3 tips to successfully enhance collaboration between IT and the rest of the business

Portfolio Product Marketing Manager, DataOps, IBM

The conversation around data preparation has been evolving. What started as a push for self-service access for specific use cases has now expanded to operationalizing a data pipeline across the enterprise. The goal is to create efficiencies and eliminate workflow silos to propel data strategy forward.

As I mentioned in my previous blog, current day business realities are shifting emphasis more on fixing data preparation challenges around the organization. However, who will take the lead to implement a lasting and effective solution? The answer should be…. Everyone.

While it is easy to say it is a problem for technology, it is also up to culture, organizational prioritization, and driving value to address this issue. In order to be successful, IT and business leaders must have a hand in data strategy and implementation. Below are three areas of consideration for those seeking to enhance collaboration between IT and the rest of the business:

1. Align IT project goals to the business needs

There has always been friction when it comes to budget allocation in an organization. For example, will investment in marketing’s campaign be prioritized over updating legacy technology this year? Those who have been around a few of these annual conversations will say: probably. Major business projects tend to showcase the return on investment during a project pitch better than technology projects competing for the same budget. The business line, by its nature, has been connecting proposed activities to revenue, customer satisfaction, and other core metrics for longer than IT has. However, with the implementation of data preparation an opportunity has emerged for IT.

According to a 2019 market study, 47 percent of business executives are deeming data preparation critical for the success of their organizations. This is most likely due to the fact that preparing data is a highly visible bottleneck to driving value with data. This is where the lightbulb turns on for IT leaders. Faster and more effective data preparation processes should become the objective of the next pilot program. This opportunity will create a lift in data governance and information architecture initiatives due to the dependency that data preparation has on them. Better quality data leads to more efficient preparation and transformation, ultimately resulting in more activities that drive value: analysis, modeling, and insights.

2. Support data literacy with tools that focus on user experience

Today’s organizations are experiencing a lack of data literacy skills. Paired with low resources to address the shortage, these realities have caused an overreliance on IT for data. While business users have identified that they would like more access and ownership of critical business data, both asks require extensive knowledge on compliance measures, lineage management, and consistent terminology to maintain the integrity of data. This challenge isn’t one that is going away either as the market continues to expect more from the business. Organizations should offer support to make the goals achievable, despite a potential lack in skills.

Here is another opening for IT to take the lead by offering tools that are straightforward and leverage contextual learning opportunities for end users, while offering a base that is IT-managed. For example, having a data preparation solution that can sit on top of an existing governed data lake can ensure that all the work that has been expelled on governance and compliance to date is able to surface once data is delivered to end users. At the same time, the UI of the tool should be intuitive, with a vocabulary that the end users will understand, and provide as much automation as possible. The most common way to do this is to enable ML-led recommendations at every step of the preparation process. By providing a strategic solution, the literacy gap tightens enough for end users to extract value from data.

3. Build a data culture across the enterprise

One of the biggest pitfalls for any project is adoption. If the people involved and/or impacted do not understand the mission and don’t see a pathway to results, it leads people to believe a project might be short-sighted in its vision or will lose investment quickly. The key, and it is a major commitment, is for an enterprise as a whole to adopt a culture tied to the results. In this case, a data culture.

This commitment needs to start at the executive level and be supported across the enterprise. As you get to the different departments, a critical piece is to set prioritization that aligns with driving a data culture. As the leadership set goals in place, it is important that they offer results at timely intervals. With realistic goals at 30, 60, 90 days and beyonda department can see tangible results quickly and be encouraged to keep going. As people see data preparation processes that used to take them weeks decreased to mere minutes, they will appreciate the powerful results for the business.

With sights set on improving data preparation, many doors are opening for technical leadership and enhanced data governance. Watch the latest 20-minute webisode of #DataDecoded for the Modern Enterprise featuring Sarah White Eagle as she shares her unique perspective as a data scientist and how she perceives the challenges and opportunities ahead for IT and the business users she sits between.

Learn more about IBM Infosphere Advanced Data Preparation by visiting