Steve Rimar, Speaker at TDWI Anaheim Leadership Summit

Steve Rimar, CEO at Analytica Consulting recently presented at the TDWI Summit in Anaheim. To watch his Case Study presentation on Transforming Healthcare with Predictive Analytics click on the video below.

He was also asked to speak at TDWI’s Orlando Conference in November. TDWI Orlando is the leading conference focused on analytics, big data, and data science training. TDWI events are well-known for providing attendees with cutting-edge, actionable insights that can be applied immediately to advance critical big data and analytics projects.

CEO Tenure & Retention Dashboard

Analytica Consulting partnered with the Community College League of California to produce the 8th Update of the CEO Tenure & Retention Study.  This update is the first version to feature a dynamic dashboard with visual representations of over 100 years of California Community College chief executive officer tenure data. The dashboard is an interactive infographic that displays the growing presence of women in CEO positions, the effects of the Great Recession on CEO retention, and provides a snapshot of the diversity of California CEOs. Through dynamic charts and tables, users can identify trends and outliers to gain additional insight into the data.

CEO Tenure & Retention Dashboard

Los Angeles Real Estate Visualization

Brent Johnson, Senior Consultant at Analytica Consulting created the dashboard below for visualizing the real estate market in Los Angeles. The dashboard breaks the market down by property type and the decade of construction. It also shows a detailed map of Los Angeles properties with a street view of each selected home. The tool is highly useful for anyone looking for a specific number of bedrooms or bathrooms, a desired zip code and cost. If you have any questions or would like more information regarding this visualization tool, contact Brent at:

Steve Rimar Presents at the State of Reform Conference

Steve Rimar, CEO and Founder of Analytica Consulting, a technology services firm that specializes in data analytics and architecture, recently presented at the State of Reform Health Policy Conference in San Diego, December 5, 2017.

The State of Reform conference focuses on bridging the gap between the health care marketplace and health care policy. This conference is one of the largest, most diverse assemblies of health care executives and health policy leaders that that influence health care policy and administration in the state. The 2017 Southern California Conference had over 70 speakers and experts from across the spectrum of care.

Mr. Rimar’s conference topic was “Technology as a Tool for Care Improvement”. He discussed how data is transforming healthcare along with case studies.  Joining him in a panel discussion was Tanya Dansky, MD: Chief Medical Officer, Care1st Health Plan and Barbara DeBuono, MD: Vice President, Clinical Strategy & Value Based Care, 3M Health Information Systems.  The panel discussed various use-cases and experiences with technology and the impact on the future of health care.

California Department of Public Health Launches Public-Facing Dashboards

Congratulations to the California Department of Public Health for releasing publicly-available interactive data visualizations!  As one of the first State Agencies to provide public data via interactive dashboards, they are setting the pace for transforming static reports into dynamic, user-friendly tools. By utilizing these tools, CDPH is providing the public with a way to connect with publicly available data like never before, while encouraging them to ask questions, explore data, and make their own unique data discoveries.

To view the dashboards, click here. For more information on how your organization can utilize data visualizations to connect users with data, please email us at


The History of the Miami-FSU College Football Rivalry

Our talented Senior Consultant, Brent Johnson, made the following interactive infographic. It chronicles the college football rivalry between his alma mater, The University of Miami, and Florida State. Any questions regarding this visualization can be sent directly to

Do You Have the Right Approach to Analytics?

Article written by Jun Lee, Senior Consultant at Analytica Consulting

In 2017, data analytics has taken huge strides in innovation. Cloud analytics, in-memory data computing, big data storage, enhanced data encryption – these are all the available tools, should you decide to get involved. However, even with all these tools, many companies continue to spend exorbitant amounts to maintain their traditional methods of collecting and analyzing their data. What is the issue?

Priority. Analytics is pervasive across every operation in a business. No longer should analytics be solely handled by the business or data analysis teams. Instead, analytics should take a more active role in each division within a company. Each team (whether customer services, engineering teams, business & finance) can contribute to the efficiency of the team performance, can link and derive errors, and predict modular solutions by using analytics. Analytics is a priority at each level of the company, and should be integrated into both distributed and local work.

Innovation. Having the latest tech and infrastructure is only a tool to reach your end goal: analyses that drive a business successfully. Adopting the proper analytics infrastructure is more than setting up the right technology, it’s creating a learning culture that evolves data sets and analytics applications. People who are data driven and embrace the many solutions Business Intelligence (BI) technology offers, are a necessity for any company looking to create an effective analytics environment. The company needs the mindset to create a cohesive ecosystem, with exchanges of data and structured ideas that build a package of knowledge. Only with this mindset can we fully capture and realize the impact of analytics.

How do we get there?

The road to efficient analytics varies case by case. Here are a couple of ways to launch innovative analytics:

  • Receive feedback from your teams and decide how to centralize the management of your data.
  • Create granular levels of operation for analytics (Trainings, consistent meetings and presentation sessions, organized request and task structures, scoping of high level analytics projects).
  • Present a model example of a data visualization and explain how the data leads to new business insights. Preferably, each team should receive a model data visualization to see how they can utilize analytics. Focusing on an outcome can inspire others to try innovative methods and open minds to create insightful analytics.
  • Recognize the tools and experience your company needs. Often times, innovative analytics and ideas are spurred by the lack of proper tools for data insights. Analytics projects are decided based on the traditional methods and applications available to them.  However, many available BI tools excite innovation, improve efficiency of the extraction, transformation, and loading (ETL) process, create appealing data visualizations, and reduce the time to make core and detailed analyses.  Provide your company with a robust BI tool and trainings to break past limits.

Let’s talk about the financial details:

Initial Upfront Costs Recurring Costs
Server set up Security and data governance
BI tools purchase (Tableau, Qlik, Splunk, etc.) BI tools subscription
Training sessions and development strategy Analytics development

The risks of staying behind your competition, losing potential insights on your data, and lacking efficiency in data analysis far outweigh the costs to correctly set up an effective analytics environment. If you’d like to take your analytics to the next level, please consider Analytica Consulting. Feel free to contact us at: 858-272-8260 or

Steve Rimar Presents at Qlik Qonnections 2017

This May, Qlik Qonnections, the premierBI conference, was held in Orlando, Florida. Steve Rimar, CEO & Founder of Analytica Consulting presented: Big Data Analytics with Qlik & Splunk to over 140 conference attendees

What is Spunk? Splunk is generally used as a machine data/log analytics platform and has a powerful search index that can hold massive amounts of information. This can be web server logs, device logs from millions/billions of users, etc. One thing it lacks is the visualization and interactivity available in the Qlik product suite. Steve’s presentation on the Splunk Connector, covered how to integrate the two technologies to create a world-class solution for analyzing big data on top of log files with Qlik.

For more information on the Splunk Connector, click here.  Steve’s PowerPoint presentation can be viewed by clicking here. For a Splunk Connector demo, email us at:

Data Science Platforms – New Trend or Necessity?

Article written by Josh Karpen, Senior Consultant at Analytica Consulting

Data science, predictive analytics, machine learning – these methodologies are increasingly becoming a necessary part of the toolkit for modern organizations to compete effectively. Many companies are building data science teams to meet that challenge rather than having one data scientist.  One of the reasons they are opting for a team effort is that the mythical data scientist “unicorn” is simply too difficult to find or too expensive when they are found.  Another reason is that most data scientists simply cannot handle the workload alone as more departments start making requests for their project time.

Data scientists may not be cheap to employ, but the software most of them are using now is usually free and open source. For example, free tools like R and Python are constantly being updated and new features are added in the form of packages that extend their functionality.  If, however, you have an entire team trying to use these tools and not just one individual, there is a downside.  The constant software updates and the flexibility with these tools make it difficult to keep projects organized across multiple team members.

When an organization finally puts together a data science team, often times they discover that the individuals on the team are speaking a different language.  Then, when the team starts working on a project, there is no cohesion which results in longer project completion time and scattered results.

Here are some other issues organizations can run into when a data science team uses various tools and not one platform:

  • Ensuring everyone is on the same version of their chosen software and is using the same version for all packages. If a team member installs a new package, everyone else must install it as well to run their code. How do you implement this and who makes sure this happens?  This could be added work if not executed properly.
  • Version control is not built in to these tools. Data and code files often need to be sent around to the various team members, which can lead to data duplication or mistakes if the wrong file is used.
  • Most tools do not provide a good way to keep the various runs of a model organized and properly labeled. A typical data scientist will find their project directory overflowing with multiple copies of the same code, slightly tweaked as they tried different parameters. Or, they may have runs that are built into a single code file that go on forever, with maybe some commented lines separating them. The commenting line may be done haphazardly depending on the programmer.
  • When a new project is started, there is not an easy way to search through past results to see what has been done before. This can lead to the same preliminary steps and data cleaning being repeated multiple times, a waste of time and money.
  • Data scientists are not software engineers, so they may not be the best person to implement their models into an actual production environment. This is not always ideal in a team scenario.
The Benefits of a Data Science Platform for your Team

Some companies have chosen Github, which can help with some of these problems, but it was designed primarily for software engineers, not data scientists – and to be honest, there is a bit of a learning curve to make use of Git properly.

However, there are other options and the relatively new concept of a data science platform can revolutionize the way your team works together.  What should a useful data science platform do?

  • Make it easy for the entire team to work on the same version of every software and package they utilize.
  • Allow version control, so Team Member A can create a branch off of Team Member B’s model and test out some ideas without breaking the original version.
  • Bring all of your data scientist’s code into one, searchable repository.
  • Organize and label the results of every model in a manner that allows for sorting and searching.
  • Make it easier to funnel the results of the optimal model into your real-world, production environment, whatever that might be.
  • Create great documentation – the days of trudging through badly written explanations and walls of text should be far behind us.
  • Generate a well-designed user interface that is easy to navigate through.

There are a number of data science platforms on the market that have all or most of these capabilities.  For example, yhat, Domino Data Labs and RapidMiner are a few of the strongest at the moment, but there are many others. All of them allow you to view a demo online of the tool in action, and most offer a free trial as well.

We will be reviewing some data science platforms over the next few months.  Keep an eye out for our first review!

What is your opinion – are data science platforms a trend or a necessity?  Feel free to share your comments on our LinkedIn page.  Is there a particular platform that you are interested in and would like us to review?  If so, email us at: and we will consider your request.

San Diego Qlik User Group Meeting

The San Diego Qlik User Group meeting was a great success! The event was on May 3 at AleSmith Brewing Company and had over 25 in attendance, including people from AMN Healthcare, Sony, BD, Prometheus Labs, GreatCall, Hologicand and UCSD Health. We had a few brief presentations going over QlikView/QlikSense tips and tricks, use-cases and product roadmap. However, there was plenty of time for networking with cold beers and appetizers. The next Qlik User Group meeting will be in Orange County on Thursday, June 1. Click here to register for this fun and informative event.