Poor data quality and acceptance make it difficult for companies to derive value from their data. Data experts must take on the role of business experts to translate business goals into data projects. 

Companies receive a wealth of data. Yet they often don’t know what to do with it or how to benefit from it. And is the data correct? “Data quality within organizations is a big issue,” notes Ziad A Fayad, Data Cloud Specialist Lead at Salesforce. Also sitting at the table are Christophe Robyns, Managing Partner at Agilytic, Mathias Coopmans, Cloud and Architecture Lead at SAS and Thijs Paepen, Account Manager at Ctac. Together, the experts discuss the role of data in companies, its quality and value, and the introduction of data and technologies within an organization.

I have data, now what?

“Data quality and data silos still pose major challenges for companies, even after years of technological advances,” begins Fayad. Paepen agrees: “Many companies still struggle with their data: from data clutter to duplicates or incorrect data.”

This poor data quality means that companies cannot derive sufficient benefit from it. “That’s why they often stay at a simple level when it comes to analytics,” says Paepen. “Even simple questions like, ‘What business problems can I solve with my data?’ remain unanswered,” Robyns adds.

Data in silos

One of the most common challenges in data management is data silos. These are separate collections of data within the same organization, stored in isolation and not easily accessible to other departments. This leads to inefficiencies and obstacles to using the data effectively. Coopmans sees a possible explanation for the existence of data silos. He emphasizes that data silos often represent power in large organizations, which can lead to their deliberate maintenance. “Not all causes of silos make sense,” he notes. “Domestic politics certainly also play a role. The solution to this will therefore not be technical in nature, but will focus on change management.

Beyond the hype

“Data projects have been around for at least twenty years,” says Fayad. However, such projects still carry a great deal of weight. “If someone sells a solution that has nothing to do with data, implementation will likely be quick. “As soon as you start talking about data, most people automatically think that the project will take years,” explains Fayad. Robyns agrees and adds an important first step before talking to clients about data.

“We don’t initially ask for their data, but we want to know the company’s vulnerabilities. What are the business goals?” Robyns illustrates: “There was an organization that had developed an impressive model. Months after implementation, they discovered that no one in the company was using the model. The reason? It did not meet the needs of the company and therefore proved useless.” This highlights the importance of clear goals and business proposals.

assumption

Companies have a lot of data but don’t know what to do with it. “A lot of it has to do with adoption,” Coopmans continues. “Adoption within the same organization is slowed by different speeds in how teams and departments approach data and new technologies.”

Adoption within the same company is slowed by different speeds in how teams and departments approach data and new technologies.

Mathias Coopmans, cloud and architecture lead at SAS

Paepen also points out this discrepancy. “Some teams are already working with advanced data models, while others are still struggling with basic processes like managing Excel spreadsheets.” In other words, there are big differences in maturity, not just between companies, but also between internal departments.

Paepen also points out the importance of changes in the way we work and think. “We spend thirty percent of the time in our projects on change management so that new technologies can be successfully implemented,” says Paepen.

Fayad builds on this by emphasizing that companies need to focus on making data accessible and understandable to all levels of an organization to lower the barrier to adoption.

IT as a moderator

Robyns already emphasized the importance of defining business goals before starting the technical part. Paepen also states that the IT department is increasingly taking on the role of intermediary and must translate between business requirements and technical implementation. “The role of the IT department has changed from a driving force to a support function that helps solve business problems.”

The role of the IT department has changed from a driving force to a support function that helps solve business problems.

Thijs Paepen, Account Manager at Ctac

Coopmans agrees: “Today, data scientists and engineers not only need technical skills, but also need to be able to translate business problems into technical solutions.” This requires a hybrid approach where data experts also understand the business context.” Data ultimately needs to contribute to business goals.

Look before you leap

Data quality and competency as well as data silos remain common challenges. Today the media gives us the idea that everything is AI and everyone works with data, but in practice we see that many companies are not ready for this at all.

“The fundamental question in AI and data projects often revolves around the quality and suitability of the data: Is your data ready?” says Fayad. “It is important to determine your business goals and assess the feasibility of your project. Based on a feasibility matrix that also takes into account the availability and quality of data, we can prioritize the right projects,” concludes Robyns.

This is the first editorial article in a three-part series on data and analytics. Click on our topic page to see all roundtable articles, the video and our partners.