Why Do We Need Data Analytics Instead of Reporting?

Need data analytics

Data analytics is an increasingly important asset for growing and improving businesses. But what exactly is data analytics, and what is it good for?

Data analytics — a relatively young field — is the process of analyzing data to gain insights and create recommendations. Successful data analytics relies on high-quality software programs and skilled interpretation.

Most data analytics programs use sophisticated software to collect information. For instance, at DataSync, we often use IBM’s Cognos, TM1, and SPSS to collect data and make forecasts. These programs help to collect and consolidate business intelligence, recruitment, human resource forecasts, financial forecasts, budgets and workflow processes. We also create customized systems to collect information from social media, GPS locations and other types of data.

Although machine learning and sophisticated software helps, effective data analytics relies on the insights of skilled analysts. Data analysts usually have highly refined technical skills, but they also excel in the soft skills that machines can’t handle — such as identifying the right questions, finding relevant data sources and interpreting results.

Data analytics also relies on good communication. CEOs shouldn’t need to master R queries or advanced statistics to make decisions. It should be the data analysts who look for important information, stripping away irrelevant data so the CEO can focus on making those important decisions.

Benefits of Analytical Reporting Over Operational Reporting

In the past, many businesses made crucial decisions based on their operational reporting. Although this is possible, decisions made without analytics are often more limited. In addition, the effectiveness of recommendations is based on the reader’s knowledge of data.

In today’s economy, data analytics provides more value to businesses than operational reporting alone. That’s influenced by changes in information management and the types of questions that businesses today face.

There are two big influences driving the trend toward data analysis over operational reporting:

1. Companies Have More Data Available

Businesses have more information available to them than ever before, and they create more information faster than ever as well. The forms this information takes have changed dramatically, too. For example, data now includes GPS locations, social media comments, images and sensor readings.

It’s no wonder that information management and analysis has changed over the years, and smart companies have learned to combine and leverage different types of information. Data analytics allows you to merge varying sources of data to understand the stories and trends that are influencing your business.

Because of the sheer amount of data available to most companies, those that focus on simply reporting don’t use most of the information available to them. As a result, their strategies and decisions are less informed and nuanced than companies that use data analytics.

2. Operational Reporting Doesn’t Show Inefficiencies

Companies turn to data analysis because they want to streamline their existing processes and workflows. Many businesses have limited resources, and customers want products faster than ever before, so identifying any inefficiencies is key to success.

Regular business reporting, however, often fails to identify inefficiencies in workflows or products. This is because a static snapshot rarely provides information about production over time. Most processes also usually have a number of inputs, from delivery routes to customer preferences to pricing trends.

Identifying ways to streamline these processes requires businesses to analyze large amounts of data. For instance, many utility companies have begun to use data analysis vs. reporting in this way. By aggregating and analyzing information from utility meters, weather patterns, customer usage trends and pricing variations, utility companies are able to recommend effective energy-saving techniques for large numbers of customers with similar behaviors.

Without data analytics, this process would be too time consuming and unwieldy for the utility companies to use.

The Competitive Advantage of Data Analytics

The use of data analytics provides businesses with a competitive advantage. In fact, one survey showed that 63 percent of businesses with a data analytics program think that data analytics provided their business with a competitive advantage.

competitive advantage of data analytics

Although many businesses think data analytics gives them an advantage, not all businesses understand the impact this advantage can have.

The amount of data businesses, competitors and customers produce each day is growing exponentially. By mining this data for hidden patterns and insights, businesses can produce better products faster than their competitors. It’s possible to segment customer markets more narrowly, respond to consumers faster and minimize risks that other businesses haven’t spotted yet.

advantages of data analytics

It’s big. As an example, using data analytics throughout the U.S. healthcare system could add more than $300 billion of value to the industry each year. Additionally, by using data to determine where to reduce expenses, researchers estimated that the industry could cut costs by nearly two-thirds.

Other industries could see similar benefits. Data analytics is already being used to improve shipping routes for a wide variety of companies — and these businesses are seeing lower fuel costs and payroll expenses than their competitors. Some retailers, for instance, that use data analytics throughout the supply chain have decreased their operating margins by more than 60 percent.

How Does Data Analytics Provide This Competitive Advantage?

Data analytics makes it possible to review large pools of data efficiently. Businesses and customers produce more information than ever before, and reviewing this data via old fashioned reports is too time consuming to be useful.

more-than-60-percent

Data analytics also provides information and recommendations quickly. Businesses are able to use real-time information from social media, GPS and other sources to identify and respond to customer desires quickly. This allows companies to use demand-driven pricing and to determine consumer confidence in real time.

One of the biggest issues with operational reporting is the creation of data silos — information that is looked at in isolation from other company data. Without data analytics, combining this data and reviewing it for insights is expensive and time consuming — yet it’s this combined format that can create a more nuanced picture of the market.

That’s why many businesses cite the ability to merge data from different departments as one of their biggest competitive advantages. With data analytics, you can search many different types of data for correlations and insights. This helps to identify new growth opportunities and market trends early in the game.

The Differences Between Operational Reporting and Analytical Reporting

Many businesses think that they’re already using data analytics. These businesses may gather reports on product success and customer retention. They may even have a full-fledged operational reporting program — but they may not be actually using data analytics.

Operational reporting is very different than analytical reporting, but what exactly are the differences?

Operational reporting generally:

  • Gathers data from specific products, programs or services
  • Provides a snapshot of past business performance in a given time period
  • Is “pushed” to users via email or other mediums
  • Relies on readers to analyze and contextualize data

Reporting is the process of collecting and summarizing business information, and it’s usually used to monitor existing programs or products. Business reporting requires users who are informed about the context of the information because they need to be able to generate meaningful insights and actions from the data directly.

Data analytics, on the other hand:

  • Incorporates data from multiple sources
  • Provides information about trends and predictions about future performance
  • Is pulled from multiple sources to answer a specific question
  • Provides recommendations based on the question being addressed

Data analytics is the process of pulling information — sometimes from business reports — to collect and analyze data. Data analysis is normally used to identify areas to expand and to introduce new products and new strategic approaches.

Businesses that use data analytics have a decision-making process that’s based on the information they’ve collected. Instead of relying on guesswork, these businesses have hard data to back up their decisions. The decision-making process usually has five steps:

  • Data reporting or operational reporting: Data about business practices, products and revenue is gathered.
  • Data analysis: This data is used to identify or help solve a problem that the business is facing.
  • Recommendations: Suggestions for future actions are developed based on the insights gained from the analysis.
  • Action: The company enacts one or more of the recommendations created from their data analytics.
  • Value: The company is streamlined, experiences growth or solves a problem based on the action they’ve taken. Data from the new strategies can be collected to help shape future strategies.

As you can see, operational reporting is just one step in the chain of data analytics and strategic improvement.

How Businesses Use Data Analytics

Since data analytics is a new field, the way that businesses use it is changing rapidly. From the types of data that can be used, to the problems that businesses attempt to solve, the range of applications is growing daily.

Companies that use data analytics

Because it’s not always easy to imagine the impact of data analytics, we’ve rounded up a few real world examples. The companies that follow have all used data analytics to make big changes to their supply chain, operating costs and how they do business:

1. Wal-Mart Stores, Inc.

Wal-Mart developed their data analytics software, Polaris, in house. It analyzes text and synonyms in search terms to improve relevant search results. Customers often used different terms or unusual syntax in their searches, so by mining previous search and purchase data, Wal-Mart was able to create a better product search tool.

This meant consumers were now more likely to find the products they’re looking for. After Wal-Mart began using Polaris, the number of online shoppers that completed purchases jumped by 10 percent.

2. Los Angeles Police Department

Big data can benefit more than businesses. The Los Angeles Police Department teamed up with a group of professors to develop a way to use data analytics to predict crimes. They tweaked software that’s used for reporting earthquakes to highlight where crimes are likely to occur. The software analyzed past crime reports to look for trends and correlations, then suggested where crimes were likely to occur in the future.

Several neighborhoods and jurisdictions in LA now use this data to predict crime in their neighborhoods. By improving police visibility in neighborhoods where crime was predicted, they were able to decrease the number of actual crimes. With the help of their data analytics, the LAPD has reduced burglaries by 33% and violent crime by 21%.

3. UPS

UPS uses data analytics to streamline their delivery services. Analyzing their past data about delivery routes, how long trucks idle as well as maintenance and service patterns, UPS looked at how to speed up their deliveries and reduce fuel use.

Since they began optimizing their fleet based on data analysis, UPS has saved over 39 million gallons of fuel and reduced their driving distances by 394 million miles.

Other Business Outcomes Improved by Data Analytics

Because data analytics focuses on developing solutions, it’s an ideal tool to help businesses solidify their market reach and develop new products. But many explanations of data analytics focus on technical components instead of on your business.

We think this is the wrong approach.

Data analytics should start with your business. It’s a tool to help you capitalize on opportunities and identify weaknesses.

Examples of the types of problems that data analytics can help solve include:

  • Identifying your most profitable customers and figuring out how to serve them better
  • Finding new revenue opportunities
  • Creating strategies to reduce fraud and theft
  • Managing business risk by connecting information with the departments that benefit from it
  • Identifying new customers and how to reach them
  • Finding emerging trends and determining how to take advantage of them
  • Identifying unmet customer needs
  • Improving the effectiveness of services
  • Optimizing supply chains and development processes
  • Creating new business models

Answering these questions can make a big impact on your business.

And because the tools and techniques used in data analytics are advancing constantly, new applications for data analytics are always being found.

Not too long ago, the benefits using data analytics vs. reporting to improve national security would have been unheard of. Then government contractors asked whether they could use data analytics to identify security threats. Today, data analytics can be used to create sophisticated algorithms that identify terrorist cells communicating on social media.

Using data analytics is a proven way to help keep your business on top of new trends. Looking for more information about how data analysis can help your business? Sign up for our newsletter today!