There is a lot of hype surrounding Business Analytics and Big Data, and deservedly so. With access to better/faster computing and global analytics expertise, an organization can reap huge benefits in business knowledge - and thus decision making - using data analysis. Understanding exactly what Business Analytics and Big Data are, and more importantly, how they can be utilized for maximum value within your unique organization, is not so easily understood.

We'll try to provide some clarity on the issue.

What is Business Analytics?

Business Analytics (BA) refers to the collection of technologies, applications, and methodologies used to investigate business performance, and provide insight for further business planning. More than simply providing basic data reporting to an organization, BA tools can provide context and meaning within the data, thus increasing business intelligence and driving improved decision making.

A complete BA toolset incorporates Descriptive Analytics, Predictive Analytics, Prescriptive Analytics, and Visual Analytics.

What Really is Big Data?

“Big Data” is the catch-all term that describes the vast amounts of information being collected from a myriad of sources, systems and devices. Compared to traditional structured data commonly stored in a relational database, Big Data combines structured and unstructured data - e-mail messages, word processing documents, video and audio files, photos, presentations, case notes, journals, WebPages, social media interactions, and new sources of data from internet connected devices known as the Internet of Things (IoT). It's estimated that 80-90% of business data is unstructured data.

Big Data Characteristics:

Volume: Reflecting the "Big" in Big Data, the sheer amount of data being stored is astounding. We're not talking about Terabytes anymore, but Zettabytes and Brontobytes. In addition to data generated by historical transactions and traditional business operations, massive amounts of data is being generated from sources like social media, web logs, machine inputs, and device sensors.

Velocity: The rate at which the volume of data is increasing is a significant issue. We are collecting data at an exponential rate, and analyzing the torrents of data in a timely manner is a major challenge for most organizations.

Variety: As previously mentioned, Big Data is characterized by a variety of types and sources. Each organization brings a unique mix of data to be managed and analyzed.

Veracity: Data can be messy. Veracity refers to the quality, accuracy and overall trustworthiness of the data. Is the data meaningful to the problem being analyzed? In considering your data strategies, you need to evaluate your data cleanliness, and manage processes to keep ‘dirty data’ from accumulating in your systems.

Value: The goal of Big Data analytics is, of course, value! Whether it's increasing revenue, reducing costs, improving customer relations, enhancing productivity and efficiency, and numerous other impacts - gaining value from the analysis of Big Data for your organization is the ultimate goal.

Variability: On a day to day data understanding and analysis focuses on finding as much value as possible, however, variability in Big Data can be biggest bottleneck; requires one to be prepared for the surprises it may shows us. For example, asking the business questions from 5 years ago or even 2 years ago with the same methods on Big Data could produce differential variance. Organizations acting upon it would find an essence of such data from this type of variability.

Moving Towards a More Data Driven Culture

While analytics can be a powerful tool for improving business performance, there are issues to be considered. Valuable information can get lost in layers of inefficiency, and care must be taken to use the data to your advantage rather than just a form of record-keeping. Organization, leadership, and analytics-based decision making are critical aspects of competing in business analytics.

In BA, a major goal is to improve decision making. Creating an environment where trust in new analytics-based processes also needs to be established. To do this it's important to evaluate those decisions after the fact to help ensure the accuracy of the model, and ensure continuous improvements in decision making in the future.

It helps everyone within the organization to move from a "gut" based approach to decision-making, to one based on effective data analysis. In time, the organization develops a repeatable approach for better decision making, and a road-map for developing better decision makers.

Examples from Healthcare

In the healthcare field, Business Analytics strategies can significantly improve clinical outcomes by reducing errors and recognizing opportunities. Data analysis within a medical, wellness and social context can provide improved insights about individual patients. Identifying risk factors, promoting healthier choices, and developing more effective, early engagement from the entire care system can positively impact clinical outcomes.

Obstacles to Getting Started

Many companies have recognized the need for Big Data analytics and are building up their capabilities. But shifting the culture of your organization to embrace analytics-based decision making can pose a challenge.

It's helpful for leaders to understand the analytical tools, and the importance of shifting to analytics-based decisions, prior to communicating this shift to their organization, and fostering support for the contribution of statistical analysis.

How Can BA Fail You?

Businesses need to move fast -- they want to meet customer needs, now -- whereas IT has been hired to primarily think long term: secure the systems, scale the solution and make it available for everyone. Often, those two worlds are in conflict. Hence it is of significance to train your resources in your IT and business units in Business Analytics (BA). Unfortunately, if not done properly, that’s where it can fail you!

The mistake in BA training programs is sending users to the standard classroom training given by the vendor. Every training environment is different in some fashion. And in most cases, the BA content used for training (descriptive analytics reports, visualizations, etc.) is stale and generic and not relevant to what the users will be doing on a daily basis. So when they go to use the tools in their own environments, they're often confused and find it hard to relate what they've seen to their daily use cases.

Another issue is that the functional training isn't customized enough for the users, which relates back to content as well. Training would be far more effective if the exercises were geared toward how the users will be using the BA tools and data on a daily basis. In addition to conducting the training in the users' specific environments with the data they'll be using, BA leaders should go one step further and create exercises that will help users do their jobs more efficiently and make better decisions. Most of the training delivered in classrooms -- and even in custom training settings -- doesn't account for what the users truly need to be proficient with the new tools.

Periodic classes with a focus on continuous improvement are extremely beneficial, and organizations that frequently refine and extend their business intelligence training programs are the most successful. The best way to do this is to make the content you've developed readily available, by offering tips-and-tricks documents, posting how-to videos on employee mobile directly and providing other easy-to-access resources. Users like to be able to refer back to what they've learned as they move forward on BA applications; opportunity and willingness to fail and learn is important for successful BA initiatives.

How Can Fluent Analytics Help?

We provide data exploration methods that yield detailed, specific, actionable information that is specific to YOUR company's business needs, in a method of communication that is easy for you to understand. We understand that Business analytics, both retrospective and predictive, can be the catalyst to ensuring that critical health information reaches the right people, at the right time, so they can better monitor performance, detect trends, predict outcomes and deliver more efficient patient care. By linking individual and team performance to organizational goals, analytic capabilities such as scorecards or dashboards can help users determine how their roles drive institutional performance, and quickly spot delivery trends, which in turn can better support critical quality initiatives.

To deliver improved, more efficient patient care; to better detect trends; to more effectively predict outcomes and monitor performance; and ensure that vital health information reaches the appropriate person at the right time, we believe that Business Analytics, both retrospective and predictive, can act as the catalyst in achieving these goals. For better support of critical quality initiatives, we help connect team and individual accomplishments to organizational goals, and implement analytic capabilities like dashboards or scorecards to help users understand how their roles are driving improved institutional performance.

Over time, decision-makers can then assess how newly implemented Business Analytics are impacting improvements in different locations, and across selected service categories. Making the often-difficult decisions on which initiatives, resources, locations, or services may no longer be sustainable becomes easier with this level of data-backed insight. Consequently, it permits decision-makers to eliminate inefficient processes, develop productivity improvements through streamlining workflows, realign marketing efforts, and roll out new service lines.

Cost reduction initiatives become something more than just identifying and managing unanticipated shifts in resources, volumes, quality measures and contracts; identification and actionable methods of improving the clinical outcomes of their patients can be achieved in the process. Business Analytics from Fluent Analytics allows our clients to transform raw data into new insight and intelligence. It allows healthcare organizations to make critical decisions, providing them their best opportunity to provide the highest-quality care to their communities.

For example, healthcare data from medication and supply dispensing systems can be used to:

• Deliver actionable and intuitive dashboards, reports, and proactive alerts that assist you in analyzing your medication and supply chains.
• Help you prioritize action items that keep you in compliance, lower costs, and improve patient care.

Due to continuous decreases in cash reserves, healthcare organizations have to keep a close vigil on the business aspect of healthcare delivery. This often presents challenges on prioritizing initiatives and allocating resources, in both finance and work-force related endeavors. Particularly in the face of stiff competition, organizations must continually tighten up operations to make sure the whole organization is operating at peak efficiency. A thorough understanding of how well the organization is running in relation to its peers, historical trends and the overall market is required.

To gain a 360 degree view of organizational efficiency, decision-makers can use key performance indicators rather than measuring performance in absolutes. Administrators can track performance relative to peer groups, strategic objectives, or market growth rather than basic single-point measures, such as revenues per service line or patients per month.

Fluent Analytics re-examines how the mountains of information at your fingertips can be better used to drive high quality care by asking questions such as how to provide more cost-efficient and safer care to patients? What is the expected financial impact of can shorter hospital stays and faster recoveries? How can we unsure optimal care by adjusting our variety of services at specific locations?