Top 8 Data Management Challenges for Organizations

When it comes to data-driven working, few organizations wouldn't aspire to it. But there are always a few bumps on the road to “Data Nirvana”. Data-driven working thrives on a functioning foundation for your data, and that's where Data Management comes in. This blog looks at some of the barriers you encounter with Data Management and how you can overcome them.

#1 Lack of knowledge and manpower

When you talk about Data Management, many of us think of Data Warehouses and ETL (Extract, Transform, and Load), one of the ways to populate a Data Warehouse. The how and what of setting up a DWH requires specialist knowledge and experience that is scarce, and the current labor market conditions make that scarcity worse.

Where in the year 2000 you could still shout “Developers! Developers! Developers!”, now you want to deploy those scarce employees where they can really make a difference.

#2 Long lead times

When you want to fulfill new requests from the organization, you don't want to only be able to deliver next calendar year. Yet this is often the reality if you have a complex landscape of tools, or have to operate across many links in the chain. The result is often that the business solves it themselves, resulting in a new pipeline based on different tools.

#3 Patchwork of tools

The more traditional setup of Data Management is often a hodgepodge of all kinds of systems, software, and workarounds that have since become permanent: a “patchwork of tools”. Because data platforms often grow organically and aren't always set up according to a predetermined plan, at some point it becomes harder and harder to integrate and manage new sources and technology.

#4 Overview and data lineage

When problems are reported, when new employees join a team, when you want to get insight across the full breadth of your platform: these are all moments where a patchwork of tools works against you in keeping an overview. Where does the data in this report come from? How is this calculation put together? Which user accounts have access to this financial data? You'd expect to be able to answer that easily, but the reality is often different.

#5 Error-proneness

Despite everyone doing their best, people aren't able to deliver perfect work every time. A copy-paste mistake or another forgotten detail is always lurking. When you manually configure systems and develop code to manage your Data Management, you have to take human nature into account.

#6 Documentation

Everyone enjoys developing new things and implementing improvements, but recording them in documentation is harder to motivate people for. In today's world, continuous change is also a given, which means manually documenting things simply can't keep up anymore.

#7 Updates

The world doesn't stand still, and neither does the tooling you use in your Data Management landscape. Vendors are increasingly moving to short-cycle releases, which can mean you constantly have to roll out updates to your infrastructure and application landscape. This can mean you're forced to adjust code or scripts to new versions of tooling, or that you have to make changes to safeguard the security of your platform. The more you develop manually, the more dependencies you build in that can become a problem when you need to update things.

#8 Future-proofing

At the start of a project where you're building your data foundation, it can be hard to estimate which resources you'll need. Ideally you want enough so you're not hindered, but also not so over-dimensioned that the costs become a problem. And once you've made a choice, you also want to be able to change it if that turns out to be necessary later. Cloud services and virtualization are solutions that make things scalable, but what if you want to move from a database to a lake? That might mean you have to start over, resulting in a long process.

The solution

Fortunately, within the field of Data Management there's a solution that makes these challenges less challenging: TimeXtender. TimeXtender is a central orchestrator for a Data Platform, aiming to automate as many of the tasks you need to do as possible. Because TimeXtender works as much as possible via drag-and-drop, you spend much more time on what needs to happen instead of how. TimeXtender automates the infrastructure you have available and can therefore oversee how your entire platform is put together.

Because TimeXtender generates script from your project, you're much less affected by the usual little mistakes that creep in when you have to write code by hand. Changing infrastructure also has no far-reaching consequences, because TimeXtender, for example, generates code that matches the version of SQL Server you're going to work with.

TimeXtender uses metadata to generate all code and infrastructure; this metadata is also used to create up-to-date documentation and gain insight into lineage. With TimeXtender's capabilities, you can automate and manage your data flows from your source systems all the way to the back end of your BI tooling. Because as many tasks as possible are automated, the learning curve is a lot flatter than with other tools, allowing people with knowledge of business processes to also find their way around.

Next steps

Do these obstacles sound familiar, and are you curious how TimeXtender can remove them for you? Join a live demo and experience it for yourself! Want to read more about experiences with TimeXtender first? Then check out one of our reference cases below.

Written by Ruairidh Smith,
Senior consultant at E-mergo