Data and Analytics Strategy as an Enabler for Digital Journeys
Without strategy, execution is aimless. Without execution, strategy is useless. When it comes to data, it may be doubly so.
I remember being a in a multiple-day workshop aimed at defining a data strategy at a company I was working for. Several hours of that workshop focused on data virtualization because access to data was a significant struggle for the data analysts.
I couldn’t shake the feeling that we were already defining technologies before properly understanding the requirements and business problems. After several days, we left those workshops with almost nothing in hand beyond ideas, and no path forward that everyone agreed to walk together. It was as if we were at an impasse between what Business Analysts wanted and what the Information Technology team was willing to provide – each side stacking up policies and technologies to try to help solve the problem. What was missing was a strategy - an approach - that would help guide policy, process, and technology investments.
Achieve Strategic Consensus
Sometimes, that’s just where the problem is – the lack of consensus regarding the approach to one of a company’s most important assets: data. Achieving strategic consensus around data may seem like a simple problem to solve, but it’s almost comical how frequently it is ignored. Companies fund entire organizations dedicated for optimizing human capital, but many of those same organizations have very little data to define what “optimized human capital” really means.
Engineering firms propose millions in equipment and automation that promise ROI with no way to monitor it or be able to prove through real data whether the equipment is accomplishing the expected benefit. It’s as if we’re so passionate about an employee program or a piece of new equipment that we almost forget about objectively defining what success looks like and how we plan to get the data to measure it.
As data professionals, we can get distracted and frustrated when we see a digital journey that’s so wrapped up in digitizing /automating process that the team misses defining how a process should be measured and improved upon. I’ve been guilty in the past of thinking that I see an opportunity, creating a data model, and creating a dashboard that was never used because the business team wasn’t convinced of the value or just wasn’t ready to change the way they worked to take advantage of the new information we were providing them.
A Cohesive Data Strategy
The advantage to having a cohesive data strategy creates a standard set of warehousing and virtualization strategies for a variety of business scenarios. The beauty of doing that in the cloud is that we can start small using modern tools that unlock opportunities for Data Science and Artificial Intelligence and scale indefinitely based on business need. We’re no longer pinned into using legacy data warehouses or aging databases that result in subpar user experiences. Having the strategic consensus on the future direction of your company’s data allows a data team to focus more on achieving ROI for an application or modernization initiative rather than the technology and architecture of the “future” data platform. Setting a “True North” as a standard allows a data professional on a project to spend less time focused on technology and integration, and more time on ensuring effort for a project are put in the right place.
Here’s an example of finding that “True North”:
What is the standard approach to using data or analytical result? Will we look at it every morning and take some action? Will we review it in a daily stand-up meeting? Will we talk about it during specialized meetings? How can analytics help us define key process performance indicators, predictors, or AI that might help us optimize the process?
If the project team can’t indicate how success should be measured, a dedicated workshop might be required to brainstorm.
Ensure that analytics generated for an initiative or project are the right ones to spend time on because they will be integrated into the business process or be the standard way of proving the value of an initiative.
Define It. Defining the data and analytics strategy for an organization is worth the investment and companies should be sure to have one in hand before embarking down new digital initiatives. IT can help standardize approaches and define policy around that strategy and allow data professionals to hone in on the most impactful work. Additionally, it provides an avenue for companies to spend less time enhancing legacy data warehouse technologies and more time on modern warehousing and virtualization strategies. Over time, it lowers the hurdle rate for a company to completely drop legacy warehouse solutions for modern data platforms.
About the Author
Brad Zorn is an Industry Solution Architect for eLogic with extensive manufacturing, business, and technical experience across Manufacturing Operations, Microsoft and SAP platforms, Data and Analytics, Digital Manufacturing, Enterprise Architecture, and Industry 4.0 Strategy. He is a proven Data & Analytics leader with nearly two decades of experience designing industry-leading analytics that drive Enterprise and Plant Operational Improvements that enable data-driven decisions.