Steve Miller is co-founder and President of Inquidia Consulting, a Chicago-based business intelligence, analytics, and professional services firm formerly known as OpenBI. In September, 2017, Inquidia was acquired by Hitachi Vantara, which also owns the Pentaho big data platform.
He has more than three decades of knowledge and experience in big data analytics, data architecture, data strategy, and data engineering, among other disciplines. He has also designed and deployed best-practice business intelligence systems. Through Inquidia, Mr. Miller builds traditional data infrastructure and intelligence solutions that help clients get the most from their growing data assets. Prior to co-founding Inquidia, Mr. Miller served as the Vice President of Piocon Technologies, Executive Vice President of Braun Consulting, and Sr. Principal for Oracle Consulting.
Mr. Miller once described himself as a “data geek” and continues to stay on the leading edge of data analytics and intelligence. He shares news, trends, insights through various publications and outlets, including the analytics and IT publication DATAVERSITY, Information Management, and Inquidia’s official website. Mr. Miller studied Quantitative Methods and Statistics at Johns Hopkins University, the University of Wisconsin-Madison, and the University of Illinois.
[OnlineEducation.com] Data science and analytics is applicable in many different industries, and to many different ends. While terms like “analytics” and “big data” have become quite prevalent, not everyone knows how data is used on a basic level, and by whom. Can you please share what you do, including how you use data and apply its insights?
[Mr. Miller] Inquidia is all about learning from data. We were established 10 years ago, as OpenBI, a business intelligence professional services firm focused on implementing open source software solutions. After a few years, we found open source too constraining and changed our name to Inquidia Consulting.
The Inquidia partners are all long-time business intelligence (BI) and data warehousing experts. The focus of our work is entirely on helping businesses use data more effectively for both revenue-producing products and decision-making.
A foundational competency of Inquidia is designing and building analytics data infrastructures for our customers. We use relational databases, Hadoop, and Cloud-based analytic data stores to house customer intelligence data, building and managing contents using integration platforms like Pentaho and Informatica.
A second area of focus is helping customers build entire analytical applications to supplement existing software as a service (SAAS) offerings.
Finally, we’re analytics and data science experts that work with customers to jump-start data-driven initiatives.
There are two basic ways our customers use insights from the analytic infrastructures we help them build: first is basic performance management in which analytics help businesses evaluate and manage their performance. The second is data/analytics as a product, in which we help customers build the data products they ultimately sell to their customers.
Besides databases and Hadoop along with data integration platforms like Pentaho, Inquidia has expertise in agile statistical programming with Python and R, as well as bonafides in analytical platforms Tableau and QlikView.
Our customers aren’t confined to a single industry; rather, our work revolves on companies that are committed to evidence-based decisions. That said, we do have a wealth of marketing, healthcare and government customers, and often work with new companies whose products are data/analytics.
[OnlineEducation.com] Someone researching analytics careers for the very first time could come across seemingly different disciplines within the field, particularly data science, data analytics, and business intelligence. What are some of the similarities and difference among these programs and how do those variations translate to the career field?
[Mr. Miller] BI, analytics, and data science all obsess on data-driven decision-making. BI is the oldest and now the most mature, governed, and IT-centric of the three, focusing on the data warehouse, reporting and online analytical processing (OLAP). After 25 successful years, BI is ceding to Enterprise Analytics (EA), which gives up some IT control/centralization for more flexible data integration and analytics. In essence, IT still governs the core intelligence data, but allows the business to extend the data and analytical assets in ways not initially envisioned. EA is a more agile approach that leverages both the governed and ungoverned data assets as inputs for a more technically-savvy analytics team.
Data scientists are much more independent of traditional IT than either BI or EA, though that’s changing as DS becomes more pervasive and there emerges a division of labor in the discipline. And the raison d’etre for data science is often the creation of new data products, as opposed to the performance management of BI and EA. Analytics and DS both contrast with statistics in their emphasis on prediction over causality and their general use of observational in contrast to experimental methods. In addition, I’ve always seen analytics as applied statistics/machine learning in the work world, more data-focused and computational than statistics, but less so than data science. When challenged to define the “point” that separates analytics from DS, however, I can’t, and argue feebly there’s a continuum from analytics to data science on a data/computation axis with endpoints “not so much” and “lots” — and predictive analytics in the middle. That’s certainly how my company, Inquidia Consulting, sees it.
[OnlineEducation.com] You write articles for data science publications that spotlight various analytics skills, trends, and issues, including some of its more technical aspects. What technical skills and expertise would you encourage rising data analytics professionals to know? What programming languages and software suites should they learn, and how might they get started?
[Mr. Miller] Computation skills are no doubt critical for Inquidia’s new college hires, along with a solid general quantitative background. It’s easier to teach statistics and machine learning to programmers than it is to teach stats majors how to compute. Specific majors aren’t overly critical, but we generally hire students focusing on math/stats/econ, business, engineering, CS, and the physical sciences. More schools have added focused certificate programs that consist of roughly 4 courses. A programming/data science certificate is quite attractive.
The ideal “newbie” would have a solid background in SQL, Python, R and perhaps Java or C++. Matlab/Octave are good also. Often as not, an internship or student job/research shapes the computation experience more than classes. Obsession with the evidenced-based power of data is a differentiator.
There’s no shortage of free online teaching resources for SQL, R, Python, and data science. We encourage all employees to extend their skills with online curricula such as Coursera Johns Hopkins Data Science program. One college hire learned Python in a between-quarters immersion course. Another was a philosophy major who cut his computation teeth with Coursera. Other pertinent online training courses/specializations include Python, big data, machine learning, and statistical methods. We also hire MS Analytics students but often feel we can teach new grads more efficiently for our needs than can universities.
[OnlineEducation.com] What are some of the non-technical skills, aptitudes, and personal qualities employers prize in analytics candidates? What about educational qualifications, including degrees and professional certificates? How else might future professionals set themselves apart?
[Mr. Miller] An obsession with data and a data/computation-intensive internship are differentiators. Inquidia’s a professional services firm, so strong interpersonal skills and the ability to function in a collegial environment are critical. We also look for those we think can develop as consultants, first building tech, analytic and business skills, then progressing in project/client management.
Inquidia’s a big proponent of online courses, and encourage our consultants to continuously learn, both formally and informally. As noted above, we’re also excited about focused certificate programs offered by many schools. Skepticism born from rigorous exposure to the scientific method in university/research settings is a nice-to-have.
I’m pretty familiar with many of MS in Analytics/Data Science programs. I think in general they’ve improved quite a bit over the years, adding more rigorous computation focus to a foundation in statistics/machine learning. Undergraduate statistics curricula are also in a positive computational evolution.
For senior-level professionals, Inquidia looks for consulting experience in addition to technical/analytical skills. Business, industry, and technical architecture knowledge, plus project management experience, are key. The ability to conceptualize, architect, design and build a data warehouse is particularly important, as is strong expertise with data integration platforms such as Pentaho and Informatica.
For college hires, see question 3.
[OnlineEducation.com] You have more than 30 years of BI and analytics experience. How has the field changed over the course of your career, and what role has technology played? What are some of the most important or novel uses of data you have observed? Where do you think the field will stand in another five or ten years?
[Mr. Miller] I’ve had the good fortune to spend most of my 35-year career “doing data”. I started in health care research after statistics grad school and learned early on that data and computation trumped stats in those environments. Managing perinatal and cancer registries was about 80% data/programming and 20% statistical analysis. And that divide really hasn’t changed over the years. What has changed is that I could easily have managed the work that took two minicomputers in the early 80’s on my notebook now. And the programming tools are so much better today. A SAS whiz back in the day (when SAS was king), I’ll take Python/R/SQL today in a heartbeat. Inquidia now does a lot of work in the Cloud, using Hadoop, Spark, and Cloud DBMS’s like Redshift and Snowflake to replace on-premise databases.
For my first 25 years or so of data-driven business, analytics were used more for decision support — to bring evidence to bear for business decision-making. Now, for many Inquidia customers, at least, data/analytics is the product in and of itself. These companies engage analytics in the transactions of their customers to increase revenues and lower costs. Often as not they’re compensated on the come.
In the next 5-10 years, Cloud and Hadoop computing will be the norm, supplanting on-premise infrastructures. We can see the evolution in programming Hadoop with the emergence of Spark, which seems to me like the productivity increase of moving from assembler to 3GL programming back in the day. Hadoop computation will look like pretty vanilla Python and R in the not-too-distant future. And the ability to fire up and extinguish sophisticated computational environments at a moment’s notice will make analytical life much easier.
[OnlineEducation.com] You have achieved tremendous success in your career. One would think you also learned a lot along the way. What advice would you offer rising data professionals who want to lead similarly successful careers? Is there anything you know now that you wish you knew in the beginning?
[Mr. Miller] Thanks. I wish what you say were true!
I had the good fortune to choose a data science-like career before it was commonplace, so for that I’m lucky – and grateful. And my undergraduate and graduate schooling in quantitative social science, even way back then, was great preparation for analytics and data science. Indeed, some centers in social science academy are ideal training bases for data science. Thirty-five years later, I only wish I’d taken even more advantage of the excellent learning opportunities while in school.
My advice to aspiring Steve Miller‘s emerging from college is to take their evidence-based obsession to a “data” company or a consultancy like Inquidia to hone their data/computational/analytical skills. They should commit to life-long learning, investing heavily in online education. After five years or so, they may wish to get a computationally-intensive Masters in a program such as one of these, though, with online learning, I don’t think it’s essential.