocities and varieties of data, the data flow is becoming unpredictable, which is a challenge.
Every business needs to stay updated with the newest trends on social media and how to manage daily, seasonal, or event-triggered data loads.

VERACITY:
Refers to the quality of data. Data today is collected through various sources, making it difficult to link, match, cleanse, and transform data across systems. Therefore, businesses need to connect and correlate relationships, hierarchies, and multiple data linkages to control their data flow.

Because we now understand what big data is and how it is produced, let us look at how and why using the collected data is a good idea.

Why and how to use big data

McKinsey Global Institute estimated that the potential value of adding artificial intelligence and data science in the retail business is between $400 to $800 billion globally []. This is just one example of an area data science can contribute with economic value.

Many companies still don’t know how to use their data properly. For example, an analysis made by technical ethnographer and TedTalk’er Tricia Wans shows that 73% of all big data projects are not profitable [], and a study made by Gartner reveals that in 2022 only 20% of analytic insights will deliver business outcomes [].

Five ways to use big data in business

The purpose of the following five examples is to understand some of the areas big data can help your company, which hopefully will get your mind wondering where you can adapt big data analyses in your business. First, you must know that the opportunity with big data is endless — only the imaginary is setting the limit.

Data can increase and optimize every part of your business, and sometimes the same datasets can create value across departments if you just look at it differently.

1. Big data and risk management

Times changes, and you never know what tomorrow will bring — which is why every company needs to have a strategic approach on how to handle future risks. There are many factors to examine when working with risk management but understanding the risk in a statistical and data-driven way — is one of the most important. By analyzing collected data, companies can rely on predictive analysis to create an intelligent risk foresight.

UOB bank from Singapore created a risk management system based on their big data. The purpose of the system was to minimize the time it took for the bank to calculate risk as a loan application, which initially took about 18 hours. The risk management system could do the same job with higher accuracy in only a few minutes [].

2. Big data and customer understanding

Your customers are the reason for your company. By analyzing big data, companies can observe various customers and identify related patterns and trends in the customer’s behavior. Your customers are giving you a lot of data every day. Data from social media comments to online sales transactions reveals a lot of information about your customer’s profiles, sentiment, and buying behavior in your different customer segments.

By understanding your individual customer, you can improve product or service offers, which allows you to reach the highest level of customer satisfaction.

Alibaba.com is one of the pioneers in e-commerce. By implementing a cross-selling strategy based on sales transactions and basket analysis, they increased their conversation rate by 20% [].

3. Big data in marketing

Understanding your customer’s behavior allows you to create personalized marketing strategies for each customer segment.

By knowing the most likely customer behavior in the conversion funnel or after a purchase, you can target each customer with personalized advertising. Furthermore, the insight also identifies when customers are about to churn, which gives you the opportunity to react by example gives them a special offer to have them stay as your customers.

AdrianCamp increased their booking amount by 60% by implementing a marketing strategy based on a data analysis [].

4. Competitive advantages with big data

You can access some of your competitor’s big data, which can be used to increase your competitiveness. For instance, data can provide you with information about their pricing model and how customers perceive it. As a result, you can get insight into your competitors’ perceptions and identify why some customers choose your competitors’ products or services rather than yours.

The well-known tool manufacturer Bosch used a competitive analysis to identify which customers were better satisfied by DeWalt and Makita and why. Not only did the analysis revealed every competitor’s weaknesses and strengths, but enabled Bosch to determine the technical reasons for their success, which guided their focal point to the most profitable direction [].

5. Big data used in product development

Understand your data can help you innovate and redevelop your products. For example, knowing why some customers choose your competitor’s products or services over yours allows you to redevelop your products to contain the features the customer demands.

When implementing data insight for product development, “gut feeling” plays a much smaller part in the decision process because you know which exact unique selling point your customers want.

The consumer good manufacturer Procter and Gamble (P&G) applied big data analysis in their product development by aggregating consumer data from various touchpoints. For example, they’ve used data to determine how molecules in a particular dishwashing liquid reacted over time with the purpose to refine the product [].

Take-Aways

Investing in big data is essential if you want to grow your business. Implementing insight from collected data businesses worldwide can achieve a competitive advantage, reduce the cost of operations and drive customer retention.

Big data has endless applications in every business. By uncovering the hidden patterns and correlations between various datasets companies, can today make proper business decisions.

Originally published at https://www.borbaki.com on June 18, 2021.