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'Companies Continue To Struggle With 'Data' And How To Fully Leverage And Achieve An ROI With Data'

Interview with Nicole Mills, VP of Strategic Planning at Suffolk Construction

15Nov

Nicole is VP of Strategic Planning at Suffolk Construction. She is a seasoned strategy professional focused on building executable strategies that succeed in driving long-term growth, achieved through an aligned, transparent, and collaborative operating system of research, planning, change management, analytics (business metrics & predictive models), & corporate governance. 

We sat down with her ahead of his presentation at the Predictive Analytics Innovation Summit, taking place in Chicago this November 29-30.

How do you think companies have changed in their attitudes to data over the course of your career?

I think that companies continue to struggle with 'data' and how to fully leverage and achieve an ROI with data. For non-analytics or non-IT people, data is perceived as numbers or information in various systems or reports. The data itself is not necessarily exciting, and most people don’t have an appreciation for how critical data architecture, data hygiene, and data governance are to fully capitalize upon the information. To properly architect and manage data requires tremendous investment in terms of dollars and resources, as well as rigorous and deliberate planning around systems, business definitions, and data capture processes. It isn’t the kind of exciting innovation that is 'sexy' or 'marketable', however it is the critical foundation from which advanced analytics emerge. I have seen companies focus too heavily on the analytics end game without addressing the data health upstream in the process, and in the long run it holds them back. If we can start looking at data and analytics holistically as a value system within an enterprise, I think that we can more directly tie the analytics return back to the original data investment.

How do you get analytics consumers excited about your analytics solutions?

Our team spends an incredible amount of time partnering directly with our analytics consumers, and making them a core part of the process. Each solution is routed in a critical business question that the consumer is trying to answer, and therefore there is an immediate demand for the solution before it is even created. As we start conducting our analysis, we make certain to check in with our partner at regular intervals to share interim findings, and get direct feedback and input. By the time the final solution is completed, there is a vested partner waiting to receive the insights, who shares joint ownership over the outcomes!

In addition to creating deep partnerships with our internal stakeholders around new analytics solutions, we also focus heavily on the data visualization and user experience so that people who don’t have a background in advanced statistics and analysis can clearly understand the story that the data is telling. We have developed a 'brand guide' and 'UX Guide' for all of our analytics solutions, so regardless of the business question that we are trying to answer the end user will have the same easy, and intuitive experience to navigate through the solution and fully leverage the insights!

What are the main challenges facing you in your role at Suffolk Construction? Are there any new technologies and innovations with data do you think will help you to overcome these?

It’s all about the data, and data continues to be our biggest challenge! While we have made huge strides in our advanced analytics capabilities, we are still behind in terms of data alignment & integration, data quality, and data governance. The pace at which we can effectively architect critical data lags substantially behind our capabilities to use our data! One of the technologies that our analytics team has been leveraging is Alteryx, which has strengthened the business user side of accessing and manipulating data, so that we can still move forward with critical analytics solutions. Alteryx has bi-passed the need to rely upon a data warehouse in many instances, and gives the business user the ability to scrub, blend, and perform numerous other operations with the data to accelerate our analytics development. We have found it to be incredibly advantageous when we are blending internal and external data, as it has tremendous processing speeds and has eliminated the need for us to have to build a data lake.

With machine learning algorithms now helping to automate certain elements of data science, what do you see as the future role of the analyst? Will human input always be required?

I think that major advances in platforms with built in algorithms and machine learning algorithms have facilitated an acceleration in speed to market with new analytics solutions, but have not replace the role or need for the analyst themselves. I see an increasing need for analysts to have strong business acumen above and beyond the traditional core of analytics and programming skills. The root of analytics development, despite new technological efficiencies, has to be around the question(s) that need to be answered by the end user. I believe that analysts will need to be able to clearly understand organizational needs, challenges, and requirements so that they can effectively structure the right solutions that will sustainably answer critical questions. As new analytics solutions are developed, there still needs to be validation with the business that the findings have merit, and that critical human dynamics or systemic variables were not missed in the analysis. I don’t believe that machine learning will ever fully be able to replace the need for human testing and validation of analytics solutions, based upon real-world conditions.

What will you be discussing in your presentation?

Now-a-days it’s hard to find anyone more excited about advanced analytics than an analyst! Businesses are investing incredible resources into data solutions, analytics staff and capabilities, consultant partnerships, and new platforms to provide insights to the business leaders who can apply them. But what happens when our analytics consumers are not as excited about our solutions, or are not able to fully leverage our insights? Let’s explore some of the root causes that lead to ROI misalignment, and discuss easy prescriptions to boost business alignment, data literacy, and analytics impact. 

You can hear from Nicole, along with other experts in data analytics, at the Predictive Analytics Innovation Summit. View the full agenda here.

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