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For more than two decades, business executives and business owners have been obsessed with capturing as much data as possible, from customers, employees, suppliers, developers and other relevant sources. . Because of the relatively small amount 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 yesterday and now is abundance.

According to the International Data Corporation, more than 59 zettabytes of data were created in 2020, a volume 30 times greater than that generated just 10 years ago. The amount of data available has exploded, far more than any business can handle. Therefore, how a business consumes data is more important to success than the amount of data it collects.

Let’s explore the importance of data consumption and see what resources businesses can use to process and get the most from their data.

Related: The Insane Amounts of Data We Use Every Minute (Infographic)

Why data consumption matters

There are two types of data:

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

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

Raw data is unusable, while processed data is essential for making business decisions.

But how do you create structured data?

The answer lies in the consumption of data.

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

In many ways, the way a business consumes data is similar to gold mining. Gold, a precious metal, only gains value once it is mined from the earth, refined, and melted down into functional objects like jewelry or microchips (a more valuable end product.)

A company’s approach to data consumption is also key to finding actionable insights to increase sales, improve customer experience and retention, reduce operating costs, and capture greater market share. .

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

The most important point here is that, regardless of its size, every organization must consume data wisely to maintain its longevity.

Related: Why Quantitative And Qualitative Data Is Vital For Results-Driven Businesses

Four modes of data consumption

There are four proven ways a business can analyze data:

  • Descriptive analysis evaluates historical data to identify patterns and trends. Descriptive analysis is the most common approach to data consumption because it focuses on synthesizing data and identifying general trends.

  • Diagnostic analysis examines the data to determine why an event has occurred in the past. Through data mining and correlation, diagnostic analysis detects trends and anomalies, an operation that goes beyond descriptive analysis functions.

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

  • Prescriptive analysis applies descriptive and predictive data to test multiple variables and calculates the best possible outcome. Prescriptive analysis 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 example, auto mechanics trying to determine why an engine failed can use diagnostic scans. Meanwhile, a company like Amazon could use predictive and prescriptive analytics to recommend new products to consumers and drive sales.

Likewise, commercial real estate operators are increasingly using predictive analytics to forecast future income by tracking several factors in real time, including traffic counts, neighborhood trends, local development, analysis of heat maps, etc.

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 for evaluating their data, it is essential that they select the right resources to interpret it.

The most popular data consumption tools include:

  • Third-party analyzes. Many tech companies, such as Google and Facebook, specialize in collecting and analyzing data from other companies and providing basic analytics to owners. These scans are a great tool for small businesses with limited budgets or who don’t need in-depth statistics.

  • Internal data analysts. Medium and large businesses sometimes hire a dedicated data analyst whose sole responsibility is to oversee the processing and organization of all of their data. Calling on such a specialist offers the greatest flexibility, although it is costly.

  • Tailored data platforms. Organizations that process massive amounts of data can build custom data analysis platforms from scratch. These platforms include robust dashboards that analyze millions of data sets in real time. Their implementations have streamlined the operations of many multinational companies.

As a commercial real estate owner, you need to start by assessing the size, goals, and budget of your business to determine the right data analysis tools for your operations.

Also, consider taking the initial small step of integrating technology to streamline and automate your processes. This can simplify the transition to integrating data analytics into your operations, as it did for Bellwether Enterprise, a commercial, multi-family real estate banking company with offices across the country.

Rather than immediately launching into data analytics, the company slowly began to introduce AI to facilitate its data collection, which in 2019 helped the company take out $ 7.9 billion in loan volume. and manage a $ 31.2 billion portfolio.

It was only recently that Bellwether Enterprise added reporting modules to provide the management team with detailed data analysis. The firm’s path offers a model approach to 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 in data analysis

Our digital economy has evolved beyond the point where data collection alone can facilitate success. Moving forward now requires collecting and evaluating data to gather critical insight into market demand, run lean, and generate consistent returns.

There was a time when accumulating and interpreting data 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 – prioritizing the consumption of mission-critical data.

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