InetSoft Webinar: Navigating the Sea of Big Data
This is the continuation of the transcript of a Webinar hosted by InetSoft on the topic of "How to Implement Business Analytics." The speaker is Abhishek Gupta, Product Manager at InetSoft.
As we talk about navigating the sea of big data, it’s also natural to wonder what's in it for me. While some of the answers may be qualitative, they’re all extremely important to building that stronger foundation of data ultimately leading to the kinds of business improvements that I’ll talk about in a few slides. But in this data management study, they asked companies to rank various aspects of the data environment on a scale of 1 to 10.
This chart here shows the percentage of companies that reported the following factors were high, meaning that an answer of seven or higher on that scale of 1 to 10. So in terms of trust, we see the best companies are more likely to report a strong sense of trust in the relevance and the cleanliness of their business data, but also in the systems used to manage and organize that data.
From an organizational standpoint, top companies also report far higher instances of adherence to the data policies they set forward, allowing for a more secure and effective data environment. With these policies in place, the analytical systems created are much more likely to see executive-level support at top companies. This helps to improve the chances of success for these programs. So these are some of the benefits of that data-driven course.
Transforming Data into Insight
And as we talk about this process of transforming data into insight, we move beyond the conversation specifically around data management and start talking about the process of creating those better decisions. A big part of that boils down to timeliness of data, probably not surprisingly. Earlier I showed data that talked about the shrinking decision window. Well, the chart on the left here shows how that window actually breaks down for most companies.
A little more than 20% of decision makers who were surveyed said that they needed actionable information in real time or near real time, essentially within a few minutes of that being available. Another 10% said they needed it hourly or within the hour. The majority of these people say that they do need information on a daily basis, or at least not sooner.
So how often do they get it within that window? Well, for leading companies, it's the vast majority of the time. Eighty-six percent of the time decision makers at these top companies get information on time within that decision window compared with just over half of the time for laggards. That is a lot of late information, a lot of frustrated decision makers, and a lot of potentially lost business opportunities, candidly, at these laggard companies.
The data here really underscores the importance of timely data. But how is it that the best companies are able to do this? What are the tools that help them get information into the hands of decision makers when they need it? The answer to that question is obviously multifaceted, but here I wanted to hone in on one of the aspects of the best performance strategy. It helps them get quicker information to their users through the use of a mobile device. The overall usage of mobile business intelligence today is still relatively low, but it is growing as more companies see the value in arming the distributed workforces with the ability to make data-driven decisions.
So the best companies are more than three times as likely as all other companies to mobilize analytical activity and capability. Some of the aspects of the analytics that the companies are leveraging on a mobile platform include mobile dashboards. But beyond the delivery of insight via a mobile device, they take it a step further and allow for drill-down capability to ask the inevitable questions of why that accompany the delivery of that dashboard or report on a mobile device.
And even a step further, they enable their users to annotate, collaborate, and share data via a mobile device to help add richness and multiple perspectives on the interpretation of the reports and the dashboards that they look at. All of this leads to faster delivery of information, faster understanding of what all the information means, and then faster business decisions at the end of the day.
Big Data Analytics in Personalized Medicine
One compelling application of big data analytics is in personalized medicine, where vast datasets from genomic sequencing, electronic health records, and wearable devices are harnessed to tailor treatments to individual patients. Traditional medicine often relies on generalized protocols, but big data enables the analysis of genetic variations, lifestyle factors, and environmental influences at scale. For instance, by processing petabytes of genomic data from initiatives like the Human Genome Project and patient cohorts, algorithms can identify biomarkers that predict how a person might respond to specific drugs. This shift from one-size-fits-all approaches to precision therapies has revolutionized fields like oncology, where tumors are sequenced to match them with targeted therapies, potentially improving survival rates and reducing side effects.
The mechanics of big data analytics in personalized medicine involve advanced techniques such as machine learning and predictive modeling. Tools like Hadoop and Spark process unstructured data from diverse sources, including imaging scans, lab results, and real-time biometrics from fitness trackers. Natural language processing extracts insights from medical literature and patient notes, while deep learning models correlate genetic mutations with disease progression. A notable example is IBM Watson Health, which analyzes millions of medical papers and patient records to suggest customized treatment plans for cancer patients, often uncovering patterns that human clinicians might overlook due to the sheer volume of information.
The benefits of this application are profound, extending beyond individual care to public health outcomes. By predicting disease risks through population-level data analysis, healthcare providers can implement preventive measures, such as early screenings for high-risk groups identified via familial genetic patterns. This not only enhances patient outcomes but also reduces healthcare costs by minimizing ineffective treatments and hospital readmissions. In rare diseases, where data scarcity once hindered progress, aggregating global datasets has accelerated drug discovery, as seen in collaborations like the Global Alliance for Genomics and Health, which shares anonymized data to foster breakthroughs.
Despite its promise, implementing big data analytics in personalized medicine faces challenges like data privacy concerns and integration hurdles across disparate systems. Regulations such as HIPAA in the US aim to protect sensitive information, but the risk of breaches persists in an era of interconnected databases. Looking ahead, advancements in edge computing and AI ethics could mitigate these issues, paving the way for even more innovative applications, such as real-time adaptive therapies that evolve with a patient's changing health metrics. As data volumes continue to explode, personalized medicine stands as a testament to how big data can transform human health on a global scale.
| Previous: Why Do Companies Need Analytics? |