Emerging technologies are evolving at an incredible pace, and companies are eager to take advantage of the new tools and solutions available on the market. One of the most prominent fields is artificial intelligence (AI), driving innovation and investment thanks to its diverse range of use cases. From making real-time data analytics accessible to transforming customer-facing applications, 78 percent of enterprise leaders expect to increase their overall spending on AI in the next fiscal year.

However, the appetite for new tech solutions presents IT teams with new challenges, prompting them to rethink their data management practices. Innovation doesn’t happen in a vacuum, and the IT infrastructure needs to be able to play well with any newly implemented technology or application in order to achieve tangible results. Especially for companies with significant amounts of historical data stored on legacy systems like the mainframe, finding the right data management strategy is fundamental for successful implementation.

Data discovery for increased visibility

Data discovery is step one because not all data is created equal. The decision on where to store the data will come down to individual circumstances and priorities, but data discovery needs to precede any kind of strategic decision.

Due to the fact that any AI initiative will only ever be as good as the data that fuels it, the return on investment will be directly influenced by the data the AI has access to. Organizations could miss out on using some of their best quality data if the environment isn’t mapped properly.

This makes data discovery and appropriate data management not just helpful but critical, and it’s not limited to companies that operate on the mainframe either. In fact, 80 percent of organizations that have native and hybrid cloud solutions reportedly struggle with network blind spots that impede efficacy within the system.

Data discovery enables companies to determine where their data is located, how they can access it, and what pre-existing dependencies they have to account for before introducing any new elements to that environment. This will include locating the metadata as well, which will further contextualize the data and confirm its lineage. IT teams can then leverage advanced analytics tools to determine the possible outcomes of that data being moved from its location. Would other applications break, or would the business have to contend with a new security risk?

Without those tools in place, introducing new endpoints becomes a risky move that many organizations will shy away from with good reason. If the system lacks transparency, a company could end up moving sensitive information to an unsecured location and inadvertently grant access to individuals who were not meant to have it. Mapping out the data landscape will drastically reduce the risk of failure when making changes to the existing infrastructure and eliminate the potential for unexpected ripple effects.

Finding the right strategy

Data discovery can be a monumental task, but modern tools with automation capabilities can help ease the pressure on internal teams and simplify the process. Once completed, teams will be able to see the full picture and make stronger analytical decisions, while cutting down on time and cost spent. The next step is to decide on a strategy that aligns with the organization’s needs and the requirements of the new piece of technology they’re looking to implement.

For instance, companies that store their data on the mainframe might decide to leave it there instead of moving it to a cloud environment, so they can take advantage of the stability and security a mainframe environment offers. This will be increasingly true for organizations operating in highly regulated industries such as finance and healthcare, where data security is paramount.

In this case, the IT team may determine that it would be too risky to expose that data to the perils of the cloud and instead decide to replicate it. This way, they are still able to implement innovative AI tools without having to commit to a complete cloud migration. In other cases, like with most analytics tools, virtualization of the data may be enough. Finding the best strategy can be tricky when there are so many considerations to take into account, but working with a trusted partner can enable the internal IT teams to navigate the process with confidence.

The cost of innovation starts with data management

When a business invests in cutting-edge technology, return on investment is a key component of that decision, and the costs that come with a new data management strategy are necessarily part of that. Organizations want their most secure and reliable data to drive their AI initiatives and fuel their real-time applications, and they want to better understand their own data to drive their business analytics. Good quality data is the foundation for innovation.

A huge part of devising a data management strategy is about cost. From data storage to the tools that the team is using for management and integration to the cost of implementing new models or new pieces of software, these expenses can quickly add up.

IT teams are often charged with managing their own budgets, and it becomes a balancing act of maintaining an environment that supports the day-to-day operation of the business while also encouraging growth and investment in innovation. As business operations are now underpinned by data, its management will be felt in every area, directly impacting return on investment, efficiency, user friendliness, and security.

Companies that are considering implementing innovative solutions and want to stay ahead in their industries must first understand their own data. Eliminating data dark spots is the foundation for innovative applications to run smoothly and ensures continued compliance with regulations like DORA and NIS2. Implementing new pieces of technology is always a calculated risk for businesses, but that risk can be significantly reduced by creating the right foundation for it.

IT teams that dedicate the time and resources to complete data discovery will be able to make more strategic decisions, drive meaningful results, and set the entire organization up for success.