Can Small Businesses Benefit from Big Data?

All organizations irrespective of their size generate volumes of data. However, for SMBs, the question is, does the cost and effort justify the value to be derived from data? Data analytics provides deep insights that complement human judgement. Forrester describes the power of big data for small business as “A major disruption in the business intelligence and data management landscape.”

There are several success stories of small businesses benefiting from data analytics. One interesting story is of a zoo in Washington State that was unable to plan daily staffing commensurate with attendance. The zoo’s major source of income was through attendance, which was highly dependent on weather.

By parsing historical data, and analyzing it against decades of local weather data, they found some predictable intelligence. This helped them to fine-tune their plans regarding staffing and promotional activities.

The fear of big data is probably related to the word `big,’ and small companies wonder if they have enough data to qualify for big data. It does not matter. Any data, including visitor logs from a website, is enough to provide vital information on customer behavior.

Another reason why SMBs shy away from big data could be the lack of streamlined processes and information silos. A lease-management company in North Carolina, that manages nearly 1,000 rental properties in the Outer Banks, was unable to accurately predict profitability for homeowners through tourist rentals. With data stored in spreadsheets, the management found it impossible to analyze the data that they had amassed over the years. The company opted for a business analytics tool, which distilled the data and simplified the available information.

Based on the analytics, the company could share vital information with its guests. They could now make rental-pricing recommendations to owners based on seasonal trends and so forth. The business has grown by over 10 percent and costs reduced by 15 percent in the last three years. Big data analytics for small business also helped this company to identify invoice-processing errors, and overall it saved $50,000, annually.

Leverage our Big Data Services to get insights from your Structured and Unstructured Data Repositories

Smaller organizations focused on business needs may not have the time, or even not see the need for streamlined processes. Big data makes allows us to think about the current strategy, economic environment, and competitive landscape. To move from small to medium and from there to large requires processes. Incorporating them now can help to mine data, which will be useful in the short and long term.

To summarize, big data for small business helps small organizations to watch and learn about their customers and their preferences. Even if it is just from their website, it is still intelligence. For retaining customers and acquiring new ones, for up selling and cross-selling, for streamlined processes, which lead to operational efficiency, big data has a hoard of benefits that simply cannot be ignored!


Big Data Analytics Can Play an Important Role in Healthcare

Before you dive into the importance of big data analytics in healthcare, you can learn about the importance of small data vs big data in healthcare.

Global healthcare is in a state of flux with big data analytics emerging as a powerful tool to transform clinical, operational, and administrative functions among others.  The healthcare IT market has grown from basic EMR solutions to specialized hospital information management solutions and healthcare information exchange systems and the Healthcare IT Solutions Market Report predicts growth at a CAGR of 13.57 percent till 2022.  This growth is being fuelled by the increasing role played by big data to manage patient care, reduce costs, and improve quality while keeping one eye steadily focused on operational efficiency.

Related: Innovative Healthcare solutions for ISV’s & Providers

Realizing the potential of big data

Probably realizing the value of big data, The Health Information Technology for Economic and Clinical Health (HITECH) Act created a $30 billion federal grant as an incentive to adopt EHRs, which has helped to generate tons of structured and unstructured data.  This data is finding its uses across functions and services.

Value-Added Services

Insurance companies, for example, are changing their models from a fee-for-service to value-based data-driven payments by using electronic health records that enable high-quality patient care. In the value-based model, doctors, hospitals, and insurance work together to deliver care that is measured by patient satisfaction and this model relies on data from EHRs.

Cost Savings

The same data from EHRs have also helped in mitigating fraud thereby increasing cost savings.  For example, the Centers for Medicare and Medicaid Services prevented more than $210.7 million in healthcare fraud in one year alone.  Insurance companies have also experienced a higher return on investment.  United HealthCare generated a 2200% return on investment in a single year.  Big data analytics has helped these companies to take large unstructured information with regard to historical claims and by using machine learning to detect patterns and anomalies.  This has helped to control overutilization of services, patients receiving the same services from multiple hospitals, and filling out identical prescriptions in various locations.

Predictive Patient Care

By analyzing structured and unstructured data, and using predictive modeling on EHR data, it is now possible to diagnose various illnesses which is helping to reduce mortality rates.  To elaborate, devices are helping to monitor patients’ glucose levels, blood pressure etc.  When combined with machine learning IoT, proactive care for patients is a reality.  Advanced big data analytics is able to work with the unstructured data generated by these sensors.

To summarize, evidence-based medicine relies on patient data which is now growing more in availability.  Capturing data is, however, only the first step.  The next one requires analytics which will not only result in better patient care and engagement but also eliminate redundant testing, reduce expensive errors, and help save lives.