7 common pitfalls with manual processes and DIY data manipulation

pitfalls of manual data manipulation

Outsourcing, cloud services and financial pressures are constant realities for IT leaders.

When it comes to data-driven decision making, speed is essential; from real-time querying to personalized customer service. Many organizations will lean towards ease and convenience, using manual processes to get their results fast.

But speed isn't enough. Your business must also be able to trust its data in the long-term. With that in mind, here are the common pitfalls that come with in-house, DIY data manipulation.

1. 'Spaghetti infrastructure'

Without a central place for everything to live, a manual process can become very fragmented.

The challenge with DIY data manipulation is to avoid a 'spaghetti infrastructure', where tools and rules are built on top of one another.

Adding, replacing or mixing data can become a logistical nightmare. Without guidance on your architecture, you may encounter costly mistakes further down the line.

A data integration platform will erase the border between your data and applications. With one platform, you can conduct simple jobs as well as complex integrations between applications and people.

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2. "We know what we're doing"

It's fair to say that getting started with a data project that's managed in-house is going to be a lot easier when you have the team and tools in place. But oftentimes, the perceived ease of getting started quickly can overshadow proper planning.

It's quite tempting for technically able people to 'play with toys' and get distracted. From what originally looks like a couple of days or weeks of work, you might realize that your most experienced staff are spending a significant amount of time on a project that's seemingly justified, but also distracts them from what they should be doing.

A thorough assessment of how much time is required to work on a project is part of the real cost of doing it yourself. Planning is crucial. Failing to undertake a full scoping of the documentation and testing that's required for the task may lead to future doubts in the process.

Investing in a tool that allows you to better build out your processes and data architecture - alongside the 'DIY' freedom of a homegrown solution - means that you can shake off the status quo and focus on structure and innovation at the same time.

3. Reliance on a single user

You'll need highly technical users to design and operate your data solution.

If you have an experienced internal team member from similar projects, then it's great to be able to utilize that knowledge.

However, as projects grow and data volume increases, the integrity of your entire data project may become dependent on one person. That's a dangerous place to be in, as they then become the only person who knows where things are and how things work.

As projects, data and the pressures on your manual solution grow, it becomes more labor-intensive to prepare the data. If you're building the process around someone who knows what they're doing and they leave - that unique knowledge disappears with them.

You can get your solutions up and running quickly with a tool that consolidates your team's skills from siloed projects into a single design and operations platform. That way, you get peace of mind and high-performance all in one.

4. The hidden cost of not moving

The real cost of a legacy process is invisible. While development tools can be initially free, it's important to watch out for costs that come further down the line.

Manual processes become more difficult to automate because all the resources are focused specifically on day-to-day jobs.

As time goes on, the linear increase in data volume will start to double and triple time complexity, exhaust your resources, require constant maintenance, and create the need to hire more people. Ultimately, the problem snowballs.

You can support your data project with a data management platform that addresses your needs and saves your business money in the long run.

Watch The Webinar: Why Automation Is Better Than Excel Data Manipulation

5. Operational inefficiency

With DIY data manipulation, the implementation phase tends to attract most of the focus.

For a manual system to be effective, staff need to be able to consistently enter data cleanly without errors, otherwise data quality can be affected.

When data work takes more time to execute than the task itself, you know you're in an inefficient cycle.

Having a highly-skilled professional spend most of their day writing SQL scripts isn't a good use of their time.

An automated system, however, only needs to learn your process once, saving you the manual effort.

6. Hard to scale

Manual solutions for businesses often originate as 'one-off' instances that, in time, become periodical. They act as a 'good for now' solution that can keep time and budgets under control.

However, when you inevitably need to scale up your solution, you're going to meet a brick wall. After all, there's no such thing as small data processing.

The moment when you're re-using scripts and building on top of them is the moment your DIY data manipulation is no longer working. At this point, a controlled, automated way to manage data is required.

Growth becomes difficult when others depend on your DIY ETL project. Moving to a mature platform that supports your ambitions will be able to monitor, troubleshoot, and easily modify and adapt your data.

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7. Security

Using manual processes can compromise the security of the system and put your business at risk of regulatory fines.

Data integrity can be compromised through:

  • Human error
  • Security threats, such data breaches and viruses/malware
  • Lack of ability to debug and investigate data
  • Infrequent scanning and updating of libraries
  • Compromised hardware

The room for error with manual processes is huge and can create inconsistencies that impact the accuracy of your reporting. However, counteracting this with review and quality checks to ensure accuracy is another additional cost and use of time.

A platform that can help you to standardize data reconciliation processes for simple reporting, as well as manage your data from a single control point, will make it easier for you to stay compliant.

Build a better solution

When approaching a data project, in most cases, your business needs to choose between handling a solution in-house or using a full-featured data management platform.

There's no denying that managing your own manual process can bring fast results. But in time, as your process requirements grow, it'll eventually cause security, time and resource headaches.

With a platform such as CloverDX, you get the safety of having your data when and where you need it, coupled with the freedom to innovate. You'll get assistance with labor-intensive tasks, improve repeatability and precision and allow rapid and predictable scaling.

To find out how CloverDX could help you move away from DIY data processes and implement scalable, manageable systems, get in touch with us.

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Posted on February 02, 2021

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