A Google search for “big data analytics” yields a long list of resources. And many of these sources provide big data platforms and tools that support the analytics process, such as data integration, data preparation, and other types of data management software. Let’s focus for a minute on tools that meet the following criteria:

• They provide the analyst with advanced analytics algorithms and models.
• They’re engineered to run on big data platforms such as Hadoop or specialty high-performance analytics systems.
• They’re easily adaptable to use structured and unstructured data from multiple sources.
• Their performance is capable of scaling as more data is incorporated into analytical models.
• Their analytical models can be or already are integrated with data visualization and presentation tools.
• They can be easily integrated with other technologies.

Who uses advanced analytics?

While some individuals in the organization are looking to explore and devise new predictive models, others look to embed these models within their business processes, and still others want to understand the overall impact that these tools will have on the business. In other words, organizations that continue to evolve and deploy big data analytics need to accommodate a variety of users, such as the ones below:

• The data scientist, who likely performs analyses involving complex data types and is familiar with how underlying models are designed and implemented to assess inherent dependencies or biases.
• The business analyst, who is likely a more casual user looking to use the tools for proactive data discovery or visualization of existing information, as well as some predictive analytics.
• The business manager, who is looking to understand the models and conclusions.
• IT developers, who support all the prior categories of users.

All of these roles typically work together in the model development lifecycle. The data scientist subjects a swath of big data sets to the undirected analyses provided, and looks for any patterns that would be of business interest.

After engaging the business analyst to review how the models work and evaluate how each of those discovered models or patterns could potentially positively affect the business, the business manager and IT teams are brought in to embed or integrate the models into business processes or devise new processes around the models.

As appetites for data expand among companies across an array of industries, big data analytics has found a place in a wider corporate population, including healthcare organizations. In the past, the cost factors for a large-scale analytics platform would have limited the adoption to only the very largest businesses.

However, the availability of utility-style hosted big data platforms (such as those available via Amazon Web Services) and the ability to instantiate big data platforms such as Hadoop on-premises without a large investment have reduced the barrier to entry. In addition, open data sets and accessibility to fire hose data feeds from social media channels provide the raw material for larger-scale data analyses when blended with internal data sets.

Larger businesses may still opt for high-end big data analytics tools, but lower-cost alternatives deployed on cost-effective platforms enable small and medium-size businesses to evaluate and launch big data analytics programs and achieve the desired business improvement results.

You can get your organization healthier with a process for improvements based upon a lean delivery system and the data analytics that support savvy, cost-effective decisions.

That’s where and how you will see not only a greater adoption of predictive analytics, but far greater results.

Do your research. Ask a lot of questions. Engage a successful, seasoned team of data integration and planning analysts. We cost less and deliver more. Our Austin, Texas location keeps us at the epicenter of planning analytics and data-driven healthcare innovation and keeps our global capabilities and services at the forefront of our industry.

Email us at kcerny@mia-consulting.com or call us at 512.478.3848 to start a friendly, productive conversation.