Data Isn't Important, But How You Consume It Is Two decades ago, data was the buzzword in the business world.

For more than two decades, corporate executives and business owners have been obsessed over capturing as much data as possible, from clients, employees, suppliers, developers and other relevant sources. Due to the relatively modest volume of data created at the time, whoever held the most data had the greatest competitive advantage. But the thing that has changed in the data space between then and now is abundance.

According to the International Data Corporation, more than 59 zettabytes of data were created in 2020 — 30 times greater in volume than generated just 10 years ago. The sheer amount of data available has exploded, much more than any single company can handle. As a result, how a company consumes data is a more significant determinant of success than how much data they collect.

Let's explore the importance of data consumption and see the resources businesses can use to optimally process and leverage their data.

Related: The Insane Amounts of Data We're Using Every Minute (Infographic)

Why data consumption is important

There are two types of data:

1. Raw, unstructured data, consisting of numbers or lines of code.

2. Processed data, which is raw data that has been cleaned, organized and transformed into information (i.e., knowledge).

Raw data is unusable, while processed data is key to driving business decisions.

But how do you create structured data?

The answer is through data consumption.

Data consumption is the process of transforming raw data into processed information readable by business intelligence (BI) software, which then extracts meaningful analyses and patterns.

In many ways, how a company consumes data is similar to gold mining. Gold, a precious metal, only becomes valuable once it's excavated from the earth, refined and melted into functional items like jewelry or microchips (a more valuable end-product.)

A business's approach to data consumption is also critical to finding actionable information for increasing sales, improving customer experience and retention, reducing operating costs and capturing a larger market share.

A recent example of effective data consumption can be found in Netflix's success with quantitative data. The billion-dollar online streaming platform captures viewing information from its hundreds of millions of subscribers and uses the data to make personalized recommendations for new shows. This efficient application of data analysis helps the company retain 93% of its customers and ensures 80% of its content is streamed.

The larger point here is, no matter the size, every organization must consume data judiciously to maintain its longevity.

Related: Why Both Quantitative and Qualitative Data Are Vital for Results-Driven Businesses

Four methods of data consumption

There are four proven ways a business can analyze data:

• Descriptive Analytics evaluates historical data to identify patterns and trends. Descriptive analytics is the most common approach to data consumption as it centers on summarizing data and identifying general trends.

• Diagnostic Analytics reviews data to determine why an event occurred in the past. Through data mining and correlation, diagnostic analytics spots trends and anomalies — an operation that's a step beyond the functions of descriptive analytics.

• Predictive Analytics analyzes data to forecast or predict future patterns. Predictive analytics, popular among large corporations, involves consuming data in a way that proactively drives business decisions and revenue.

• Prescriptive Analytics applies descriptive and predictive data to test multiple variables and calculates the best possible outcome. Prescriptive analytics compiles the results of all other methods to provide recommended courses of action.

When processing data, there is no one-size-fits-all method.

For instance, auto mechanics trying to determine why an engine failed may use diagnostic analytics. Meanwhile, a company like Amazon could employ predictive and prescriptive analyses to recommend new products to consumers and stimulate sales.

In the same vein, commercial real-estate operators are increasingly using predictive analysis to forecast future revenue by tracking several factors in real time, including traffic counts, neighborhood trends, local development, heatmap analysis and more.

The best data-processing method for your organization will depend on the type of data you collect and your goals.

Tools that drive data analysis

Once companies have identified the best approach to evaluate their data, it's essential they select the right resources to interpret it.

The most popular data consumption tools include:

• Third-party analytics. Numerous tech companies, such as Google and Facebook, specialize in collecting and analyzing the data of other businesses and providing elementary analytics to owners. These analytics are an excellent tool for small businesses with limited budgets or no need for deep statistics.

• Internal data analysts. Mid- to large-size companies sometimes hire a dedicated data analyst whose sole responsibility is to oversee the processing and organization of all their data. Bringing on such a specialist provides the greatest, though pricey, flexibility.

• Custom-built data platforms. Organizations that process a massive amount of data can custom-build data analytics platforms from scratch. These platforms include robust dashboards that analyze millions of datasets in real time. Their implementations have streamlined the operations of many multinational corporations.

As a commercial real-estate owner, you should begin by evaluating your company's size, goals and budget to determine the right data analytics tools for your operations.

Additionally, consider taking the small, initial step of integrating technology to streamline and automate your processes. Doing so can simplify the transition to incorporating data analytics into your operations, as it did for Bellwether Enterprise, a commercial and multifamily real-estate banking company with offices nationwide.

Rather than taking the immediate dive into data analytics, the firm began slowly by introducing AI to facilitate its data collection, which in 2019 helped the company underwrite $7.9 billion in loan volume and manage a $31.2 billion portfolio.

Only recently did Bellwether Enterprise add reporting modules to provide the management team with detailed data analytics. The firm's path offers a model approach for a commercial real-estate operator to select the best resources to analyze their data.

Related: How Entrepreneurs Can Use Data Aggregation to Grow Their Business

The future is data analysis

Our digital economy has evolved beyond the point where data collection alone can facilitate success. Now, getting ahead requires collecting and evaluating data to gather crucial insights on market demand, running lean and generating consistent returns.

There was a time when data accumulation and interpretation seemed a daunting task. But in the 2020s, we have concrete methods, tools and resources to streamline both processes.

It's time to equip your business to achieve exponential growth — and scale with confidence — by prioritizing strategic data consumption.