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May 28, 2018
Estimated Read Time: 5 mins
Business Analytics, Business Intelligence, Data Strategy… regardless of what you call it, the demand for the usage of data in business decision-making across the live audience sphere has grown immensely over the course of the past ten years. Many major league sports organizations have invested significantly in standalone departments and technologies meant to help organizations understand their customers better, price smarter and ultimately mine data to answer questions at a scale never before possible.
Within sports organizations, data teams have grown considerably in the past few years. These teams take on many different shapes depending on who’s sponsoring the department, what the organization’s priorities are, and how much senior leadership values data-based decision-making.
As is usually prudent after a period of investment growth (think budgets and headcounts associated with building out data teams), we wanted to reflect on some of the opportunities we still see for the industry and their usage of business data. A combination of factors both inside the front-office as well as on the exterior are changing the way data can be leveraged to create value and we wanted to explore some of this with you.
In this post, we’ve identified 7 critical areas of opportunity for organizations looking to make the most of analytics into the future.
The creation of analytics-specific departments has been one strategy used to accelerate data-based decision-making across organizations. After years of investment and growth in this area we are now learning that the simple creation of a department isn’t enough to drive value. Analytics departments have, in many cases, become silos in their own right as organizations have struggled to operationalize a culture of data-focused decision-making.
One major gap we see is a reluctance on the part of organizational leaders and decision-makers to actively manage and participate in setting the analytics agenda. Fear of change, lack of time and an inability to effectively prioritize and communicate new data-driven approaches can result in disconnect between organizational revenue drivers and the analytics department.
Department heads are arguably the most important piece of the analytics puzzle. These individuals have the expertise in their specific areas that can provide focus to the analytics department. With that collaboration, analytics departments can provide more targeted data for leaders to take action on to achieve a return on analytics investment.
Much of the success of data-based decision-making comes down to a culture decision on the part of every decision-maker to embrace and own analytics as it pertains to them and their organization. Analytics is very much a horizontal band that should cut across the organization as opposed to standing alone.
Organizations get hung up on not being able to show value from data until it is spotlessly assembled, cleansed and structured. We believe that this is holding organizations back significantly. We recommend that organizations start with use cases to show value because it will result in not only cleaner and better structured data, but will also significantly accelerate the return on analytics investment.
The word data warehouse has been used as an end goal term with little to no thought being given to what will be done with the data once it’s assembled. The strategy of trying to fit as many data sources as possible into a data warehouse has, in many cases, not born fruit. As organizational demand for access to data insights and capabilities continues to grow, we see organizations with many of the same pain points that existed before strategies were employed.
It’s important to remember that data assembly, cleansing, and performance is an ongoing journey not an endpoint. The value really comes from how these master data management practices support the use cases that will ultimately optimize spend, show results, and make organizations more data driven as a whole.
A big component of building out analytics capabilities in the recent past has involved staffing up. This has often included a strategy of carving out existing staff members from other departments while enhancing and adding bodies to create the team. Every organization has different roles, titles, and skillsets within their unique analytics departments. These analytics departments often involve some form of CRM administration, Marketing Automation specialization and Data Analysis capabilities to go along with a department head to represent the group to the organization.
Unfortunately, what we often see is that analytics teams are under-resourced and under-supported by the business decision-makers that ultimately determine the success of the team. And the answer in this case isn’t throwing bodies at problems. The number of analytics personnel should not be the measure of a successful and sophisticated organizational culture. Having the skillsets and capabilities that are able to drive insight out of data in an automated, actionable and integrated way is really the future of our industry. Some would argue that the future will involve less bodies and more reliance on technology, as opposed to more.
If you are just starting your analytics journey, it’s an exciting time. The opportunity to freely make decisions and invest in technology that is more capable than ever before, at a fraction of the cost, is available to you.
Outside of analytics, the other big area of investment over the past ten years has been digital. Unfortunately, analytics teams and digital personnel, have in many cases, not come together to the mutual benefit of the organization. The opportunity and need for organizations to connect digital and analytics teams has never been higher. As data continues to grow in sheer volume, velocity, and variability, having both digital and analytics minds at the table is paramount in terms of maximizing any investments in data. The first step to this is ensuring departments are aligned around priorities and outcomes that create value. Digital teams are often operating the tools that breed fan engagement, capture and grow total audience size, while converting the anonymous fan into the known buyer. The analytics team enhances these efforts by testing, automating, and optimizing the interpretation of the data exhaust from these interactions and ultimately providing the segmentation and customer lifecycle insights that maximize customer lifetime value. Although both departments are leveraging data, there is still a lot of unknowns in terms of what the other does and how they fit together. Intentionally exploring this is something we are convinced will reap major rewards for the organizations of tomorrow.
When it comes to analytics, one of the biggest issues we see in the business is that organizations tend to spread capabilities out and analyze things on a manual, ad hoc basis at the expense of understanding the impact of decision-making. Lean organizations with a lack of automation and clear direction around priorities often find themselves trying to “keep the lights on” as opposed to adding value and understanding how to get smarter in order to scale.
How much time did your organization spend strategizing, designing and implementing that last sales campaign? Now how much time was spent measuring, optimizing, and pivoting mid-campaign?
We believe that forcing the notion of return on analytics investment results in better, more focused analyses, learnings, and systems as well as accountability for the organization’s investment in this area.
For too long, analytics efforts have been linear exercises lacking a feedback loop. Storytelling and actionability of data have never been more important. This is also an area where we see many of today’s live audience organizations struggling. Organizational reporting infrastructures have grown unwieldy and expensive, with over 95% of attention generally going to 5% of the reports. Further, these reports often do a poor job of telling the stories behind the data and aren’t focused on answering the questions that business decision-makers have, which results in extremely low adoption rates. Data usability, actionability, and performance are critical elements of building adoption and democratizing data across the organization.
Designing a data systems architecture that is flexible is something that we at StellarAlgo strive for every day. With the data and technology landscape shifting so swiftly, we believe it is critical to build out capabilities that will last into the future. Many open-source technologies have become best-in-class, and leveraging these can yield major results in the areas of cost, performance, and capability. Aligning with partners to facilitate this, and designing your systems architecture in a way that allows you to easily pivot and position for the future, has never been so important.