Data Analytics is Shaping Business Fortunes
The most profitable businesses in the world are leveraging data at unprecedented rates, and the results are showing up in their P&L. Research from The Business Application Research Center (BARC) shows that companies leveraging their data efficiently see an average increase in profitability of 8% and a 10% reduction in costs.
It is results like these that have boosted the business intelligence market size to $24 billion in 2022. Data analytics for business is expected to grow to $43 billion by 2028, with a compound annual growth rate (CAGR) of 8.7%, according to Fortune Business Insights. The rapid digitization and collection of data, strong demand for data personalization, and the need to make data-driven business decisions fuel this growth.
Yet, many businesses remain challenged when it comes to utilizing all their data. In this article, we’ll explain the benefits of data analytics, examples of how companies are using data science, and ways to overcome the challenges.
What Is Data Analytics?
Data analytics is a discipline that uses data to draw conclusions by analyzing raw data in various forms. Data scientists and researchers use data to produce business intelligence, forecasting, and trends to help organizations stay on track and make better decisions.
The information generated by data analytics can be used in multiple ways, including reducing costs, optimizing and increasing productivity, product development, marketing, formulating growth strategies, driving revenue, and more.
What Are the Four Types of Data Analytics?
There are four main types of data analytics:
- Descriptive data analysis
- Diagnostic data analysis
- Predictive data analysis
- Prescriptive data analysis
Descriptive Analytics
Descriptive analytics is the type of data analytics used in reporting, dashboards, and many business intelligence tools. Researchers pull reports on-demand, such as a monthly summary of revenue and expenses. Real-time dashboards for data visualization are also common in BI tools to evaluate current performance, such as traffic to a website or social media engagement.
Descriptive analytics answer questions such as:
- What have we done in the past?
- What are we doing currently?
Diagnostic Analytics
With diagnostic analytics, data scientists examine past performance. This helps to understand what happened and why over the course of time. Often, historical data is used to create baselines against which future data and statistics are evaluated for performance. An example might be analyzing marketing campaigns to see which channels produced the highest conversion rates.
Diagnostic analytics answer questions such as:
- Why are we seeing these results?
- Can we determine causation from specific events?
Predictive Analytics
Predictive analytics is the most common category of data analytics in business. Organizations use predictive analytics to identify statistical models, trends, and correlations that can help them plan for future events. Examples include sales forecasting, customer churn, or risk assessment.
Predictive analytics can answer questions such as:
- What trends are we seeing?
- What is the likely outcome based on these trends?
Prescriptive Analytics
With prescriptive analytics, companies use artificial intelligence (AI)and Big Data to not only predict outcomes but also to identify what actions to take. Organizations use prescriptive analytics to run scenarios to anticipate outcomes. Data analysts commonly use optimization and random testing tactics.
Descriptive analytics can answer questions such as:
- What actions should we take next?
- Are specific actions likely to have positive benefits?
The Importance of Data Analytics in Today’s World
Data analytics has become an essential business tool. Data tells stories, and the stories are important for companies to understand their current state and predict future outcomes. Data lets companies understand the current marketplace and evaluate changes.
For example, the disruptions in supply chains over the past few years have profoundly affected shipping costs, inventory levels, and revenue generation for retailers. Big data analytics can help eCommerce companies prepare better for continued disruptions.
Challenges of Optimizing Data Analytics
Organizations have three significant challenges in data analytics. The first is data locked away in silos.
What Are Data Silos?
Over time, as companies build data stores and applications, data often becomes segregated into different programs, databases, or departments that have access to only some data. This makes the use of data analytics more difficult.
Without access across an organization, business leaders may only know part of the picture or draw incorrect solutions from inadequate or conflicting data. A good environment for statistical analysis can unify data by eliminating data and cloud silos, democratizing data by making it available to all authorized users, eliminating data silos, and allowing analysis of all the data sets.
What Are Knowledge Gaps?
The second challenge is the significant knowledge gap that exists within organizations. Businesses today gather large amounts of data. 60% or more of the data gathered goes unanalyzed. Companies often don’t know what information the data contains and are unable to leverage all of this data to find hidden insights.
Making business decisions without the benefit of business analytics is like playing roulette.
What Is Data Gravity?
When organizations work with large datasets, moving the data from one data lake, data warehouse, or application to another can be time-consuming and expensive. It’s easy for data to get out of date or require businesses to pay significant ingest and egress costs for data handling. This is known as data gravity, and it can hinder your agility and ability to analyze data.
The larger your data grows, the more challenging it becomes for business professionals to manage. Hosting, duplicating, replicating, and syncing data sets can be a serious concern.
How Data Analytics Used in Business & How Companies Benefit From their Data
Enterprises can benefit from their data in multiple ways to make better business decisions. Here are a few ways businesses are benefiting from data analytics along with examples.
Business Intelligence
Business intelligence encompasses a broad range of functions and can be used in a variety of ways to improve business. For example, companies can evaluate the impact of changes to products or pricing to determine how it impacts customer demands. Predictive analytics can forecast the results, while A/B testing can validate real-world results and determine whether to roll out full-scale changes.
BI can often spot trends and patterns that open up new markets. Data analytics can pave the way for innovation. For example, the London Stock Exchange now makes more money from the data it produces than from the securities trading it facilitates.
Customer Intelligence
Data analytics allows companies to have a deeper understanding of their customers and how they interact with products and respond to marketing. Data analysis can influence product or feature development to improve customer service, customer satisfaction, and customer experience.
Market Intelligence
Business doesn’t happen in a vacuum. Enterprises need to pay attention to market dynamics. Consumer behavior changes over time. Failing to recognize these trends can put you at a competitive disadvantage and lead to missed opportunities. Financial institutions, for example, benefit from fast access to both structured and unstructured data to react to shifting market trends.
Your competitors are also constantly evolving. Market intelligence can help you anticipate what your competitors are doing or understand the impact of pricing changes or new products on the marketplace. This becomes incredibly useful information to help position your company against your competitor’s offerings.
Streamlined Operations
Companies can improve their operational efficiency by gathering and analyzing process data. Data analytics are being used in retail to anticipate demand to maintain optimal inventory, including automatic reorder points based on future forecasting. Business executives can better plan for multiple variables and take advantage of an emerging opportunity that arises.
Cost Management
Business intelligence can also provide significant savings by managing inventory levels more efficiently, matching staffing levels with demand, and navigating the complexities of supply chains for more efficient routing. Companies with large data stores use AI to analyze data storage and network traffic costs and dynamically routing traffic for cost-efficiency.
Risk Mitigation
Data mining can help mitigate risk. Banks can use AI and machine learning to predict when loans are likely to go into default. A retail chain might use a statistical model to determine stores at higher risk for theft to deploy appropriate security levels or stock up certain locations to meet the growing demand for regional favorites.
Unifying Data
Pulling data together from all of your data sources allows you to draw better conclusions. For example, a multi-cloud solution unifies data into one copy and makes it simultaneously accessible to all authorized users on all clouds. A single data set provides enterprises with a complete view of their data and one source of truth for business foresight. Unifying data reduces IT overhead, lowers the cost of data storage, and minimizes the time and effort required for data management.
The Impact of the Exponential Growth of Data on Business Fortunes
Companies that use data analytics are driving better outcomes by improving their metrics, earnings, and profit margins.
The obvious examples of using Big Data to grow business are the market leaders in eCommerce and online streaming. Amazon does more than one trillion dollars in revenue annually and attributes a significant amount of its retail success due to its recommendation engine, which pairs user behavior with historical purchases data and trends to anticipate customer needs.
Netflix knows that higher levels of engagement reduce churn rates. Netflix uses its vast treasure chest of data to predict which piece of content users are most likely to be interested in. Its algorithms may contain as many as 10,000 images for a single title and display the one it believes will most entice you to watch. As much as 80% of viewing on the streaming service comes from personalized recommendations from various data points, which Netflix says generates $1 billion a year from customer retention.
Fintech uses data analytics to detect potential signs of fraud and make better loan assessments. Manufacturers are using predictive models for proactive maintenance to minimize downtime. Home improvements stores are marrying online and offline data to make inventory decisions for individual stores.
The list goes on and on. Data-driven companies are 58% more likely to beat revenue goals, according to Forrester Consulting. Businesses that are efficient and data-savvy see even better performance, beating competitors that aren’t using their data well by as much as 162% in revenue attainment.
McKinsey agrees that leveraging data leads to improved understanding and operations. The business consulting and research company did a deep dive into the data collection and data practices of telecommunication companies. It found that only about five percent of telcos are maximizing their data science potential. McKinsey believes that operators could increase customer satisfaction by as much as 30% and revenue by as much as 10% with better data analytics from data they already have.
Unfortunately, many companies are simply drowning in data and struggling to extract its value. Data is hidden across multiple sources of data or stored in disparate locations. It is often out of sync with current data or sitting unknown.
Setting Your Organization Up for Future Business Success with a Multi-Cloud Solution
Faction’s Multi-cloud Data Services Platform connects your organization to best-in-class services from multiple cloud providers simultaneously, so you can leverage the full value of your data for a more effective business strategy.
Faction’s solutions unify data from all of your data sources into one copy of an organization’s data and makes it simultaneously accessible to all authorized users. Faction single data sets provide enterprises with a complete view of their data and one source of truth for business foresight. Unifying data reduces IT overhead, lowers the cost of data storage, and lowers the time and effort required for data management.
This frees organizations from the confines of legacy architecture and infrastructure. With a single source of truth, users always know they have the most up-to-date and accurate information. Faction unlocks data silos and allows businesses to leverage their full power of data science.
To learn more about multi-cloud data services and to maximize your data assets for business growth, book an intro call with the data science experts at Faction today.
