There are no shortage of data and analytics challenges that business and IT leaders face when launching data and analytics projects. In fact, it is fair to say there is a crisis of confidence in the ability to realize data and analytics investments, with the vast majority of projects often failing to deliver expected results.
In our experience working with enterprise organizations on their data and analytics initiatives, here are the five most common obstacles facing data leaders today – with advice from the experts on how to overcome them.
Data analytics initiative challenge #1
Lack of alignment to business outcomes
The biggest challenge we see is that data and analytics initiatives are often disconnected from business objectives and instead treated like one-off technology implementations.
Recent stats suggest that 70-80% of data initiatives fail to deliver the expected value. That’s not surprising. After all, it’s hard to get the organization excited about a new technology if it doesn’t deliver business value.
Data initiatives have traditionally leveraged project management techniques, which focus on collecting requirements, adhering to a rigid development plan, and completing sequential tasks.
Instead of focusing on completing tasks and shipping a one-and-done technology implementation, the product management approach prioritizes empathizing with the business user, defining and solving business problems, ensuring end-user adoption, and leveraging agile thinking.
On the Data-Driven Leaders podcast, Ceridian CDO David Lloyd advised that CDOs take time to reflect on what they hope to achieve with a data and analytics initiative:
“We’re so busy in our day-to-day lives and our business lives that I’m not sure that we’re stepping back and asking ourselves, ‘What are the four or five things that we would love to know about our business? […] What’s the root benefit of it? Do we capture data that is actually going to matter in answering those questions?’ I think that’s the starting point.” – Using Data to Fuel Your Company’s Performance
It’s important that data leaders work closely with business unit leaders to identify the best opportunities to leverage data that creates business value.
Finally, remember that data is not a project. It’s a business asset, and the best part is that it doesn’t reduce when you use it. In fact, data becomes more powerful the more it’s used.
Former Minto Properties COO George Van Noten weighed in on data as an asset when he joined us on Data-Driven Leaders:
“You need to think of data as if it were actually money,” he said. “If you think about the diligence and the care that you would put into a cash register, you should treat data in the same way because it has inherent value.” – How Data Brings Value to Your Business
With that perspective in mind, it’s hard to see data and analytics as anything other than business-critical initiatives.
Data analytics initiative challenge #2
Too many data silos
Organizational data silos can severely hamper data and analytics initiatives. A recent report found that 90% of organizations cite data silos as a roadblock to achieving their business goals.
David Dadoun, Head of Data at Bombardier Recreational Products, put it succinctly on Data-Driven Leaders:
“You can’t be on an island in a silo when you’re working in data.” – Data is Not a Project
Data sharing enables everyone in the organization to access the same data, which helps reduce misinformation and ensures you can see the big picture of what’s going on in your business. It reduces inefficiencies, increases collaboration, and opens up new opportunities for business leaders.
Breaking down data silos is no easy task. But the rewards are worth it: Gartner found that organizations that promote internal data sharing will outperform their peers on most business metrics.
There are several ways to break down data silos and encourage data sharing at your organization, including:
- Ditch the outdated “data ownership” mentality and instead recognize and celebrate natural data stewards.
- Recognize the organizational biases that affect data sharing and take steps to correct them.
- Prepare your environment for data sharing by establishing data management and data governance strategies.
- Work with business leaders to establish enterprise-wide data literacy programs.
Data analytics initiative challenge #3
Poor data quality
Poor data quality has far-reaching consequences for data leaders who wish to drive business value through data initiatives. In fact, MIT Sloan Management Review estimates the cost of bad data to be 15% to 25% of revenue for most companies.
Common data quality challenges include duplicate and/or hidden data (data silos again) and inaccurate data (often due to human error and data decay).
“Institutions are not placing enough emphasis on the quality of data,” said Railz CTO and co-founder Derek Manuge on Data-Driven Leaders.
He shared his view on the key attributes of data quality – and why data leaders should care:
“Data quality can be broken down into a number of different characteristics. There’s the characteristic of completeness which is crucial if you’re looking at financial data. If you’re missing half of the balance sheet or a hundred transactions, that’s no good. And you’re going to end up making poor decisions as a result of that. If you want to expand upon data quality, you can look at things like accuracy or validity source, and of course, the timeliness.
These are some of the things you want to combat against if you’re thinking about truly designing a proper data management strategy where you want to optimize decision-making on top of it.” – Why Data Quality is Essential in Data Management
This is where data governance comes in. Data governance establishes a common definition of data elements and with the help of data stewards, ensures that the data used in business analytics and business decision-making is trustworthy.
You can even make data quality the focus of your data governance program. CMO Magazine published a case study by Ram Kumar, Chief Data and Analytics Officer at CIGNA, about one of the fastest growing and more successful general insurance companies in India. The case study outlines how the organization significantly reduced risk with a company-wide data quality program.
The organization used unique and creative tactics to ensure the program succeeded, including branding it with a parrot mascot named “DeeKew”, giving out data quality awards to individual employees and the best-performing branches, and offering discounts to customers and brokers who entered quality data.
Starting a data governance program can feel daunting, but it doesn’t have to be. We recommend implementing a data governance framework that embraces Agile principles. Agile thinking is iterative, consisting of phases that build on top of the previous. It focuses on delivering scalable and repeatable business use cases that deliver value quickly — exactly what you need to ensure that your data initiative is successful.
Data analytics initiative challenge #4
No data-driven culture
Data-driven organizations are those that use data to make decisions, to optimize the business, and to innovate. These organizations have a few things in common:
- Their strategies are aligned with the broader business objectives of the organization
- They understand the value of their data assets
- They have strategies for how to use their data assets
These organizations aren’t just concerned about data for the sake of it. They want to leverage it for business insights. Data-driven cultures are ones where everyone has ready access to information that is relevant and actionable — from executives and managers all the way down to individual contributors and front-line staff.
CDOs and CIOs are typically tasked with building a data-driven culture at their organization, but it’s often an uphill battle. Business leaders who are used to making decisions based on gut instinct, or who aren’t comfortable interpreting data, can be resistant to attempts at culture change.
Change management is a major component of encouraging a data-driven culture. Data leaders need to lead the charge – and do it in a way that speaks to the business.
Rupinder Dhillon, Head of Enterprise Data Office at Sobeys explained on the Data-Driven Leaders podcast that her job is to bring data closer to the people.
”I can lead a conversation about data governance and the difference between a data steward and data owner. But let’s face it, people don’t care,” she said.
“The approach that we’re taking, with the help of Gensquared, is to target very specific business processes and problems that are the result of data or have an impact on data so that we’re talking the language of the business. We’re going to be addressing things that impact the day-to-day.” – How to Create a Successful Data Strategy
In addition to speaking the language of the business, hiring practices can go a long way towards encouraging a data-driven culture.
“It starts with making sure that the people that you’re hiring have a focus on actually understanding or utilizing data,” said David Lloyd. “Everybody can’t think like a data scientist. Curiosity about data is what I would look for when I’m hiring.”
Widespread data literacy and a deeper understanding of the importance of data will lead to stronger buy-in from stakeholders across the business. The stronger your data-driven culture, the more likely your data initiative will succeed.
Data analytics initiative challenge #5
Skills and knowledge gap
Data leaders are feeling the effects of The Great Resignation. The skills and knowledge gap in data teams makes launching new data initiatives challenging.
What’s the solution? First, do everything you can to hold onto your team. Don’t forget that culture matters, and people are your biggest asset.
“It’s very difficult to find data scientists and folks that really understand how to take advantage of new technologies or emerging technologies. If you’ve got them, hold on to them, and build up your team,” said Holt Renfrew SVP of IT Alicia Samuels.
She also advocates for leveraging external resources when needed:
“We utilize our vendors and our consulting partners to help us understand the possible and what’s coming. If we have the ability to leapfrog, then we’ll look for opportunities to be able to take advantage of that. And our vendors and our consulting partners have been phenomenal in helping define what that looks like.” How to Connect Data and People to Drive Greater Customer Experiences
(Learn about the Gensquared Data Team as a Service (DTaaS) business model.)
Data professionals aren’t just technologists – they also need “soft skills” to bring data initiatives to the finish line.
”We often talk about how our role sometimes feels like the company counselor,” said Rupinder Dhillon. “Every part of the organization has different challenges and opportunities, and because it is so people-driven and ties all of your processes together, you have to take a nuanced approach to every part of the organization. You get to see what motivates people.”
There is no silver bullet for solving the skills and knowledge gap, but creating a company culture that employees love, leveraging the skillsets of partners, and taking a more holistic approach to hiring data talent are all good places to start.
Solving data and analytics initiative challenges
There are many things in the world of data and analytics that can trip up a new project, but with careful planning and execution, any of these obstacles can be overcome.
The retail company HBC was struggling with many of the challenges identified in this piece, in particular poor data quality and data silos. Learn how we helped them eliminate silos, improve overall data quality, and enable business users make data-driven business decisions, faster.