A leading management advisory and consulting company had 20,000 contacts in its database. On the surface, this sounds like great news for the organization. But it posed a problem: the contact records featured errors, duplicates and old information. Rather than empowering the company to make quick, meaningful connections with clients or potential customers, the contact records in the database were unreliable data sources that threatened the company’s efficiency.
While the consulting company’s problem affected them specifically, it is not an uncommon corporate dilemma. In fact, on average, leaders of American companies say that a quarter of their data is inaccurate. 81 percent of those companies report encountering problems when they try to generate meaningful business intelligence. Working off of bad data is also an expensive problem, costing businesses somewhere between 15 and 25 percent of revenue.
For the consulting company, the solution to their bad data problem included a technology tool that could cleanse and enrich more than 600 contacts per day. Now working with an accurate database, the company is able to connect with clients and customers efficiently and generate meaningful business insights.
Contact databases are one of several examples of the ways in which big data and business analytics can overlap to create positive outcomes for companies. Organizations that strategically use big data to support business analytics tend to see positive results in everything from company culture to marketplace success.
What is Big Data?
Big data refers to a collection of structured, semi-structured and unstructured data that is too difficult for traditional processing methods to handle. It could be that the data sets are too big, complex, or rapidly generated. Gartner defines big data as information that is:
Such information requires advanced, often innovative methods of data processing so that companies can make the most of their big data and use it to support business analytics.
How Does Big Data Support Business Analytics?
Organizations with the means for analyzing large amounts of data can produce advanced analytics that are useful for a range of tasks. Such analytics empower companies to enhance revenue, motivate staff, improve performance and increase market share. Consider how successful companies have leveraged big data applications for better business intelligence in financial records processing, fraud detection, streamlining processes and inventory optimization.
Dropbox: Cutting Costs with Automated Financial Records Processing
With 600 million users across 180 countries, Dropbox needed a comprehensive technology solution that could process big data and produce actionable business analytics insights. Dropbox’s many monthly transactions resulted in high volumes of financial data that needed cleansing and processing. The answer was a cloud infrastructure from Oracle that was equipped for analyzing large amounts of data. The solution automated, consolidated and managed the monthly invoices and transactions.
As a result, Dropbox:
- Cut the cost of financial records processing by 80 percent
- Reduced the volume of transactions
- Sped up the accounts receivable process
With the time and money saved by the cloud solution, Dropbox leaders were better able to focus on leveraging automated analytics to improve other processes like cash flow, product modeling and resource allocation.
“We needed a solution with built-in machine learning that integrated with our ERP, eliminated complexity and simplified application development,” says Vikram Singhvi, Head of Enterprise Applications at Dropbox. “Running on Oracle Cloud Infrastructure means we close our accounts receivable process four times faster.”
State Auto: Enhancing the Insurance Life Cycle by Improving Fraud Detection
Insurance fraud costs American consumers at least $308.6 billion every year and occurs in approximately 10 percent of property-casualty insurance losses. In 2020, State Automobile Mutual Insurance Company (State Auto), which provides insurance in nine lines of business across 33 states, had already seen real gains from implementing big data solutions and leveraging business intelligence, including cost savings, efficiency gains and improved user experience. Those positive outcomes encouraged company leaders to continue building more solutions through Amazon Web Services in additional stages of the insurance life cycle, including fraud detection.
Big data processing tools have revolutionized the State Auto fraud detection process. With tools that extract handwritten text from scanned documents, combine data for analytics and apply machine learning to fraud detection, State Auto can now:
- Assess 83 percent more potential fraud claims
- Recognize suspicious claims 3 days sooner
- Identify the 20 percent of dubious claims that previously would have not been noticed
Mark Skaggs, IT director of platform engineering, sees the big data applications State Auto has implemented as enhancing the company overall. “Using AWS services has increased our overall agility and flexibility in developing solutions and facilitated a faster, less costly and better delivery of our capabilities across the board,” he says.
Al Tannan: Streamlining Processes via Data Visualization
Dubai-based kitchen, home and toy company Al Tannan prides itself on delivering an excellent customer experience. But the organization struggled to manage its supply chain and had a long list of tasks that had to be completed manually each day. With a cloud-based system from Microsoft, Al Tannan began to track, control and manage its processes and workflows in ways that reduced human error and saved time.
Al Tannan’s business intelligence solution empowers functions like sales, inventory management and lead generation through dynamic data visualizations that communicate accurate insights. The company has seen an increase in sales, as well as a major upgrade in their operational efficiencies, since they implemented a solution that leverages their big data through advanced analytics and data visualization.
“We no longer have people running around trying to fix issues,” explains Managing Director Hassan Tamimi. “They are spending time more productively on other matters. With everything plugged in together, they can look at all the information and easily identify the technical problems. These small things have made a huge difference in our operations.”
Abercrombie & Fitch: Fulfilling Customer Demand through Inventory Forecasting
Global apparel and accessories retailer Abercrombie & Fitch had data from their 865 store locations documented in siloed spreadsheets. The limitations of their data-processing system kept the company from discovering customer insights quickly and accurately. They decided to try a more holistic approach that reduced their information silos and analyzed their big data sets to facilitate data-driven decisions about inventory.
With the Tableau Server consolidating their data sources and providing advanced analytics about regional customer habits and inventory trends, Abercrombie & Fitch is able to prioritize the consumer.
“Customer service now has to be the center of everything we do,” explains Daniel Timmer, Senior Manager of Product Facing Solutions. “And we’ve got to be ahead of it before the customer even knows it.”
Tableau’s Server enables the company team to visualize trends, observe anomalies and capture localized affinity for various products. The company has improved its ability to understand customers at each store—one location may sell more casual pieces, for example, while the store just a few miles over in the next town trends dressier. With understandable representations of real-time data, the Abercrombie & Fitch team is able to better serve their customers by providing inventory that appeals to them.
Leverage Big Data as a Leader in Business Intelligence
Do you want to work with big data and business analytics to create positive outcomes for companies and customers? Are you interested in the insights that advanced analytics can reveal about everything from consumer trends to preventing fraud? If so, the Online Doctorate of Business Administration (DBA) - Business Intelligence at Marymount University can equip you to make the most of forward-thinking business analytics tools that derive meaningful information from structured, semi-structured and unstructured data.
The fully online DBA in Business Intelligence offers students an in-depth curriculum covering relevant topics like data visualization, data strategy and data-driven decision making.
A course titled Using Data for Business Intelligence covers the technological and management aspects of developing and using data to support advanced decision making. Students employ business intelligence solutions to optimize business operations. also develop a data strategy for an organization of their choice and then build a platform to create data-driven business intelligence. Learning how to work with big data, students are taught to examine the phases of data exploitation including collection, storage, analysis and visualization using state-of-the-art tools. They research and apply Artificial intelligence and Robotic Process Automation to a business problem of their choosing— such as finding new revenue sources, improving employee productivity and enhancing the customer experience.
Additionally, students can take a course called Artificial Intelligence Applications. In this class, online DBA students learn how to leverage data through the use of artificial intelligence tools. They study machine learning models and implement tools to support business intelligence using available data.
Courses like these and others in the online DBA in BI prepare students to work as expert professionals in the world of business intelligence. The curriculum includes an applied research project, which helps students prepare to leverage big data in business analytics in the marketplace. This cost-effective degree program can be completed in as few as three years.
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