Over the years, the technology industry has had fantastic inventions and innovations. Business leaders who adopted them have also reported tremendous improvement in their firms’ productivity and revenue. One of the major adoption of technology in business is using insights gathered from data to predict market behavior and strategic use of resources to maximize results.
These insights are deduced using data analytics tools. While big companies are embracing the latest tools, including artificial intelligence, small business owners have been observed to need to catch up.
A survey report by the Singapore Institute of Technology and the Institute of Singapore Chartered Accountants discovered that about 70 percent of Small and Medium-sized Enterprises (SMEs) are yet to adopt data analytics solutions and services. A similar report published by the Service Corps of Retired Executives (SCORE) said that 51 percent of small businesses agree data analytics are critical, but only 45 percent indeed track required data.
This trend was well captured by Robert Waterman decades ago in his book In Search of Excellence when he observed companies were “data rich and information poor.”
What is Data Analytics?
Data analytics is the process of collecting, transforming, organizing, and analyzing raw data to draw valuable conclusions, make predictions and drive intelligent actions to optimize performance.
Many data analytics techniques and processes have been automated with technology, making big data processing easier with few clicks and keyboard punches.
You are likely familiar with spreadsheets and databases, but companies now use advanced data analytics solutions to handle different data sizes, especially big data.
Big Data
Big data describes data with large, diverse sets of information that tend to grow at ever-increasing rates. It is characterized by three Vs: Variety, Volume, and Velocity.
The 3 Vs of Big Data
1. Variety. Big data can be structured or unstructured and sometimes semistructured. This is a result of technological improvement. Data can be collected in text, audio, and video format and, sometimes, as a mixture, hence the need for a processing tool that can efficiently derive meaning from them.
2. Volume. Big data are not named “big” for nothing; they are generally large, as much as tens of terabytes or hundreds of petabytes.
3. Velocity. Some business products operate in real-time and require instant processing of received data. Big data are designed to stream directly into memory and instantly processed for results.
Types of Data Analytics
To effectively leverage the power of data, it is vital you understand the four basic types of data analytics.
Descriptive analytics
This is considered the simplest type of data analytics. It is paramount for any other types of analytics as it is concerned with the description of what has happened or is currently happening,
Descriptive Analytics can be used to deduce details about a surge in seasonal sales for a specific product.
Diagnostic analytics
Diagnostic analytics building on the details extracted by descriptive analysis will present reports on why something is happening.
For instance, after descriptive analytics identifies the sales surge of a product during a seasonal season, diagnostic analytics will find out why such is happening.
It can help identify what the company is doing right and areas it can improve.
Predictive analytics
This analyses data from what has happened and what is happening to predict things to expect in the future.
The insight about a product’s sales during a season and why it does can be used to assess certain activities of a firm to predict what to expect in the future.
Prescriptive analytics
This suggests how a firm’s mode of operation can be optimized for a better result.
In continuation of the example of the surge in a product sale during a season, the prescriptive analysis will use insights into what was done to achieve such sales to recommend actionable takeaways a business can leverage to improve revenue.
Role of data analytics in small business success
Studies have shown that most of our daily actions are driven by repetition, while repetition builds habit. Meanwhile, a study by scientists at the University College London discovered that it takes, on average, 66 days for a new behavior to become automatic.
For managers used to traditional data processing methods, employing data analytics solutions may initially be uncomfortable, but the benefits outweigh the inconvenience. It can become part of a company’s daily routine with enough repetition.
Below are some of the roles of data analytics in the success of a business:
Finding and retaining customers
Over 70 percent of small businesses use data analytics to find new customers. Another 67 percent are using it to retain their existing customers.
Businesses, especially those with a digital presence, can track their prospective customers’ digital footprints, such as the products they check out on their e-commerce websites.
Data analytics can offer insights into these customers’ needs, preferences, and browsing behavior. This information can also help deploy an effective marketing strategy by targeting the demographic most likely to be interested in your product or service. This significantly reduces the acquisition cost and improves return on investment (ROI).
Identifying opportunities
Predictive and prescriptive analytics have the potential to help pinpoint opportunities to explore and also identify areas proactive steps can help improve revenue.
With machine learning (ML) and artificial intelligence (AI), data analytics now enhance companies with real-time reports of opportunities in their targeted markets even before competitors notice.
Enhance decision-making capabilities
A company is only as good as its decision-makers. Unfortunately, many human decision-makers have shortfalls, such as judging every occurrence with experience even when the parameters differ.
Some managers hesitate to explore a new market opportunity solely because they are unfamiliar with its operation despite the firm’s competence and human resources. This is usually true of managers who are scared of deploying technology. They trust traditional methods better.
Insights from data analytics make it easier to diagnose a decision and predict all possible outcomes. Prescriptive data analyses can also help determine how to navigate an obstacle while exploring a new market.
Improve productivity
Advanced data analytics can help small business owners assess the effectiveness of their mode of operation, analyze the outcomes, and optimize the processes for improved productivity.
It can pinpoint areas that need improvement and educate employees about their work habits and activities.
Another edge data analytics have over typical productivity assessment is that it executes a continuous process. This means there will be constant recommendations for optimizing your company’s productivity.
Keep track of the competition
Business is a race. You are either the first or the underdog threatened with bankruptcy.
Competitors also come in different sizes, some with more budget than you and others with better strategy.
As a small business, you may not have the budget to hire top-ranking executives for specific roles. Still, with data analytics, you can get first-hand insights into what your competitors are doing right and areas you can outshine them.
Competitive data may include your competitor’s products, pricing, targeted market, and promotional strategies. All of these can be analyzed using data analytics tools to provide you with details of their strength and weaknesses.
Michael Zhou is a Senior VP of Business Intelligence Development and has assisted the Fortune 1000 company with expertise in the web as a whole, including ground-zero marketing efforts that benefit both consumer and vendor. He is also a contributor on Esprittoday.
Data stock image by Andrey_Popov/Shutterstock
