By Ryan Ayers
Big data is here to stay. It’s rapidly transforming enterprises, helping executives improve business models and disrupting many industries. Along with a wealth of data, however, information overload has worked its way into the fabric of society. People who are affected by this condition typically suffer from a reduced capacity to make quality decisions.
Surprisingly, information overload is not a new phenomenon. In fact, political science professor Bertram Gross coined the term in 1964. It went mainstream in the 70s with Alvin Toffler’s book called “Future Shock.”
Importance of Data in Business
Big data systems are helping enterprises improve their marketing and operational strategies. However, forward-thinking business leaders are also affected by information overload.
As the Internet of Things (IoT) matures and enters the mainstream, the proliferation of big data is expanding at a breakneck pace. Simultaneously, emerging technologies such as machine learning, artificial intelligence (AI) and the latest big data systems are helping enterprise leaders mine enormous banks of information for meaningful insights. However, big data analyses are of no use if they start with bad information, making it vitally important that today’s enterprises understand the components of what’s called the insights value chain.
The insights value chain allows enterprises to capitalize on information resources by planning big data analyses around internal technical capabilities and operational best practices. In general, quality big data analyses begins with quality information. Next, the technical ability of an organization’s information technology (IT) professionals heavily impacts their ability to effectively analyze and visualize data reports. Finally, the usefulness of big data reports is either augmented or undermined by the domain of knowledge within an organization. In addition, organizations that leverage structures such as cross-functional integration are more likely to achieve success in making the most of big data reports.
Extracting value from big data analyses requires excellence throughout each stage of the insights value chain. Accordingly, enterprise leaders that want to promote successful big data initiative outcomes must work to ensure that each stage of the chain is effective.
Adding Fuel to the Fire
Data overload is affecting people who work in fields such as finance and education. Additionally, an increasing number of healthcare organizations are facing penalties issued by the U.S. Department of Health and Human Services (HHS) for failure to comply with the Health Insurance Portability and Accountability Act (HIPAA).
The HHS has established strict audit protocols due to a recent rise in violations. As a result, the rules mandated by HIPAA are extensive. Audits might investigate the integrity of information system activities, risk management policies, security procedures and protocols as well as other organizational standards. This increased scrutiny has resulted in the need for organizations to increase their efforts to comply with HIPAA regulations.
Healthcare professionals who violate HIPAA regulations can face severe penalties. Violations might include improper storage of electronic health records (EHRs), failure to acquire patient consent or improper sharing of photos on social media platforms. Also, when data breaches occur, compromised care providers must inform patients of the incident. Failure to comply with these rules can result in the Office for Civil Rights (OCR) issuing significant fines as well as the incarceration of accountable personnel.
Analyzing Data to Make an Impact
In many instances, it’s questionable whether data is accurate or helping enterprise leaders make a meaningful impact. As a result, today’s firms need qualified IT professionals who are skilled at working with modern big data systems.
Big data studies must start with relevant information. Experts recommend developing research initiatives by finding similar case studies and then pinpointing the appropriate analysis model. In some instances, it’s more important to use data that’s been gathered over an extensive period. On other occasions, it’s more important to gather the newest available data.
Today, big data studies are helping enterprises optimize many consumer-facing tasks. Cases that apply to activities such as pricing, churn mitigation and promotion optimization help enterprise leaders improve gross profit, while cases regarding variables such as employee sentiments and internal processes typically help company heads maximize net profits. The latter can also involve processes such as supply chain optimization and fraud prevention. The use of cases is becoming increasingly important as the IoT expands, and the invaluable insights developed by big data studies based on these cases helps business leaders develop innovative and effective organizational models.
We’re living in an era of data, and evolving technologies are continually forcing business leaders to adapt. Across all industries, corporate leaders need skilled and talented IT professionals who can cherry-pick useful data and produce meaningful, actionable reports.
Delineating between useful and inconsequential information aids today’s big data professionals in producing reports that help business leaders overcome information overload. While there’ll always be more knowledge than anyone can possibly hope to absorb, starting out with the right data can help business leaders navigate their way through massive stockpiles of information.
Ryan Ayers has consulted a number of Fortune 500 companies within multiple industries including information technology and big data. After earning his MBA in 2010, Ayers also began working with start-up companies and aspiring entrepreneurs, with a keen focus on data collection and analysis. You can find more from Ryan on Twitter at @TheBizTechGuru.